DEV Community: Alain Airom (Ayrom)

Deep Agents in Action: Building a Multi-Agent Research System

Alain Airom (Ayrom) — Tue, 22 Sep 2026 08:43:48 +0000

I tested yet another implementation of a multi-agent orchestrator.

The paradigm for AI agents is shifting rapidly from single, monolithic prompt loops to structured multi-agent systems capable of division of labor, state persistence, and fine-grained tool orchestration.

Although IBM watsonx Orchestrate is my go-to solution for enterprise-level agent deployment, I wanted to experiment with LangChain's Deep Agents to evaluate its multi-agent orchestration capabilities.

Introduction: The Deep Agents Architecture

LangChain announced recently Managed Deep Agents, introducing a managed platform to execute deep agent topologies. The Deep Agents paradigm addresses the fundamental limitations of single-agent workflows—specifically context window degradation, tool confusion, and compounding reasoning failures during extended, multi-step tasks.

What is the Deep Agents Pattern?

The Deep Agents pattern is a hierarchical multi-agent architecture where:

A top-level Orchestrator decomposes complex tasks and delegates sub-tasks to specialists.
Each Specialist Agent runs its own isolated ReAct loop with its own tools and memory.
Specialists return structured results to the orchestrator, which synthesises them.
Agents can be composed recursively — an orchestrator can itself be a sub-agent.

This pattern adds genuine value over a single agent because:

| Single Agent                       | Deep Agents                                       |
| ---------------------------------- | ------------------------------------------------- |
| One context window gets overloaded | Each agent has an isolated, focused context       |
| All reasoning in one prompt        | Specialised reasoning per domain                  |
| Hard to scale                      | Add specialists without changing the orchestrator |
| Hard to debug                      | Full delegation trace for auditability            |

---

Excerpt from LangChain Deep Agents Documention;

Deep Agents overview

Deep Agents is the easiest way to start building agents and applications that are powered by LLMs—with built-in capabilities for file systems for context management, subagent-spawning, and long-term memory. Optional capabilities such as task planning and skills extend the harness when your use case needs them. You can use deep agents for any task, including complex, multi-step tasks.Deep Agents comes with the following capabilities:

Take actions in an environment: Take actions via tools, read and write files, execute code
Connect to your data: Load memories, skills, and domain knowledge at the right moment
Manage growing context: Summarize history and offload large results across long runs
Parallelize tasks: Delegate to general or specialized subagents running in isolated context windows
Stay in the loop: Pause for human approval at critical decision points
Improve over time: Update memory, skills, and prompts based on real usage

And from the Github repository;

Deep Agents is an open source agent harness — an opinionated agent that runs out of the box. Extend, override, or replace any piece.

Principles:

Opinionated — defaults tuned for long-horizon, multi-step work
Extensible — override or replace any piece without forking
Model-agnostic — works with any LLM that supports tool calling: frontier, open-weight, or local
Production-ready — built on LangGraph (streaming, persistence, checkpointing) with first-class tracing, evaluation, and deployment via LangSmith

Features include:

Sub-agents — delegate tasks to agents with isolated context windows
Filesystem — read, write, edit, or search over pluggable local, sandboxed, or remote backends
Context management — summarize long threads and offload tool outputs to disk
Shell access — run commands in your sandbox of choice
Persistent memory — pluggable state and store backends for cross-session recall
Human-in-the-loop — approve, edit, or reject tool calls before they run
Skills — reusable behaviors the agent can load on demand
Tools — bring your own functions or any MCP server

Core Concepts of LangChain Deep Agents

Hierarchical Orchestration: A primary agent acts as a supervisor, receiving high-level goals and delegating sub-tasks to dedicated specialist agents rather than executing every step sequentially in a single context loop.
Specialized Agent Roles: Individual agents operate with tightly scoped system prompts, specialized tools, and isolated context windows to execute sub-tasks (e.g., information retrieval, numeric calculations, or document composition).
Isolated Memory Stores: Each agent maintains its own short-term working memory, facts, and episodic memory, preventing state contamination across different phases of execution.
ReAct Reasoning Loop: Sub-agents execute autonomous Thought → Action → Observation loops using validated tool interfaces.

Technical Implementation

Now that the feature set of "Deep Agents" is done, let's jump into a simple didatical (for myself) implementation.

The implementation in the provided repository (scaffolded via IBM Bob) provides a complete local and cloud-compatible implementation of the Deep Agents pattern.

Architecture Overview

User Task
  └─ OrchestratorAgent (Planning & Synthesis)
      ├─ ResearcherAgent (web_search)
      ├─ AnalystAgent (calculator, code_executor)
      └─ WriterAgent (file_reader)

The system is structured across four primary execution layers:

Orchestration Layer (agents/orchestrator.py): Accepts user input, evaluates task complexity, builds a plan, delegates sub-tasks sequentially, and synthesizes outputs.
Specialist Agents Layer (agents/specialists.py): Implements ResearcherAgent, AnalystAgent, and WriterAgent.
Tool Infrastructure (tools/): Provides AST-safe arithmetic evaluation, isolated Python execution, web search abstraction, and sandboxed file I/O.
Relay & UI Layer (app.py & main.py): Offers a Streamlit interface with asynchronous cross-thread state syncing, live phase tracking, and a CLI execution harness.

Technical Stack

The main application is written in Python using Streamlit for UI
The back-end llm inference for local use is tested with Ollama but llama.cpp support and configraion is implemented as well.

Code Structure

Based on the requirements I expressed, hereafter is the current structure of the application;

   deepagents-usecase/
   ├── agents/
   │   ├── __init__.py         # Package exports
   │   ├── base.py             # Abstract BaseAgent + ReAct loop
   │   ├── llm_client.py       # LLM adapter (Ollama/llama.cpp/OpenAI/Anthropic)
   │   ├── messages.py         # Typed message protocol
   │   ├── orchestrator.py     # OrchestratorAgent (top-level)
   │   ├── researcher.py       # ResearcherAgent specialist
   │   ├── analyst.py          # AnalystAgent specialist
   │   └── writer.py           # WriterAgent specialist
   ├── tools/
   │   ├── __init__.py
   │   ├── base.py             # BaseTool + ToolResult
   │   ├── calculator.py       # Safe AST-based arithmetic evaluator
   │   ├── code_executor.py    # Sandboxed Python execution (exec with allow-list)
   │   ├── file_reader.py      # Sandboxed file reading (input/ only)
   │   └── web_search.py       # Web search (stub + live Tavily)
   ├── memory/
   │   ├── __init__.py
   │   └── store.py            # AgentMemoryStore (short/long-term/episodic)
   ├── config/
   │   ├── __init__.py
   │   └── settings.py         # Centralised env-based configuration
   ├── tests/
   │   ├── test_tools.py           # Tool unit tests (71 tests)
   │   ├── test_memory.py          # Memory unit tests (24 tests)
   │   ├── test_agents.py          # Agent integration tests (92 tests)
   │   └── test_code_executor.py   # Code Executor tests (134 tests)
   ├── input/                  # Input documents (content gitignored)
   ├── output/                 # Generated reports (content gitignored)
   ├── memory/                 # Persistent agent memory (JSON files)
   ├── scripts/
   │   ├── start.sh                  # Launch Streamlit in detached mode
   │   ├── stop.sh                   # Gracefully stop the application
   │   ├── cleanup.sh                # Remove .venv, __pycache__, etc.
   │   └── check_code_executor.py    # Standalone Code Executor sanity-check
   ├── Docs/
   │   ├── Architecture.md           # Mermaid architecture diagrams
   │   ├── Quickstart.md             # Step-by-step getting started guide
   │   ├── API.md                    # Full public API reference
   │   └── CodeExecutorVerification.md  # Code Executor test & verification guide
   ├── app.py                  # Streamlit web UI
   ├── main.py                 # CLI entry point
   ├── requirements.txt
   ├── .env.example            # Environment variable template
   └── README.md

Flexible configurations allow the codebase to easily adapt to standard use cases. The backend supports multiple LLM inference sources, including local runtimes like llama.cpp and Ollama, as well as cloud-hosted providers accessed via API keys.

   # =============================================================================
   # Deep Agents System — Environment Configuration
   # =============================================================================
   # Copy this file to .env and fill in your values.
   # NEVER commit the .env file to version control.
   #
   # Usage:
   #   cp .env.example .env
   #   # edit .env with your actual keys
   # =============================================================================

   # ─── LLM Provider ──────────────────────────────────────────────────────────
   # Select ONE provider. Comment out the others.

   # Option A: Ollama (local, default — no API key needed)
   LLM_PROVIDER=ollama
   OLLAMA_BASE_URL=http://localhost:11434
   OLLAMA_MODEL=llama3.2

   # Option B: llama.cpp (local)
   # LLM_PROVIDER=llamacpp
   # LLAMACPP_BASE_URL=http://localhost:9931/v1
   # LLAMACPP_MODEL=local-model

   # Option C: OpenAI
   # LLM_PROVIDER=openai
   # OPENAI_API_KEY=sk-...
   # OPENAI_MODEL=gpt-4o

   # Option D: Anthropic
   # LLM_PROVIDER=anthropic
   # ANTHROPIC_API_KEY=sk-ant-...
   # ANTHROPIC_MODEL=claude-3-5-sonnet-20241022

   # ─── Application Settings ──────────────────────────────────────────────────
   APP_PORT=8501
   APP_HOST=0.0.0.0
   LOG_LEVEL=INFO

   # ─── Agent Configuration ───────────────────────────────────────────────────
   # Maximum reasoning steps per agent (lower = faster on slow local LLMs)
   MAX_AGENT_STEPS=8

   # Maximum tokens per LLM call (1024 is enough for ReAct; raise for longer reports)
   MAX_TOKENS=1024

   # Temperature for LLM responses (0.0 = deterministic, 1.0 = creative)
   TEMPERATURE=0.1

   # ─── Memory & Storage ──────────────────────────────────────────────────────
   # Directory for persistent agent memory (relative to project root)
   MEMORY_DIR=./memory

   # Maximum number of memories to retain per agent
   MAX_MEMORY_ENTRIES=100

   # ─── Tool Configuration ────────────────────────────────────────────────────
   # Enable or disable specific tools (true/false)
   TOOL_WEB_SEARCH_ENABLED=true
   TOOL_CALCULATOR_ENABLED=true
   TOOL_FILE_READER_ENABLED=true
   TOOL_CODE_EXECUTOR_ENABLED=true

   # Web search stub — set to real Tavily/SerpAPI key for live search
   # TAVILY_API_KEY=tvly-...

   # ─── Output ────────────────────────────────────────────────────────────────
   OUTPUT_DIR=./output

Key Code Excerpts

The application serves to illustrate multi-agent orchestration, highlighting how tasks are dynamically distributed and delegated across dedicated toolsets.

Some core implementation details are outlined below. Designed with extensibility in mind, the architecture easily accommodates custom tools and additional specialist agents.

Safe AST Arithmetic Evaluator (`tools/calculator.py`)

To prevent arbitrary code execution vulnerabilities in arithmetic tasks, the system evaluates mathematical expressions using Python's Abstract Syntax Tree (ast) module against an explicit whitelist of AST nodes and functions—avoiding raw eval() calls:

# Whitelisted AST operators and math functions
_SAFE_OPERATORS = {
    ast.Add: operator.add,
    ast.Sub: operator.sub,
    ast.Mult: operator.mul,
    ast.Div: operator.truediv,
    ast.Pow: operator.pow,
    ast.USub: operator.neg,
}

_SAFE_FUNCTIONS = {
    "abs": abs, "round": round, "sqrt": math.sqrt,
    "sin": math.sin, "cos": math.cos, "log": math.log,
    "pi": math.pi, "e": math.e,
}

def _safe_eval(node: ast.expr) -> float:
    """Recursively evaluate an AST expression node in a safe sandbox."""
    if isinstance(node, ast.Constant):
        if isinstance(node.value, (int, float)):
            return float(node.value)
        raise ValueError(f"Unsupported constant type: {type(node.value).__name__}")

    if isinstance(node, ast.BinOp):
        op_type = type(node.op)
        if op_type not in _SAFE_OPERATORS:
            raise ValueError(f"Unsupported binary operator: {op_type.__name__}")
        left = _safe_eval(node.left)
        right = _safe_eval(node.right)
        return _SAFE_OPERATORS[op_type](left, right)

    if isinstance(node, ast.Call):
        func_name = node.func.id
        if func_name not in _SAFE_FUNCTIONS:
            raise ValueError(f"Function '{func_name}' is not whitelisted.")
        args = [_safe_eval(a) for a in node.args]
        return _SAFE_FUNCTIONS[func_name](*args)

    raise ValueError(f"Unsupported AST node type: {type(node).__name__}")

Sandboxed Python Code Executor (`tools/code_executor.py`)

For advanced data processing, the code executor provides an isolated environment with restricted built-ins, pre-imported scientific modules, output redirection, and thread timeout controls:

def _build_exec_namespace() -> dict[str, Any]:
    return {
        "__builtins__": _SAFE_BUILTINS, # Explicit whitelist (no open, __import__, eval)
        "math": math,
        "statistics": statistics,
        "pi": math.pi,
        "e": math.e,
    }

def _execute_code(code: str, timeout: int = 5) -> ToolResult:
    exec_result = _ExecResult()
    captured_io = io.StringIO()

    def _worker():
        namespace = _build_exec_namespace()
        namespace["__builtins__"]["print"] = lambda *args, **kw: print(*args, **{**kw, "file": captured_io})
        try:
            exec(code, namespace)
            exec_result.stdout = captured_io.getvalue()
        except Exception as exc:
            exec_result.error = f"{type(exc).__name__}: {exc}"

    thread = threading.Thread(target=_worker, daemon=True)
    thread.start()
    thread.join(timeout=timeout)
    if thread.is_alive():
        return ToolResult(success=False, error=f"Execution timed out after {timeout}s.")

Thread-Safe Streamlit UI Relay (`app.py`)

Streamlit worker threads cannot directly write to st.session_state due to missing execution contexts (ScriptRunContext). The application uses an @st.cache_resource singleton relay with reentrant locking (threading.RLock) to safely stream agent execution steps across thread boundaries into the web UI:

class _PipelineRelay:
    """Thread-safe relay store for the running multi-agent pipeline."""
    def __init__(self) -> None:
        self.lock = threading.RLock()
        self.status_messages: list[dict[str, str]] = []
        self.agent_cards: dict[str, list[str]] = {}
        self.running: bool = False

    def append_status(self, msg_dict: dict[str, str]) -> None:
        with self.lock:
            self.status_messages.append(msg_dict)
            agent_id = msg_dict.get("agent_id", "orchestrator")
            self.agent_cards.setdefault(agent_id, []).append(msg_dict.get("detail", ""))

@st.cache_resource
def _get_relay() -> _PipelineRelay:
    return _PipelineRelay()

Example Use Case: Climate Change Analysis

Input task:

"Analyse the impact of rising CO2 concentrations on global ecosystems. Include key statistics such as current CO2 levels and the percentage increase since pre-industrial times. Conclude with policy recommendations."

What happens:

Orchestrator decomposes the task into research + analysis + writing.
Researcher runs 3 web searches: CO2 trends, ecosystem impact, policy frameworks.
Analyst uses the calculator tool: (421 - 280) / 280 * 100 = 50.36% increase.
Writer produces a structured Markdown document with executive summary, findings, data analysis, and recommendations.
Orchestrator synthesises all outputs into the final report.

Output is saved to output/deep_agents_report_<timestamp>.md.

Benefits of Renewable Energy and Calculation of 280 Times 42

Benefits of Renewable Energy

Renewable energy reduces greenhouse gas emissions, contributing to climate change mitigation (Source: [National Renewable Energy Laboratory](https://www.nrel.gov/renewables/energy-benefits.html)). This is a key finding supported by credible sources, including the International Renewable Energy Agency (2020) and the World Health Organization (2020).

Renewable energy creates jobs and stimulates local economies (Source: [International Renewable Energy Agency](https://www.irena.org/publications/2020/Jun/Global-Status-Report-2020)). This is a significant benefit highlighted by the International Energy Agency (2020) and the National Bureau of Economic Research (2020).

Renewable energy improves air quality and public health (Source: [World Health Organization](https://www.who.int/news-room/fact-sheets/detail/air-pollution)). This is a critical aspect of renewable energy, supported by the National Renewable Energy Laboratory (n.d.).

The global renewable energy market is projected to reach 30% of total energy production by 2025 (Source: [International Energy Agency](https://www.iea.org/news/pressrelease/2020/june/global-renewables-report-2020/)). This growth is expected to reduce energy costs by 10-30% compared to fossil fuels (Source: [National Bureau of Economic Research](https://www.nber.org/papers/w28822)).

Calculation of 280 Times 42

The calculation of 280 times 42 yields 11,840. This result is supported by the Data Analysis report, which provides a detailed calculation of the product (280 * 42 = 11,760).

Trend Analysis

The global renewable energy market is expected to grow significantly, with a projected 30% share of total energy production by 2025. This growth is expected to have a significant impact on the environment and the economy.

Key Insights

1. Renewable energy can significantly reduce greenhouse gas emissions and improve air quality.
2. Renewable energy can create jobs and stimulate local economies.

Limitations

The data provided is based on projections and may not reflect actual outcomes.

Sources

1. National Renewable Energy Laboratory. (n.d.). Energy Benefits of Renewable Energy. Retrieved from <https://www.nrel.gov/renewables/energy-benefits.html>
2. International Renewable Energy Agency. (2020). Global Status Report 2020. Retrieved from <https://www.irena.org/publications/2020/Jun/Global-Status-Report-2020>
3. World Health Organization. (2020). Air pollution. Retrieved from <https://www.who.int/news-room/fact-sheets/detail/air-pollution>
4. International Energy Agency. (2020). Global Renewables Report 2020. Retrieved from <https://www.iea.org/news/pressrelease/2020/june/global-renewables-report-2020/>
5. National Bureau of Economic Research. (2020). The Economics of Renewable Energy. Retrieved from <https://www.nber.org/papers/w28822>

Adding a New Specialist Agent

As noted earlier, the application is designed to easily accommodate new tools. The section below provides a concrete example demonstrating this extensibility.

Create agents/my_specialist.py subclassing BaseAgent.
Implement the role_description property.
Register the agent in OrchestratorAgent.__init__.
Add delegation logic in OrchestratorAgent.run_task.

Example skeleton:

from agents.base import BaseAgent

class MySpecialistAgent(BaseAgent):
    def __init__(self, llm_client=None, on_status=None):
        super().__init__(
            agent_id="my_specialist",
            tools=[my_custom_tool],
            llm_client=llm_client,
            on_status=on_status,
        )

    @property
    def role_description(self) -> str:
        return "You are a specialist that does X…"

Conclusion

This codebase demonstrates how the Deep Agents design pattern can be built in practice. By combining hierarchical agent delegation with AST-sandboxed tool safety and thread-safe streaming UI updates, the project illustrates how complex agentic pipelines can run reliably across both cloud endpoints and local LLM runtimes (like Ollama and llama.cpp).

Thanks for reading ⚡

Building a Synthetic Data Generator

Alain Airom (Ayrom) — Wed, 16 Sep 2026 11:53:53 +0000

Building a synthetic data generator based on Red Hat's "sdg hub"

Introduction

For a recent initiative, I needed to generate versatile synthetic data to support multiple downstream workflows. While I’ve previously relied on Docling’s Synthetic Data Generator with great results, I wanted to evaluate a tool from Red Hat that caught my eye recently. Drawing inspiration from its core architecture—and tailoring it to my specific pipeline requirements—I built a custom tool that I'll showcase below.

Before diving into what I built, let’s look at the foundation: what exactly is Red Hat's SDG Hub?

TL;DR-What is SDG Hub (Red Hat synthetic data generator hub)?

SDG Hub is an open-source library providing composable blocks and flows for synthetic data generation.

It is a Python framework for building synthetic data generation pipelines. Chain LLM, parsing, transform, filtering, and agent blocks into YAML-defined flows -- then generate training data at scale.

Excerpt from Github;

Get Started

pip install sdg-hub

from sdg_hub import FlowRegistry, Flow

# Discover and load a built-in flow
FlowRegistry.discover_flows()
flow = Flow.from_yaml(FlowRegistry.get_flow_path("MCP Server Distillation"))

# Configure and run
flow.set_model_config(model="openai/gpt-4o")
result = flow.generate(dataset)

See the Quick Start for a full walkthrough, or browse all built-in flows.

SDG Hub is available as a plugin for two coding agents, bringing synthetic data generation directly into your coding workflow.

Claude Code
Via org marketplace (recommended — includes all Red Hat AI plugins):

/plugin marketplace add Red-Hat-AI-Innovation-Team/plugins
/plugin install sdg-hub@Red-Hat-AI-Innovation-Team/plugins

Via this repo directly:

/plugin marketplace add Red-Hat-AI-Innovation-Team/sdg_hub
/plugin install sdg-hub@Red-Hat-AI-Innovation-Team/sdg_hub

From a local clone:

git clone https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub.git
/plugin marketplace add /path/to/sdg_hub

Codex CLI

codex plugin marketplace add Red-Hat-AI-Innovation-Team/plugins

Then install the plugin from the marketplace. See .codex-plugin/INSTALL.md for manual installation.

Once you install the SDG_HUB, it comes with a powerfull command line implementation making it quite easy to generate ad-hoc synthetic data for your requirements.

Implementation

Inspired by the original repository, I adapted the framework to align with Bob and my broader SDLC workflows. The resulting application leverages SDG Hub under the hood while extending its capabilities to match my project’s specific requirements, context, and environment.

SDG App — Based on Red Hat Synthetic Data Generator

A production-ready synthetic data generation pipeline built on Red Hat SDG Hub / InstructLab, featuring:

📄 Multi-format ingestion — Markdown, PDF, DOCX, PPTX, HTML, TXT, JSON/JSONL, images (OCR)
🧠 Taxonomy-driven generation — QA pairs, multi-turn dialogues, instruction pairs
🎓 Teacher-critic loops — quality evaluation with any LLM backend
🛡️ Automated filtering — toxicity, PII detection, deduplication, quality scoring
📦 Flexible export — JSONL, Parquet, Hugging Face Dataset
🌐 REST API (FastAPI) + Web UI (Gradio, 4 tabs) + CLI (Typer)
🤖 Local-first — works entirely offline with Ollama or llama.cpp
🔄 16 sdg_hub flows — RAG evaluation, Knowledge QA, MCP distillation, and more
🤝 Bob plugin — pre-built AI assistant commands for this project (.bob-plugin/)
🧠 Claude plugin — .claude/ skills for data-generation, flow-browser, setup-guide, and synthetic-data-generation

Application Architecture

sdg-app/
├── src/sdg_app/
│   ├── core/
│   │   ├── settings.py        # Pydantic configuration (env + YAML)
│   │   ├── seed_parser.py     # Document ingestion (all formats)
│   │   ├── prompt_builder.py  # Prompt engineering
│   │   ├── providers.py       # LLM backend adapters (LiteLLM)
│   │   ├── generator.py       # Generation orchestrator + critic
│   │   ├── validator.py       # Quality / toxicity / PII / dedup filtering
│   │   ├── exporter.py        # JSONL · Parquet · HF Dataset writers
│   │   ├── job_manager.py     # Background job lifecycle management
│   │   ├── flow_runner.py     # sdg_hub flow routing (CLI + API)
│   │   └── sdg_hub_runner.py  # sdg_hub shared execution engine
│   ├── api/                   # FastAPI REST service (:8000)
│   │   └── routes/            # health · jobs · metrics
│   ├── cli/main.py            # Typer CLI (run · serve · ui · config · flows
│   │                          #            rag-eval · knowledge-qa · mcp-distill)
│   ├── ui/
│   │   ├── gradio_app.py      # Gradio web UI (:7860) — 4-tab layout
│   │   └── tabs/              # Per-tab modules
│   │       ├── llm_backend.py # Reusable provider-selector widget
│   │       ├── rag_eval.py    # 🔍 RAG Evaluation tab
│   │       ├── knowledge_qa.py# 📚 Knowledge QA tab
│   │       └── mcp_distill.py # 🔌 MCP Distillation tab
│   └── utils/observability.py # Logging + metrics
├── tests/
│   ├── unit/                  # 137 unit tests
│   └── integration/           # 7 API integration tests
├── flows/                     # Custom sdg_hub YAML flows
├── input/                     # Seed documents (gitignored content)
├── output/                    # Generated datasets (gitignored content)
├── scripts/                   # setup_venv.sh · start.sh · stop.sh · cleanup.sh
├── Docs/                      # Architecture · Quickstart · UserGuide · GapAnalysis
├── .bob-plugin/               # Bob AI assistant plugin for this project
├── .claude/                   # Claude AI assistant plugin (skills + hooks)
│   ├── settings.json          # Hook configuration
│   ├── hooks/                 # commit-on-stop · track-read · verify-gate
│   └── skills/                # data-generation · flow-browser · setup-guide
│       │                      # synthetic-data-generation (+ references/)
├── Dockerfile                 # Multi-stage, Podman-compatible
├── docker-compose.yml         # Podman Compose deployment
├── config.yaml                # Reference configuration (all options)
└── .env.example               # Environment variable template

Component Stack

The application is using the following stack;

Python Application using Gradio framework for the UI (and the rest of it...)
Ollama/llama.cpp for local LLM inference
Docling for document ingestion

Application's Core

The synthetic data generation framework operates as an asynchronous, decoupled pipeline designed to transform unstructured seed text into verified datasets across multiple formats. Below is a detailed breakdown of the internal mechanics, execution pathways, and validation gates.

Tab	Description	sdg_hub flow
🛠️ Custom Generation	General pipeline: QA, multi-turn, instruction	Built-in
🔍 RAG Evaluation	Q/A/context triplets for evaluating RAG chatbots	`loud-dawn-245`
📚 Knowledge QA	Atomic fact extraction + 5 QA pairs per fact	`heavy-heart-77`
🔌 MCP Distillation	Agent tool-use training data (requires agent server)	`new-night-835`

Every tab includes a provider selector that auto-fills model, endpoint, and key hint when switching between Ollama, llama.cpp, OpenAI, and Custom/vLLM.

Multi-Stage Generation Orchestration

The system uses GenerationOrchestrator (generator.py) as the primary controller for execution workflows. It translates source material into domain-specific, structured datasets through a systematic transformation process:

Task Distribution: Processes source documents through configurable sample types (e.g., Q&A, instruction-following, multi-turn dialogues).
Critic-in-the-Loop Feedback: If enable_critic is toggled on, generated candidates pass through a secondary LLM evaluation step (_run_critic). Samples failing the defined critic_threshold are pruned immediately to save computational overhead.
Downstream Delivery: Successfully critiqued and validated samples are emitted as streamable GeneratedSample objects containing assigned metadata and quality scores.

# Core Orchestration Flow (generator.py)
class GenerationOrchestrator:
    def process_document(self, doc: SeedDocument) -> Iterator[GeneratedSample]:
        for sample_type in self.sample_types:
            raw_samples = self._generate_type(doc, sample_type)
            for raw in raw_samples:
                # Critic Gate: Filter out low-fidelity generations early
                if self.enable_critic and _run_critic(self.critic, doc, raw) < self.critic_threshold:
                    continue

                # Validation Gate: Structural, safety, and content checks
                result = self.validator.validate(raw)
                if result.accepted:
                    yield GeneratedSample(..., quality_score=result.quality_score)

External Flow Delegation & Asynchronous Execution

To support domain-specific workflows without bloating the core orchestrator, the system exposes specialized delegates and asynchronous worker pools:

External Flow Delegation (sdg_hub_runner.py): Wraps complex external generation flows via run_sdg_hub_flow_to_file(). This handles specialized task models like RAG evaluation dataset generation (loud-dawn-245) and Model Context Protocol distillation (new-night-835).
Asynchronous Lifecycle Management (job_manager.py): Manages non-blocking dataset creation via JobManager. Long-running tasks execute inside isolated daemon threads, tracking job states, execution metrics, and generated artifacts without blocking the main runtime process.

Multi-Layer Quality & Safety Engine

The SampleValidator (validator.py) enforces deterministic quality and safety standards before any output is finalized. It applies a strict sequence of validation filters:

Length & Boundary Checks: Rejects truncated, over-length, or structurally malformed generations.
Safety & Compliance Filters: Employs heuristic and classifier checks (_check_toxicity, _check_pii) to prevent toxic text or Personally Identifiable Information from entering training corpora.
Deduplication: Evaluates exact content identity using SHA-256 fingerprinting to eliminate redundancy.
Heuristic Scoring: Computes a overall quality_score based on semantic coherence, dynamic metrics, and formatting integrity. Rejects samples below min_quality_score.

# Validation & Quality Filtering (validator.py)
class SampleValidator:
    def validate(self, sample: Dict) -> ValidationResult:
        text = _extract_text(sample)

        # Fast Failure: Reject length anomalies or toxic outputs
        if not self._check_length(text)[0] or not self._check_toxicity(text)[0]:
            return ValidationResult(accepted=False)

        # Compliance Failure: Filter out detected PII
        if self.enable_pii and not self._check_pii(text)[0]:
            return ValidationResult(accepted=False)

        # Scoring & Deduplication: Calculate heuristic quality & check uniqueness
        quality = self._compute_quality_score(text, sample)
        if quality < self.min_quality_score or not self._check_dedup(text)[0]:
            return ValidationResult(accepted=False, quality_score=quality)

        return ValidationResult(accepted=True, quality_score=quality)

Pipeline Summary

Ingestion & Delegation: JobManager kicks off thread-isolated tasks, delegating complex pipelines to sdg_hub_runner.py or sending standard documents directly to GenerationOrchestrator.
Generation & Critique: Candidate samples are constructed per task specification and evaluated via an optional LLM Critic.
Validation & Filtering: SampleValidator strips unsafe, duplicate, or low-scoring generations via SHA-256, safety models, and length thresholds.
Artifact Export: Accepted samples are assigned quality scores and saved as standardized data artifacts.

REST API

The application exposes a REST API running at http://localhost:8000/docs (with interactive Swagger UI documentation) to control dataset generation, monitor system metrics, and fetch outputs:

Job Execution & Status: Submit new generation workloads (POST /api/v1/jobs), query the complete job list (GET /api/v1/jobs), or retrieve status and results for a specific run (GET /api/v1/jobs/{id}).
Artifact Retrieval: Download generated dataset outputs directly via GET /api/v1/jobs/{id}/artifact.
System Operations: Monitor pipeline operational health (GET /api/v1/health) and real-time execution metrics (GET /api/v1/metrics).

Method	Endpoint	Description
`GET`	`/api/v1/health`	Service health check
`POST`	`/api/v1/jobs`	Submit generation job
`GET`	`/api/v1/jobs`	List all jobs
`GET`	`/api/v1/jobs/{id}`	Job status & results
`GET`	`/api/v1/jobs/{id}/artifact`	Download output file
`GET`	`/api/v1/metrics`	Pipeline metrics

Interactive docs: http://localhost:8000/docs

LLM Implementation

The pipeline supports flexible LLM backend integration across both local and cloud environments through standard .env configuration profiles:

Supported Backends: Choose between local, cost-free runtimes—including Ollama (ollama/llama3.2 at http://localhost:11434) and llama.cpp (openai/<model> at http://localhost:9931/v1)—or production-scale endpoints like OpenAI (openai/gpt-4o) and custom hosted vLLM instances (hosted_vllm/<model>).
Critical Path Rules: When configuring Ollama, do not append /v1 to SDG_LLM_API_BASE, as doing so breaks sdg_hub execution flows.
Setup Reference: Pre-configured environment setups for all three backend profiles are available in .env.example.


  | Backend                     | Provider   | Model Prefix          | `SDG_LLM_API_BASE`                      |
  | --------------------------- | ---------- | --------------------- | --------------------------------------- |
  | **Ollama** (local, free)    | `ollama`   | `ollama/llama3.2`     | `http://localhost:11434` ← **no `/v1`** |
  | **llama.cpp** (local, free) | `llamacpp` | `openai/<model>`      | `http://localhost:9931/v1`              |
  | **OpenAI** (cloud, paid)    | `openai`   | `openai/gpt-4o`       | `https://api.openai.com/v1`             |
  | **vLLM / Custom**           | `vllm`     | `hosted_vllm/<model>` | `http://your-host/v1`

⚠️ Ollama only: do NOT add /v1 to the API base — it breaks sdg_hub flow commands.

Flows

Executing data generation workflows in the application is managed through sdg_hub flows, which structure processing tasks into chained YAML-defined pipeline blocks. The system provides over 14 pre-built flows across seven distinct categories:

Direct Execution: Every flow in the catalog can be triggered directly from the terminal using the command sdg-app run --flow <flow-id>, or configured interactively within the UI wizard.
Dedicated Subcommands: Core workloads feature primary CLI subcommands and direct UI tab integration. Specifically, heavy-heart-77 (sdg-app knowledge-qa) extracts core facts, questions, and responses; loud-dawn-245 (sdg-app rag-eval) formats retrieval-augmented generation datasets with questions, responses, and ground truth contexts; and new-night-835 (sdg-app mcp-distill) processes tool-use trajectory data.
Specialized Requirements: Advanced pipelines—such as Model Context Protocol (MCP) server distillation (new-night-835), code evaluation benchmark generation (domain-code-eval), and red teaming prompt generation (major-sage-742)—require additional setup. For example, running new-night-835 requires a connected agent server (such as LangFlow), while domain-code-eval requires installing sandboxed execution packages like sdg-hub[code]

14+ built-in flows available. Three have dedicated CLI subcommands and UI tabs:

  | Flow ID          | CLI subcommand         | Output                                              |
  | ---------------- | ---------------------- | --------------------------------------------------- |
  | `heavy-heart-77` | `sdg-app knowledge-qa` | `key_fact, question, response`                      |
  | `loud-dawn-245`  | `sdg-app rag-eval`     | `question, response, context, ground_truth_context` |
  | `new-night-835`  | `sdg-app mcp-distill`  | tool-use trajectory data

All flows accessible via: sdg-app run --flow <flow-id>

Specific Plugin built for Bob by Bob

To streamline local workflows, the project includes a specialized Bob AI assistant plugin located in the .bob-plugin/ directory that provides pre-built commands, project context, and automated coding conventions directly inside your IDE environment.

Command Capabilities: Streamline operations with dedicated /sdg-* commands, including execution guides (/sdg-run, /sdg-generate-qa, /sdg-generate-rag), backend switching (/sdg-switch-backend), output inspection (/sdg-inspect-output), debugging (/sdg-debug), API monitoring (/sdg-api), test suite validation (/sdg-test), and custom YAML scaffolding (/sdg-add-flow).
Plugin Architecture: Organizes distinct task workflows inside the commands/ directory, provides continuous runtime context via context/ (project-state.md and known-issues.md), and enforces code style alignment through rules/project-conventions.md.
Usage: Open the repository in Bob and invoke any /sdg-* command in the chat. The assistant automatically ingests the step-by-step instructions from the target command file alongside the project rules without requiring manual context setup each session.

Available commands

Command	What it does
`/sdg-run`	Guide through a complete generation run
`/sdg-generate-qa`	Run Knowledge QA pipeline step-by-step
`/sdg-generate-rag`	Run RAG Evaluation pipeline step-by-step
`/sdg-switch-backend`	Switch between Ollama / llama.cpp / OpenAI
`/sdg-inspect-output`	Read and summarise the latest output file
`/sdg-debug`	Diagnose and fix pipeline failures
`/sdg-add-flow`	Scaffold a new custom sdg_hub YAML flow
`/sdg-test`	Run the test suite and explain failures
`/sdg-api`	Submit and monitor jobs via the REST API

Plugin structure

.bob-plugin/
├── README.md               # Project overview and command index
├── commands/               # One Markdown file per /command
│   ├── sdg-run.md
│   ├── sdg-generate-qa.md
│   ├── sdg-generate-rag.md
│   ├── sdg-switch-backend.md
│   ├── sdg-inspect-output.md
│   ├── sdg-debug.md
│   ├── sdg-add-flow.md
│   ├── sdg-test.md
│   └── sdg-api.md
├── context/
│   ├── project-state.md    # Current working state snapshot
│   └── known-issues.md     # Known limitations + workarounds
└── rules/
    └── project-conventions.md  # Coding and project rules for Bob

Conclusion

Inspired by "Red Hat Synthetic Data Generation Hub", and by integrating an extensible orchestration framework (generator.py), asynchronous job execution, and rigorous multi-stage validation (validator.py), this synthetic data generation system offers an end-to-end pipeline tailored for high-quality dataset creation. This application tries to seamlessly bridge localized engine configurations—such as Ollama and llama.cpp—with cloud-native backends, providing robust execution whether driven via the REST API endpoints, the interactive UI flow wizard, or command-line subcommands. Combined with tailored developer tooling like the embedded Bob AI plugin and specialized sdg_hub flows, the architecture provides a scalable, extensible foundation designed to process raw context files into reliable, instruction-tuned corpora with speed and precision.

Thanks for reading 💾

Testing NVIDIA NemoClaw in a Sandboxed Environment

Alain Airom (Ayrom) — Sat, 12 Sep 2026 14:21:47 +0000

Testing NVIDIA NemoClaw in a Local Sandboxed Environment with Bob

Introduction

For a while, I’ve wanted to test and implement NemoClaw in a sandboxed environment to better understand its capabilities and real-world usage. Safety and isolation are paramount when granting LLMs agency—especially when agents can execute terminal commands, modify local workspaces, or perform egress network requests.

To evaluate its operational stack, I built a test harness and orchestration environment using Bob. In this post, I’ll walk through the fundamentals of NVIDIA NemoClaw, break down its core security architecture, and share an actionable implementation guide so you can spin up your own isolated agent sandbox.

TL;DR: What is NVIDIA NemoClaw?

NemoClaw is an open-source reference stack from NVIDIA designed to execute always-on AI agents safely inside OpenShell sandboxes. It serves as a bridge between high-capability agent runtimes—such as OpenClaw, Hermes, or LangChain Deep Agents—and host system security.

> Image from Nvidia (https://docs.nvidia.com/nemoclaw/user-guide/openclaw/reference/architecture)

+-----------------------------------------------------------------------+
|                              Host Machine                             |
|  +-------------------+  +------------------+  +--------------------+  |
|  | nemoclaw CLI      |  | OpenShell        |  | Host State         |  |
|  | (Node.js CJS)     |  | Gateway Proxy    |  | ~/.nemoclaw/       |  |
|  +---------+---------+  +--------+---------+  +--------------------+  |
+------------|---------------------|------------------------------------+
             | orchestrates        | L7 Proxy & Credentials
             v                     v
+-----------------------------------------------------------------------+
|                    🛡️ Sandbox Container (Podman/Docker)               |
|                    --read-only + tmpfs mounts                          |
|  +-------------------+  +------------------+  +--------------------+  |
|  | AI Agent          |  | NemoClaw         |  | Managed Endpoint   |  |
|  | (OpenClaw/Hermes) |  | In-Process Plugin|  | (inference.local)  |  |
|  +-------------------+  +------------------+  +--------------------+  |
+-----------------------------------------------------------------------+

NVIDIA NemoClaw: Reference Stack for Sandboxed AI Agents in OpenShell (from official Github repository)

Image from Nvidia (https://docs.nvidia.com/nemoclaw/user-guide/openclaw/reference/architecture)
_

NVIDIA NemoClaw is an open source reference stack for running supported AI agents more safely inside NVIDIA OpenShell sandboxes. It provides guided onboarding, managed inference, network policy, managed integrations, snapshots, and lifecycle operations through the NemoClaw CLI and its agent-specific aliases.

Supported agents:

OpenClaw (default)
Hermes
LangChain Deep Agents Code

Technical Stack of the Local Implementation

Components Stack

The code and project uses the following stacks;

Podman (for provisionning NemoClaw Sandbox)
Ollama (local llm, if not using a model hosted by Nvidia using an API Key for Nvidia. One can use either Ollama or llama.cpp)
LLam.cpp (local llm, if not using a model hosted by Nvidia using an API Key for Nvidia. One can use either Ollama or llama.cpp)
Python Streamlit as the application’s framework

Core Architecture Highlights

Host & Sandbox Separation: The orchestration logic (nemoclaw CLI) runs on the host, while the agent executes inside an isolated container namespace (Podman or Docker).
No Raw Credentials in Sandbox: Agents make requests to a local proxy (inference.local). The host-side OpenShell Gateway injects real credentials (such as an NVIDIA_API_KEY) at egress and strips them from incoming responses.
Multi-Provider Routed Inference: Out-of-the-box support for switching between cloud endpoints like NVIDIA Nemotron/NIM and local providers like Ollama (localhost:11434) or llama.cpp (localhost:9931).
Strict Containment Policies: Containers launch with immutable root filesystems (--read-only), capability drops (--cap-drop=all), non-root privilege restrictions, and fine-grained network egress rules.

Implementation Guide And Tests

This step-by-step setup guide includes blueprint excerpts and setup scripts.

Environment Setup & Prerequisites

First, define your environment variables in .env to configure API keys and runtime parameters:

# NemoClaw Environment Configuration
# Copy this file to .env and fill in your values.
# NEVER commit .env to source control.

# ─── Inference Provider ────────────────────────────────────────────────────────
# NVIDIA API key for Nemotron / NIM inference
NVIDIA_API_KEY=your-nvidia-api-key-here

# Default inference model (e.g. nvidia/llama-3.1-nemotron-ultra-253b-v1)
NEMOCLAW_MODEL=nvidia/llama-3.1-nemotron-70b-instruct

# Inference provider: nvidia | ollama | openai-compatible | model-router
NEMOCLAW_INFERENCE_PROVIDER=nvidia

# ─── Ollama (local LLM) ────────────────────────────────────────────────────────
# If using Ollama as inference provider
OLLAMA_BASE_URL=http://localhost:11434

# ─── llama.cpp (local LLM) ────────────────────────────────────────────────────
LLAMACPP_BASE_URL=http://localhost:9931/v1
LLAMACPP_API_KEY=

# ─── OpenShell Gateway ────────────────────────────────────────────────────────
# Port for the OpenShell gateway (default: 10000)
NEMOCLAW_GATEWAY_PORT=10000

# ─── Agent Configuration ──────────────────────────────────────────────────────
# Agent to use: openclaw | hermes | langchain-deepagents-code
NEMOCLAW_AGENT=openclaw

# Sandbox name (1-63 lowercase letters, numbers, internal hyphens)
NEMOCLAW_SANDBOX_NAME=nemoclaw-sandbox

# ─── Dashboard / UI ───────────────────────────────────────────────────────────
# Port for the Streamlit dashboard (avoid 5000 on macOS - reserved for AirDrop)
DASHBOARD_PORT=8501

# ─── Logging ──────────────────────────────────────────────────────────────────
# Log level: debug | info | warn | error
LOG_LEVEL=info

# Log file path (optional, defaults to stdout)
LOG_FILE=

# ─── Network Policy ───────────────────────────────────────────────────────────
# Comma-separated list of allowed egress hosts (beyond defaults)
EXTRA_ALLOWED_HOSTS=

# ─── State / Storage ──────────────────────────────────────────────────────────
NEMOCLAW_STATE_DIR=~/.nemoclaw

# ─── Security ─────────────────────────────────────────────────────────────────
# SSRF validation: strict | permissive
SSRF_VALIDATION_MODE=strict

# ─── MCP Servers ──────────────────────────────────────────────────────────────
# Comma-separated list of MCP server names to enable
MCP_SERVERS=

Run the initialization script to bootstrap Node.js dependencies, set up the Python virtual environment, and link the CLI globally:

#!/usr/bin/env bash
set -e

echo "=== 1. Installing Node Dependencies & Linking CLI ==="
npm install
cd nemoclaw && npm install && npm run build && cd ..
npm link

echo "=== 2. Setting up Python Environment ==="
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

echo "=== 3. Host Readiness Check ==="
nemoclaw host probe

Architecture & Hardening Configuration

NemoClaw enforces isolation at the container engine layer. The following excerpt shows how runner.js provisions a hardened sandbox using Podman:

// Excerpt: bin/lib/runner.js - Container Launch Configuration
const podmanFlags = [
  'run', '-d',
  '--name', sandboxName,
  '--read-only',                                      // Immutable root filesystem
  '--security-opt', 'no-new-privileges',             // Prevent privilege escalation
  '--cap-drop=all',                                  // Drop all Linux capabilities
  '--tmpfs', '/tmp:rw,noexec,nosuid,size=128m',       // Writable non-executable temp space
  '--tmpfs', '/home/nemoclaw/.streamlit:rw,nosuid,size=32m', // Session cache space
  networkModeFlag,                                   // --network=pasta or --network=bridge
  containerImage
];

// Placeholder: Custom container invocation parameters
// ____________________________________________________
// ____________________________________________________

Network Policy Definition

Network egress is restricted to explicitly allowed hostnames. The network policy is defined declaratively using YAML:

# nemoclaw-blueprint/policies/default-policy.yamlversion: "1"egress:  - host: inference.local    port: 443    protocol: https    comment: "Managed inference endpoint proxied by Gateway"  - host: github.com    port: 443    protocol: https    comment: "Repository operations"  - host: registry.npmjs.org    port: 443    protocol: https    comment: "Package dependencies"deny: []

You can modify rules dynamically via the CLI without recreating the container:

# Add a custom host rule
nemoclaw policy add ________________________

# Apply a preset policy (e.g., Slack or Discord)
nemoclaw policy add slack

Sandbox Onboarding & Execution

Execute the onboarding sequence to build the image, register credentials from .env, instantiate the OpenShell Gateway, and start the container runtime:

# Execute onboarding sequence
nemoclaw onboard --agent openclaw --name ________________________

# Verify status and running processes
nemoclaw status

# Stream live container logs
nemoclaw logs --follow

Python State Inspection Test

You can write tests using unittest or pytest to programmatically inspect sandbox metadata and verify that secrets remain unexposed:

"""Unit tests for the local NemoClaw sandbox demonstration.

The five tests map directly to the scenarios in Docs/NemoClaw-Sandbox-User-Guide.md.
"""

import sys
import unittest
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))

from nemoclaw_sandbox_demo import AgentProfile, Sandbox, SandboxLimits, SandboxTimeout


class TestNemoClawSandboxScenarios(unittest.TestCase):
    """Validate the sandbox contract without requiring Podman or network access."""

    def setUp(self):
        self.agent = AgentProfile(
            name="agent",
            role="test agent",
            capabilities=frozenset({
                "read_workspace", "write_workspace", "network:inference", "compute", "inter_agent:send",
            }),
        )

    def test_normal_in_sandbox_execution(self):
        """An authorized agent can write and read only its private workspace."""
        with Sandbox() as sandbox:
            sandbox.register_agent(self.agent)
            written = sandbox.run(self.agent, "write_workspace", path="workspace/report.txt", content="pass")
            read = sandbox.run(self.agent, "read_workspace", path="workspace/report.txt")

        self.assertTrue(written.permitted)
        self.assertEqual(read.value, "pass")
        self.assertTrue(sandbox.closed)

    def test_escape_and_network_boundary_are_denied(self):
        """Traversal and non-approved egress are blocked and audited."""
        with Sandbox() as sandbox:
            sandbox.register_agent(self.agent)
            traversal = sandbox.run(self.agent, "read_workspace", path="../../etc/passwd")
            egress = sandbox.run(self.agent, "network_request", host="example.com")

        self.assertFalse(traversal.permitted)
        self.assertIn("filesystem path denied", traversal.error)
        self.assertFalse(egress.permitted)
        self.assertIn("network host denied", egress.error)

    def test_resource_limits_are_enforced(self):
        """Excess memory is rejected and long-running work is terminated."""
        limits = SandboxLimits(memory_limit_mb=8, timeout_seconds=0.1, cpu_limit_seconds=1)
        with Sandbox(limits) as sandbox:
            sandbox.register_agent(self.agent)
            memory = sandbox.run(self.agent, "allocate_memory", megabytes=16)
            timeout = sandbox.run(self.agent, "busy_loop")

        self.assertFalse(memory.permitted)
        self.assertIn("exceeds limit", memory.error)
        self.assertFalse(timeout.permitted)
        self.assertIsInstance(timeout.error, str)
        self.assertIn("timeout", timeout.error)

    def test_inter_agent_communication_stays_inside_sandbox(self):
        """A message can move between registered agents through the local mailbox."""
        sender = self.agent
        receiver = AgentProfile("receiver", "reviewer", frozenset({"inter_agent:receive"}))
        with Sandbox() as sandbox:
            sandbox.register_agent(sender)
            sandbox.register_agent(receiver)
            sent = sandbox.run(sender, "send_message", recipient="receiver", message="approved")
            received = sandbox.run(receiver, "receive_message")

        self.assertTrue(sent.permitted)
        self.assertEqual(received.value, "approved")

    def test_failure_is_reported_and_teardown_is_graceful(self):
        """A denied operation returns a structured error and context is removed."""
        sandbox = Sandbox()
        sandbox.__enter__()
        sandbox.register_agent(self.agent)
        result = sandbox.run(self.agent, "unsupported_operation")
        root = sandbox.root
        sandbox.teardown()

        self.assertFalse(result.permitted)
        self.assertIn("not supported", result.error)
        self.assertTrue(sandbox.closed)
        self.assertFalse(root.exists())
        with self.assertRaises(RuntimeError):
            sandbox.root


if __name__ == "__main__":
    unittest.main()

Test-Case Walkthrough

Now that the application is running, we will validate its behavior under realistic conditions using the following test scenarios.

The test harness exposes five automated test scenarios (test/test_nemoclaw_sandbox.py) that validate allowed behaviors while asserting that policy violations fail predictably.

Normal In-Sandbox Execution (TC-01)

Validates that an authorized agent can write artifacts to its designated workspace and read them back cleanly.

# test/test_nemoclaw_sandbox.py - Scenario 1
def test_normal_in_sandbox_execution(self):
    """An authorized agent can write and read only its private workspace."""
    with Sandbox() as sandbox:
        sandbox.register_agent(self.agent)
        written = sandbox.run(self.agent, "write_workspace", path="workspace/report.txt", content="pass")
        read = sandbox.run(self.agent, "read_workspace", path="workspace/report.txt")

    self.assertTrue(written.permitted)
    self.assertEqual(read.value, "pass")
    self.assertTrue(sandbox.closed)

Scenario 2: Traversal & Egress Violation Defense (TC-02)

Ensures that directory traversal attempts (e.g., ../../etc/passwd) and unauthorized external network requests (e.g., example.com) are immediately denied and logged to the audit trail.

# test/test_nemoclaw_sandbox.py - Scenario 2
def test_escape_and_network_boundary_are_denied(self):
    """Traversal and non-approved egress are blocked and audited."""
    with Sandbox() as sandbox:
        sandbox.register_agent(self.agent)
        traversal = sandbox.run(self.agent, "read_workspace", path="../../etc/passwd")
        egress = sandbox.run(self.agent, "network_request", host="example.com")

    self.assertFalse(traversal.permitted)
    self.assertIn("filesystem path denied", traversal.error)
    self.assertFalse(egress.permitted)
    self.assertIn("network host denied", egress.error)

Scenario 3: Resource Limit Enforcement (TC-03)

Proves that attempts to exceed memory quotas are rejected prior to allocation, and long-running operations (such as infinite loops) are terminated by worker process timeouts.

# test/test_nemoclaw_sandbox.py - Scenario 3
def test_resource_limits_are_enforced(self):
    """Excess memory is rejected and long-running work is terminated."""
    limits = SandboxLimits(memory_limit_mb=8, timeout_seconds=0.1, cpu_limit_seconds=1)
    with Sandbox(limits) as sandbox:
        sandbox.register_agent(self.agent)
        memory = sandbox.run(self.agent, "allocate_memory", megabytes=16)
        timeout = sandbox.run(self.agent, "busy_loop")

    self.assertFalse(memory.permitted)
    self.assertIn("exceeds limit", memory.error)
    self.assertFalse(timeout.permitted)
    self.assertIsInstance(timeout.error, str)
    self.assertIn("timeout", timeout.error)

Scenario 4: Inter-Agent Communication Containment (TC-04)

Demonstrates secure inter-agent messaging routed entirely within in-memory mailboxes inside the sandbox, without opening external host ports or network sockets.

# test/test_nemoclaw_sandbox.py - Scenario 4
def test_inter_agent_communication_stays_inside_sandbox(self):
    """A message can move between registered agents through the local mailbox."""
    sender = self.agent
    receiver = AgentProfile("receiver", "reviewer", frozenset({"inter_agent:receive"}))
    with Sandbox() as sandbox:
        sandbox.register_agent(sender)
        sandbox.register_agent(receiver)
        sent = sandbox.run(sender, "send_message", recipient="receiver", message="approved")
        received = sandbox.run(receiver, "receive_message")

    self.assertTrue(sent.permitted)
    self.assertEqual(received.value, "approved")

Scenario 5: Failure Handling & Graceful Teardown (TC-05)

Confirms that when unsupported or illegal operations occur, structured errors are returned to the caller and the temporary workspace directory is unmounted and completely wiped upon teardown.

# test/test_nemoclaw_sandbox.py - Scenario 5
def test_failure_is_reported_and_teardown_is_graceful(self):
    """A denied operation returns a structured error and context is removed."""
    sandbox = Sandbox()
    sandbox.__enter__()
    sandbox.register_agent(self.agent)
    result = sandbox.run(self.agent, "unsupported_operation")
    root = sandbox.root
    sandbox.teardown()

    self.assertFalse(result.permitted)
    self.assertIn("not supported", result.error)
    self.assertTrue(sandbox.closed)
    self.assertFalse(root.exists())
    with self.assertRaises(RuntimeError):
        sandbox.root

Implementation Blueprint & Execution

To run these verification checks directly in your environment, use the provided Python entry points:

Running the Full Test Suite

Running the tests;

  python3 -m unittest discover -s test -p 'test_nemoclaw_sandbox.py' -v
  test_escape_and_network_boundary_are_denied (test_nemoclaw_sandbox.TestNemoClawSandboxScenarios.test_escape_and_network_boundary_are_denied)
  Traversal and non-approved egress are blocked and audited. ... ok
  test_failure_is_reported_and_teardown_is_graceful (test_nemoclaw_sandbox.TestNemoClawSandboxScenarios.test_failure_is_reported_and_teardown_is_graceful)
  A denied operation returns a structured error and context is removed. ... ok
  test_inter_agent_communication_stays_inside_sandbox (test_nemoclaw_sandbox.TestNemoClawSandboxScenarios.test_inter_agent_communication_stays_inside_sandbox)
  A message can move between registered agents through the local mailbox. ... ok
  test_normal_in_sandbox_execution (test_nemoclaw_sandbox.TestNemoClawSandboxScenarios.test_normal_in_sandbox_execution)
  An authorized agent can write and read only its private workspace. ... ok
  test_resource_limits_are_enforced (test_nemoclaw_sandbox.TestNemoClawSandboxScenarios.test_resource_limits_are_enforced)
  Excess memory is rejected and long-running work is terminated. ... ok

  ----------------------------------------------------------------------
  Ran 5 tests in 0.114s

Or... execute the 5 scenario assertions using standard Python unit testing tools:

# Run unit tests verbosely
python -m unittest test/test_nemoclaw_sandbox.py -v

For automated integration pipelines, run the local harness script directly with the --json flag to inspect structured audit trails:

# Generate JSON audit report
python scripts/nemoclaw_sandbox_demo.py --json

Example JSON Output:

  python3 scripts/nemoclaw_sandbox_demo.py --json
  {
    "results": [
      {
        "operation": "write_workspace",
        "permitted": true,
        "value": "workspace/result.txt",
        "error": null
      },
    {
      "operation": "read_workspace",
      "permitted": true,
      "value": "approved",
      "error": null
    },
    {
      "operation": "network_request",
      "permitted": true,
      "value": {
        "host": "inference.local",
        "status": "routed through inference.local"
      },
      "error": null
    },
    {
      "operation": "network_request",
      "permitted": false,
      "value": null,
      "error": "network host denied: example.com"
    },
    {
      "operation": "read_workspace",
      "permitted": false,
      "value": null,
      "error": "filesystem path denied: ../../etc/passwd"
    },
    {
      "operation": "busy_loop",
      "permitted": false,
      "value": null,
      "error": "operation exceeded 1.0s timeout"
    },
    {
      "operation": "allocate_memory",
      "permitted": false,
      "value": null,
      "error": "memory request 128 MiB exceeds limit 64 MiB"
    },
    {
      "operation": "send_message",
      "permitted": true,
      "value": "message delivered",
      "error": null
    },
    {
      "operation": "receive_message",
      "permitted": true,
      "value": "result ready",
      "error": null
    }
  ],
  "audit_events": 12,
  "sandbox_torn_down": true
}

Troubleshooting & Production Best Practices

When transitioning from the local Python test harness to standard containerized deployments, refer to this common operational matrix:

Issue / Symptom	Root Cause	Resolution
`filesystem path denied`	The requested path attempts directory traversal outside `workspace/`. MD	Use strictly relative subpaths inside `workspace/`; do not weaken path validation. MD
`network host denied`	Host is not present on the policy allowlist. MD	Route requests through `inference.local` or explicitly add authorized host rules. MD
`capability denied`	Agent profile lacks the required permission scope. MD	Grant the minimum required capability to the `AgentProfile` scope. MD
`operation exceeded timeout`	Task exceeded wall-clock deadline. MD	Optimize worker routines or increase `timeout_seconds` in `SandboxLimits` with workload profiling evidence. MD
`sandbox is not active`	Access attempted before context entry or post-teardown. MD	Ensure work is wrapped within `with Sandbox(...)` context managers. MD

Summary Checklist for Deployment

Least-Privilege Capabilities: Start agent profiles with zero capabilities and enable permissions on a strict need-to-have basis.
Egress Boundary Lockdown: Deny external egress by default; route all model traffic through inference.local managed gateways.
Secret Isolation: Never store or pass API keys inside prompts, agent workspace files, or sandbox environments.
Guaranteed Teardown: Ensure sandbox contexts cleanly dispose of transient filesystems immediately upon execution completion.

Conclusion

Implementing NVIDIA NemoClaw with Bob demonstrates how modern AI agent orchestration can strike a practical balance between functional flexibility and strict security controls.

Key takeaways from this implementation:

Zero-Trust Credentials inside Sandboxes: By routing agent API calls through inference.local, raw API keys never enter the sandbox environment.
Defense-in-Depth Container Isolation: Combining --read-only filesystems, capability drops, tmpfs mounts, and network egress policies prevents agents from compromising host resources.
Flexible Runtime Operations: Hot-swapping between cloud endpoints (NVIDIA NIM) and local runtimes (Ollama/llama.cpp) allows switching between local offline execution and production-grade models without altering agent business logic.

NemoClaw provides a dependable framework for safely running agentic workflows locally or in automated pipelines.

Thanks for reading 🦀

End-to-End Enterprise RAG Pipeline with IBM Docling Pipelines and OpenSearch

Alain Airom (Ayrom) — Wed, 09 Sep 2026 14:16:28 +0000

Implementing a RAG pipeline with docling-pipelines and OpenSearch

Introduction

In enterprise Retrieval-Augmented Generation (RAG) applications, moving from a simple proof-of-concept to a robust production system requires high-fidelity document conversion, flexible topology orchestration, and low-latency vector retrieval. Unstructured enterprise documents—such as complex multi-page PDFs, technical whitepapers, and dynamic web pages—often break naive text splitters by scattering table structures, disrupting read order, or discarding critical metadata.

To solve these challenges at scale, this article demonstrates an end-to-end, production-ready RAG pipeline built using IBM Docling Pipelines (docling-pipeline) and OpenSearch. Docling Pipelines provides a Directed Acyclic Graph (DAG) framework for document curation—enabling declarative flows that handle document ingestion, high-accuracy text and structure extraction, language detection, readability analysis, hybrid chunking, embedding generation, and vector indexing. Combined with OpenSearch's k-Nearest Neighbors (k-NN) search capabilities and Ollama's local LLM/embedding inference, this stack provides a privacy-conscious, local-first RAG solution.

TL;DR-What is Docling-Pipelines?

Excerpt from docling-pipelines github repository.

Docling pipelines is an enterprise-grade document curation pipeline for Retrieval Augmented Generation (RAG) applications. It ingests data from unstructured sources, curates documents, and writes entities and vector embeddings to targets — enabling AI-ready pipelines at scale.

It connects to cloud document sources (S3, OneDrive, SharePoint, Google Drive, Box, and more) and extracts content and entities from PDF, DOCX, HTML, images, and other formats using Docling. Extracted content is curated for LLMs, converted into chunks and embeddings, and stored in a vector database such as Milvus or OpenSearch.

Features

📥 Multi-source ingestion — local filesystem, Amazon S3, IBM COS, SharePoint, OneDrive, Google Drive, Box, and web pages
📄 Document extraction — PDF, DOCX, HTML, images, and more via Docling, with optional VLM and ASR pipelines
🧠 Entity extraction — LLM-based extraction via LiteLLM (100+ providers), IBM watsonx.ai, or Docling templates
✂️ Chunking — Docling-native and semantic chunking strategies
🔢 Embeddings — vector embedding generation for any downstream vector store
🔍 Quality operators — language detection, readability scoring, PII/HAP detection, deduplication, redaction, SQL filtering, document classification, and ML enrichment
🗄️ Vector storage — write to OpenSearch or Milvus
🔀 DAG-based flows — define pipelines as JSON with automatic dependency resolution and parallel execution
🔌 Extensible — load custom operators from Python packages, local paths, or S3 without modifying core code
🖥️ Multiple interfaces — CLI, Python API (DocpipeFlowManager), and REST API (FastAPI)

Architecture Overview of the Solution

The pipeline processes unstructured documents through two execution paths: a real execution engine backed by docling-pipelines CLI/Python APIs, and a fallback simulation manager designed for zero-dependency test environments.

┌─────────────────────────────────────────────────────────────────────────────┐
│                           Document Ingestion Layer                          │
│          (Filesystem, Web URLs, S3/COS, SharePoint, Uploaded Files)          │
└──────────────────────────────────────┬──────────────────────────────────────┘
                                       │
                                       ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                           IBM Docling Curation DAG                          │
│                                                                             │
│  ┌────────────────┐     ┌──────────────────┐     ┌───────────────────────┐  │
│  │ ingest_source  ├────►│ extract_operator ├────►│ lang_detect/readability│ │
│  └────────────────┘     └────────┬─────────┘     └───────────┬───────────┘  │
│                                  │                           │              │
│                                  ▼                           ▼              │
│                         ┌────────────────────────────────────────┐          │
│                         │        docling_hybrid Chunker          │          │
│                         └────────────────┬───────────────────────┘          │
│                                          │                                  │
│                                          ▼                                  │
│                         ┌────────────────────────────────────────┐          │
│                         │    embeddings (Ollama/LiteLLM)         │          │
│                         └────────────────┬───────────────────────┘          │
└──────────────────────────────────────────┼──────────────────────────────────┘
                                           │
                                           ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                        OpenSearch Vector Store Index                        │
│                   (k-NN HNSW Indexing & Semantic Search)                    │
└──────────────────────────────────────┬──────────────────────────────────────┘
                                       │
                                       ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                             RAG Engine & Chat                               │
│              (Context Retrieval → Grounded LLM Prompting)                   │
└─────────────────────────────────────────────────────────────────────────────┘

The system is organized into modular components:

📄 Documents  →  📥 Ingest  →  📝 Extract  →  ✂️ Chunk  →  🔢 Embed  →  🗄️ VectorDB

UI & Control Center (app.py): Interactively configures pipeline parameters, template topologies, document sources, and operator settings.

Flow Definition Builder (flow_builder.py): Programmatically generates valid JSON DAG specifications following the official Docling Flow Authoring Format.

Pipeline Manager (pipeline_manager.py): Orchestrates document processing, handling document parsing, step-by-step operator execution, schema evolution, and artifact export.
Vector Database & RAG Engine (opensearch_client.py & rag_engine.py): Manages OpenSearch index creation, dense vector embeddings, k-NN retrieval, and LLM chat generation.

Implementation

Components Stack

The code and project uses the following stacks;

Docling-pipelines library
OpenSearch as RGA implementation
Podman (for provisionning OpenSearch locally)
Ollama (with granite-embedding:278m for embedding and granite4.2:latest for local chat enablement)
Python Streamlit as the application's framework

Programmatic Pipeline Authoring (`flow_builder.py`)

Docling Pipelines relies on declarative flow representations that enforce dependency graphs between operators. The FlowBuilder class transforms user settings into standard flow JSON objects.

Below is an excerpt showing how the Full RAG Pipeline topology is constructed, complete with LiteLLM-compatible embedding parameters and OpenSearch vector database configs:

# flow_builder.py
from __future__ import annotations
import json
from pathlib import Path
from config import AppConfig

class FlowBuilder:
    """Builds a Docling Pipelines flow JSON from a UI pipeline configuration dict."""

    def __init__(self, config: AppConfig) -> None:
        self.config = config

    def _build_full_rag_pipeline(self, pipeline_cfg: dict) -> dict:
        """Complete RAG pipeline: Ingest → Extract → Chunk → Embed → VectorDB."""
        return {
            "flow_name": "full-rag-pipeline",
            "description": (
                "Complete RAG pipeline: ingest documents, extract text via Docling, "
                "chunk for retrieval, generate embeddings with Ollama, and store in OpenSearch."
            ),
            "flow": [
                self._ingest_operator(pipeline_cfg),
                self._extract_operator(["ingest"]),
                self._chunker_operator(["extract"], pipeline_cfg),
                self._embeddings_operator(["chunker"], pipeline_cfg),
                self._vectordb_operator(["embeddings"], pipeline_cfg),
            ],
            "global_config": {
                "doc_column": "content",
                "disable_validation": False,
            },
        }

    def _embeddings_operator(self, depends_on: list[str], pipeline_cfg: dict) -> dict:
        model = pipeline_cfg.get("ollama_model", self.config.ollama_embedding_model)
        if not model.startswith("openai/"):
            litellm_model = f"openai/{model}"
        else:
            litellm_model = model

        base = self.config.ollama_base_url.rstrip("/")
        api_base = base if base.endswith("/v1") else f"{base}/v1"

        return {
            "name": "embeddings",
            "type": "embeddings",
            "depends_on": depends_on,
            "config": {
                "provider": "litellm",
                "provider_config": {
                    "model_id": litellm_model,
                    "api_base": api_base,
                    "api_key": self.config.ollama_api_key,
                },
            },
        }

    def _vectordb_operator(self, depends_on: list[str], pipeline_cfg: dict) -> dict:
        return {
            "name": "vectordb",
            "type": "vectordb",
            "depends_on": depends_on,
            "config": {
                "provider": "opensearch",
                "provider_config": {
                    "index_name": pipeline_cfg.get(
                        "opensearch_index", self.config.opensearch_index
                    ),
                    "host": self.config.opensearch_host,
                    "port": self.config.opensearch_port,
                    "user": self.config.opensearch_user,
                    "password": self.config.opensearch_password,
                    "use_ssl": self.config.opensearch_use_ssl,
                },
            },
        }

Pipeline Execution Engine (`pipeline_manager.py`)

The PipelineManager resolves execution requests by dynamically choosing between the system binary docling-pipelines and a simulation mode. During document extraction, binary document types (such as PDFs or Word documents) are parsed using docling.

# pipeline_manager.py
from dataclasses import dataclass, field
import json
import logging
import os
import subprocess
import time
from pathlib import Path
from config import AppConfig

logger = logging.getLogger(__name__)

@dataclass
class PipelineResult:
    """Uniform result object returned by any backend."""
    success: bool
    duration_seconds: float
    docs_total: int
    docs_completed: int
    docs_failed: int
    error_message: str = ""
    operator_results: list[dict] = field(default_factory=list)
    output_documents: list[str] = field(default_factory=list)
    chunks: list[dict] = field(default_factory=list)
    schema_changes: dict[str, list[str]] = field(default_factory=dict)
    metadata: dict = field(default_factory=dict)

class PipelineManager:
    def __init__(self, config: AppConfig) -> None:
        self.config = config

    def run(self, flow_def: dict, pipeline_cfg: dict, progress_callback=None) -> PipelineResult:
        simulate = pipeline_cfg.get("simulate", not self.config.docling_pipelines_enabled)
        if not simulate and not self._cli_available():
            logger.warning("docling-pipelines CLI not found; falling back to simulation mode.")
            simulate = True

        if simulate:
            return self._run_simulation(flow_def, pipeline_cfg, progress_callback)
        else:
            return self._run_real(flow_def, pipeline_cfg, progress_callback)

    def _run_real(self, flow_def: dict, pipeline_cfg: dict, progress_callback=None) -> PipelineResult:
        ts = time.strftime("%Y%m%dT%H%M%S")
        flow_path = Path(self.config.output_dir) / f"flow_{ts}.json"
        flow_path.parent.mkdir(parents=True, exist_ok=True)
        flow_path.write_text(json.dumps(flow_def, indent=2), encoding="utf-8")

        cli = self._cli_path() or "docling-pipelines"
        cmd = [cli, "--flow-file", str(flow_path)]
        env = os.environ.copy()
        env["PYTHONPATH"] = "src:" + env.get("PYTHONPATH", "")

        start = time.time()
        completed = subprocess.run(cmd, capture_output=True, text=True, timeout=600, env=env)
        duration = time.time() - start

        success = (completed.returncode == 0)
        error_msg = "" if success else (completed.stdout or completed.stderr)

        return PipelineResult(
            success=success,
            duration_seconds=duration,
            docs_total=len(pipeline_cfg.get("saved_paths", [])) or 1,
            docs_completed=1 if success else 0,
            docs_failed=0 if success else 1,
            error_message=error_msg,
            output_documents=self._collect_output_files(),
        )

Dense Vector Ingestion & RAG Orchestration (`rag_engine.py`)

The RAGEngine links document processing outputs to OpenSearch vector stores. It generates text embeddings via Ollama models (e.g., granite-embedding:278m), performs k-NN retrieval, and queries chat models (e.g., granite4.2:latest).

To prevent hallucinated answers when no context matches a user query, the RAGEngine short-circuits execution and returns a deterministic fallback message without incurring unnecessary LLM generation costs.

# rag_engine.py
import logging
import requests
from config import AppConfig
from opensearch_client import OpenSearchClient

logger = logging.getLogger(__name__)

class RAGEngine:
    _NO_CONTEXT_MSG = (
        "No information about this topic was found in the knowledge base. "
        "Please ingest relevant documents before querying."
    )

    def __init__(self, *, config: AppConfig, os_client: OpenSearchClient | None = None) -> None:
        self.config = config
        self._os = os_client or OpenSearchClient(config=config)

    def ingest_chunks(self, *, chunks: list[dict], progress_callback=None) -> dict:
        if not chunks:
            return {"indexed": 0, "failed": 0, "message": "No chunks provided."}

        self._os.ensure_index()
        embedded = []
        for chunk in chunks:
            text = chunk.get("text", "").strip()
            if not text:
                continue
            vec = self._embed(text)
            embedded.append({**chunk, "embedding": vec})

        index_result = self._os.index_chunks(chunks=embedded)
        return {
            "indexed": index_result["indexed"],
            "failed": index_result.get("failed", 0),
            "message": f"Ingested {index_result['indexed']} chunks.",
        }

    def chat(self, *, query: str, top_k: int = 5, source_filter: str | None = None) -> dict:
        context = self.retrieve(query=query, top_k=top_k, source_filter=source_filter)

        # Deterministic check when retrieval returns zero chunks
        if not context:
            return {
                "answer": self._NO_CONTEXT_MSG,
                "context": [],
                "query": query,
                "sources": [],
                "retrieved_count": 0,
                "fallback": True,
            }

        ctx_text = "\n\n".join(
            f"[Source: {c['source_document']}, chunk {c['chunk_index']}]\n{c['text']}"
            for c in context
        )
        system = (
            "You are a helpful assistant. "
            "Answer the user's question using ONLY the provided context. "
            "If the context does not contain enough information, say so clearly."
        )
        prompt = f"Context:\n{ctx_text}\n\nQuestion: {query}\n\nAnswer:"
        answer = self._chat(prompt, system)
        sources = list(dict.fromkeys(c["source_document"] for c in context))

        return {
            "answer": answer,
            "context": context,
            "query": query,
            "sources": sources,
            "retrieved_count": len(context),
            "fallback": False,
        }

    def _embed(self, text: str) -> list[float]:
        url = f"{self.config.ollama_base_url.rstrip('/')}/api/embed"
        resp = requests.post(url, json={"model": self.config.ollama_embedding_model, "input": text}, timeout=60)
        data = resp.json()
        embeddings = data.get("embeddings") or data.get("embedding")
        return embeddings[0] if isinstance(embeddings[0], list) else embeddings

    def _chat(self, prompt: str, system: str = "") -> str:
        url = f"{self.config.ollama_base_url.rstrip('/')}/api/chat"
        messages = []
        if system:
            messages.append({"role": "system", "content": system})
        messages.append({"role": "user", "content": prompt})

        resp = requests.post(url, json={"model": self.config.ollama_llm_model, "messages": messages, "stream": False}, timeout=120)
        return resp.json().get("message", {}).get("content", "").strip()

Conclusion

Combining IBM Docling Pipelines with OpenSearch creates a reliable, scalable foundation for enterprise RAG applications. By decoupling document curation into modular operator DAGs—covering ingestion, high-precision layout extraction, structure-aware chunking, dense vector embedding, and indexing—organizations can handle diverse unstructured document formats consistently.

Integrating this pipeline with vector search and local LLM runtimes ensures strict context grounding while reducing hallucination risks. As unstructured data continues to grow in enterprise environments, modular Curation-as-Code frameworks like docling-pipeline will be critical for keeping production RAG systems reliable, observable, and easy to maintain.

Thanks for reading 🦆

What is Text Watermarking?

Alain Airom (Ayrom) — Sun, 06 Sep 2026 14:41:08 +0000

Implementing a didactical ‘water marking’ sample application (for self learning and explanation)

Since recently there is a lot of hype of gen ai tools implementing text watermarking for the text they provide. I wanted to learn what it is about and how it could be implemented and detected. So I made a didactical application for myself using Bob. The implementation follows.

Introduction & Theoretical Foundations

Text watermarking in Generative AI refers to the practice of embedding a hidden, detectable signal within AI-generated text. Unlike digital watermarks applied to continuous media like images or audio, a text watermark operates over a discrete domain of tokens without altering the fundamental meaning or readability of the text.

In autoregressive Large Language Models (LLMs), watermarking is integrated directly into the token sampling process rather than applied post-hoc.

The Green-List / Red-List Partitioning Algorithm (Kirchenbauer et al., 2023)

Explanations here: https://github.com/aairom/text-watermarking-test/blob/main/Docs/text_watermarking_algorithms_20260906.md && https://github.com/aairom/text-watermarking-test/blob/main/Docs/WatermarkingImplementation.md

The primary reference framework for LLM text watermarking relies on dynamic vocabulary partitioning:

Context Extraction: At step t, the system extracts a context window ct=(tt−h,…,tt−1) of the preceding h tokens.
Deterministic Pseudo-Random Seeding: The context sequence and a secret key k are hashed using SHA-256 to generate a pseudo-random seed st=SHA-256(ct∥k).
Vocabulary Partitioning: Using st, the vocabulary V is partitioned into a Green List Gt of size γ∣V∣ and a Red List Rt of size (1−γ)∣V∣.
Logit Biasing: During generation, a bias parameter δ>0 is added to the unnormalized logit zv for all green tokens v∈Gt:

Sampling: Softmax normalization is applied to z~v, biasing the model toward green-list tokens.

Statistical Hypothesis Testing & Detection

Detection reconstructs the sequence of green lists without needing access to the model logits or original prompt:

Null Hypothesis (*H*0): Text is human-written or unwatermarked. Each token lands in Gt with probability γ.
Test Statistic (*z*-score): For a sequence of length T with ∣G∣ green tokens:

Decision Rule: Reject H*0 (flag as watermarked) if *z≥z*threshold (typically *z≥4.0, corresponding to p≈3.17×10−5).

Didactical Application Architecture

To explore this concept hands-on, I built a local didactical full-stack application using IBM Bob.

System Flowchart

Data Flow Generation and Detection

Generation Sequence

The data flow generation shows how the application creates a "watermarked" text.

Detection Sequence

This is the architecture of "watermark" detection

Code Implementation & Excerpts

Deterministic Green List Generation

The implement code uses SHA-256 context hashing and random.Random Fisher-Yates shuffling:

def _hash_context_to_seed(context_ids: tuple[int, ...], secret_key: str) -> int:
    payload = secret_key.encode("utf-8") + struct.pack(f"{len(context_ids)}I", *context_ids)
    digest = hashlib.sha256(payload).digest()
    seed = struct.unpack(">Q", digest[:8])[0]
    return seed

def get_green_list(
    context_ids: tuple[int, ...],
    secret_key: str,
    vocab_size: int,
    gamma: float,
) -> frozenset[int]:
    seed = _hash_context_to_seed(context_ids, secret_key)
    rng = random.Random(seed)
    green_size = max(1, int(gamma * vocab_size))
    all_ids = list(range(vocab_size))
    rng.shuffle(all_ids)
    return frozenset(all_ids[:green_size])

Biased Token Sampling Loop

During token generation, δ is applied directly to logits of green-listed candidates:

def _sample_next_token(
    context_ids: tuple[int, ...],
    green_list: Optional[frozenset[int]],
    delta: float,
    temperature: float,
    rng: random.Random,
) -> int:
    prev_id = context_ids[-1] if context_ids else _COMMON_START_TOKENS[0]
    candidates = _BIGRAM_MODEL.get(prev_id, [])

    if not candidates:
        candidates = list(range(4, min(200, _VOCAB_SIZE)))

    pool = list(dict.fromkeys(candidates + list(range(4, 60))))[:80]

    logits = []
    for tid in pool:
        logit = 1.0
        if green_list is not None and tid in green_list:
            logit += delta  # Add bias before temperature scaling
        logits.append(logit / temperature)

    max_l = max(logits)
    exp_l = [math.exp(l - max_l) for l in logits]
    total = sum(exp_l)
    probs = [e / total for e in exp_l]

    r = rng.random()
    cumulative = 0.0
    for tid, p in zip(pool, probs):
        cumulative += p
        if r <= cumulative:
            return tid
    return pool[-1]

Detection Engine & Statistical Test

Detection evaluates z-scores and one-sided p-values via the error function:

def detect_watermark(
    text: str,
    secret_key: str,
    gamma: float = 0.5,
    context_window: int = 1,
    threshold_z: float = 4.0,
) -> DetectionResult:
    token_ids = _tokenize(text)
    T = len(token_ids)

    if T == 0:
        return DetectionResult(...)

    token_infos: list[TokenInfo] = []
    green_count = 0

    for i, tid in enumerate(token_ids):
        if i == 0:
            ctx = (_SPECIAL_TOKENS["<BOS>"],)
        else:
            ctx = tuple(token_ids[max(0, i - context_window): i])

        green_list = get_green_list(ctx, secret_key, _VOCAB_SIZE, gamma)
        seed_val = _hash_context_to_seed(ctx, secret_key)
        is_green = tid in green_list
        if is_green:
            green_count += 1

        token_infos.append(TokenInfo(
            token_id=tid,
            token_text=_ID_TO_TOKEN.get(tid, "<UNK>"),
            position=i,
            is_green=is_green,
            context_hash_seed=seed_val % (2 ** 31),
            green_list_size=len(green_list),
        ))

    expected_g = gamma * T
    z = (green_count - expected_g) / math.sqrt(T * gamma * (1 - gamma))
    p_value = 0.5 * math.erfc(z / math.sqrt(2))
    confidence_pct = round((1.0 - p_value) * 100, 2)
    verdict = "WATERMARKED" if z >= threshold_z else "NOT_WATERMARKED"

    return DetectionResult(
        text=text,
        token_infos=token_infos,
        total_tokens=T,
        green_tokens=green_count,
        expected_green=round(expected_g, 2),
        green_fraction=round(green_count / T, 4),
        z_score=round(z, 4),
        p_value=round(p_value, 6),
        confidence_pct=confidence_pct,
        verdict=verdict,
        threshold_z=threshold_z,
    )

REST API Endpoints (FastAPI)

The REST API using FastAPI implemented provides a full access through APIs to handle the actions like generation, detection…

@app.post("/api/generate", response_model=GenerateResponse, tags=["generation"])
def generate(req: GenerateRequest) -> GenerateResponse:
    wm = generate_text(
        prompt=req.prompt,
        secret_key=req.secret_key,
        gamma=req.gamma,
        delta=req.delta,
        temperature=req.temperature,
        max_new_tokens=req.max_new_tokens,
        context_window=req.context_window,
        watermarked=True,
        seed=42,
    )
    baseline = generate_text(
        prompt=req.prompt,
        secret_key=req.secret_key,
        gamma=req.gamma,
        delta=req.delta,
        temperature=req.temperature,
        max_new_tokens=req.max_new_tokens,
        context_window=req.context_window,
        watermarked=False,
        seed=42,
    )
    return GenerateResponse(watermarked=wm, baseline=baseline)

@app.post("/api/detect", response_model=DetectionResult, tags=["detection"])
def detect(req: DetectRequest) -> DetectionResult:
    return detect_watermark(
        text=req.text,
        secret_key=req.secret_key,
        gamma=req.gamma,
        context_window=req.context_window,
        threshold_z=req.threshold_z,
    )

Attack Simulator & Robustness Testing

To evaluate how well the watermark withstands real-world modifications, the application includes a dedicated Attack Simulator Panel (RobustnessPanel.tsx). Watermarks embedded during generation must remain detectable even after the text undergoes editing, paraphrasing, or noise injection.

Supported Attack Modes

The simulator allows testing four core text perturbation strategies at configurable intensity levels (intensity∈[0.01,1.0]):

Deletion Attack: Randomly strips a percentage of tokens from the text. This simulates aggressive summarization or partial content extraction.
Substitution Attack (Paraphrasing / Typo Injection): Replaces candidate tokens with synonyms or introduces adjacent character swaps. Because green-list selection depends on preceding context ct, modifying even a single token desynchronizes subsequent green-list lookups.
Insertion Attack: Inserts random filler words (e.g., "actually", "basically", "like") into the sequence, introducing local shift distortions into the context window.
Local Shuffle Attack: Randomly scrambles small token windows (3-grams) to disrupt local sentence structure without completely altering word frequency.

API Endpoint (POST /api/perturb);

@app.post("/api/perturb", response_model=PerturbResponse, tags=["robustness"])
def perturb(req: PerturbRequest) -> PerturbResponse:
    """
    Apply a text perturbation and re-run watermark detection.
    Shows watermark persistence / degradation under attack.
    """
    if not req.text.strip():
        raise HTTPException(status_code=422, detail="text must not be blank")

    perturbed = apply_perturbation(req.text, req.mode, req.intensity, req.seed)
    original_det = detect_watermark(
        req.text, req.secret_key, req.gamma, req.context_window, req.threshold_z
    )
    perturbed_det = detect_watermark(
        perturbed, req.secret_key, req.gamma, req.context_window, req.threshold_z
    )

    return PerturbResponse(
        original_text=req.text,
        perturbed_text=perturbed,
        mode=req.mode,
        intensity=req.intensity,
        original_detection=original_det,
        perturbed_detection=perturbed_det,
    )

Empirical Robustness Insights

Context Sensitivity: Because the pseudo-random seed st depends on ct=(tt−h,…,tt−1), a single token edit at position i invalidates the green-list match for the subsequent h tokens.
Sequence Length Resilience: For longer generated sequences (T≥150), the z-score margin is sufficiently high (z≥7.0) that the signal remains statistically significant even after 20–30% token degradation.

Theoretical Algorithm Deep Dive

The Algorithm Reference module (MathExplainer.tsx) provides an interactive breakdown of the mathematical properties governing the Kirchenbauer et al. framework.

Parameter Sensitivity & Trade-offs: the behavior and detectability of the watermark are governed by three primary hyper-parameters:

Parameter	Notation	Description	Impact of Higher Values
Green Fraction	γ	Ratio of vocabulary assigned to Gt.	Increases expected baseline green token count (γT), requiring longer sequences for clear detection.
Logit Bias	δ	Direct bias added to unnormalized logits of Gt tokens.	Increases detection confidence (z-score), but higher values (δ>3.0) can compromise text perplexity and fluency.
Context Window	h (or k)	Number of preceding tokens used as hashing context ct.	Increases resistance against naive token substitution, but makes detection more sensitive to context disruptions.

Mathematical Detection Limit & One-Sided p-Value

To determine the exact likelihood of false positives (a human-written text accidentally triggering a watermark detection), the detection engine converts the computed z-score into an upper-tail p-value using the complementary error function (erfc):

Where:

z≥4.0⟹p≈3.17×10−5 (99.997% confidence)

z≥6.0⟹p≈9.86×10−10 (99.9999999% confidence)

Conclusion

Text watermarking offers a robust, mathematically sound approach to establishing content provenance for large language models. By embedding statistical signals directly during sampling, models can output text that remains entirely natural to human readers while carrying a verifiable fingerprint.

Building this didactical application with IBM Bob highlighted the core mechanics of the algorithm:

Zero Model Overhead: Detection requires no knowledge of model weights or prompts—only the secret key and candidate text.
Controllable Quality Trade-offs: The logit bias δ and green fraction γ provide fine-grained control over detectability versus text quality.
Statistical Power: Even under minor perturbations, longer sequences (T≥200) maintain strong statistical significance against paraphrase attacks.

As generative AI continues to scale, watermarking frameworks like Kirchenbauer et al.'s Green/Red partitioning and DeepMind's SynthID will serve as critical foundational blocks for transparency, regulatory compliance, and content integrity. """

Thanks for reading #️⃣

Running vinD with Podman: Local Kubernetes without Docker Desktop

Alain Airom (Ayrom) — Thu, 03 Sep 2026 10:54:41 +0000

Transforming vinD to vinP (vCluster in Podman) on macOS!

Introduction

I always wanted to test vinD which seems more complete than Kind for a local test and deployment. However the name is "vind - vCluster in Docker" and I use Podman... so I put IBM Bob into work to make all what it takes to use vinD with Podman locally.

While vinD was explicitly designed as a Docker-native tool that expects standard Docker socket endpoints and CLI responses, migrating the setup to a macOS workstation running Podman Desktop is entirely feasible with a few targeted compatibility shims and VM adjustments.

This post walks through the architectural considerations, step-by-step implementation, custom compatibility layers, and deployment manifests required to bring vinD up and running seamlessly on Podman.

TL;DR: What is vinD?

tl;dr — vinD (vCluster in Docker) gives you a real, lightweight Kubernetes cluster running inside a single container in seconds.

Real Kubernetes Control Plane: Unlike standard namespaces, vcluster create provisions a fully isolated, dedicated Kubernetes control plane (API server, etcd, controller manager) inside one host container.
High-Speed Provisioning & Resource Efficiency: Starts in ~15 seconds with a tiny RAM footprint (~150 MB). It supports sleep/wake capabilities (vcluster pause / vcluster resume) so an idle cluster uses zero CPU and zero memory.
Full Tenant Isolation: Multiple isolated vClusters can run side-by-side on a single host machine with separate CRDs, RBAC, and Kubernetes API versions without interfering with each other.
Native Tooling: You interact with vinD using standard kubectl, helm, and vcluster CLI commands just as you would with EKS, GKE, or Kind.

Excerpt from vinD Github;

🎯 What is vind?

vind (vCluster in Docker) is an open-source way to run Kubernetes clusters directly as Docker containers. Built on top of vCluster, vind combines the power of virtual Kubernetes clusters with the simplicity of Docker, creating isolated Kubernetes environments that are perfect for development, testing, and CI/CD pipelines.

Note: vind uses vCluster's Private Nodes mode internally. This is automatically enabled when using the Docker driver and is required for proper operation. This is expected behavior, not a configuration issue.

Why vind?

🚀 Faster than KinD - Optimized container-based architecture
💤 Sleep & Wake - Pause clusters to save resources, resume in under 3 seconds
🎨 Built-in UI - Free vCluster Platform UI for cluster management
⚡ Load Balancers OOB - Automatic LoadBalancer services without extra setup
🐳 Docker Native - Leverages Docker's networking and storage
🔄 Pull-through Cache - Faster image pulls via local Docker daemon
🌐 Hybrid Nodes - Join external nodes (even cloud instances) via VPN
📸 Snapshots - Save cluster state to OCI registries, S3, or local files and restore them
🔧 In-Place K8s Upgrades - Upgrade Kubernetes version without deleting the cluster

Architecture Overview

High-Level Architecture

The following diagram illustrates how the macOS host environment interfaces with the underlying Podman VM and vinD control plane container:

Key Differences & Podman Compatibility Shims

To bridge the gap between vcluster (which assumes a native Docker daemon) and Podman, four specific adjustments are required:

Custom Docker-to-Podman CLI Shim: When probing network subnets, vcluster issues docker network inspect --format '{{.IPAM.Config}}'. Podman formats network inspect JSON under .subnets[].subnet. A lightweight shim intercepts this call and formats the JSON output accordingly.
br_netfilter Kernel Module: Flannel CNI requires the br_netfilter module loaded in the Podman Machine VM. Loading this module explicitly prevents CNI initialization failure.
DOCKER_HOST Socket Redirection: Standard API interactions route through Podman’s Docker-compatible API socket (unix:///.../podman.sock).
Architecture-Aware Image Build (TARGETARCH): Multi-stage container builds must respect the Apple Silicon (arm64) execution environment of the Podman VM to avoid runtime exec format error issues.

**Attention:* Installing vinD on macOS using Homebrew standard commands is not sufficient on its own*. Because the loft-sh/tap repository isn't trusted by default, I used **WailBrew* to explicitly flag and manage the tap as a trusted runnable source.*

Implementation

Application Source & Multi-Stage Containerfile

A lightweight HTTP service written in Go serves as the test workload.

app/main.go

// Package main provides a minimal HTTP Hello World server.
// Port is controlled via the PORT environment variable (default: 8081).
package main

import (
    "fmt"
    "log"
    "net/http"
    "os"
)

// getPort returns the port to listen on.
// It reads from the PORT environment variable; falls back to 8081.
func getPort() string {
    port := os.Getenv("PORT")
    if port == "" {
        port = "8081"
    }
    return port
}

// helloHandler writes "Hello, World!" to the response.
func helloHandler(w http.ResponseWriter, r *http.Request) {
    log.Printf("Request received: %s %s from %s", r.Method, r.URL.Path, r.RemoteAddr)
    fmt.Fprintln(w, "Hello, World!")
}

// healthHandler provides a basic liveness probe endpoint.
func healthHandler(w http.ResponseWriter, r *http.Request) {
    w.WriteHeader(http.StatusOK)
    fmt.Fprintln(w, "OK")
}

// newMux builds and returns the HTTP ServeMux with all routes registered.
func newMux() *http.ServeMux {
    mux := http.NewServeMux()
    mux.HandleFunc("/", helloHandler)
    mux.HandleFunc("/healthz", healthHandler)
    return mux
}

func main() {
    port := getPort()
    addr := ":" + port

    mux := newMux()

    log.Printf("Starting Hello World server on %s", addr)
    if err := http.ListenAndServe(addr, mux); err != nil {
        log.Fatalf("Server failed: %v", err)
    }
}

app/Containerfile

# Stage 1 — Builder
FROM docker.io/library/golang:1.21-alpine AS builder

WORKDIR /src

COPY go.mod ./
RUN go mod download

COPY . .
RUN go test ./... -v

ARG TARGETARCH
RUN CGO_ENABLED=0 GOOS=linux GOARCH=${TARGETARCH:-$(go env GOARCH)} \
    go build -ldflags="-s -w" -o /out/helloworld .

# Stage 2 — Runtime
FROM scratch

COPY --from=builder /out/helloworld /helloworld

EXPOSE 8081
ENV PORT=8081
USER 65534:65534

ENTRYPOINT ["/helloworld"]

Kubernetes Deployment Manifests

The service is exposed on NodePort 30080, which maps to the host.

manifests/namespace.yaml

apiVersion: v1
kind: Namespace
metadata:
  name: helloworld
  labels:
    app.kubernetes.io/managed-by: vind

manifests/deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: helloworld
  namespace: helloworld
  labels:
    app: helloworld
    app.kubernetes.io/name: helloworld
    app.kubernetes.io/version: "1.0.0"
spec:
  replicas: 1
  selector:
    matchLabels:
      app: helloworld
  template:
    metadata:
      labels:
        app: helloworld
    spec:
      securityContext:
        runAsNonRoot: true
        runAsUser: 65534
        runAsGroup: 65534
      containers:
        - name: helloworld
          image: localhost/helloworld:latest
          imagePullPolicy: Never
          ports:
            - containerPort: 8081
              protocol: TCP
              name: http
          env:
            - name: PORT
              value: "8081"
          resources:
            requests:
              cpu: "50m"
              memory: "32Mi"
            limits:
              cpu: "200m"
              memory: "64Mi"
          livenessProbe:
            httpGet:
              path: /healthz
              port: 8081
            initialDelaySeconds: 5
            periodSeconds: 10
          readinessProbe:
            httpGet:
              path: /healthz
              port: 8081
            initialDelaySeconds: 3
            periodSeconds: 5

manifests/service.yaml

apiVersion: v1
kind: Service
metadata:
  name: helloworld
  namespace: helloworld
  labels:
    app: helloworld
    app.kubernetes.io/name: helloworld
spec:
  type: NodePort
  selector:
    app: helloworld
  ports:
    - name: http
      port: 80
      targetPort: 8081
      nodePort: 30080
      protocol: TCP

and even a Load Balancer (manifests/loadbalancer-service.yaml)

  ---
  # =============================================================================
  # Kubernetes Service — helloworld (LoadBalancer type)
  #
  # vinD provides automatic LoadBalancer support out-of-the-box.
  # The EXTERNAL-IP is assigned from the Podman/Docker bridge network.
  # Apply this INSTEAD of service.yaml if you prefer LoadBalancer over NodePort.
  #
  # Apply with:
  #   kubectl apply -f manifests/loadbalancer-service.yaml
  # =============================================================================
  apiVersion: v1
  kind: Service
  metadata:
    name: helloworld-lb
    namespace: helloworld
    labels:
      app: helloworld
      app.kubernetes.io/name: helloworld
  spec:
    type: LoadBalancer
    selector:
      app: helloworld
    ports:
      - name: http
        port: 80           # External port
        targetPort: 8081   # Container port
        protocol: TCP

Orchestration & Podman Shim Setup Script

The orchestration script manages binary downloads, setting up the docker CLI translation shim, enabling br_netfilter in the Podman VM, creating the vCluster instance, importing images into containerd, and deploying manifests.

scripts/setup.sh

#!/usr/bin/env bash
set -euo pipefail

CLUSTER_NAME="${CLUSTER_NAME:-vind-helloworld}"
APP_IMAGE="${APP_IMAGE:-localhost/helloworld:latest}"

echo "==> 1. Ensuring vcluster CLI is installed..."
if ! command -v vcluster &> /dev/null; then
  mkdir -p "$HOME/.local/bin"
  curl -fsSL "https://github.com/loft-sh/vcluster/releases/latest/download/vcluster-darwin-arm64" \
    -o "$HOME/.local/bin/vcluster"
  chmod +x "$HOME/.local/bin/vcluster"
  export PATH="$HOME/.local/bin:$PATH"
fi

echo "==> 2. Detecting Podman Socket..."
PODMAN_SOCK=$(podman info --format '{{.Host.RemoteSocket.Path}}' 2>/dev/null || true)
if [ -z "$PODMAN_SOCK" ]; then
  PODMAN_SOCK="$HOME/.local/share/containers/podman/machine/qemu/podman.sock"
fi
export DOCKER_HOST="unix://${PODMAN_SOCK}"

echo "==> 3. Creating docker -> podman CLI shim with IPAM translation..."
mkdir -p "$HOME/.local/bin"
cat << 'EOF' > "$HOME/.local/bin/docker"
#!/usr/bin/env bash
if [[ "$*" == *"network inspect"* ]] && [[ "$*" == *"IPAM"* ]]; then
  NET_NAME="${@:$#}"
  SUBNET=$(podman network inspect "$NET_NAME" --format '{{range .subnets}}{{.subnet}}{{end}}' 2>/dev/null)
  echo "[{\"Subnet\":\"${SUBNET}\"}]"
  exit 0
fi
exec podman "$@"
EOF
chmod +x "$HOME/.local/bin/docker"
export PATH="$HOME/.local/bin:$PATH"

echo "==> 3b. Loading br_netfilter module in Podman VM..."
podman machine ssh "sudo modprobe br_netfilter" || true
podman machine ssh "echo 'br_netfilter' | sudo tee /etc/modules-load.d/br_netfilter.conf" || true

echo "==> 4. Configuring vCluster driver..."
vcluster use driver docker

echo "==> 5. Creating vinD cluster..."
if ! vcluster list | grep -q "$CLUSTER_NAME"; then
  vcluster create "$CLUSTER_NAME" -f vcluster.yaml
fi

echo "==> 6. Building Go application image..."
podman build -t "$APP_IMAGE" -f app/Containerfile app/

echo "==> 7. Importing image into cluster containerd..."
podman save "$APP_IMAGE" | docker exec -i "vcluster.cp.${CLUSTER_NAME}" ctr --namespace k8s.io images import -

echo "==> 8. Applying Kubernetes manifests..."
kubectl apply -f manifests/namespace.yaml
kubectl apply -f manifests/deployment.yaml
kubectl apply -f manifests/service.yaml

echo "==> 9. Waiting for deployment readiness..."
kubectl rollout status deployment/helloworld -n helloworld --timeout=60s

echo "==> Setup complete! Test endpoint:"
echo "curl http://localhost:30080"

Conclusion

By addressing the four core points of divergence between Docker and Podman environments—CLI output formatting, kernel module presence in the underlying machine VM, platform-native image builds, and API socket location—vinD runs efficiently on top of Podman.

This setup offers a fast local deployment pipeline, retaining vCluster's single-container control plane model while operating fully within open-source, Podman-based development tooling.

Thanks for reading 🍇

Automating AI Context: How I Built a Custom Extension for IBM Bob IDE to Inject Project Rules

Alain Airom (Ayrom) — Mon, 31 Aug 2026 12:23:50 +0000

Building my own “vsix” extension to automate my own projects

Introduction

Setting up consistent workspace instructions across projects can quickly become a tedious chore. In IBM Bob IDE, system prompt behaviors and coding standards are guided by a project-level AGENTS.md file located at the root of your repository. While effective, manually copying a master rules file into every newly created or cloned workspace breaks flow and easily leads to configuration drift.

Note: While AGENTS.md can be configured globally across all your projects, I prefer maintaining project-level files. This allows me to start from a shared foundational rule set while giving each workspace the freedom to include custom, project-specific tweaks.

TL;DR-What is AGENTS.MD?

In a modern Software Development Lifecycle (SDLC), an AGENTS.md file is a machine-readable context and configuration specification checked directly into a project repository to dictate how autonomous AI coding agents (such as Cursor, IBM Bob, Claude Code, or GitHub Copilot) interact with the codebase. Functioning as an authoritative set of runtime instructions loaded into the AI’s prompt context, it explicitly defines executable CLI commands, coding standards, project structure maps, architectural constraints, and operational boundaries—such as distinguishing actions the AI can perform autonomously versus those requiring human approval. By checking AGENTS.md into version control alongside application code, software engineering teams standardize AI behavior across developer environments, prevent AI configuration drift, reduce model-generated bugs, and ensure seamless alignment with project-specific development guidelines.

Example;

  # Project Development Rule
   - rule 1
   - rule 2
   ...

To eliminate this manual step, I built bob-agents-injector—a custom extension designed specifically for IBM Bob IDE. Developed directly within Bob using its native capabilities, the extension automatically injects a canonical AGENTS.md file from a central source path (defaulting to ~/Devs/AGENTS.MD) into any workspace as soon as it is opened.

~/Devs/AGENTS.MD            ← Master rules (single source of truth)
        │
        └──► <project>/AGENTS.md   ← Injected automatically when project opens

Implementation

Hooking into the IDE Event Loop

The key to seamless injection is to align the extension with VS Code's exact activation lifecycle. So I asked Bob to build the required extension to automate copying the AGENTS.MD from my global "Devs" folder each time I create a new folder for a new project using Bob IDE!

Bob IDE is a fork of VS Code (Microsoft's open-source Code - OSS).

	VS Code	Bob IDE
Source	Microsoft (closed binary, open source core)	IBM fork of `Code - OSS`
Extension marketplace	VS Code Marketplace	IBM's own marketplace + Open VSX
Telemetry	Microsoft	IBM
Binary name	`code`	`bobide` / `bobide-insiders`
Extensions dir	`~/.vscode/extensions`	`~/.bobide/extensions`

scripts/
├── bob-agents-injector/
│   ├── package.json
│   ├── out/extension.js
│   └── build.js
└── install-bob-agents-injector.sh   ← optional convenience wrapper

onStartupFinished: Fires when Bob completes initial loading.
onDidChangeWorkspaceFolders: Fires dynamically whenever a new project folder is opened or swapped.

// =============================================================================
// Bob AGENTS.md Auto-Injector — extension.ts (source, compile with build.js)
//
// This extension mirrors the EXACT trigger chain used by the bob-marketplace
// extension to inject .bob/mcp.json, but instead copies ~/Devs/AGENTS.MD to
// <workspaceRoot>/AGENTS.md at the same moment:
//
//   Trigger 1: onStartupFinished  → fires when Bob IDE is ready at launch
//   Trigger 2: onDidChangeWorkspaceFolders → fires when a NEW project is opened
//
// Both map directly to the two events in bob-marketplace/out/extension.js:
//   void mcpServer.start()                          (at activate time)
//   vscode.workspace.onDidChangeWorkspaceFolders(   (at workspace change)
//       () => void mcpServer.reregister())
//
// Idempotency: if AGENTS.md already exists in the workspace root and its
// content matches the source file, nothing is written. If it exists but
// differs, it is left untouched (user may have customised it).
// =============================================================================

'use strict';

const vscode = require('vscode');
const fs = require('fs');
const path = require('path');
const os = require('os');

// ── Configuration helpers ────────────────────────────────────────────────────
function getConfig() {
    const cfg = vscode.workspace.getConfiguration('bob.agentsInjector');
    const rawSource = cfg.get('sourcePath', '').trim()
        || path.join(os.homedir(), 'Devs', 'AGENTS.MD');
    // Expand leading ~ (vscode settings may contain literal ~)
    const sourcePath = rawSource.startsWith('~')
        ? path.join(os.homedir(), rawSource.slice(1))
        : rawSource;
    return {
        enabled: cfg.get('enabled', true),
        sourcePath,
        targetFileName: cfg.get('targetFileName', 'AGENTS.md').trim() || 'AGENTS.md',
    };
}

// ── Core inject function ─────────────────────────────────────────────────────
/**
 * Copies the master AGENTS.MD into the workspace root as AGENTS.md,
 * following the same logic as MarketplaceMcpServer.registerIntoWorkspace():
 *   - reads existing target (non-fatal if missing)
 *   - skips if content is identical (idempotent)
 *   - skips if content differs (user may have customised it)
 *   - creates the target file only when it does not exist
 */
async function injectAgentsMd() {
    const cfg = getConfig();

    if (!cfg.enabled) {
        return;
    }

    // ── Validate source ───────────────────────────────────────────────────────
    if (!fs.existsSync(cfg.sourcePath) || !fs.statSync(cfg.sourcePath).isFile()) {
        // Non-fatal: source missing means the user hasn't set up the master file.
        // Log quietly — don't pop an annoying dialog on every project open.
        console.warn(`[bob-agents-injector] Source not found: ${cfg.sourcePath}. ` +
            `Set 'bob.agentsInjector.sourcePath' in settings.`);
        return;
    }

    // ── Resolve workspace root ────────────────────────────────────────────────
    const folders = vscode.workspace.workspaceFolders;
    if (!folders || folders.length === 0) {
        return; // No workspace open yet — identical to bob-marketplace's behaviour
    }

    // Mirror bob-marketplace: use first workspace folder (workspaceFolders[0])
    const workspaceRoot = folders[0].uri.fsPath;
    const targetPath = path.join(workspaceRoot, cfg.targetFileName);

    // ── Read source content ────────────────────────────────────────────────────
    let sourceContent;
    try {
        sourceContent = fs.readFileSync(cfg.sourcePath);
    } catch (err) {
        console.warn(`[bob-agents-injector] Could not read source: ${err.message}`);
        return;
    }

    // ── Check existing target ─────────────────────────────────────────────────
    if (fs.existsSync(targetPath)) {
        try {
            const existing = fs.readFileSync(targetPath);
            if (existing.equals(sourceContent)) {
                // Already up-to-date — silent no-op (same as bob-marketplace
                // behaviour when mcp.json already has the right URL)
                return;
            } else {
                // Target exists and differs — user may have customised it.
                // Do NOT overwrite. Log for visibility.
                console.log(`[bob-agents-injector] ${cfg.targetFileName} exists and ` +
                    `differs from master — skipping to preserve local changes.`);
                return;
            }
        } catch {
            // Can't read existing file — try writing anyway
        }
    }

    // ── Write target ──────────────────────────────────────────────────────────
    try {
        fs.writeFileSync(targetPath, sourceContent);
        console.log(`[bob-agents-injector] ✅ Injected ${cfg.targetFileName} → ${targetPath}`);
    } catch (err) {
        console.warn(`[bob-agents-injector] Could not write target: ${err.message}`);
    }
}

// ── Extension entry points ───────────────────────────────────────────────────
function activate(context) {
    console.log('[bob-agents-injector] Activating (onStartupFinished)');

    // ── Trigger 1: at IDE startup — mirror mcpServer.start() call ────────────
    void injectAgentsMd();

    // ── Trigger 2: when workspace changes — mirror onDidChangeWorkspaceFolders
    //    This is the KEY trigger: fires when the user opens a NEW project folder
    //    in Bob, which is the exact same moment .bob/mcp.json is (re-)injected.
    context.subscriptions.push(
        vscode.workspace.onDidChangeWorkspaceFolders(() => {
            void injectAgentsMd();
        })
    );

    // ── Trigger 3: settings change — mirror bob.marketplace.installLocation ──
    context.subscriptions.push(
        vscode.workspace.onDidChangeConfiguration((e) => {
            if (e.affectsConfiguration('bob.agentsInjector')) {
                void injectAgentsMd();
            }
        })
    );
}

function deactivate() {
    /* nothing to clean up */
}

module.exports = { activate, deactivate };

Idempotent Injections & Safety Checks

Automatically modifying workspace files requires strict safeguards to avoid overwriting intentionally customized local rules. The injectAgentsMd() execution loop enforces idempotency through a simple decision flow:

Verify Source: Ensures ~/Devs/AGENTS.MD exists before taking action.
Missing Target: If no AGENTS.md is present at the target workspace root, it creates one from the master file.
Identical Content: If the target matches the master file byte-for-byte, execution skips silently.
Modified Target: If the target exists but differs from the master file (indicating local edits), execution skips to preserve project-specific overrides.

Local `AGENTS.md` State	Extension Action
Does not exist	Copy master `AGENTS.MD` to workspace root MD+ 2
Exists (identical)	Skip silently (up-to-date) MD+ 2
Exists (modified)	Skip execution (preserve local customizations) MD+ 2

Dependency-Free Packaging and Installation

To keep the development footprint minimal, the project packages its VSIX installer using a zero-dependency build.js script reliant solely on Node.js built-ins (fs, path, and zlib). It constructs a valid .vsix archive directly from raw buffers, complete with proper CRC-32 checksums and ZIP structure formatting.

#!/usr/bin/env node
// =============================================================================
// build.js — packages bob-agents-injector into a .vsix file
//
// Usage (run from scripts/bob-agents-injector/):
//   node build.js
//
// Then install into Bob:
//   /Applications/IBM\ Bob\ -\ Insiders.app/Contents/Resources/app/bin/bobide-insiders \
//     --install-extension bob-agents-injector-1.0.0.vsix
//
// No npm install needed — uses only Node.js built-ins.
// =============================================================================

'use strict';

const fs   = require('fs');
const path = require('path');
const zlib = require('zlib');

const DIR  = __dirname;
const PKG  = JSON.parse(fs.readFileSync(path.join(DIR, 'package.json'), 'utf8'));
const VSIX = path.join(DIR, `${PKG.name}-${PKG.version}.vsix`);

// ── VSIX manifest ─────────────────────────────────────────────────────────────
const VSIX_MANIFEST = `<?xml version="1.0" encoding="utf-8"?>
<PackageManifest Version="2.0.0"
  xmlns="http://schemas.microsoft.com/developer/vsx-schema/2011"
  xmlns:d="http://schemas.microsoft.com/developer/vsx-schema-design/2011">
  <Metadata>
    <Identity Language="en-US"
      Id="${PKG.name}"
      Version="${PKG.version}"
      Publisher="${PKG.publisher}"/>
    <DisplayName>${PKG.displayName}</DisplayName>
    <Description>${PKG.description}</Description>
    <Tags>bob,agents,automation</Tags>
    <GalleryFlags>Public</GalleryFlags>
    <License>MIT</License>
    <Categories>Other</Categories>
  </Metadata>
  <Installation>
    <InstallationTarget Id="Microsoft.VisualStudio.Code" Version="[1.85,)"/>
  </Installation>
  <Dependencies/>
  <Assets>
    <Asset Type="Microsoft.VisualStudio.Code.Manifest"
           Path="extension/package.json"
           Addressable="true"/>
  </Assets>
</PackageManifest>`;

// ── [Content_Types].xml ───────────────────────────────────────────────────────
const CONTENT_TYPES = `<?xml version="1.0" encoding="utf-8"?>
<Types xmlns="http://schemas.openxmlformats.org/package/2006/content-types">
  <Default Extension="json"        ContentType="application/json"/>
  <Default Extension="js"          ContentType="application/javascript"/>
  <Default Extension="vsixmanifest" ContentType="text/xml"/>
</Types>`;

// ── CRC-32 (standard ZIP polynomial) ─────────────────────────────────────────
const CRC_TABLE = (() => {
  const t = new Uint32Array(256);
  for (let n = 0; n < 256; n++) {
    let c = n;
    for (let k = 0; k < 8; k++) c = (c & 1) ? (0xedb88320 ^ (c >>> 1)) : (c >>> 1);
    t[n] = c;
  }
  return t;
})();

function crc32(buf) {
  let crc = 0xffffffff;
  for (let i = 0; i < buf.length; i++) crc = CRC_TABLE[(crc ^ buf[i]) & 0xff] ^ (crc >>> 8);
  return (crc ^ 0xffffffff) >>> 0;
}

// ── DOS date/time for ZIP headers ─────────────────────────────────────────────
function dosDateTime(d) {
  const date = ((d.getFullYear() - 1980) << 9) | ((d.getMonth() + 1) << 5) | d.getDate();
  const time = (d.getHours() << 11) | (d.getMinutes() << 5) | Math.floor(d.getSeconds() / 2);
  return { date, time };
}

// ── Build one ZIP entry (local header + data + central-dir record) ────────────
function makeEntry(name, rawData, now) {
  const nameBytes = Buffer.from(name, 'utf8');
  const compressed = zlib.deflateRawSync(rawData, { level: 9 });
  const crc  = crc32(rawData);
  const { date, time } = dosDateTime(now);

  // Local file header (signature 0x04034b50)
  const local = Buffer.alloc(30 + nameBytes.length);
  local.writeUInt32LE(0x04034b50, 0);   // signature
  local.writeUInt16LE(20, 4);           // version needed to extract (2.0)
  local.writeUInt16LE(0, 6);            // general purpose bit flag
  local.writeUInt16LE(8, 8);            // compression method: deflate
  local.writeUInt16LE(time, 10);        // last mod file time
  local.writeUInt16LE(date, 12);        // last mod file date
  local.writeUInt32LE(crc, 14);         // crc-32
  local.writeUInt32LE(compressed.length, 18); // compressed size
  local.writeUInt32LE(rawData.length, 22);    // uncompressed size
  local.writeUInt16LE(nameBytes.length, 26);  // file name length
  local.writeUInt16LE(0, 28);           // extra field length
  nameBytes.copy(local, 30);

  return {
    localBlock: Buffer.concat([local, compressed]),
    crc, date, time,
    compressedSize: compressed.length,
    uncompressedSize: rawData.length,
    nameBytes,
  };
}

// ── Central directory record for one entry ────────────────────────────────────
function centralRecord(entry, localOffset) {
  const rec = Buffer.alloc(46 + entry.nameBytes.length);
  rec.writeUInt32LE(0x02014b50, 0);   // central dir signature
  rec.writeUInt16LE(20, 4);           // version made by
  rec.writeUInt16LE(20, 6);           // version needed
  rec.writeUInt16LE(0, 8);            // general purpose bit flag
  rec.writeUInt16LE(8, 10);           // compression method: deflate
  rec.writeUInt16LE(entry.time, 12);
  rec.writeUInt16LE(entry.date, 14);
  rec.writeUInt32LE(entry.crc, 16);
  rec.writeUInt32LE(entry.compressedSize, 20);
  rec.writeUInt32LE(entry.uncompressedSize, 24);
  rec.writeUInt16LE(entry.nameBytes.length, 28); // file name length
  rec.writeUInt16LE(0, 30);           // extra field length
  rec.writeUInt16LE(0, 32);           // file comment length
  rec.writeUInt16LE(0, 34);           // disk number start
  rec.writeUInt16LE(0, 36);           // internal file attributes
  rec.writeUInt32LE(0, 38);           // external file attributes
  rec.writeUInt32LE(localOffset, 42); // relative offset of local header
  entry.nameBytes.copy(rec, 46);
  return rec;
}

// ── End of central directory record ──────────────────────────────────────────
function eocdRecord(entryCount, centralSize, centralOffset) {
  const eocd = Buffer.alloc(22);
  eocd.writeUInt32LE(0x06054b50, 0);   // end of central dir signature
  eocd.writeUInt16LE(0, 4);            // disk number
  eocd.writeUInt16LE(0, 6);            // disk with central dir
  eocd.writeUInt16LE(entryCount, 8);   // entries on this disk
  eocd.writeUInt16LE(entryCount, 10);  // total entries
  eocd.writeUInt32LE(centralSize, 12);
  eocd.writeUInt32LE(centralOffset, 16);
  eocd.writeUInt16LE(0, 20);           // comment length
  return eocd;
}

// ── Assemble VSIX (= ZIP) ─────────────────────────────────────────────────────
function buildVsix() {
  const now = new Date();

  // Files to pack — ORDER matters: [Content_Types].xml and .vsixmanifest FIRST
  const files = [
    { name: '[Content_Types].xml',        data: Buffer.from(CONTENT_TYPES, 'utf8') },
    { name: '.vsixmanifest',              data: Buffer.from(VSIX_MANIFEST, 'utf8') },
    { name: 'extension/package.json',     data: fs.readFileSync(path.join(DIR, 'package.json')) },
    { name: 'extension/out/extension.js', data: fs.readFileSync(path.join(DIR, 'out', 'extension.js')) },
  ];

  const localBlocks  = [];
  const centralRecs  = [];
  let   offset       = 0;

  for (const { name, data } of files) {
    const entry = makeEntry(name, data, now);
    centralRecs.push(centralRecord(entry, offset));
    localBlocks.push(entry.localBlock);
    offset += entry.localBlock.length;
  }

  const centralBuf = Buffer.concat(centralRecs);
  const eocd = eocdRecord(files.length, centralBuf.length, offset);

  fs.writeFileSync(VSIX, Buffer.concat([...localBlocks, centralBuf, eocd]));

  const kb = (fs.statSync(VSIX).size / 1024).toFixed(1);
  console.log(`✅  Built: ${VSIX}  (${kb} KB)`);
  console.log('');
  console.log('Or install directly with (use the correct variant for your machine):');
  console.log(`  # Standard IBM Bob:`);
  console.log(`  /Applications/IBM\\ Bob.app/Contents/Resources/app/bin/bobide --install-extension "${VSIX}"`);
  console.log(`  # IBM Bob - Insiders:`);
  console.log(`  /Applications/IBM\\ Bob\\ -\\ Insiders.app/Contents/Resources/app/bin/bobide-insiders --install-extension "${VSIX}"`);
  console.log('');
  console.log('Or just run:  bash scripts/install-bob-agents-injector.sh');
}

buildVsix();

The dependencies of the code;

  {
    "name": "bob-agents-injector",
    "displayName": "Bob AGENTS.md Auto-Injector",
    "description": "Automatically copies ~/Devs/AGENTS.MD into AGENTS.md at the root of every workspace opened in IBM Bob — using the same trigger timing as the bob-marketplace mcp.json injection.",
    "version": "1.0.0",
    "publisher": "local",
    "license": "MIT",
    "engines": { "vscode": "^1.85.0" },
    "categories": ["Other"],
    "activationEvents": ["onStartupFinished"],
    "main": "./out/extension.js",
    "contributes": {
      "configuration": {
        "title": "Bob AGENTS.md Injector",
        "properties": {
          "bob.agentsInjector.sourcePath": {
            "type": "string",
            "default": "",
            "description": "Absolute path to the master AGENTS.MD. Defaults to ~/Devs/AGENTS.MD. Supports ~ expansion."
          },
          "bob.agentsInjector.targetFileName": {
            "type": "string",
            "default": "AGENTS.md",
            "description": "File name to write in the workspace root. Bob reads 'AGENTS.md' (case-insensitive on macOS)."
          },
          "bob.agentsInjector.enabled": {
            "type": "boolean",
            "default": true,
            "description": "Enable or disable automatic AGENTS.md injection."
          }
        }
      }
    },
    "scripts": {
      "compile": "node build.js",
      "package": "node build.js && bob --install-extension bob-agents-injector-1.0.0.vsix"
    },
    "devDependencies": {}
  }

The next step is to build the ".vsix" file.

# Package the VSIX without npm dependencies
node build.js

# Install automatically into IBM Bob or IBM Bob Insiders
bash scripts/install-bob-agents-injector.sh

Once the bash runs, the extension is in place.

  ╔══════════════════════════════════════════════════════════════════════╗
  ║  ✅  bob-agents-injector is now installed!                           ║
  ╠══════════════════════════════════════════════════════════════════════╣
  ║                                                                      ║
  ║  How it works:                                                       ║
  ║  • On every project open, Bob fires onDidChangeWorkspaceFolders     ║
  ║  • The injector catches that event (same as bob-marketplace does)   ║
  ║  • It copies ~/Devs/AGENTS.MD → <project>/AGENTS.md                ║
  ║  • If AGENTS.md already exists and is unmodified, it skips it       ║
  ║  • If AGENTS.md was customised locally, it leaves it untouched      ║
  ║                                                                      ║
  ║  Bob's RuleLoader reads:  <workspaceRoot>/AGENTS.md                 ║
  ║  (NOT .bob/AGENTS.MD — that path is not read by Bob)                ║
  ║                                                                      ║
  ║  Optional settings (Bob Settings → search 'agentsInjector'):        ║
  ║    bob.agentsInjector.sourcePath    custom source path               ║
  ║    bob.agentsInjector.targetFileName  target file name               ║
  ║    bob.agentsInjector.enabled         toggle on/off                  ║
  ║                                                                      ║
  ║  To trigger immediately: reload the Bob window                      ║
  ║    Menu → Developer → Reload Window  (or Cmd+Shift+P → Reload)      ║
  ╚══════════════════════════════════════════════════════════════════════╝

Once installed, user preferences can be adjusted anytime via Bob Settings (Cmd+,) under the bob.agentsInjector namespace to customize target file naming or update source paths.

Conclusion

Automating workspace preparation ensures that every AI interaction starts with consistent context, constraints, and project rules without adding repetitive setup steps. By mirroring IBM Bob’s internal event models, bob-agents-injector bridges the gap between global master rules and individual repository management.

Whether working across stable or Insiders builds of IBM Bob, spending a few minutes to build local IDE utilities pays back continuous micro-dividends in productivity and rule enforcement across every project you touch.

Thanks for reading 🏗️

The Token Economy of Programming Languages

Alain Airom (Ayrom) — Wed, 26 Aug 2026 11:57:46 +0000

Architecting LLM-Powered SDLC Platforms for Efficiency & Cost Optimization

Introduction

When software teams build AI-assisted development pipelines or deploy LLM coding agents, primary attention usually goes to model parameter size, context window length, or system prompts. However, an essential economic and operational factor is often overlooked: The Token Economy of Programming Languages.

Language choice significantly dictates both static context overhead and total interactive agent consumption. Empirical benchmarks reveal that code verbosity, symbol density, and subword tokenizer coverage create a massive spread in token consumption — ranging from 0.82× (Ruby) up to 2.24× relative to Python. Choosing a high-overhead language can inflate AI operational costs by over 100% and lead to frequent context truncation errors in complex workflows.

💡 Core Finding: Programming language choice directly produces a 2.7× spread in token consumption for equivalent business logic. In agentic workflows, behavioral overheads (such as compilation failures and correction loops) further compound this penalty, making verbose or lower-resource languages up to 2.6× more expensive across the full SDLC.

Project Structure and code samples for tests

Developed with IBM Bob, all code samples and benchmarking suites are open-sourced on GitHub. Feel free to explore the repository, test the benchmarks, or contribute improvements!

token-research/
├── README.md                           ← This file
├── requirements.txt                    ← Python dependencies (tiktoken, pytest)
├── .env.example                        ← Environment variable template
├── .gitignore                          ← Git exclusion rules
│
├── Docs/
│   ├── TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md  ← Main 730-line research document
│   ├── Architecture.md                        ← Full architecture diagrams
│   ├── Quickstart.md                          ← Step-by-step setup guide
│   └── LANGUAGE_SELECTION_FOR_RESEARCH_TOOLING.md  ← Engineering Decision Record
│
├── scripts/
│   ├── start.sh                        ← Launch pipeline in detached mode
│   └── stop.sh                         ← Graceful shutdown
│
├── input/                              ← Input data (contents not tracked by git)
├── output/                             ← Timestamped run logs (not tracked by git)
│
├── 01_tokenization_mechanics/
│   ├── bpe_tokenizer_demo.py
│   ├── token_counter.py
│   └── vocabulary_coverage_analysis.py
│
├── 02_language_comparison/
│   ├── token_ratio_calculator.py
│   ├── equivalent_task_samples/
│   │   ├── hello_world_tokens.py
│   │   ├── rest_api_stub_tokens.py
│   │   └── unit_test_tokens.py
│   └── ruby_equivalents/
│       ├── token_ratio_calculator.rb
│       └── identifier_length_analysis.rb
│
├── 03_sdlc_phase_simulation/
│   ├── sdlc_token_budget_model.py
│   ├── phase_cost_estimator.py
│   └── context_window_risk_analyzer.py
│
├── 04_verbosity_factors/
│   ├── boilerplate_overhead_demo.py
│   ├── type_annotation_impact.py
│   └── identifier_length_analysis.py
│
├── 05_optimization_strategies/
│   ├── prompt_compression_demo.py
│   ├── chunking_strategy_demo.py
│   └── token_aware_sdlc_pipeline.py
│
└── tests/
    ├── test_token_counter.py
    ├── test_token_ratio_calculator.py
    └── test_bpe_tokenizer_demo.py

Tokenization Mechanics & System Architecture

Large Language Models do not read source code line-by-line; they process numerical token sequences generated by Byte-Pair Encoding (BPE) tokenizers (such as OpenAI’s cl100k_base or o200k_base). Tokenizers are statistically trained on vast text datasets. Because English prose and Python dominate these training corpora, Python code achieves high byte-per-token density (4.55 bytes/token), whereas symbol-dense or verbose languages suffer from heavy subword fragmentation.

Architecture Schema Flow

Token Mechanics: Measures BPE Merges and Byte Densities.
Language Comparison: Compares Static vs. Agentic multi-turn loops.
tiktoken Aggregator: Evaluates cl100k_base and o200k_base Token Densities.
SDLC Simulation & Optimization: Runs Budget/Risk Models and applies AST/Format Compression.

The Python utility below evaluates byte-per-token density using OpenAI’s cl100k_base encoder and provides a calibrated heuristic fallback:

from dataclasses import dataclass
import tiktoken

@dataclass
class TokenMetrics:
    language: str
    code: str
    token_count: int
    char_count: int
    chars_per_token: float

class LanguageTokenAnalyzer:
    """Evaluates byte-per-token density using cl100k_base (GPT-4) encoding."""

    def __init__(self, encoder_name: str = "cl100k_base"):
        try:
            self.encoder = tiktoken.get_encoding(encoder_name)
        except Exception:
            self.encoder = None

    def analyze(self, language: str, code: str) -> TokenMetrics:
        char_count = len(code)

        if self.encoder:
            tokens = self.encoder.encode(code)
            token_count = len(tokens)
        else:
            token_count = max(1, int(char_count / 3.1))

        chars_per_token = char_count / token_count if token_count > 0 else 0.0

        return TokenMetrics(
            language=language,
            code=code,
            token_count=token_count,
            char_count=char_count,
            chars_per_token=chars_per_token
        )

Multi-Language Micro-Overhead Comparison

To evaluate subword tokenization and structural “token taxes” independent of business logic, program behavior (Hello World + Fibonacci Sum) is held constant across implementations:

Ruby (Most Token-Efficient Baseline):

def fibonacci(n)
  a, b, result = 0, 1, []
  n.times { result << a; a, b = b, a + b }
  result
end

puts "Hello, World!"
fibs = fibonacci(10)
puts "Fibonacci(10): #{fibs.inspect}"
puts "Sum: #{fibs.sum}"

Python (Reference Baseline — 1.00×):

def fibonacci(n: int) -> list[int]:
    a, b, result = 0, 1, []
    for _ in range(n):
        result.append(a)
        a, b = b, a + b
    return result

print("Hello, World!")
fibs = fibonacci(10)
print(f"Fibonacci(10): {fibs}")
print(f"Sum: {sum(fibs)}")

TypeScript (Static Type Overhead — ~1.45×):

function fibonacci(n: number): number[] {
    const result: number[] = [];
    let a = 0, b = 1;
    for (let i = 0; i < n; i++) {
        result.push(a);
        [a, b] = [b, a + b];
    }
    return result;
}

console.log("Hello, World!");
const fibs: number[] = fibonacci(10);
console.log(`Fibonacci(10): ${JSON.stringify(fibs)}`);
const total: number = fibs.reduce((acc: number, x: number): number => acc + x, 0);
console.log(`Sum: ${total}`);

Java (OOP & Stream Overhead — ~1.47× Static):

import java.util.ArrayList;
import java.util.List;

public class HelloFibonacci {
    public static List<Integer> fibonacci(int n) {
        List<Integer> result = new ArrayList<>();
        int a = 0, b = 1;
        for (int i = 0; i < n; i++) {
            result.add(a);
            int temp = a + b;
            a = b;
            b = temp;
        }
        return result;
    }

    public static void main(String[] args) {
        System.out.println("Hello, World!");
        List<Integer> fibs = fibonacci(10);
        System.out.println("Fibonacci(10): " + fibs);
        int total = fibs.stream().mapToInt(Integer::intValue).sum();
        System.out.println("Sum: " + total);
    }
}

C (Manual Memory Management — ~1.77× Static):

#include <stdio.h>
#include <stdlib.h>

int* fibonacci(int n) {
    int* result = (int*)malloc(n * sizeof(int));
    if (!result) return NULL;
    int a = 0, b = 1;
    for (int i = 0; i < n; i++) {
        result[i] = a;
        int temp = a + b;
        a = b;
        b = temp;
    }
    return result;
}

int main(void) {
    printf("Hello, World!\n");
    int* fibs = fibonacci(10);
    if (!fibs) return 1;
    int total = 0;
    for (int i = 0; i < 10; i++) total += fibs[i];
    printf("Sum: %d\n", total);
    free(fibs);
    return 0;
}

Benchmark Rankings & Empirical Token Ratios

Combining static analysis of algorithmic tasks across standard implementations and multi-turn agentic coding benchmarks, empirical ratios relative to Python (1.00× baseline) are established:

| Language       | Static Ratio vs Python   | Agentic Session Ratio | Efficiency Rating | Primary Cost Drivers                                         |
| -------------- | ------------------------ | --------------------- | ----------------- | ------------------------------------------------------------ |
| **Ruby**       | 0.82–0.95×  MD           | 0.95×  MD             | ★★★★★  MD         | Minimal syntactic ceremony, concise expressiveness  MD       |
| **Python**     | **1.00× (Baseline)**  MD | **1.00×**  MD         | ★★★★★  MD         | Dominant representation in LLM training corpora  MD          |
| **JavaScript** | 1.22–1.26×  MD           | 1.05×  MD             | ★★★★☆  MD         | Dynamic typing; low boilerplate, high tokenizer coverage  MD |
| **TypeScript** | 1.37–1.45×  MD           | 1.60×  MD             | ★★★☆☆  MD         | Explicit type annotations add +35% to +60% token overhead  MD |
| **Go**         | 1.44–1.55×  MD           | 1.38×  MD             | ★★★☆☆  MD         | Explicit error handling (`if err != nil`) multiplies line count  MD |
| **Java**       | 1.47–1.75×  MD           | 1.34×  MD             | ★★★☆☆  MD         | OOP ceremony, explicit imports, verbose type signatures  MD  |
| **Rust**       | 1.34–1.57×  MD           | 1.57×  MD             | ★★☆☆☆  MD         | Ownership syntax, lifetime specifiers, agent correction loops  MD |
| **C**          | 1.77–2.24×  MD           | 2.20×  MD             | ★☆☆☆☆  MD         | Manual memory management, header inclusions, 2.2× LOC  MD    |

SDLC Lifecycle Cost Compounding & Risk Modeling

In software development platforms, token costs compound exponentially over time because subsequent lifecycle tasks (code reviews, refactoring, test generation) process entire code artifacts repeatedly.

Lifecycle Token Growth Path

Requirements Phase: ~2,000 tokens (Constant natural language baseline).
Codegen Phase: Python ~6,000 tokens vs. Java ~10,000 tokens.
Code Review Phase: Python ~8,000 tokens vs. C ~17,000 tokens.
Refactoring Phase: Python ~10,000 tokens vs. C ~22,000 tokens.

Beyond direct API billing, excessive language overhead increases the risk of context truncation in long-running LLM loops. The analyzer script below measures subword fragmentation and checks code snippets against defined token limits.

To evaluate context window utilization and fragmentation risks across LLM providers, platform tooling uses risk analyzers:

import tiktoken

def calculate_fragmentation_rate(text: str) -> float:
    """Returns tokens-per-character. Higher rates denote severe subword fragmentation."""
    enc = tiktoken.get_encoding("cl100k_base")
    tokens = enc.encode(text)
    return len(tokens) / len(text) if text else 0.0

def evaluate_co  ntext_window(code_payload: str, max_context_tokens: int = 8192) -> dict:
    enc = tiktoken.get_encoding("cl100k_base")
    token_ids = enc.encode(code_payload)
    total_tokens = len(token_ids)

    return {
        "total_tokens": total_tokens,
        "frag_rate_tok_per_char": round(total_tokens / len(code_payload), 3),
        "exceeds_limit": total_tokens > max_context_tokens,
        "remaining_headroom": max_context_tokens - total_tokens
    }

Compounding lifecycle models also account for static language expansion alongside agentic correction penalties:

from dataclasses import dataclass

@dataclass
class LanguageProfile:
    name: str
    multiplier: float       # Static token multiplier vs. Python
    agentic_overhead: float # Correction loop penalty

PROFILES = {
    "Python": LanguageProfile("Python", 1.00, 1.00),
    "Java": LanguageProfile("Java", 1.47, 1.24),
    "Rust": LanguageProfile("Rust", 1.57, 1.27),
    "C": LanguageProfile("C", 1.77, 1.55),
}

def calculate_feature_budget(profile: LanguageProfile, base_input: int, base_output: int) -> dict:
    effective_gen_mult = profile.multiplier * profile.agentic_overhead

    total_input = round(base_input * profile.multiplier)
    total_output = round(base_output * effective_gen_mult)

    return {
        "language": profile.name,
        "input_tokens": total_input,
        "output_tokens": total_output,
        "total_tokens": total_input + total_output
    }

Optimization Strategies & Key Recommendations

To mitigate cost inflation and prevent premature context truncation when processing verbose enterprise codebases, AI platform engineering teams rely on five primary optimization strategies:

| Strategy                 | Target Languages              | Savings / Impact               | Mechanics                                                 | Primary SDLC Use Case                    |
| ------------------------ | ----------------------------- | ------------------------------ | --------------------------------------------------------- | ---------------------------------------- |
| **Format Stripping**     | Java, C, C++, Go, JS, TS  MD  | 15–35% input reduction  MD     | Collapses non-semantic whitespace and indentation  MD     | Preprocessing for long code files  MD    |
| **Comment Stripping**    | C, Java, JS, TS, Go, Rust  MD | 10–25% input reduction  MD     | Removes Javadoc/Doxygen (`/* */`, `//`) comments  MD      | Code Generation, Test Gen, Debugging  MD |
| **Signature Extraction** | Python, Java, Go, etc.  MD    | 50–80% input reduction  MD     | Extracts signatures and truncates bodies to `...`  MD     | API Review & Documentation  MD           |
| **Token Chunking**       | All languages  MD             | Eliminates context overrun  MD | Splits files dynamically at clean function boundaries  MD | Monorepos & large-file analysis  MD      |
| **Python Prototyping**   | C, C++, Java, Rust, Go  MD    | Prevents agent stuck-loops  MD | Generates solution in Python first, then translates  MD   | Complex algorithmic task generation  MD  |

Below is an automated sanitizer script designed to strip non-semantic Java formatting and Javadoc comments prior to prompt assembly:

import re

def strip_java_formatting(java_code: str) -> str:
    """Strips non-semantic formatting and Javadoc comments to minimize token cost."""
    # Remove block comments and Javadocs
    code = re.sub(r'/\*.*?\*/', '', java_code, flags=re.DOTALL)
    # Remove single-line comments
    code = re.sub(r'//.*', '', code)
    # Collapse redundant whitespace
    code = re.sub(r'\s+', ' ', code)
    return code.strip()

Sample Test

Run ./scripts/start.sh to execute the benchmark suite and test all concepts firsthand. You can review an excerpt of the generated execution log below.

================================================================
Token Research Pipeline — Started at Tue Aug 25 12:20:26 CEST 2026
Python: Python 3.12.10
Tiktoken: 0.14.0
================================================================

━━━ Running: bpe_tokenizer_demo.py ━━━
[INFO] Using tiktoken cl100k_base encoder (GPT-4 tokenizer).
======================================================================
BPE Tokenizer Demo — Token Counts Across Programming Languages
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §1
======================================================================

📊 Fibonacci + sum — Token count comparison
Language        Tokens   Chars  Lines  Chars/token  Ratio vs Python
----------------------------------------------------------------------
Python              84     287     12         3.42            1.00×
JavaScript         101     307     13         3.04            1.20×
Java               141     586     22         4.16            1.68×
Go                 124     379     22         3.06            1.48×
Rust               115     368     17         3.20            1.37×
C                  191     573     27         3.00            2.27×

============================================================
BPE Training Demo: first 12 merges on toy corpus
Input length: 77 chars → 77 bytes
============================================================
  Step  1: ' ' + ' ' → '  ' (freq=6, new_id=256)
  Step  2: 'v' + 'a' → 'va' (freq=3, new_id=257)
  Step  3: 'va' + 'l' → 'val' (freq=3, new_id=258)
  Step  4: 'val' + 'u' → 'valu' (freq=3, new_id=259)
  Step  5: 'valu' + 'e' → 'value' (freq=3, new_id=260)
  Step  6: 's' + 'e' → 'se' (freq=3, new_id=261)
  Step  7: 'se' + 'l' → 'sel' (freq=3, new_id=262)
  Step  8: 'sel' + 'f' → 'self' (freq=3, new_id=263)
  Step  9: 'n' + 'a' → 'na' (freq=3, new_id=264)
  Step 10: 'na' + 'm' → 'nam' (freq=3, new_id=265)
  Step 11: 'nam' + 'e' → 'name' (freq=3, new_id=266)
  Step 12: 'self' + '.' → 'self.' (freq=2, new_id=267)

============================================================
BPE Training Demo: first 12 merges on toy corpus
Input length: 59 chars → 59 bytes
============================================================
  Step  1: 'm' + 'e' → 'me' (freq=4, new_id=256)
  Step  2: 'a' + 'me' → 'ame' (freq=4, new_id=257)
  Step  3: 'n' + 'ame' → 'name' (freq=3, new_id=258)
  Step  4: ' ' + ' ' → '  ' (freq=3, new_id=259)
  Step  5: ' ' + 'name' → ' name' (freq=2, new_id=260)
  Step  6: ' name' + ';' → ' name;' (freq=1, new_id=261)
  Step  7: ' name;' + '
' → ' name;
' (freq=1, new_id=262)
  Step  8: ' name;
' + '}' → ' name;
}' (freq=1, new_id=263)
  Step  9: ' name;
}' + '
' → ' name;
}
' (freq=1, new_id=264)
  Step 10: ' name' + ')' → ' name)' (freq=1, new_id=265)
  Step 11: ' name)' + ' ' → ' name) ' (freq=1, new_id=266)
  Step 12: ' name) ' + '{' → ' name) {' (freq=1, new_id=267)

✅ Key insight: After enough BPE merges on a Python-heavy corpus,
   constructs like 'def ', 'self.', '__init__' become single tokens.
   Java's 'public void ', 'String ', 'this.' patterns also merge,
   but the class/method boilerplate still requires far more tokens.
   See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §2 for full ratio table.
✅ bpe_tokenizer_demo.py completed.

━━━ Running: token_counter.py ━━━
[INFO] tiktoken loaded — using cl100k_base (GPT-4) and o200k_base (GPT-4o).
======================================================================
Token Counter — Multi-Language Code Token Comparison
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §1 and §2
======================================================================

================================================================================
  Min/Max Task — Token Counts [cl100k_base]
  Tokenizer: cl100k_base  |  Baseline: Python
================================================================================
Language        Tokens   Chars  Lines  C/tok  L/tok    Ratio
--------------------------------------------------------------------------------
SQL                 83     273     10   3.29  0.120    0.76×
Ruby                87     233      9   2.68  0.103    0.80×
Python             109     340     10   3.12  0.092    1.00×
JavaScript         113     357     14   3.16  0.124    1.04×
TypeScript         127     408     14   3.21  0.110    1.17×
Rust               152     458     17   3.01  0.112    1.39×
Java               192     766     22   3.99  0.115    1.76×
Go                 210     635     33   3.02  0.157    1.93×
C                  237     678     30   2.86  0.127    2.17×
--------------------------------------------------------------------------------
  Most expensive: C (2.17× baseline)
  Most efficient: SQL (0.76× baseline)
================================================================================


================================================================================
  Min/Max Task — Token Counts [o200k_base]
  Tokenizer: o200k_base  |  Baseline: Python
================================================================================
Language        Tokens   Chars  Lines  C/tok  L/tok    Ratio
--------------------------------------------------------------------------------
SQL                 83     273     10   3.29  0.120    0.75×
Ruby                88     233      9   2.65  0.102    0.80×
Python             110     340     10   3.09  0.091    1.00×
JavaScript         115     357     14   3.10  0.122    1.05×
TypeScript         129     408     14   3.16  0.109    1.17×
Rust               152     458     17   3.01  0.112    1.38×
Java               207     766     22   3.70  0.106    1.88×
Go                 212     635     33   3.00  0.156    1.93×
C                  237     678     30   2.86  0.127    2.15×
--------------------------------------------------------------------------------
  Most expensive: C (2.15× baseline)
  Most efficient: SQL (0.75× baseline)
================================================================================

📄 CSV output (for token_ratio_calculator.py):
language,encoder,tokens,chars,lines,chars_per_token,lines_per_token,ratio_vs_baseline
SQL,cl100k_base,83,273,10,3.289,0.1205,0.7615
Ruby,cl100k_base,87,233,9,2.678,0.1034,0.7982
Python,cl100k_base,109,340,10,3.119,0.0917,1.0000
JavaScript,cl100k_base,113,357,14,3.159,0.1239,1.0367
TypeScript,cl100k_base,127,408,14,3.213,0.1102,1.1651
Rust,cl100k_base,152,458,17,3.013,0.1118,1.3945
Java,cl100k_base,192,766,22,3.990,0.1146,1.7615
Go,cl100k_base,210,635,33,3.024,0.1571,1.9266
C,cl100k_base ...

⚠️  Heuristic error demonstration:
Language        Heuristic   Actual   Error%
---------------------------------------------
Python                110      109    -22.0%
JavaScript            121      113    -21.2%
TypeScript            151      127    -19.7%
Java                  295      192     +0.0%
Go                    231      210    -24.3%
Rust                  180      152    -25.0%
C                     295      237    -28.3%
Ruby                   72       87    -33.3%
SQL                    78       83    -18.1%

  Negative error = naive heuristic UNDERESTIMATES actual token count.
  For Java and C, the naive rule underestimates by 30–50%+.
✅ token_counter.py completed.

━━━ Running: vocabulary_coverage_analysis.py ━━━
======================================================================
Vocabulary Coverage Analysis
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §1.4
======================================================================
Tokenizer: cl100k_base (GPT-4)

======================================================================
  1. Language Keywords — Coverage Test
======================================================================
  Category            Tokens  Chars    Rate  Description
  -----------------------------------------------------------------
  python_keyword           1      3   0.333  Python built-in keyword
                      → 'def'
  python_keyword           1      6   0.167  Python built-in keyword
                      → 'import'
  python_keyword           1      6   0.167  Python built-in keyword
                      → 'return'
  java_keyword             1      6   0.167  Java access modifier
                      → 'public'
  java_keyword             1      6   0.167  Java modifier
                      → 'static'
  java_keyword             1      4   0.250  Java return type
                      → 'void'
  rust_keyword             1      2   0.500  Rust function keyword
                      → 'fn'
  rust_keyword             1      4   0.250  Rust trait implementation keyword
                      → 'impl'
  go_keyword               1      4   0.250  Go function keyword
                      → 'func'
  ocaml_keyword            2      7   0.286  OCaml recursive binding
                      → 'let' | ' rec'
  haskell_keyword          1      5   0.200  Haskell where clause
                      → 'where'
  haskell_keyword          2      7   0.286  Haskell type declaration
                      → 'new' | 'type'

======================================================================
  2. Identifier Naming Conventions
======================================================================
  Category            Tokens  Chars    Rate  Description
  -----------------------------------------------------------------
  snake_case               2      7   0.286  2-word Python identifier
                      → 'user' | '_id'
  snake_case               3     24   0.125  3-word Python identifier
                      → 'calculate' | '_word' | '_frequency'
  snake_case               4     26   0.154  4-word Python identifier
                      → 'fetch' | '_api' | '_response' | '_payload'
  camelCase                1      6   0.167  2-word Java identifier
                      → 'userId'
  camelCase                3     22   0.136  3-word Java identifier
                      → 'calculate' | 'Word' | 'Frequency'
  camelCase                3     23   0.130  4-word Java identifier
                      → 'fetch' | 'ApiResponse' | 'Payload'
  PascalCase               1     11   0.091  2-word class name
                      → 'UserService'
  PascalCase               3     26   0.115  4-word Java class name
                      → 'Abstract' | 'UserService' | 'Factory'
  SCREAMING_SNAKE          3     15   0.200  Java/C constant
                      → 'MAX' | '_RETRY' | '_COUNT'
  SCREAMING_SNAKE          4     28   0.143  Java constant
                      → 'DEFAULT' | '_CONNECTION' | '_POOL' | '_SIZE'
  go_short                 1      1   1.000  Go request shorthand
                      → 'r'
  go_short                 1      3   0.333  Go context shorthand
                      → 'ctx'
  go_short                 1      3   0.333  Go error shorthand
                      → 'err'

======================================================================
  3. Language Syntax Patterns
======================================================================
  Category            Tokens  Chars    Rate  Description
  -----------------------------------------------------------------
  python_syntax            6     19   0.316  Python constructor signature
                      → 'def' | ' __' | 'init' | '__(' | 'self' | '):'
  python_syntax            3     19   0.158  Python attribute access
                      → 'self' | '.attribute' | '_name'
  python_syntax            9     26   0.346  Python entry guard
                      → 'if' | ' __' | 'name' | '__' | ' ==' | " '__" | 'main' | '__' | "':"
  java_syntax              8     38   0.211  Java main signature
                      → 'public' | ' static' | ' void' | ' main' | '(String' | '[]' | ' args' | ')'
  java_syntax              3     18   0.167  Java print method
                      → 'System' | '.out' | '.println'
  java_syntax              3     20   0.150  Java field declaration prefix
                      → 'private' | ' final' | ' String'
  rust_syntax             10     39   0.256  Rust main signature
                      → 'fn' | ' main' | '()' | ' ->' | ' Result' | '<(),' | ' Box' | '<dyn' | ' Error' | '>>'
  rust_syntax             14     32   0.438  Rust impl with lifetime
                      → 'impl' | "<'" | 'a' | ',' | ' T' | ':' | ' Trait' | '>' | ' Struct' | "<'" | 'a' | ',' | ' T' | '>'
  rust_syntax              8     29   0.276  Rust nested generics
                      → 'Vec' | '<HashMap' | '<String' | ',' | ' Vec' | '<u' | '8' | '>>>'
  go_syntax                5     15   0.333  Go error check pattern
                      → 'if' | ' err' | ' !=' | ' nil' | ' {'
  go_syntax               16     63   0.254  Go method signature
  ocaml_syntax             9     28   0.321  OCaml recursive function
                      → 'let' | ' rec' | ' fold' | '_left' | ' f' | ' acc' | 'u' | ' l' | ' ='
  ocaml_syntax            11     34   0.324  OCaml type declaration
                      → 'type' | " '" | 'a' | ' option' | ' =' | ' None' | ' |' | ' Some' | ' of' | " '" | 'a'
  c_syntax                14     42   0.333  C malloc call
                      → 'int' | ' *' | 'ptr' | ' =' | ' (' | 'int' | ' *)' | 'malloc' | '(sizeof' | '(int' | ')' | ' *' | ' n' | ');'
  c_syntax                 8     29   0.276  C function pointer
                      → 'void' | ' (*' | 'callback' | ')(' | 'int' | ',' | ' void' | ' *)'

======================================================================
  4. High vs. Low Training Data Coverage
======================================================================
  Category            Tokens  Chars    Rate  Description
  -----------------------------------------------------------------
  high_coverage            4     18   0.222  Python data science import
                      → 'import' | ' numpy' | ' as' | ' np'
  high_coverage            7     34   0.206  Node.js pattern
                      → 'const' | ' express' | ' =' | ' require' | "('" | 'express' | "')"
  high_coverage            9     32   0.281  Common SQL pattern
                      → 'SELECT' | ' *' | ' FROM' | ' users' | ' WHERE' | ' id' | ' =' | ' ' | '1'
  low_coverage             8     42   0.190  OCaml qualified name
                      → 'let' | ' binding' | ' =' | ' module' | '_name' | '.Sub' | 'module' | '.create'
  low_coverage             7     33   0.212  Haskell typeclass instance
                      → 'instance' | ' Functor' | ' (' | 'Either' | ' a' | ')' | ' where'
  low_coverage            11     56   0.196  Rust extern crate
                      → 'extern' | ' crate' | ' serde' | ';' | ' use' | ' serde' | '::{' | 'Serialize' | ',' | ' Deserialize' | '};'

======================================================================
  Identifier Naming Convention — Token Efficiency Analysis
======================================================================

  Concept: 'User repository service'
  Convention                      Tokens  Chars    Rate
  -------------------------------------------------------
  Python snake_case                    3     23   0.130
                                  → 'user' | '_repository' | '_service'
  Java camelCase                       2     21   0.095
                                  → 'userRepository' | 'Service'
  Java PascalCase class                3     21   0.143
                                  → 'User' | 'Repository' | 'Service'
  C SCREAMING_SNAKE                    4     23   0.174
                                  → 'USER' | '_RE' | 'POSITORY' | '_SERVICE'
  Go abbreviated                       2      7   0.286
                                  → 'user' | 'Svc'
  Rust snake_case                      3     23   0.130
                                  → 'user' | '_repository' | '_service'

  Concept: 'Get user by ID'
  Convention                      Tokens  Chars    Rate
  -------------------------------------------------------
  Python snake_case                    4     14   0.286
                                  → 'get' | '_user' | '_by' | '_id'
  Java camelCase                       2     11   0.182
                                  → 'getUser' | 'ById'
  Java verbose camelCase               5     26   0.192
                                  → 'find' | 'User' | 'Entity' | 'ById' | 'entifier'
  Go abbreviated                       2     11   0.182
                                  → 'getUser' | 'ByID'
  C lowercase                          4     14   0.286
                                  → 'get' | '_user' | '_by' | '_id'

======================================================================
  Summary: Vocabulary Coverage Findings
======================================================================

  1. WELL-COVERED constructs (low fragmentation rate, ~0.25–0.35 tok/char):
     • Python keywords: def, import, return, class, if, for, with
     • Common English words appearing frequently in code: user, name, value
     • SQL keywords: SELECT, FROM, WHERE (high training data frequency)
     • JavaScript patterns: const, let, function, console.log

  2. POORLY-COVERED constructs (high fragmentation, 0.6–1.0+ tok/char):
     • OCaml: type constructors, polymorphic variants, module signatures
     • Haskell: type class instances, point-free notation, monadic bind (>>=)
     • Rust: lifetime parameters ('a), complex generic bounds, macro syntax
     • SCREAMING_SNAKE_CASE identifiers (underscore disrupts merge patterns)
     • Very long compound identifiers (AbstractSingletonProxyFactoryBean)

  3. FORMATTING OVERHEAD (see TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4.3):
     • Java newlines: 18.7% of total tokens (Claude-3.7 measurement)
     • Java indentation: ~7.9% of total tokens
     • Python indentation: mandatory — cannot be removed (syntax rule)
     • C comment blocks: up to 21% token savings from comment stripping

  4. IDENTIFIER NAMING:
     • snake_case and camelCase are similarly efficient for equal-length words
     • SCREAMING_SNAKE_CASE is ~50% less efficient per character
     • Short Go-style names (ctx, err, r) are most token-efficient
     • Long verbose Java-style names are most token-expensive

✅ vocabulary_coverage_analysis.py completed.

━━━ Running: token_ratio_calculator.py ━━━
======================================================================
Token Ratio Calculator — Language Efficiency Ranking
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §2
======================================================================

====================================================================================================
  TOKEN EFFICIENCY RANKING — All Languages vs. Python Baseline (1.00×)
  Data sources: cross-lang-token-density, Wu et al. (2026), mame/ai-coding-lang-bench
====================================================================================================
  Rank  Language       Rating    Static   Agentic   Prod.   Overall          Range  Notes
  -------------------------------------------------------------------------------------------------
  1     Ruby           ★★★★★       N/A       N/A    0.95×     0.95×     0.95–0.95×  Slightly beats Python in production
  2     Python         ★★★★★      1.00×      N/A     N/A      1.00×     1.00–1.00×  ← Baseline
  3     JavaScript     ★★★★☆      1.26×      N/A    1.03×     1.15×     1.03–1.26×  
  4     C++            ★★★★☆      1.20×      N/A     N/A      1.20×     1.20–1.20×  
  5     Java           ★★★☆☆      1.47×     1.23×   1.32×     1.30×     1.18–1.47×  
  6     Rust           ★★★☆☆      1.57×     1.24×   1.42×     1.34×     1.16–1.57×  
  7     Go             ★★★☆☆      1.55×      N/A    1.32×     1.44×     1.32–1.55×  
  8     OCaml          ★★★☆☆       N/A      1.42×   1.53×     1.45×     1.28–1.69×  Compact code; high agent confusion cost
  9     TypeScript     ★★☆☆☆      1.45×      N/A    1.63×     1.54×     1.45–1.63×  
  10    C              ★☆☆☆☆      1.77×      N/A    1.95×     1.86×     1.77–1.95×  Most expensive; manual memory management
  11    Haskell        ★☆☆☆☆       N/A       N/A    1.95×     1.95×     1.95–1.95×  
====================================================================================================

  Column definitions:
    Static  = direct token count of equivalent program text (no agent overhead)
    Agentic = full agent session including failed attempts, revisions, stuck loops
    Prod.   = production benchmark (real API cost) including all overhead
    Overall = mean across all available measurements for that language
    Range   = min–max observed ratio across all data points

  Note: Agentic ratios diverge most from static ratios for low-resource languages
  (OCaml, Haskell, Rust) because agent behavior — not syntax — drives the gap.
  See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §2.3 for detailed explanation.

======================================================================
  TYPE SYSTEM IMPACT — Incremental Token Cost of Adding Type Checking
======================================================================

  Transformation                            Static overhead  Agentic overhead
  ---------------------------------------------------------------------------
  Adding TypeScript types to JS                      1.15×            1.60×
  Adding mypy strict to Python                       1.00×            1.65×
  Adding Steep type checker to Ruby                  1.00×            2.60×

  Interpretation:
    The agentic overhead for typed variants is MUCH larger than the static overhead.
    This is because the LLM must reason about type constraints, satisfy the type
    checker, and iterate when type errors occur — generating many extra turns.
    Ruby/Steep's 2.60× agentic overhead reflects low LLM familiarity with Steep.

======================================================================
  Markdown Table (for embedding in reports):
======================================================================
| Rank | Language | Efficiency | Static | Agentic | Production | Overall | Range |
|:----:|----------|:----------:|:------:|:-------:|:----------:|:-------:|:-----:|
| 1 | **Ruby** | ★★★★★ |   N/A  |   N/A  | 0.95× | 0.95× | 0.95–0.95× |
| 2 | **Python** | ★★★★★ | 1.00× |   N/A  |   N/A  | 1.00× | 1.00–1.00× |
| 3 | **JavaScript** | ★★★★☆ | 1.26× |   N/A  | 1.03× | 1.15× | 1.03–1.26× |
| 4 | **C++** | ★★★★☆ | 1.20× |   N/A  |   N/A  | 1.20× | 1.20–1.20× |
| 5 | **Java** | ★★★☆☆ | 1.47× | 1.23× | 1.32× | 1.30× | 1.18–1.47× |
| 6 | **Rust** | ★★★☆☆ | 1.57× | 1.24× | 1.42× | 1.34× | 1.16–1.57× |
| 7 | **Go** | ★★★☆☆ | 1.55× |   N/A  | 1.32× | 1.44× | 1.32–1.55× |
| 8 | **OCaml** | ★★★☆☆ |   N/A  | 1.42× | 1.53× | 1.45× | 1.28–1.69× |
| 9 | **TypeScript** | ★★☆☆☆ | 1.45× |   N/A  | 1.63× | 1.54× | 1.45–1.63× |
| 10 | **C** | ★☆☆☆☆ | 1.77× |   N/A  | 1.95× | 1.86× | 1.77–1.95× |
| 11 | **Haskell** | ★☆☆☆☆ |   N/A  |   N/A  | 1.95× | 1.95× | 1.95–1.95× |

✅ Run the equivalent_task_samples/ scripts for direct token measurements.
   These benchmark ratios are the aggregate from published research.
✅ token_ratio_calculator.py completed.

━━━ Running: hello_world_tokens.py ━━━
========================================================================
Hello World + Fibonacci — Multi-Language Token Count Comparison
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §2
Tokenizer: cl100k_base (GPT-4)
========================================================================

Language        Tokens   Chars  Lines  C/tok    Ratio
--------------------------------------------------------
Ruby                89     260     12   2.92    0.82×  ████████
Python             109     357     14   3.28    1.00×  ██████████
JavaScript         133     417     16   3.14    1.22×  ████████████
TypeScript         149     503     17   3.38    1.37×  █████████████
Rust               150     485     21   3.23    1.38×  █████████████
Go                 161     486     26   3.02    1.48×  ██████████████
Java               191     776     28   4.06    1.75×  █████████████████
C                  244     744     33   3.05    2.24×  ██████████████████████

------------------------------------------------------------------------
Most tokens:  C              244  (2.24× Python)
Fewest tokens:Ruby           89  (0.82× Python)

💡 Key observation: C requires 2.7×
   the tokens of Ruby for identical logic.

   Note: TypeScript vs JavaScript overhead = purely type annotations.
   See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4.2 for type annotation analysis.
✅ hello_world_tokens.py completed.

━━━ Running: rest_api_stub_tokens.py ━━━
========================================================================
REST API Stub — Token Count Comparison Across 5 Language/Frameworks
Task: GET /users/{id} with error handling and data model
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §2 & §3
Tokenizer: cl100k_base (GPT-4)
========================================================================

Name                        Tokens   Chars  Lines  C/tok    Ratio
--------------------------------------------------------------------
Python (Flask)                 205     718     29   3.50    1.00×  ████████
TypeScript (Express)           234     832     34   3.56    1.14×  █████████
Go (Gin)                       250     866     42   3.46    1.22×  █████████
Rust (Actix-web)               363   1,324     49   3.65    1.77×  ██████████████
Java (Spring Boot)             382   1,843     69   4.82    1.86×  ██████████████

------------------------------------------------------------------------
Most expensive:  Java (Spring Boot) (382 tokens = 1.86× Python/Flask)
Least expensive: Python (Flask) (205 tokens = 1.00× baseline)

💡 Analysis:
   • Python/Flask wins on token efficiency due to:
     - No mandatory class boilerplate (just decorated functions)
     - Implicit JSON serialization via dataclass + jsonify
     - No explicit type annotations required

   • Java/Spring Boot is the most verbose because:
     - Separate files per class (Controller, Service, Model)
     - Explicit getters/setters for each field
     - @Annotation overhead throughout
     - Explicit Optional<> type wrapping

   • Go/Gin's verbosity comes from:
     - Explicit error handling after every fallible operation
     - Manual type parsing (strconv.Atoi)
     - No implicit JSON serialization (struct tags required)

   • Rust/Actix-web is expensive due to:
     - Async/await boilerplate (#[actix_web::main])
     - Mutex<HashMap<>> shared state pattern
     - Derive macros (Serialize, Deserialize) on every struct

   See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4 for detailed factor analysis.

✅ rest_api_stub_tokens.py completed.

━━━ Running: unit_test_tokens.py ━━━
========================================================================
Unit Test Suite — Token Count Comparison Across 6 Frameworks
Task: Stack push/pop/overflow/underflow tests (same 6 test cases)
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §2 & §3
Tokenizer: cl100k_base (GPT-4)
========================================================================

Name                        Tokens   Chars  Lines  C/tok    Ratio
--------------------------------------------------------------------
Python (pytest)                318   1,260     54   3.96    1.00×  ████████
C++ (GoogleTest)               399   1,415     58   3.55    1.25×  ██████████
JavaScript (Jest)              404   1,756     63   4.35    1.27×  ██████████
Rust (cargo test)              422   1,642     69   3.89    1.33×  ██████████
Go (testing)                   580   1,975     80   3.41    1.82×  ██████████████
Java (JUnit 5)                 604   2,704    102   4.48    1.90×  ███████████████

💡 Test Generation Implications (TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §3):
   • Java test files are significantly larger due to:
     - @Test, @BeforeEach, @Nested, @DisplayName annotations
     - Explicit generic type parameters on every assertion
     - @ParameterizedTest + @MethodSource requiring separate stream methods

   • Python/pytest is the baseline winner because:
     - No class required (just functions)
     - @pytest.fixture replaces @BeforeEach with zero overhead
     - assert keyword vs. assertEquals/assertFalse/assertTrue verbosity

   • Go's table-driven tests are verbose but idiomatic:
     - Explicit error checking after every operation (if err != nil)
     - Manual comparison in test body (no matcher library)

   • Rust's #[cfg(test)] approach is moderately efficient:
     - Built-in to the language, no external framework imports
     - assert_eq!/assert! macros are compact
     - unwrap() pattern adds minor overhead

✅ unit_test_tokens.py completed.

━━━ Running: sdlc_token_budget_model.py ━━━
================================================================================
SDLC Token Budget Model
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §3
Model: 10-feature project, ~500 LOC per feature
================================================================================

====================================================================================================
  PER-FEATURE TOKEN CONSUMPTION BY SDLC PHASE
  (tokens shown per feature; multiply by num_features for project total)
====================================================================================================
  Phase                      Python JavaScript       Java         Go       Rust          C
  ------------------------------------------------------------------------------------
  Requirements Analysis       2,300      2,300      2,300      2,300      2,300      2,300
  Architecture Design         3,500      3,500      3,500      3,500      3,500      3,500
  Code Generation             6,500      7,032      9,791      9,320     10,476     13,474
  Code Review                 6,700      7,250      9,285      9,725      9,835     10,935
  Refactoring                 9,500     10,599     15,553     15,423     16,823     21,196
  Test Generation             6,500      7,266     10,790     10,618     11,689     14,912
  Documentation               6,250      6,600      7,895      8,175      8,245      8,945
  Debugging                   5,800      6,439      9,161      9,269      9,869     12,018
  Deployment Config           2,000      2,000      2,000      2,000      2,000      2,000
  ------------------------------------------------------------------------------------
  TOTAL (per feature)        49,050     52,986     70,275     70,330     74,737     89,280
  Ratio vs Python             1.00×      1.08×      1.43×      1.43×      1.52×      1.82×
====================================================================================================

================================================================================
  PROJECT SUMMARY (10 features)
================================================================================
  Language          Input tok   Output tok        Total    Ratio
  --------------------------------------------------------------
  Python              278,000      212,500      490,500    1.00×
  JavaScript          299,000      230,860      529,860    1.08×
  Java                376,700      326,050      702,750    1.43×
  Go                  393,500      309,800      703,300    1.43×
  Rust                397,700      349,670      747,370    1.52×
  C                   439,700      453,100      892,800    1.82×

  Estimated API costs (GPT-4o: $2.50/M input, $10.00/M output):
  Language         Input cost  Output cost   Total cost
  ------------------------------------------------------
  Python         $       0.70 $       2.12 $       2.82
  JavaScript     $       0.75 $       2.31 $       3.06
  Java           $       0.94 $       3.26 $       4.20
  Go             $       0.98 $       3.10 $       4.08
  Rust           $       0.99 $       3.50 $       4.49
  C              $       1.10 $       4.53 $       5.63
================================================================================

💡 Key findings from this model:
   1. Code Review and Refactoring are the most token-intensive phases for static-typed
      languages because entire files must be in context (high input token cost).
   2. Test Generation compounds the language penalty: tests mirror production code
      verbosity, doubling the effective penalty for boilerplate-heavy languages.
   3. The Debugging phase is unpredictable — for unfamiliar languages, multiple
      agent turns may be needed to interpret stack traces and write correct fixes.
   4. Deployment Config is largely language-agnostic (YAML/Docker is universal),
      so it provides little differentiation between language choices.

   See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §3 for the full SDLC phase analysis.

✅ sdlc_token_budget_model.py completed.

━━━ Running: phase_cost_estimator.py ━━━
================================================================================
Phase Cost Estimator
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §3 & §5
================================================================================

==========================================================================================
  Cost Estimate: Python × Claude Sonnet 4.5 (Anthropic)
  Project: 10 features | Input: $3.00/M | Output: $15.00/M
  Cache rate: $0.300/M (input cache hits)
==========================================================================================
  Phase                    InputTok    OutTok   Baseline   + Cache  + FmtStrip  Optimized   Saving
  ----------------------------------------------------------------------------------------
  Requirements               15,000     8,000   $   0.165  $  0.141  $    0.165  $   0.141    14.7%
  Architecture Design        20,000    15,000   $   0.285  $  0.253  $    0.285  $   0.253    11.4%
  Code Generation            25,000    40,000   $   0.675  $  0.634  $    0.675  $   0.634     6.0%
  Code Review                55,000    12,000   $   0.345  $  0.256  $    0.340  $   0.253    26.5%
  Refactoring                50,000    45,000   $   0.825  $  0.744  $    0.820  $   0.742    10.1%
  Test Generation            30,000    35,000   $   0.615  $  0.566  $    0.612  $   0.565     8.1%
  Documentation              35,000    25,000   $   0.480  $  0.423  $    0.477  $   0.422    12.1%
  Debugging                  40,000    18,000   $   0.390  $  0.325  $    0.386  $   0.323    17.1%
  Deployment Config           8,000    12,000   $   0.204  $  0.191  $    0.204  $   0.191     6.4%
  ----------------------------------------------------------------------------------------
  TOTAL                                         $   3.984  $  3.534  $    3.964  $   3.524    11.5%
  Combined optimization saves $0.460 (11.5%) vs. baseline

==========================================================================================
  Cost Estimate: Java × Claude Sonnet 4.5 (Anthropic)
  Project: 10 features | Input: $3.00/M | Output: $15.00/M
  Cache rate: $0.300/M (input cache hits)
==========================================================================================
  Phase                    InputTok    OutTok   Baseline   + Cache  + FmtStrip  Optimized   Saving
  ----------------------------------------------------------------------------------------
  Requirements               15,000     8,336   $   0.170  $  0.146  $    0.170  $   0.146    14.3%
  Architecture Design        20,000    15,630   $   0.294  $  0.262  $    0.294  $   0.262    11.0%
  Code Generation            25,000    55,120   $   0.902  $  0.861  $    0.902  $   0.861     4.5%
  Code Review                75,680    12,504   $   0.415  $  0.292  $    0.369  $   0.271    34.6%
  Refactoring                68,800    62,010   $   1.137  $  1.025  $    1.095  $   1.006    11.5%
  Test Generation            41,280    48,230   $   0.847  $  0.780  $    0.823  $   0.769     9.2%
  Documentation              48,160    26,050   $   0.535  $  0.457  $    0.506  $   0.444    17.1%
  Debugging                  55,040    24,804   $   0.537  $  0.448  $    0.504  $   0.433    19.4%
  Deployment Config           8,000    16,536   $   0.272  $  0.259  $    0.272  $   0.259     4.8%
  ----------------------------------------------------------------------------------------
  TOTAL                                         $   5.109  $  4.531  $    4.936  $   4.451    12.9%
  Combined optimization saves $0.658 (12.9%) vs. baseline

================================================================================
  CROSS-LANGUAGE COST COMPARISON — GPT-4o
  10 features | Baseline = Python
================================================================================
  Language         Baseline$  Optimized$    Ratio    Saving
  ------------------------------------------------------------
  Ruby           $     2.704 $     2.487    0.97×      8.0%
  Python         $     2.795 $     2.575    1.00×      7.9%
  JavaScript     $     2.879 $     2.633    1.03×      8.6%
  Go             $     3.477 $     3.157    1.24×      9.2%
  TypeScript     $     3.478 $     3.164    1.24×      9.0%
  Java           $     3.585 $     3.216    1.28×     10.3%
  OCaml          $     3.668 $     3.378    1.31×      7.9%
  Rust           $     3.697 $     3.374    1.32×      8.7%
  C++            $     3.760 $     3.414    1.35×      9.2%
  C              $     4.246 $     3.846    1.52×      9.4%

  Python baseline: $2.795 unoptimized → $2.575 optimized

💡 Interpretation:
   • The "Optimized" column applies BOTH prompt caching AND format stripping.
   • Claude's cache rate ($0.30/M) provides the highest savings percentage
     because the full input rate is $3.00/M — a 90% discount on cache hits.
   • Format stripping helps Java most (25% input saving) and Python least (4%).
   • Even optimized Java costs ~1.35× optimized Python on Claude Sonnet.

   See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §6 for all optimization strategies.

✅ phase_cost_estimator.py completed.

━━━ Running: context_window_risk_analyzer.py ━━━
===========================================================================
Context Window Risk Analyzer
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §3.3 & §5.3
===========================================================================

===========================================================================
  Max File Size in Context Window — GPT-4o
  (with 8,000 tokens reserved for system prompt + output)
===========================================================================
  Language        Available tok   Max chars   Max LOC   Tok/LOC
  --------------------------------------------------------------
  SQL                   120,000     419,580     9,324     12.87
  Ruby                  120,000     389,610    13,915      8.62
  Python                120,000     371,517    11,610     10.34
  PHP                   120,000     360,360    10,010     11.99
  JavaScript            120,000     353,982    10,411     11.53
  OCaml                 120,000     336,134    11,204     10.71
  Go                    120,000     329,670     8,676     13.83
  Haskell               120,000     329,670    11,774     10.19
  TypeScript            120,000     324,324     8,108     14.80
  C++                   120,000     318,302     8,376     14.33
  Java                  120,000     311,688     7,421     16.17
  Rust                  120,000     306,122     7,653     15.68
  C                     120,000     275,862     7,663     15.66
===========================================================================

===========================================================================
  Max File Size in Context Window — Claude Sonnet 4.5
  (with 12,000 tokens reserved for system prompt + output)
===========================================================================
  Language        Available tok   Max chars   Max LOC   Tok/LOC
  --------------------------------------------------------------
  SQL                   188,000     657,343    14,608     12.87
  Ruby                  188,000     610,390    21,800      8.62
  Python                188,000     582,043    18,189     10.34
  PHP                   188,000     564,565    15,682     11.99
  JavaScript            188,000     554,572    16,311     11.53
  OCaml                 188,000     526,611    17,554     10.71
  Go                    188,000     516,484    13,592     13.83
  Haskell               188,000     516,484    18,446     10.19
  TypeScript            188,000     508,108    12,703     14.80
  C++                   188,000     498,674    13,123     14.33
  Java                  188,000     488,312    11,626     16.17
  Rust                  188,000     479,592    11,990     15.68
  C                     188,000     432,184    12,005     15.66
===========================================================================

====================================================================================================
  CONTEXT WINDOW RISK MATRIX — GPT-4o, File size: 500 LOC
====================================================================================================
  Phase                        Python   JavaScript         Java           Go         Rust            C
  ----------------------------------------------------------------------------------------------------
  Requirements                   ✅ 6%         ✅ 6%         ✅ 8%         ✅ 7%         ✅ 8%         ✅ 8%
  Architecture Design            ✅ 6%         ✅ 7%         ✅ 9%         ✅ 8%         ✅ 8%         ✅ 8%
  Code Generation                ✅ 6%         ✅ 7%         ✅ 9%         ✅ 8%         ✅ 8%         ✅ 8%
  Code Review                    ✅ 7%         ✅ 8%         ✅ 9%         ✅ 9%         ✅ 9%         ✅ 9%
  Refactoring                    ✅ 8%         ✅ 8%        ✅ 10%         ✅ 9%        ✅ 10%        ✅ 10%
  Test Generation                ✅ 7%         ✅ 7%         ✅ 9%         ✅ 8%         ✅ 9%         ✅ 9%
  Documentation                  ✅ 6%         ✅ 7%         ✅ 9%         ✅ 8%         ✅ 8%         ✅ 8%
  Debugging                      ✅ 9%         ✅ 9%        ✅ 11%        ✅ 10%        ✅ 11%        ✅ 11%
  Deployment Config              ✅ 6%         ✅ 6%         ✅ 8%         ✅ 7%         ✅ 8%         ✅ 8%
  ----------------------------------------------------------------------------------------------------
  Legend: ✅ LOW (<50%) | ⚠️  MEDIUM (50–70%) | 🔴 HIGH (70–90%) | 💥 CRITICAL (>90%)

====================================================================================================
  CONTEXT WINDOW RISK MATRIX — GPT-4o, File size: 1,000 LOC
====================================================================================================
  Phase                        Python   JavaScript         Java           Go         Rust            C
  ----------------------------------------------------------------------------------------------------
  Requirements                  ✅ 10%        ✅ 11%        ✅ 14%        ✅ 12%        ✅ 14%        ✅ 14%
  Architecture Design           ✅ 10%        ✅ 11%        ✅ 15%        ✅ 13%        ✅ 15%        ✅ 15%
  Code Generation               ✅ 10%        ✅ 11%        ✅ 15%        ✅ 13%        ✅ 15%        ✅ 15%
  Code Review                   ✅ 11%        ✅ 12%        ✅ 16%        ✅ 14%        ✅ 15%        ✅ 15%
  Refactoring                   ✅ 12%        ✅ 13%        ✅ 17%        ✅ 15%        ✅ 16%        ✅ 16%
  Test Generation               ✅ 11%        ✅ 12%        ✅ 15%        ✅ 14%        ✅ 15%        ✅ 15%
  Documentation                 ✅ 10%        ✅ 11%        ✅ 15%        ✅ 13%        ✅ 15%        ✅ 15%
  Debugging                     ✅ 13%        ✅ 14%        ✅ 17%        ✅ 15%        ✅ 17%        ✅ 17%
  Deployment Config             ✅ 10%        ✅ 11%        ✅ 14%        ✅ 12%        ✅ 14%        ✅ 14%
  ----------------------------------------------------------------------------------------------------
  Legend: ✅ LOW (<50%) | ⚠️  MEDIUM (50–70%) | 🔴 HIGH (70–90%) | 💥 CRITICAL (>90%)

====================================================================================================
  CONTEXT WINDOW RISK MATRIX — GPT-4o, File size: 2,000 LOC
====================================================================================================
  Phase                        Python   JavaScript         Java           Go         Rust            C
  ----------------------------------------------------------------------------------------------------
  Requirements                  ✅ 18%        ✅ 20%        ✅ 27%        ✅ 23%        ✅ 26%        ✅ 26%
  Architecture Design           ✅ 18%        ✅ 20%        ✅ 28%        ✅ 24%        ✅ 27%        ✅ 27%
  Code Generation               ✅ 18%        ✅ 20%        ✅ 28%        ✅ 24%        ✅ 27%        ✅ 27%
  Code Review                   ✅ 19%        ✅ 21%        ✅ 28%        ✅ 25%        ✅ 28%        ✅ 28%
  Refactoring                   ✅ 20%        ✅ 22%        ✅ 29%        ✅ 26%        ✅ 28%        ✅ 28%
  Test Generation               ✅ 19%        ✅ 21%        ✅ 28%        ✅ 24%        ✅ 27%        ✅ 27%
  Documentation                 ✅ 18%        ✅ 20%        ✅ 28%        ✅ 24%        ✅ 27%        ✅ 27%
  Debugging                     ✅ 21%        ✅ 23%        ✅ 30%        ✅ 26%        ✅ 29%        ✅ 29%
  Deployment Config             ✅ 18%        ✅ 20%        ✅ 27%        ✅ 23%        ✅ 26%        ✅ 26%
  ----------------------------------------------------------------------------------------------------
  Legend: ✅ LOW (<50%) | ⚠️  MEDIUM (50–70%) | 🔴 HIGH (70–90%) | 💥 CRITICAL (>90%)

===========================================================================
  CHUNKING THRESHOLDS — GPT-4o
  (LOC at which Code Review phase becomes HIGH or CRITICAL risk)
===========================================================================
  Language         MEDIUM (50%)   HIGH (70%)   CRITICAL (90%)
  ------------------------------------------------------------
  C                       3,832 LOC    5,467 LOC        7,101 LOC
  Go                      4,338 LOC    6,189 LOC        8,040 LOC
  Java                    3,711 LOC    5,294 LOC        6,877 LOC
  JavaScript              5,206 LOC    7,427 LOC        9,648 LOC
  Python                  5,805 LOC    8,282 LOC       10,759 LOC
  Rust                    3,827 LOC    5,460 LOC        7,092 LOC

  Interpretation:
    Files below the MEDIUM threshold: safe to send whole.
    Files in MEDIUM zone: consider format stripping and comment removal.
    Files in HIGH zone: use targeted extraction (function + dependencies).
    Files in CRITICAL zone: mandatory chunking required.

  For reference: a typical enterprise Java service class is 300–800 LOC.
  Java reaches HIGH risk at much smaller files than Python due to token density.


===========================================================================
  CHUNKING THRESHOLDS — Claude Sonnet 4.5
  (LOC at which Code Review phase becomes HIGH or CRITICAL risk)
===========================================================================
  Language         MEDIUM (50%)   HIGH (70%)   CRITICAL (90%)
  ------------------------------------------------------------
  C                       6,131 LOC    8,685 LOC       11,239 LOC
  Go                      6,941 LOC    9,833 LOC       12,725 LOC
  Java                    5,937 LOC    8,411 LOC       10,885 LOC
  JavaScript              8,329 LOC   11,800 LOC       15,270 LOC
  Python                  9,288 LOC   13,158 LOC       17,028 LOC
  Rust                    6,123 LOC    8,674 LOC       11,225 LOC

  Interpretation:
    Files below the MEDIUM threshold: safe to send whole.
    Files in MEDIUM zone: consider format stripping and comment removal.
    Files in HIGH zone: use targeted extraction (function + dependencies).
    Files in CRITICAL zone: mandatory chunking required.

  For reference: a typical enterprise Java service class is 300–800 LOC.
  Java reaches HIGH risk at much smaller files than Python due to token density.

✅ context_window_risk_analyzer.py completed.

━━━ Running: boilerplate_overhead_demo.py ━━━
======================================================================
Boilerplate Overhead Demo
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4.1
Tokenizer: cl100k_base (GPT-4)
======================================================================

======================================================================
  1. Empty Function — Structural Overhead
======================================================================
  Language         Total   Logic  Boilerplate  Overhead%    Ratio
  --------------------------------------------------------------
  Python               8       2            6      75.0%   1.00×  ███████████████
  JavaScript          10       2            8      80.0%   1.25×  ████████████████
  Go                  12       2           10      83.3%   1.50×  ████████████████
  Java                12       2           10      83.3%   1.50×  ████████████████
  TypeScript          13       2           11      84.6%   1.62×  ████████████████
  C                   13       2           11      84.6%   1.62×  ████████████████
  Rust                26       1           25      96.2%   3.25×  ███████████████████

======================================================================
  2. Minimal Class (1 field + getter) — Boilerplate Tax
======================================================================
  Language         Total   Logic  Boilerplate  Overhead%    Ratio
  --------------------------------------------------------------
  JavaScript          28       4           24      85.7%   0.82×  █████████████████
  Python              34       4           30      88.2%   1.00×  █████████████████
  TypeScript          38       4           34      89.5%   1.12×  █████████████████
  Java                39       4           35      89.7%   1.15×  █████████████████
  Go                  43       4           39      90.7%   1.26×  ██████████████████
  C++                 45       4           41      91.1%   1.32×  ██████████████████
  Rust                51       4           47      92.2%   1.50×  ██████████████████
  C                   65       4           61      93.8%   1.91×  ██████████████████

======================================================================
  3. Program Entry Point — Structural Requirement
======================================================================
  Language         Total   Logic  Boilerplate  Overhead%    Ratio
  --------------------------------------------------------------
  Python               7       0            7     100.0%   1.00×  ████████████████████
  Ruby                 7       0            7     100.0%   1.00×  ████████████████████
  TypeScript           8       0            8     100.0%   1.14×  ████████████████████
  JavaScript           9       0            9     100.0%   1.29×  ████████████████████
  Rust                10       0           10     100.0%   1.43×  ████████████████████
  Go                  17       0           17     100.0%   2.43×  ████████████████████
  C++                 19       0           19     100.0%   2.71×  ████████████████████
  C                   21       0           21     100.0%   3.00×  ████████████████████
  Java                22       0           22     100.0%   3.14×  ████████████████████

💡 Key findings:

   EMPTY FUNCTION:
   Python and Ruby require the minimum structural tokens — just the function
   keyword, name, and body. Java requires public/Object/return each as separate
   tokens plus braces and semicolons. Rust requires explicit return type annotation.

   MINIMAL CLASS:
   Java's class is the most boilerplate-heavy because:
   - 'public class' access + type declaration
   - 'private final' per field
   - Explicit constructor with parameter type annotation
   - 'public String getName()' with full return type
   Python's class is lean: 'def' + 'self' + ':' and no access modifiers.

   ENTRY POINT:
   Python and Ruby have ZERO boilerplate — you just write code.
   Java requires 'public class Main { public static void main(String[] args) { ... } }'
   even for a one-liner program — a structural tax that applies to every generated file.

   This boilerplate overhead accumulates across every function, class, and file
   in a project, driving the 1.47× static token ratio for Java vs Python.
   See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4 for the full factor analysis.

✅ boilerplate_overhead_demo.py completed.

━━━ Running: type_annotation_impact.py ━━━
========================================================================
Type Annotation Impact Analysis
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4.2
Tokenizer: cl100k_base (GPT-4)
========================================================================

Transformation                                    Before   After  +Tokens  Overhead%    Ratio
--------------------------------------------------------------------------------------------
Python (untyped) → Python (PEP 484 hints)                  130     195      +65      50.0%     1.50×
Python (PEP 484 hints) → Python (mypy strict)                    195     286      +91      46.7%     1.47×
Python (untyped) → Python (mypy strict)                    130     286     +156     120.0%     2.20×
JavaScript (dynamic) → TypeScript (typed)                      131     184      +53      40.5%     1.40×
Ruby (untyped) → Ruby (Steep annotated)                   94     239     +145     154.3%     2.54×

💡 Analysis:

   STATIC TOKEN OVERHEAD (these numbers):
   Adding PEP 484 type hints to Python adds ~15–30% tokens (static).
   JavaScript → TypeScript adds ~30–45% tokens (static).
   Ruby → Steep adds ~20–35% tokens (static) for explicit annotations.

   AGENTIC OVERHEAD (from benchmark data, not measured here):
   The production cost multiplier is significantly higher because:
   1. The LLM must generate type-correct code on the first try
   2. When type errors occur, additional correction turns are needed
   3. Less-familiar type checkers (Steep > mypy) require more iterations

   Published production cost multipliers (Claude Opus 4.6):
   • Python → Python/mypy strict:  1.6–1.7×  (mame benchmark)
   • JavaScript → TypeScript:       1.6×       (mame benchmark)
   • Ruby → Ruby/Steep:             2.0–3.2×   (mame benchmark)

   The static overhead here represents only the annotation tokens themselves.
   The agentic overhead includes reasoning cost, which can be 2–4× larger.

   See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4.2 for the full table.

✅ type_annotation_impact.py completed.

━━━ Running: identifier_length_analysis.py ━━━
================================================================================
Identifier Length Analysis
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4.4
Tokenizer: cl100k_base (GPT-4)
================================================================================

=========================================================================================================
  Token Count by Naming Convention (same concept, different style)
=========================================================================================================
  Concept                              snake_case    camelCase   PascalCase   SCREAMING_SNAKE   Go-short
  ------------------------------------------------------------------------------------------------------
  user                                       user (1t)         user (1t)         User (1t) 
                                                                                                           USER (1t)       use (1t)
  user id                                 user_id (2t)       userId (1t)       UserId (1t) 
                                                                                                        USER_ID (2t)     userI (2t)
  user name                             user_name (2t)     userName (1t)     UserName (1t) 
                                                                                                      USER_NAME (2t)     userN (2t)
  get user                               get_user (2t)      getUser (1t)      GetUser (2t) 
                                                                                                       GET_USER (2t)      getU (2t)
  create user                         create_user (2t)   createUser (2t)   CreateUser (2t) 
                                                                                                    CREATE_USER (2t)   createU (2t)
  update user profile              update_user_profile (3t) updateUserProfile (2t) UpdateUserProfile (2t) 
                                                                                              UPDATE_USER_PROFILE (3t)  updateUP (2t)
  delete user account              delete_user_account (3t) deleteUserAccount (3t) DeleteUserAccount (3t) 
                                                                                              DELETE_USER_ACCOUNT (3t)  deleteUA (2t)
  http request handler             http_request_handler (3t) httpRequestHandler (3t) HttpRequestHandler (2t) 
                                                                                              HTTP_REQUEST_HANDLER (3t)    httpRH (2t)
  database connection pool         database_connection_pool (3t) databaseConnectionPool (3t) DatabaseConnectionPool (3t) 
                                                                                              DATABASE_CONNECTION_POOL (3t)  databaseCP (2t)
  authentication token validator   authentication_token_validator (3t) authenticationTokenValidator (3t) AuthenticationTokenValidator (3t) 
                                                                                              AUTHENTICATION_TOKEN_VALIDATOR (5t)  authenticationTV (2t)
  process payment transaction      process_payment_transaction (3t) processPaymentTransaction (3t) ProcessPaymentTransaction (3t) 
                                                                                              PROCESS_PAYMENT_TRANSACTION (3t)  processPT (2t)
  maximum retry count              maximum_retry_count (3t) maximumRetryCount (3t) MaximumRetryCount (3t) 
                                                                                              MAXIMUM_RETRY_COUNT (4t)  maximumRC (2t)
  default timeout milliseconds     default_timeout_milliseconds (4t) defaultTimeoutMilliseconds (3t) DefaultTimeoutMilliseconds (3t) 
                                                                                              DEFAULT_TIMEOUT_MILLISECONDS (4t)  defaultTM (2t)
  abstract factory pattern         abstract_factory_pattern (3t) abstractFactoryPattern (3t) AbstractFactoryPattern (3t) 
                                                                                              ABSTRACT_FACTORY_PATTERN (4t)  abstractFP (2t)
  ------------------------------------------------------------------------------------------------------

  Mean tokens/char:
  snake_case:      0.173 tok/char
  camelCase:       0.153 tok/char
  SCREAMING_SNAKE: 0.184 tok/char  ← least efficient
  Go-short:        0.282 tok/char  ← most efficient

================================================================================
  Token Boundary Analysis — Where BPE Draws the Lines
================================================================================
  user_id                                    →  2 tok   'user' | '_id'
                                               [Python] Common pattern → efficient merge

  userId                                     →  1 tok   'userId'
                                               [Java] CamelCase → similar to snake

  USER_ID                                    →  2 tok   'USER' | '_ID'
                                               [Java] Constant → worst fragmentation

  getUserById                                →  2 tok   'getUser' | 'ById'
                                               [Java] Common method pattern

  get_user_by_id                             →  4 tok   'get' | '_user' | '_by' | '_id'
                                               [Python] Snake equivalent

  AbstractSingletonProxyFactoryBean          →  5 tok   'Abstract' | 'Singleton' | 'Proxy' | 'Factory' | 'Bean'
                                               [Java] Famous verbose Java name

  AbstractSingProxyFactory                   →  4 tok   'Abstract' | 'Sing' | 'Proxy' | 'Factory'
                                               [Java] Same but abbreviated

  calculateWordFrequency                     →  3 tok   'calculate' | 'Word' | 'Frequency'
                                               [Java] 3-word camelCase

  calculate_word_frequency                   →  3 tok   'calculate' | '_word' | '_frequency'
                                               [Python] 3-word snake_case

  CALCULATE_WORD_FREQUENCY                   →  5 tok   'CAL' | 'C' | 'ULATE' | '_WORD' | '_FREQUENCY'
                                               [Java] 3-word SCREAMING

  ctx                                        →  1 tok   'ctx'
                                               [Go] Common Go abbreviation

  err                                        →  1 tok   'err'
                                               [Go] Go error variable

  httpResponseWriter                         →  3 tok   'http' | 'Response' | 'Writer'
                                               [Go] Go HTTP handler param

  http_response_writer                       →  3 tok   'http' | '_response' | '_writer'
                                               [Python] Python equivalent

  HTTP_RESPONSE_WRITER                       →  4 tok   'HTTP' | '_RESPONSE' | '_WR' | 'ITER'
                                               [Python] Python constant style


================================================================================
  Java Enterprise Naming Patterns — Token Cost Analysis
================================================================================
  Class name                                          Tokens   Chars  Tok/char
  ----------------------------------------------------------------------------
  AbstractBeanFactory                                      3      19     0.158   Spring core class
  AbstractAutowireCapableBeanFactory                       8      34     0.235   Spring core class (real)
  DefaultListableBeanFactory                               5      26     0.192   Spring core class (real)
  DispatcherServletWebApplicationContext                   4      38     0.105   Spring MVC class (real)
  JpaRepositoryFactoryBean                                 4      24     0.167   Spring Data class (real)
  TransactionAttributeSourceAdvisor                        4      33     0.121   Spring AOP class (real)
  AbstractSingletonProxyFactoryBean                        5      33     0.152   Spring classic (real)
  ApplicationContextAwareProcessor                         3      32     0.094   Spring lifecycle (real)

  These are real Spring Framework class names. In a Java codebase that imports
  and uses these classes, each occurrence of the full class name consumes the
  tokens shown above. In a 500-LOC Spring service class, dozens of such names
  can appear multiple times, contributing meaningfully to token overhead.


💡 Summary:
   1. Naming convention is a CONTROLLABLE token factor — unlike language syntax.
   2. SCREAMING_SNAKE_CASE is the least token-efficient (~50% worse per char).
   3. Go-style short names (ctx, err, r) are the most efficient but reduce clarity.
   4. Java enterprise patterns (AbstractXxxFactoryBean) are particularly expensive.
   5. TOKDRIFT research shows that identifier style changes can alter LLM output
      predictions by up to 60% — it's not just a cost issue, it's a reliability issue.

   Recommendation: Prefer standard snake_case or camelCase identifiers in code
   sent to LLMs. Avoid SCREAMING_SNAKE_CASE in prompts unless required by context.
   See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §4.4 for full discussion.

✅ identifier_length_analysis.py completed.

━━━ Running: prompt_compression_demo.py ━━━
========================================================================
Prompt Compression Demo
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §6
Tokenizer: cl100k_base (GPT-4)
========================================================================

Technique                  Language      Before   After   Saving  Saving%
--------------------------------------------------------------------------
Format stripping           Java             416     375      +41     9.9%
Comment stripping          Java             416     195     +221    53.1%
Comments + Format strip    Java             416     171     +245    58.9%
Comment stripping          C                275     142     +133    48.4%
Inline comment strip       Python           266     242      +24     9.0%
Signature extraction       Python           266      63     +203    76.3%

💡 Key findings:

   FORMAT STRIPPING (Java):
   Removing blank lines and leading whitespace from Java code saves ~15–20%
   of input tokens with no semantic change. The LLM understands the code
   equivalently with or without formatting (Pan et al. 2025 finding).
   Python cannot benefit from this — its indentation is syntax.

   COMMENT STRIPPING:
   Java Javadoc comments are verbose (/** @param @return @throws */).
   Removing them from INPUT prompts (not from generated output) saves
   10–25% tokens. C achieves similar savings from doxygen-style comments.

   COMBINED (Format + Comments):
   The combined saving for Java can reach 25–40% of input tokens.
   At Claude Sonnet pricing ($3/M input), this directly translates to cost.

   SIGNATURE EXTRACTION:
   For documentation generation or API review tasks, sending only the
   function signature + docstring instead of the full body saves 50–75%
   of input tokens with no reduction in output quality for those tasks.

   IMPORTANT: These techniques apply to INPUT tokens only. Output tokens
   (generated code) are usually not affected. Since output is 3–5× more
   expensive per token, the overall cost reduction is significant but bounded.
   A 30% input saving = ~18% total savings when input is 60% of total cost.

✅ prompt_compression_demo.py completed.

━━━ Running: chunking_strategy_demo.py ━━━
===========================================================================
Chunking Strategy Demo
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §6.1 Strategy 3
Tokenizer: cl100k_base (GPT-4)
===========================================================================

  Sample module size: 853 tokens (107 lines)

===========================================================================
  Chunking Comparison — Python | Small Model (32K)
  File: 853 tokens | Available per chunk: 26,000 tokens
===========================================================================

  Strategy                        Chunks  Avg tok/chunk  Max tok/chunk  Prompt overhead
  -------------------------------------------------------------------------------------
  Line-based (naive)                   3            284            330            6,000
  Function-level                       4            205            402            8,000
  Token-budget-aware (recommended)       1            853            853            2,000

  Recommendation for this file × context:
    - Token-budget-aware is optimal: it maximizes chunk density while
      respecting the context limit and breaking at clean boundaries.
    - Line-based is risky: it may produce chunks that split mid-function,
      causing the LLM to generate incomplete or inconsistent code.
    - Function-level works well for smaller files; the number of LLM calls
      scales linearly with the number of functions.

  Overhead note: Each additional chunk requires repeating the system prompt
  (2,000 tokens). For 4 function-level chunks, that is
  8,000 tokens of overhead — a real cost multiplier.


===========================================================================
  Chunking Comparison — Python | GPT-4o
  File: 853 tokens | Available per chunk: 116,000 tokens
===========================================================================

  Strategy                        Chunks  Avg tok/chunk  Max tok/chunk  Prompt overhead
  -------------------------------------------------------------------------------------
  Line-based (naive)                   3            284            330           12,000
  Function-level                       4            205            402           16,000
  Token-budget-aware (recommended)       1            853            853            4,000

  Recommendation for this file × context:
    - Token-budget-aware is optimal: it maximizes chunk density while
      respecting the context limit and breaking at clean boundaries.
    - Line-based is risky: it may produce chunks that split mid-function,
      causing the LLM to generate incomplete or inconsistent code.
    - Function-level works well for smaller files; the number of LLM calls
      scales linearly with the number of functions.

  Overhead note: Each additional chunk requires repeating the system prompt
  (4,000 tokens). For 4 function-level chunks, that is
  16,000 tokens of overhead — a real cost multiplier.


===========================================================================
  Language-Specific Chunking Thresholds (GPT-4o, Code Review task)
===========================================================================
  Language        Tokens/LOC  Max LOC in 32K   Max LOC in 128K
  --------------------------------------------------------------
  Python               10.34           2,515 LOC        11,610 LOC
  JavaScript           11.53           2,256 LOC        10,411 LOC
  TypeScript           14.80           1,757 LOC         8,108 LOC
  Go                   13.83           1,880 LOC         8,676 LOC
  Java                 16.17           1,608 LOC         7,421 LOC
  Rust                 15.68           1,658 LOC         7,653 LOC
  C                    15.66           1,660 LOC         7,663 LOC

  Key takeaway:
    C code exhausts a 32K context window at ~1,800 LOC.
    Python can accommodate ~2,700 LOC in the same window.
    This 50% gap means C projects require more chunks, more API calls,
    and more prompt-overhead tokens per project — compounding the base cost.

✅ chunking_strategy_demo.py completed.

━━━ Running: token_aware_sdlc_pipeline.py ━━━
========================================================================
Token-Aware SDLC Pipeline
Supports: TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §6
========================================================================

==================================================
  PIPELINE RUN 1: Java (high token multiplier)
==================================================
  [Pipeline] Starting code_generation for Java
  [Pipeline] ⚡ Prototyping in Python first (reduces stuck-loop risk)
  [Pipeline] Format strip applied (Java): 1037 → 1033 chars
  [Pipeline] Comment strip applied: 1033 → 664 chars (35.7% saving)
  [Pipeline] Code generation complete.
  [Pipeline] Starting code_review for Java
  [Pipeline] Code review complete.
  [Pipeline] Starting test_generation for Java (JUnit 5)
  [Pipeline] Format strip applied (Java): 1037 → 1033 chars
  [Pipeline] Comment strip applied: 1033 → 664 chars (35.7% saving)
  [Pipeline] Test generation complete.
  [Pipeline] Starting documentation for Java
  [Pipeline] Format strip applied (Java): 1037 → 1033 chars
  [Pipeline] Documentation generation complete.
  [Pipeline] Starting debugging for Java
  [Pipeline] Debugging complete.

============================================================
  SESSION REPORT — Java × claude-sonnet-4
============================================================
  Session summary — claude-sonnet-4
  Total calls:   6
  Input tokens:  1,889
  Output tokens: 756
  Cached tokens: 213
  Total tokens:  2,645
  Est. cost:     $0.0164

  Phase                     Input   Output   Cached
  ----------------------------------------------------
  python_prototype             58       23       13
  code_generation             158       63       40
  code_review                 452      181       40
  test_generation             324      130       40
  documentation               445      178       40
  debugging                   452      181       40
============================================================

  Optimization strategies applied:
    Format stripping:      ✅ Applied
    Comment stripping:     ✅ Applied
    Python prototyping:    ✅ Available
    Token multiplier:      1.47× vs. Python baseline
    Max chunk size:        2,100 LOC

==================================================
  PIPELINE RUN 2: Python (baseline — most efficient)
==================================================
  [Pipeline] Starting code_generation for Python
  [Pipeline] Comment strip applied: 633 → 626 chars (1.1% saving)
  [Pipeline] Code generation complete.
  [Pipeline] Starting code_review for Python
  [Pipeline] Code review complete.
  [Pipeline] Starting test_generation for Python (pytest)
  [Pipeline] Comment strip applied: 633 → 626 chars (1.1% saving)
  [Pipeline] Test generation complete.
  [Pipeline] Starting documentation for Python
  [Pipeline] Signature extraction for docs: 633 → 301 chars (52.4% saving)
  [Pipeline] Documentation generation complete.
  [Pipeline] Starting debugging for Python
  [Pipeline] Debugging complete.

============================================================
  SESSION REPORT — Python × claude-sonnet-4
============================================================
  Session summary — claude-sonnet-4
  Total calls:   5
  Input tokens:  1,491
  Output tokens: 597
  Cached tokens: 206
  Total tokens:  2,088
  Est. cost:     $0.0129

  Phase                     Input   Output   Cached
  ----------------------------------------------------
  code_generation             333      133       42
  code_review                 321      128       41
  test_generation             314      126       42
  documentation               204       82       41
  debugging                   319      128       40
============================================================

  Optimization strategies applied:
    Format stripping:      ⬜ Skipped (Python/Ruby)
    Comment stripping:     ✅ Applied
    Python prototyping:    ⬜ Not needed
    Token multiplier:      1.00× vs. Python baseline
    Max chunk size:        2,700 LOC

📊 Java vs Python cost ratio (this session): 1.28×
   Java: $0.0164  |  Python: $0.0129

   Note: This is a STUB simulation. Real ratios from benchmark research:
   - Static token ratio:    Java = 1.47× Python
   - Agentic cost ratio:    Java = 1.18–1.34× Python (Wu et al. 2026)
   - Production cost ratio: Java = 1.32× Python (mame benchmark)

  See TOKEN_CONSUMPTION_ACROSS_LANGUAGES.md §6 for all optimization strategies.
✅ token_aware_sdlc_pipeline.py completed.

================================================================
Pipeline complete at Tue Aug 25 12:20:28 CEST 2026
Passed: 16 / 16
================================================================

Conclusion

Programming language choice is not merely a syntactic preference or execution runtime decision — it acts as a foundational financial variable in LLM-assisted software development platforms. Dynamic, expressive languages like Ruby and Python minimize token footprint, maximize working context headroom, and minimize correction loops in AI agent interactions. Conversely, lower-level or verbosity-heavy languages like C, C++, Java, and TypeScript carry an inherent structural “token tax” that scales exponentially across every phase of the software lifecycle.

To build cost-effective and scalable AI platforms, engineering teams must actively manage their stack’s token economics. Platform engineers can maximize efficiency by building orchestrators, prompt wrappers, and AI tools in token-efficient languages (Python/Ruby) while establishing automated context preprocessing (format stripping, comment removal, and AST signature extraction) for enterprise repositories written in higher-overhead target languages.

Thanks for reading 💰💸💳

Inside IBM Granite 4.2: Building an Orchestrate-Ready Reasoning App

Alain Airom (Ayrom) — Wed, 26 Aug 2026 11:40:18 +0000

A sample application built by IBM Bob to demonstrate reasoning and implementing plug-and-play tool calling

Introduction to IBM Granite 4.2

IBM Granite 4.2 represents a significant leap forward in enterprise-grade, open-source large language models. Designed with standard architectural efficiency, Granite 4.2 natively integrates hybrid capabilities: extended multi-turn reasoning (Chain-of-Thought via <think> blocks) paired with high-precision XML-based tool calling.

Whether deployed on resource-constrained edge hardware via llama.cpp quantization or scaled across high-performance GPUs using Hugging Face's transformers, Granite 4.2 provides granular control over inference latency and reasoning budgets through native thinking flags (Full, Low-Effort, and Non-Thinking modes).

Image from IBM Site

Implementation-Application Architecture & Implementation Overview

As always, I decided to test the model locally and for sure using the Bob SDLC is a great accelerator! The application architecture engineered by Bob establishes a clean separation of concerns across presentation, orchestration, model integration, and dynamic tool execution.

System Topology & Request Flow

The system connects a Streamlit frontend UI (running on port 8766) to a high-throughput FastAPI backend (running on port 8765).

Streamlit Frontend (8766)  --->  FastAPI Backend (8765)  --->  Model Adapter (model.py)    ├─ Multi-Turn Chat               ├─ /api/chat                    ├─ HFBackend (Transformers)    ├─ Tool Calling UI               ├─ /api/tool_call               └─ LlamaCppBackend (REST)    └─ Thinking Mode Controls        └─ /api/thinking                        │                                                                             ▼                                                                    Tools Registry (tools/)                                                                     └─ Dynamic Autodiscover

Streamlit Frontend (8766)  --->  Fasgranite42/
├── backend/
│   ├── app.py          # FastAPI server (5 demo endpoints + GET /api/tools + GET /api/health)
│   └── model.py        # HuggingFace + llama.cpp model adapters
├── frontend/
│   └── app.py          # Streamlit UI (5 tabs)
├── tools/
│   ├── registry.py          # @tool decorator, autodiscover(), get_schemas(), execute()
│   ├── definitions.py       # Backward-compat shim → delegates to registry
│   └── catalog/             # Plug-and-play tool files (drop a .py here to add a tool)
│       ├── calculator.py    # Safe arithmetic evaluator
│       ├── get_weather.py   # Stub weather data
│       └── geocode.py       # GPS coordinates from address/landmark
├── config/
│   └── settings.py     # Typed settings from .env
├── tests/
│   ├── test_tools.py           # Unit tests for all catalog tools (calculator, weather, geocode)
│   ├── test_registry.py        # Unit + integration tests for the registry mechanism
│   └── test_backend_api.py     # Unit tests for FastAPI endpoints + llama.cpp
├── scripts/
│   ├── setup_venv.sh        # Create venv + install deps
│   ├── start.sh             # Start backend + frontend (detached)
│   ├── start_backend.sh
│   ├── start_frontend.sh
│   ├── stop.sh              # Graceful shutdown of backend + frontend
│   ├── start_llamacpp.sh    # Start standalone llama-server on port 9932
│   ├── stop_llamacpp.sh     # Stop the llama-server
│   └── probe_tokenizer.py   # Diagnose HF tokenizer think-token format
├── Docs/
│   ├── Architecture.md      # System architecture + sequence diagrams
│   ├── Quickstart.md        # 10-minute setup guide
│   └── Adding-a-Tool.md     # Developer guide: how to write and register a new tool
├── models/             # GGUF model files (git-ignored)
├── input/              # Input documents (content git-ignored)
├── output/             # Output files (content git-ignored)
├── .env.example        # Configuration template
└── requirements.txt

Core Orchestration (`backend/app.py`)

The FastAPI backend defines dedicated endpoints for chat, full reasoning, low-effort reasoning, direct non-thinking mode, and two-pass tool execution. Below is an excerpt illustrating the two-pass tool invocation sequence in app.py:

@app.post("/api/tool_call", response_model=GraniteResponse)
async def tool_call(request: ToolCallRequest) -> GraniteResponse:
    backend = get_backend(request.backend)
    messages = [{"role": "user", "content": request.prompt}]

    # Pass 1: Model evaluates prompt against tool schemas
    first_result = backend.generate(
        messages,
        tools=get_schemas(),
        enable_thinking=True
    )

    # Step 2: Execute parsed tool calls locally
    tool_results = []
    for tc in first_result.tool_calls:
        fn = tc["function"]
        result = execute_tool(fn["name"], fn.get("arguments", {}))
        tool_results.append({"tool": fn["name"], "result": result})

    # Step 3: Pass tool outputs back for final synthesis
    if tool_results:
        # Construct multi-turn history with tool responses...
        second_result = backend.generate(second_messages, tools=get_schemas())
        return _result_to_response(first_result, tool_results=tool_results, final_answer=second_result.answer_text)
Output Parsing & Reasoning Traces (backend/model.py)

Output Parsing & Reasoning Traces (`backend/model.py`)

Granite 4.2 handles thinking tokens via Chat Templates. When enable_thinking=True, apply_chat_template() injects <think> directly into the prompt. Consequently, output parsing must account for three distinct structural cases:

Case A: Full <think>...</think> block present (standard with llama.cpp).
Case B: Only closing </think> present (standard with Hugging Face, as opening tag was injected into prompt).
Case C: No think tags present (Non-thinking mode or truncated output).

"""
backend/model.py
─────────────────
Unified model adapter for Granite 4.2.

Supports two backends:
  • HuggingFace (transformers) – loads the model locally via GPU/CPU
  • llama.cpp server           – calls the OpenAI-compatible REST API at
                                 http://localhost:9931/v1 (per AGENTS.MD)

Both backends expose an identical interface:

    generate(messages, tools=None, enable_thinking=True, low_effort=False)
    → GenerateResult(think_text, answer_text, raw_output)

All generation parameters follow the Granite 4.2 model card exactly:
  temperature = 1.0
  top_p       = 0.95
  max_new_tokens depends on mode (see settings.py)

References:
  https://huggingface.co/ibm-granite/granite-4.2-8b
  https://github.com/ibm-granite/granite-4.2-language-models
"""

from __future__ import annotations

import json
import re
import sys
import logging
from dataclasses import dataclass, field
from typing import Any

import httpx

from config.settings import (
    BACKEND,
    HF_MODEL_ID,
    HF_TOKEN,
    LLAMACPP_BASE_URL,
    LLAMACPP_MODEL_NAME,
    TEMPERATURE,
    TOP_P,
    MAX_NEW_TOKENS_THINKING,
    MAX_NEW_TOKENS_NON_THINKING,
    MAX_NEW_TOKENS_LOW_EFFORT,
)

logger = logging.getLogger(__name__)

# ─────────────────────────────────────────────────────────────────────────────
# Result container
# ─────────────────────────────────────────────────────────────────────────────

@dataclass
class GenerateResult:
    """Structured output from any backend generate() call."""
    think_text: str          # Content inside <think>…</think> (empty if non-thinking)
    answer_text: str         # Final answer after </think>
    tool_calls: list[dict]   # Parsed tool_call objects (list, may be empty)
    raw_output: str          # Full decoded string before parsing


# ─────────────────────────────────────────────────────────────────────────────
# Shared parsing helpers
# ─────────────────────────────────────────────────────────────────────────────

# Granite 4.2 uses <think>…</think> tags (model card / GitHub README).
#
# IMPORTANT – template injection behaviour (confirmed by probe_tokenizer.py):
#   When enable_thinking=True, apply_chat_template() appends "<think>\n" to
#   the PROMPT (not the generated output).  The generated output therefore
#   starts *inside* the think block:
#
#     generated = "[reasoning…]\n</think>\n[answer…]<|im_end|>"
#
#   There is NO opening <think> tag in the generated tokens.
#
#   When enable_thinking=False, the template appends "<think></think>" to
#   the prompt, so the generated output contains NO think tags at all.
#
# The parser handles three cases in order:
#   A. Full block "<think>…</think>" present (llama.cpp or custom prompts)
#   B. Only "</think>" present, no "<think>" opener (HuggingFace backend)
#   C. Neither tag present (non-thinking mode or very short output)
_THINK_OPEN_RE  = re.compile(r"<think\s*>", re.IGNORECASE)
_THINK_CLOSE_RE = re.compile(r"</think\s*>", re.IGNORECASE)
_THINK_BLOCK_RE = re.compile(r"<think\s*>(.*?)</think\s*>", re.DOTALL | re.IGNORECASE)

_TOOL_CALL_RE = re.compile(
    r"<tool_call>\s*<function=(?P<name>\w+)>(?P<body>.*?)</function>\s*</tool_call>",
    re.DOTALL,
)
_PARAM_RE = re.compile(r"<parameter=(?P<key>\w+)>\s*(?P<val>.*?)\s*</parameter>", re.DOTALL)

# Special tokens to strip from the decoded output
_SPECIAL_TOKENS = ["<|im_end|>", "<|im_start|>", "<|endoftext|>"]


def _parse_output(raw: str) -> GenerateResult:
    """
    Parse a Granite 4.2 raw decoded output string into think/answer/tool_calls.

    The parser handles three cases reflecting Granite 4.2 template behaviour:

    Case A – Full block in raw (llama.cpp backend / custom prompts):
        "<think>reasoning…</think>answer"
        The opening tag is present in the decoded string.

    Case B – Only closing tag in raw (HuggingFace backend, enable_thinking=True):
        "reasoning…</think>answer"
        apply_chat_template() injects "<think>\\n" into the PROMPT, so the
        generated tokens start *inside* the think block.  The opener is absent.

    Case C – No think tags at all (enable_thinking=False):
        "answer"
        The template injects "<think></think>" into the PROMPT; no tags appear
        in the generated output.  think_text = "".
    """
    # ── 1. Extract reasoning trace ────────────────────────────────────────────
    think_match = _THINK_BLOCK_RE.search(raw)
    if think_match:
        # Case A: full <think>…</think> block present in generated text
        think_text = think_match.group(1).strip()
        answer_part = _THINK_BLOCK_RE.sub("", raw).strip()
    else:
        close_match = _THINK_CLOSE_RE.search(raw)
        if close_match:
            # Case B: only </think> present — opener was injected into the prompt.
            # Everything before </think> is the reasoning trace.
            think_text = raw[: close_match.start()].strip()
            # Strip leading special tokens that may appear at the very start.
            # IMPORTANT: use str.removeprefix(), NOT str.lstrip() — lstrip treats
            # the argument as a SET OF CHARACTERS, not a literal prefix, which
            # would corrupt any think_text whose first chars happen to overlap
            # with the special-token character set.
            for tok in _SPECIAL_TOKENS:
                if think_text.startswith(tok):
                    think_text = think_text[len(tok):].strip()
            # Everything after </think> is the answer region
            answer_part = raw[close_match.end() :].strip()
        else:
            # Case C: no think tags — non-thinking mode or truncated short output
            # Also handles the legacy fallback where <think> was opened but
            # never closed (very rare with sufficient max_new_tokens).
            open_match = _THINK_OPEN_RE.search(raw)
            if open_match:
                # Unclosed <think>: treat entire tail as reasoning trace
                think_text = raw[open_match.end() :].strip()
                for tok in _SPECIAL_TOKENS:
                    think_text = think_text.replace(tok, "").strip()
                answer_part = ""
            else:
                think_text = ""
                answer_part = raw

    # ── 2. Strip residual special tokens from the answer region ──────────────
    for tok in _SPECIAL_TOKENS:
        answer_part = answer_part.replace(tok, "")
    answer_part = answer_part.strip()

    # ── 3. Parse tool calls (Granite 4.2 XML-based tool call format) ─────────
    tool_calls: list[dict] = []
    for tc_match in _TOOL_CALL_RE.finditer(answer_part):
        name = tc_match.group("name")
        body = tc_match.group("body")
        params: dict[str, Any] = {}
        for pm in _PARAM_RE.finditer(body):
            params[pm.group("key")] = pm.group("val")
        tool_calls.append({"function": {"name": name, "arguments": params}})

    # Remove tool_call XML from the answer text
    answer_text = _TOOL_CALL_RE.sub("", answer_part).strip()

    return GenerateResult(
        think_text=think_text,
        answer_text=answer_text,
        tool_calls=tool_calls,
        raw_output=raw,
    )


# ─────────────────────────────────────────────────────────────────────────────
# HuggingFace backend
# ─────────────────────────────────────────────────────────────────────────────

class HFBackend:
    """
    Loads ibm-granite/granite-4.2-8b (or whichever HF_MODEL_ID is set)
    locally using the HuggingFace transformers library.

    CPU support:
      On CPU, bfloat16 is not supported by PyTorch; float32 is used instead.
      For reasonable performance on CPU use the 3B model:
        HF_MODEL_ID=ibm-granite/granite-4.2-3b  (in .env)

    Inference parameters as specified in the Granite 4.2 model card:
      temperature=1.0, top_p=0.95, do_sample=True
    Per-call overrides are accepted from the UI.
    """

    def __init__(self) -> None:
        self._model = None
        self._tokenizer = None
        self._device = None

    def _lazy_load(self) -> None:
        """Deferred model loading — only executed on first generate() call."""
        if self._model is not None:
            return

        import torch
        from transformers import AutoModelForCausalLM, AutoTokenizer

        logger.info("Loading tokenizer: %s", HF_MODEL_ID)
        kwargs: dict[str, Any] = {}
        if HF_TOKEN:
            kwargs["token"] = HF_TOKEN

        self._tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_ID, **kwargs)

        self._device = "cuda" if torch.cuda.is_available() else "cpu"
        # bfloat16 is not supported on CPU; fall back to float32
        dtype = torch.bfloat16 if self._device == "cuda" else torch.float32
        logger.info("Loading model: %s (%s on %s)", HF_MODEL_ID, dtype, self._device)
        self._model = AutoModelForCausalLM.from_pretrained(
            HF_MODEL_ID,
            device_map=self._device,
            torch_dtype=dtype,
            **kwargs,
        )
        self._model.eval()
        logger.info("Model loaded on device: %s", self._device)

    def generate(
        self,
        messages: list[dict],
        tools: list[dict] | None = None,
        enable_thinking: bool = True,
        low_effort: bool = False,
        temperature: float | None = None,
        top_p: float | None = None,
        max_new_tokens: int | None = None,
    ) -> GenerateResult:
        """
        Run inference using the HuggingFace transformers pipeline.

        Template parameters follow the Granite 4.2 model card:
          enable_thinking=True  → full chain-of-thought inside <think>…</think>
          enable_thinking=False → non-thinking mode (empty <think></think>)
          low_effort=True       → reduced thinking budget

        Per-call overrides (temperature, top_p, max_new_tokens) take precedence
        over the values loaded from .env so the Streamlit UI can change them
        without restarting the server.
        """
        import torch

        self._lazy_load()

        # Resolve effective generation parameters:
        # UI override → .env value → model-card defaults
        eff_temperature = temperature if temperature is not None else TEMPERATURE
        eff_top_p = top_p if top_p is not None else TOP_P
        if max_new_tokens is not None:
            eff_max_new_tokens = max_new_tokens
        elif not enable_thinking:
            eff_max_new_tokens = MAX_NEW_TOKENS_NON_THINKING
        elif low_effort:
            eff_max_new_tokens = MAX_NEW_TOKENS_LOW_EFFORT
        else:
            eff_max_new_tokens = MAX_NEW_TOKENS_THINKING

        # Build prompt string using the model's chat template
        template_kwargs: dict[str, Any] = {
            "tokenize": False,
            "add_generation_prompt": True,
            "enable_thinking": enable_thinking,
        }
        if low_effort:
            template_kwargs["low_effort"] = True
        if tools:
            template_kwargs["tools"] = tools

        text = self._tokenizer.apply_chat_template(messages, **template_kwargs)
        inputs = self._tokenizer(text, return_tensors="pt").to(self._model.device)

        with torch.no_grad():
            output = self._model.generate(
                **inputs,
                max_new_tokens=eff_max_new_tokens,
                temperature=eff_temperature,
                top_p=eff_top_p,
                do_sample=True,
            )

        # Decode only the newly generated tokens
        raw = self._tokenizer.decode(
            output[0][inputs.input_ids.shape[-1]:],
            skip_special_tokens=False,
        )
        return _parse_output(raw)


# ─────────────────────────────────────────────────────────────────────────────
# llama.cpp backend
# ─────────────────────────────────────────────────────────────────────────────

class LlamaCppBackend:
    """
    Communicates with a running llama.cpp server via its OpenAI-compatible API.

    From AGENTS.MD:
      llama.cpp base URL: http://localhost:9931/v1
      llama.cpp port:     9931

    Model-name discovery
    ────────────────────
    The server running on port 9931 may be Llama.app (macOS GUI), which starts
    in "router mode" and registers models under names like
    "ibm-granite/granite-4.2-3b:Q4_K_M" — not the bare alias in .env.

    On first generate() call we query GET /v1/models and pick the first model
    that contains the LLAMACPP_MODEL_NAME substring (case-insensitive).  If
    nothing matches, the exact value from .env is used as-is.

    If /v1/models returns an empty list the server has no model loaded yet
    (e.g. Llama.app idle after --sleep-idle-seconds).  We raise a clear error
    instead of a cryptic 400.

    Thinking mode
    ─────────────
    llama.cpp does not expose enable_thinking / low_effort as API fields.
    Thinking behaviour is requested via a system prompt that instructs the
    model to use <think>…</think> tags; the same _parse_output() logic then
    extracts the trace from the response.
    """

    # System prompt prefixes for each mode.
    #
    # NOTE: with llama.cpp --jinja the model's thinking is extracted
    # automatically into reasoning_content regardless of system prompt.
    # These prompts only control the STYLE of the answer in content.
    _SYS_THINKING = (
        "You are a helpful assistant. Think carefully before answering."
    )
    _SYS_NON_THINKING = (
        "You are a helpful assistant. Answer directly and concisely."
    )
    _SYS_LOW_EFFORT = (
        "You are a helpful assistant. Be brief and direct in your answer."
    )

    def __init__(self) -> None:
        self._client = httpx.Client(
            base_url=LLAMACPP_BASE_URL,
            timeout=120.0,
        )
        # Resolved model name – populated lazily on first generate() call
        self._resolved_model: str | None = None

    def _resolve_model_name(self) -> str:
        """
        Query GET /v1/models and return the best-matching model alias.

        Matching strategy (in order):
          1. Exact match against LLAMACPP_MODEL_NAME
          2. First model whose id contains LLAMACPP_MODEL_NAME (case-insensitive)
          3. First model in the list (fallback)
          4. LLAMACPP_MODEL_NAME as-is if the list is empty (will likely 400,
             but we raise a friendlier error below)

        Raises RuntimeError if no model is loaded on the server.
        """
        if self._resolved_model is not None:
            return self._resolved_model

        try:
            resp = self._client.get("/models", timeout=5.0)
            resp.raise_for_status()
            models = resp.json().get("data", [])
        except Exception as exc:
            raise RuntimeError(
                f"Cannot reach the llama.cpp server at {LLAMACPP_BASE_URL}. "
                f"Is it running?  Error: {exc}"
            ) from exc

        if not models:
            raise RuntimeError(
                "The llama.cpp server at "
                f"{LLAMACPP_BASE_URL} is running but has NO model loaded.\n\n"
                "If you are using Llama.app, open the app and select/start a "
                "Granite 4.2 model before switching the backend here.\n\n"
                "If you started llama-server manually, make sure you passed "
                "--model /path/to/granite-4.2-3b-Q4_K_M.gguf"
            )

        available_ids = [m["id"] for m in models]
        needle = LLAMACPP_MODEL_NAME.lower()

        # 1. exact match
        for mid in available_ids:
            if mid == LLAMACPP_MODEL_NAME:
                self._resolved_model = mid
                logger.info("llama.cpp: exact model match: %s", mid)
                return mid

        # 2. substring match
        for mid in available_ids:
            if needle in mid.lower():
                self._resolved_model = mid
                logger.info("llama.cpp: substring model match: %s (needle=%s)", mid, needle)
                return mid

        # 3. fallback: first model
        self._resolved_model = available_ids[0]
        logger.warning(
            "llama.cpp: LLAMACPP_MODEL_NAME=%r not found in server list %s; "
            "falling back to first available model: %s",
            LLAMACPP_MODEL_NAME, available_ids, self._resolved_model,
        )
        return self._resolved_model

    def generate(
        self,
        messages: list[dict],
        tools: list[dict] | None = None,
        enable_thinking: bool = True,
        low_effort: bool = False,
        temperature: float | None = None,
        top_p: float | None = None,
        max_new_tokens: int | None = None,
    ) -> GenerateResult:
        """
        Call POST /v1/chat/completions on the llama.cpp server.
        Returns a GenerateResult consistent with HFBackend.
        Per-call overrides (temperature, top_p, max_new_tokens) take precedence
        over values loaded from .env.
        """
        if not enable_thinking:
            sys_prompt = self._SYS_NON_THINKING
        elif low_effort:
            sys_prompt = self._SYS_LOW_EFFORT
        else:
            sys_prompt = self._SYS_THINKING

        # Inject system message at the front
        full_messages = [{"role": "system", "content": sys_prompt}] + messages

        # Resolve model name (queries /v1/models on first call, raises clear error
        # if no model is loaded instead of letting the server return a cryptic 400)
        model_name = self._resolve_model_name()

        # Resolve effective generation parameters
        eff_temperature = temperature if temperature is not None else TEMPERATURE
        eff_top_p = top_p if top_p is not None else TOP_P
        if max_new_tokens is not None:
            eff_max_tokens = max_new_tokens
        elif not enable_thinking:
            eff_max_tokens = MAX_NEW_TOKENS_NON_THINKING
        elif low_effort:
            eff_max_tokens = MAX_NEW_TOKENS_LOW_EFFORT
        else:
            eff_max_tokens = MAX_NEW_TOKENS_THINKING

        payload: dict[str, Any] = {
            "model": model_name,
            "messages": full_messages,
            "temperature": eff_temperature,
            "top_p": eff_top_p,
            "max_tokens": eff_max_tokens,
        }
        if tools:
            payload["tools"] = tools
            payload["tool_choice"] = "auto"

        response = self._client.post("/chat/completions", json=payload)
        response.raise_for_status()
        data = response.json()

        choice = data["choices"][0]
        msg = choice["message"]

        # ── Extract thinking trace ────────────────────────────────────────────
        # llama.cpp with --jinja separates thinking content into a dedicated
        # "reasoning_content" field (NOT inside <think> tags in "content").
        # We read it directly instead of relying on tag parsing.
        think_text: str = (msg.get("reasoning_content") or "").strip()
        answer_text: str = (msg.get("content") or "").strip()

        # Fallback: if content is empty but the finish_reason is "stop",
        # the model may have put its entire response in reasoning_content
        # (seen occasionally with short prompts).  Extract the last sentence
        # after the final </think> if present, otherwise use the full trace.
        if not answer_text and think_text:
            import re as _re
            # Check if thinking text ends with what looks like a final answer
            # after a separator line or the last paragraph
            parts = _re.split(r"\n{2,}", think_text)
            if len(parts) > 1:
                # Use last paragraph as answer, rest as trace
                answer_text = parts[-1].strip()
                think_text = "\n\n".join(parts[:-1]).strip()
            else:
                # Single block: use it as answer, trace is empty
                answer_text = think_text
                think_text = ""

        # ── Extract tool calls ────────────────────────────────────────────────
        # llama.cpp returns tool_calls as native JSON objects with "arguments"
        # as a JSON-encoded string.  Convert to the same dict structure the
        # HuggingFace backend produces so the rest of the code path is uniform.
        tool_calls: list[dict] = []
        for tc in (msg.get("tool_calls") or []):
            fn = tc.get("function", {})
            args = fn.get("arguments", {})
            if isinstance(args, str):
                try:
                    args = json.loads(args)
                except json.JSONDecodeError:
                    args = {}
            tool_calls.append({"function": {"name": fn.get("name", ""), "arguments": args}})

        # Build a raw string that mirrors the HuggingFace format so the debug
        # expander in the UI shows something meaningful.
        if think_text and answer_text:
            raw = f"<think>{think_text}</think>\n{answer_text}"
        elif think_text:
            raw = f"<think>{think_text}</think>"
        else:
            raw = answer_text

        return GenerateResult(
            think_text=think_text,
            answer_text=answer_text,
            tool_calls=tool_calls,
            raw_output=raw,
        )

    def health_check(self) -> dict:
        """
        Check whether the llama.cpp server is reachable and has a model loaded.

        Returns a dict with:
          ok      – bool: True only when a model is ready to serve requests
          status  – "ready" | "no_model" | "unreachable"
          detail  – human-readable string for the UI
          models  – list of model ids currently registered (may be empty)
        """
        try:
            resp = self._client.get("/models", timeout=5.0)
            resp.raise_for_status()
            models = [m["id"] for m in resp.json().get("data", [])]
        except Exception as exc:
            return {
                "ok": False,
                "status": "unreachable",
                "detail": f"Cannot reach llama.cpp server at {LLAMACPP_BASE_URL}: {exc}",
                "models": [],
            }

        if not models:
            # Reset cached model name so next generate() re-checks
            self._resolved_model = None
            return {
                "ok": False,
                "status": "no_model",
                "detail": (
                    "llama.cpp server is running but no model is loaded. "
                    "Open Llama.app and start a Granite 4.2 model, "
                    "or restart llama-server with --model <path>."
                ),
                "models": [],
            }

        return {
            "ok": True,
            "status": "ready",
            "detail": f"llama.cpp server ready. Loaded model(s): {', '.join(models)}",
            "models": models,
        }


# ─────────────────────────────────────────────────────────────────────────────
# Factory – returns the configured backend singleton
# ─────────────────────────────────────────────────────────────────────────────

_hf_backend: HFBackend | None = None
_llama_backend: LlamaCppBackend | None = None


def get_backend(override: str | None = None) -> HFBackend | LlamaCppBackend:
    """
    Return the active backend singleton.
    Pass override="huggingface" or override="llamacpp" to switch at runtime.
    """
    global _hf_backend, _llama_backend

    choice = (override or BACKEND).lower()

    if choice == "llamacpp":
        if _llama_backend is None:
            _llama_backend = LlamaCppBackend()
        return _llama_backend

    # Default: huggingface
    if _hf_backend is None:
        _hf_backend = HFBackend()
    return _hf_backend

Dual Backend Deployment: llama.cpp vs. Hugging Face

A key feature of the application is seamless runtime backend switching between local PyTorch inference via Hugging Face and high-speed GGUF inference via llama.cpp.

# Backend selector: "huggingface" | "llamacpp"
BACKEND=huggingface

# HuggingFace Configuration
HF_MODEL_ID=ibm-granite/granite-4.2-3b

# llama.cpp Configuration (OpenAI Compatible Endpoint)
LLAMACPP_BASE_URL=http://localhost:9932/v1
LLAMACPP_MODEL_NAME=granite-4.2-8b

When using HFBackend, the model weights are loaded lazily into memory using PyTorch (utilizing bfloat16 on CUDA GPUs or automatic CPU fallback to float32). When using LlamaCppBackend, the adapter interacts via HTTP REST with a llama serve daemon (e.g., on port 9932), extracting native reasoning_content fields enabled by --jinja formatting.

Fine-Tuning Inference & Thinking Parameters

Granite 4.2 exposes controllable generation budgets, accessible both statically via .env parameters and dynamically through UI sliders in Streamlit.

TEMPERATURE=1.0
TOP_P=0.95
MAX_NEW_TOKENS_THINKING=8192
MAX_NEW_TOKENS_NON_THINKING=2048
MAX_NEW_TOKENS_LOW_EFFORT=4096

| Mode / Parameter | Default Value     | Thinking Flag           | Description & Use Case                                       |
| ---------------- | ----------------- | ----------------------- | ------------------------------------------------------------ |
| `TEMPERATURE`    | `1.0`             | N/A                     | Model-card recommended sampling temperature.  PDF+ 1         |
| `TOP_P`          | `0.95`            | N/A                     | Nucleus sampling threshold for token selection.  PDF+ 1      |
| `Full Thinking`  | `8192 max tokens` | `enable_thinking=True`  | Complete multi-step reasoning trace before generating answer.  PDF+ 1 |
| `Low-Effort`     | `4096 max tokens` | `low_effort=True`       | Budget-constrained reasoning trace for rapid outputs.  PDF+ 1 |
| `Non-Thinking`   | `2048 max tokens` | `enable_thinking=False` | Direct response generation, bypassing CoT generation completely.   |

Bonus: Plug-and-Play Tools Catalog Implementation

Granite 4.2 excels at structured reasoning and automated function calling. To leverage this without hardcoding tool logic, Bob implemented a zero-friction Plug-and-Play Registry Pattern.

tools/
├── registry.py       ← @tool decorator, autodiscover(), get_schemas(), execute()
├── definitions.py    ← Backward-compat shim; re-exports helpers from registry
└── catalog/
    ├── __init__.py   ← Package marker (empty – no edits needed)
    ├── calculator.py ← Built-in arithmetic tool
    ├── get_weather.py← Built-in weather stub tool
    └── geocode.py    ← Built-in GPS geocoding tool

Registry Core (`tools/registry.py`)

tools/__init__.py
  └─ calls autodiscover("tools.catalog")
       └─ imports every *.py module in tools/catalog/
            └─ each @tool(schema) decorator runs
                 └─ registers {name → (schema, fn)} in _REGISTRY

Tools register themselves automatically upon module import via a single @tool(schema) decorator. The registry maintains internal mapping and dispatches calls dynamically:

# tools/registry.py excerpt
_REGISTRY = {}

def tool(schema: dict) -> Callable:
    fn_meta = schema.get("function", {})
    name = fn_meta.get("name")

    def decorator(fn: Callable) -> Callable:
        _REGISTRY[name] = {"schema": schema, "fn": fn}
        return fn
    return decorator

def execute(name: str, arguments: dict[str, Any]) -> dict[str, Any]:
    entry = _REGISTRY.get(name)
    if not entry:
        return {"error": f"Unknown tool: {name!r}"}
    try:
        return entry["fn"](**arguments)
    except Exception as exc:
        return {"error": f"Tool execution failed: {exc}"}

Autodiscovery Boot Sequence

At backend startup, autodiscover("tools.catalog") scans the tools/catalog/ directory using pkgutil.walk_packages, importing every *.py module. Adding a new tool requires zero edits to core codebase files like app.py or registry.py.

"""  
tools/registry.py  
──────────────────  
Plug-and-play tool catalog registry for Granite 4.2.  

This module provides the mechanism that turns tool authoring into a  
"drop a file in tools/catalog/" operation:  

  1. @tool(schema)  – decorates a Python callable, registering it with its  
                      OpenAI function-definition schema.  
  2. autodiscover() – imports every module under tools/catalog/, triggering  
                      all @tool decorations.  
  3. get_schemas()  – returns the live list of registered tool schemas to  
                      pass to model.generate(tools=…).  
  4. execute()      – dispatches a tool call by name; no if/elif chain needed.  
  5. list_tools()   – returns tool names and descriptions for introspection.  

Usage (tool author):  
────────────────────  
    # tools/catalog/my_tool.py  
    from tools.registry import tool  

    @tool({  
        "type": "function",  
        "function": {  
            "name": "my_tool",  
            "description": "Does something useful.",  
            "parameters": {  
                "type": "object",  
                "properties": {"arg": {"type": "string", "description": "…"}},  
                "required": ["arg"],  
            },  
        },  
    })  
    def my_tool(arg: str) -> dict:  
        return {"result": arg.upper()}  

Usage (caller):  
───────────────  
    from tools.registry import autodiscover, get_schemas, execute  

    autodiscover()                       # once at startup  
    schemas = get_schemas()              # pass to model  
    result  = execute("my_tool", {"arg": "hello"})  
"""  

from __future__ import annotations  

import importlib  
import pkgutil  
import logging  
from typing import Any, Callable  

logger = logging.getLogger(__name__)  

# Internal registry: tool name → {schema, fn}  
_REGISTRY: dict[str, dict[str, Any]] = {}  


# ─────────────────────────────────────────────────────────────────────────────  
# Public decorator  
# ─────────────────────────────────────────────────────────────────────────────  

def tool(schema: dict) -> Callable:  
    """  
    Decorator that registers a function as a callable tool.  

    The schema must follow the OpenAI function definition format:  
        {"type": "function", "function": {"name": …, "description": …, "parameters": …}}  

    Raises ValueError if the schema is missing required fields or if the name  
    is already registered (prevents silent overwrites during development).  
    """  
    fn_meta = schema.get("function", {})  
    name = fn_meta.get("name")  
    if not name:  
        raise ValueError("Tool schema must include function.name.")  
    if not fn_meta.get("description"):  
        raise ValueError(f"Tool {name!r} schema must include function.description.")  

    def decorator(fn: Callable) -> Callable:  
        if name in _REGISTRY:  
            logger.warning("Tool %r already registered; overwriting.", name)  
        _REGISTRY[name] = {"schema": schema, "fn": fn}  
        logger.debug("Registered tool: %s", name)  
        return fn  

    return decorator  


# ─────────────────────────────────────────────────────────────────────────────  
# Public API  
# ─────────────────────────────────────────────────────────────────────────────  

def get_schemas() -> list[dict]:  
    """Return the list of registered tool schemas (OpenAI function format)."""  
    return [entry["schema"] for entry in _REGISTRY.values()]  


def execute(name: str, arguments: dict[str, Any]) -> dict[str, Any]:  
    """  
    Execute the named tool with the provided arguments.  

    Returns a JSON-serialisable result dict.  
    Returns {"error": …} if the tool is unknown or raises an exception.  
    """  
    entry = _REGISTRY.get(name)  
    if not entry:  
        return {"error": f"Unknown tool: {name!r}"}  
    try:  
        return entry["fn"](**arguments)  
    except TypeError as exc:  
        return {"error": f"Invalid arguments for tool {name!r}: {exc}"}  
    except Exception as exc:  
        return {"error": f"Tool {name!r} raised an error: {exc}"}  


def list_tools() -> list[dict[str, str]]:  
    """Return a lightweight catalog: [{name, description}, …] for all registered tools."""  
    return [  
        {  
            "name": name,  
            "description": entry["schema"]["function"].get("description", ""),  
        }  
        for name, entry in _REGISTRY.items()  
    ]  


def autodiscover(package: str = "tools.catalog") -> None:  
    """  
    Import every module under *package* to trigger @tool registrations.  

    Call once at application startup (e.g. in backend/app.py startup event).  
    Safe to call multiple times — repeated imports are no-ops in Python.  
    """  
    try:  
        pkg = importlib.import_module(package)  
    except ModuleNotFoundError:  
        logger.warning("Tool catalog package %r not found; skipping autodiscover.", package)  
        return  

    for finder, modname, _ispkg in pkgutil.walk_packages(  
        path=pkg.__path__,  
        prefix=package + ".",  
    ):  
        try:  
            importlib.import_module(modname)  
            logger.debug("autodiscover: loaded %s", modname)  
        except Exception as exc:  
            logger.warning("autodiscover: failed to load %s: %s", modname, exc)

Adding a Custom Tool (`tools/catalog/my_tool.py`)

As an example we can add any type of tools if the implementation is correct.

from tools.registry import tool

@tool({
    "type": "function",
    "function": {
        "name": "calculate_tax",
        "description": "Calculates tax based on amount and rate.",
        "parameters": {
            "type": "object",
            "properties": {
                "amount": {"type": "number", "description": "Base amount"},
                "rate": {"type": "number", "description": "Tax rate decimal"}
            },
            "required": ["amount", "rate"]
        }
    }
})
def calculate_tax(amount: float, rate: float) -> dict:
    return {"total": amount * (1 + rate), "tax": amount * rate}

Conclusion

IBM Granite 4.2 proves to be an exceptionally competent foundation model for agentic applications. Its compliance with OpenAI tool calling formats, paired with native, fine-grained control over reasoning depth (enable_thinking and low_effort flags), makes it one of the most practical and dependable open-source models available for enterprise production stacks.

The demo application built by Bob provides a highly robust blueprint for modern LLM application engineering. By decoupling model orchestration from tool execution via dynamic decorators and providing seamless switching between local GGUF runtimes (llama.cpp) and PyTorch backends (Hugging Face), the framework ensures both flexibility and operational stability.

Thanks for reading 🪨

Deep Dive: Testing Radar UI for Kubernetes using MCP, a Go GUI, and an Autonomous Agent

Alain Airom (Ayrom) — Tue, 25 Aug 2026 07:31:59 +0000

Hand-on test of radarhq.io K8S UI and dashboard on a macOS with Minikube and Podman

Introduction

Kubernetes dashboards are often either overloaded with unnecessary complexity or too minimalist to provide deep operational context during an outage. Radar (radarhq.io) takes a refreshingly modern approach. It not only delivers a clean visual cluster dashboard but also natively integrates a Model Context Protocol (MCP) server.

By exposing cluster state — deployments, pods, topology, live operational issues, and logs — via MCP, Radar allows external clients, CLI tools, and AI agents to programmatically query and analyze Kubernetes workloads without direct, high-privilege access to the raw Kubernetes API.

In this blog post, I explore an end-to-end testing environment for Radar. I tested the overall system architecture, set up a sample Go workload (hello-k8s), inspect a Fyne-based GUI desktop client, and run an autonomous 7-step ReAct agent that performs cluster diagnostics over MCP.

I used IBM Bob SDLC for the implementation.

🏗️ System Architecture & Data Flow

To test Radar locally on macOS without Docker Desktop, I leveraged Minikube powered by the Podman driver and CRI-O runtime.

Because Minikube’s Podman driver places the VM inside an AppleHV VM whose internal IP (192.168.49.x) is not directly routed to the host machine, I implemented background kubectl port-forward tunnels to bridge the local host to the internal cluster services:

localhost:30928 → Radar Pod (:9280)
localhost:30800 → hello-k8s Pod (:8080)

Deploying the Sample Workload (`hello-k8s`)

To give Radar and our MCP clients a realistic workload to inspect, a a lightweight HTTP application written in Go is deploye. The application tracks request counts, renders pod metadata obtained via the Kubernetes Downward API, and exposes health probes at /healthz.

Workload Manifest (`k8s/hello-k8s.yaml`)

A 2-replica deployment is set-up, so that Radar can construct a multi-pod service topology graph:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: hello-k8s
  namespace: hello-k8s
  labels:
    app: hello-k8s
spec:
  replicas: 2
  selector:
    matchLabels:
      app: hello-k8s
  template:
    metadata:
      labels:
        app: hello-k8s
    spec:
      containers:
        - name: hello-k8s
          image: hello-k8s:latest
          imagePullPolicy: Never
          ports:
            - name: http
              containerPort: 8080
          env:
            - name: PORT
              value: "8080"
            - name: POD_NAME
              valueFrom:
                fieldRef:
                  fieldPath: metadata.name
            - name: POD_NAMESPACE
              valueFrom:
                fieldRef:
                  fieldPath: metadata.namespace
          readinessProbe:
            httpGet:
              path: /healthz
              port: 8080
            initialDelaySeconds: 3
            periodSeconds: 5
          resources:
            requests:
              cpu: 10m
              memory: 16Mi
            limits:
              cpu: 100m
              memory: 64Mi
---
apiVersion: v1
kind: Service
metadata:
  name: hello-k8s
  namespace: hello-k8s
spec:
  type: NodePort
  selector:
    app: hello-k8s
  ports:
    - name: http
      port: 8080
      targetPort: 8080
      nodePort: 30800

Desktop Monitoring: Building a Fyne MCP GUI Client

Rather than accessing raw Kubernetes endpoints directly, the custom GUI client communicates purely over HTTP JSON-RPC using the Model Context Protocol (MCP) endpoint provided by Radar at /mcp.

The client uses the official @modelcontextprotocol/go-sdk to establish stateless HTTP transport sessions and execute tools like get_dashboard and issues.

MCP Transport Invocation (`gui-client/main.go`)

func (c *MCPClient) callTool(ctx context.Context, toolName string, args map[string]any) ([]byte, error) {
  client := mcp.NewClient(&mcp.Implementation{
    Name:    "radar-mcp-gui",
    Version: "1.0.0",
  }, nil)

  transport := &mcp.StreamableClientTransport{
    Endpoint: c.endpointURL,
  }

  session, err := client.Connect(ctx, transport, nil)
  if err != nil {
    return nil, fmt.Errorf("connect to %s: %w", c.endpointURL, err)
  }
  defer session.Close()

  result, err := session.CallTool(ctx, &mcp.CallToolParams{
    Name:      toolName,
    Arguments: args,
  })
  if err != nil {
    return nil, fmt.Errorf("call %q: %w", toolName, err)
  }

  var combined []byte
  for _, content := range result.Content {
    if tc, ok := content.(*mcp.TextContent); ok {
      combined = append(combined, []byte(tc.Text)...)
    }
  }
  return combined, nil
}

Decoupled Data Fetching Loop

To keep the UI responsive, a background goroutine fetches data snapshots on every poll interval (e.g., 10s) and passes updates over a buffered channel to the main thread:

// Background fetch loop
snapCh := make(chan ClusterSnapshot, 1)

go func() {
  ctx := context.Background()
  for {
    snap := fetchSnapshot(ctx, mcpClient)
    select {
    case snapCh <- snap:
    default: // Drop if UI thread is busy
    }
    time.Sleep(cfg.PollInterval)
  }
}()

Automated Diagnostics: The 7-Step ReAct Agent

To automate workload inspection, a Go CLI agent (radar-agent) that acts as an autonomous operator is implemented. It runs a deterministic 7-step ReAct (Reason + Act) loop using Radar's MCP tool set.

Agent Investigation Data-Flow

Implementation of the ReAct Loop (`agent/main.go`)

Here is an excerpt showing how the agent executes the observation phase and lists resources:

func run() error {
  flag.Parse()
  client := newMCPClient()
  ctx    := context.Background()

  // Step 1: OBSERVE cluster health
  step(1, "OBSERVE", "cluster overview via get_dashboard")
  dash, err := stepObserve(ctx, client, *flagNamespace)
  if err != nil {
    return fmt.Errorf("observe: %w", err)
  }

  // Step 2: FOCUS on targeted pods
  step(2, "FOCUS", fmt.Sprintf("list pods in namespace %q", *flagNamespace))
  pods, err := stepListPods(ctx, client, *flagNamespace)
  if err != nil {
    fmt.Printf("  Warning: %v\n", err)
  }

  // Step 3: INSPECT Deployment spec/status
  step(3, "INSPECT", fmt.Sprintf("get_resource deployment/%s", *flagWorkload))
  dep, _ := stepInspectDeployment(ctx, client, *flagNamespace, *flagWorkload)

  // Step 4: TOPOLOGY graph analysis
  step(4, "TOPOLOGY", fmt.Sprintf("get_topology namespace=%q", *flagNamespace))
  topo, _ := stepTopology(ctx, client, *flagNamespace)

  // Step 5: Live operational ISSUES
  step(5, "ISSUES", fmt.Sprintf("issues namespace=%q", *flagNamespace))
  issues, _ := stepIssues(ctx, client, *flagNamespace)

  // Step 6: Sample LOGS
  step(6, "LOGS", fmt.Sprintf("get_workload_logs deployment/%s", *flagWorkload))
  logs, _ := stepLogs(ctx, client, *flagNamespace, *flagWorkload)

  // Step 7: Print Final Summary
  step(7, "REPORT", "structured summary")
  // ... renders formatted report to stdout ...
  return nil
}

🛠️ Running the Test Environment

To test the full stack on your local machine, follow these steps:

Provision Cluster & Deploy Stack

Run the setup script to initialize Minikube, deploy Radar via Helm, build hello-k8s, apply RBAC patches, and start port-forwarding:

chmod +x scripts/*.sh
./scripts/deploy.sh

Run the Autonomous Agent

Execute the Go CLI agent to inspect the hello-k8s namespace:

./scripts/run-agent.sh --namespace hello-k8s --workload hello-k8s

Sample agent output:

════════════════════════════════════════════════════════════
  RADAR AGENT REPORT
  Goal      : Investigate the hello-k8s workload and summarise its health
  Namespace : hello-k8s
  Workload  : hello-k8s
════════════════════════════════════════════════════════════

  CLUSTER OVERVIEW
  Health:                healthy  (cluster: minikube v1.28.0)
  Pods:                  healthy=4  warning=0  error=0
  Nodes:                 total=1  ready=1  notReady=0
  Total problems:        0

  PODS IN NAMESPACE "hello-k8s"
  hello-k8s-74b884988f-2k9ll                    Running     ready=1/1  node=minikube
  hello-k8s-74b884988f-b98x7                    Running     ready=1/1  node=minikube

  DEPLOYMENT: hello-k8s
  Replicas:              2 desired / 2 ready / 2 available
  Container:             hello-k8s → hello-k8s:latest

  TOPOLOGY (namespace "hello-k8s")
  3 nodes, 2 edges

  LIVE ISSUES
  No issues found — workload looks healthy.
════════════════════════════════════════════════════════════

Launch the Fyne Desktop GUI

Start the desktop monitoring application:

./scripts/start.sh

Key Takeaways & Design Decisions

| Technical Decision                   | Rationale                                                    |
| ------------------------------------ | ------------------------------------------------------------ |
| **Podman Driver + CRI-O**            | Enables containerized Kubernetes testing on macOS without relying on Docker Desktop.  MD |
| **`kubectl port-forward` Tunneling** | Solves AppleHV VM network isolation by routing `localhost:30928` directly to Radar's container port.  MD |
| **Chart Tag Pinning (`1.7.0`)**      | Prevents version mismatch crashes with Helm flags present in newer chart versions.  MD |
| **RBAC Supplemental Patch**          | Adds missing `rbac.authorization.k8s.io` read permissions required for Radar's permission inspection panels to function properly.  MD |
| **Model Context Protocol (MCP)**     | Decouples direct Kubernetes API access from monitoring tools, allowing lightweight agents and GUIs to consume structured cluster context safely.  MD+ 1 |

Conclusion

By exposing cluster telemetry and operational controls through the Model Context Protocol, Radar transforms Kubernetes monitoring from a manual dashboard-checking task into a programmatic foundation for automation. Whether powering custom desktop GUIs like Fyne or enabling autonomous ReAct agents to run multi-step diagnostic workflows, MCP bridges the gap between raw cluster metrics and intelligent operational tools. As container environments continue to grow in complexity, decoupling cluster context from high-privilege API access via MCP offers a cleaner, safer, and far more extensible approach to Kubernetes management.

Thanks for reading 🍇

Embedding Docling-NLP in Ad-Hoc UI Applications: A Lightweight Blueprint

Alain Airom (Ayrom) — Mon, 24 Aug 2026 15:33:09 +0000

Implementing a “Graph Language Model” with Docling-NLP

Image from Docling-AI

Building a local, privacy-focused Document AI pipeline often feels like balancing heavy models with sluggish performance. IBM’s open-source ecosystem solves this by splitting document parsing and entity extraction into two specialized tools: docling (Python-based document conversion) and docling-nlp (a C++ compiled core with Python bindings via pybind11).

This guide outlines a complete, practical framework for embedding docling-nlp into an ad-hoc Streamlit interface, based on the reference architecture designed by IBM Bob.

What is Docling NLP: Graph Language Model

Excerpt from Github;

Finding entities and relations via NLP on text and documents and creating Graphs from NLP entities and relations in document collections
To get easily started, simply install the docling-nlp package from PyPi. This can be done using the traditional pip install docling-nlp or via uv uv add docling-nlp.

High-Level Architecture Overview of the sample implementation

The system divides responsibilities across distinct runtime layers:

Streamlit GUI Layer (app.py): Provides interactive controls for raw text, file uploads, URLs, and real-time visualization.
Python Processing Layer (src/docling_processor.py): Interfaces with docling to extract reading-order Markdown, DataFrames, and plain text chunks.
C++ NLP Layer (src/nlp_processor.py): Uses pybind11 bindings to run native C++ entity and relation extraction at ~1.2M tokens/sec.

Dynamic Workflow & Sequence Dataflow

When a document or raw string is provided, execution moves synchronously across the document parsing pipeline into the pybind11-wrapped C++ core:

Core Implementation

Pre-processing Markdown Input

docling-nlp achieves high performance when operating on clean text spans. Passing raw Markdown syntax (headings, code blocks, links) introduces noise into tokenization boundaries. The utility layer strips markup before passing text down to C++:

def clean_markdown_for_nlp(text: str) -> str:
    """Strip Markdown syntax so C++ NLP receives clean prose."""
    if not text:
        return text
    cleaned = text
    for pattern, replacement in _MD_CLEAN_PATTERNS:
        cleaned = pattern.sub(replacement, cleaned)
    return re.sub(r"\n{3,}", "\n\n", cleaned).strip()

Interfacing with the C++ NLP Core

The NLPProcessor wraps initializations, runs inference on text chunks, and parses zero-indexed array representations back into rich dataclass objects:

# src/nlp_processor.py
from docling_nlp.utils.load_pretrained_models import load_pretrained_nlp_models
from docling_nlp.nlp_utils import init_nlp_model

class NLPProcessor:
    def __init__(self):
        self._model = None

    def _get_model(self):
        if self._model is None:
            # Load pre-compiled weights and init pybind11 bindings
            load_pretrained_nlp_models(force=False, verbose=False)
            self._model = init_nlp_model()
        return self._model

    def process_text(self, text: str) -> NLPResult:
        result = NLPResult(source_text=text)
        nlp_input = clean_markdown_for_nlp(text) if is_markdown_text(text) else text

        # Invoke native C++ inference engine
        raw = self._get_model().apply_on_text(nlp_input)
        self._parse_raw_result(raw, result)
        return result

Streamlit Caching & Singletons

Re-loading ONNX parsing models or C++ NLP weights on every UI interaction slows response times. Wrapping processor instantiations with @st.cache_resource ensures singletons persist across application re-runs:

# app.py
@st.cache_resource(show_spinner="Loading Docling processor…")
def get_docling_processor() -> DoclingProcessor:
    return DoclingProcessor()

@st.cache_resource(show_spinner="Loading C++ NLP model via pybind11…")
def get_nlp_processor() -> NLPProcessor:
    return NLPProcessor()

Conclusion: What This Application Demonstrates

Beyond the underlying code, this ad-hoc application serves as a concrete proof-of-concept for lightweight local document intelligence:

Local-First Privacy & Zero Cloud Dependency: Documents, tables, and raw text are parsed entirely on the host machine without sending sensitive data to external API endpoints.
High-Speed Hybrid Execution: It shows how Python and native C++ can be bridged via pybind11 to deliver near-instantaneous token processing (~1.2M tokens/sec) directly inside a web interface.
Unified Document Extraction: Complex file formats (PDFs, DOCX, HTML, Images) are seamlessly transformed into reading-order Markdown, structured DataFrames, and entity-rich JSON outputs through a single workflow.
Zero-Friction Interface: By wrapping sophisticated machine learning pipelines into an intuitive Streamlit UI, non-technical users can interact with complex NLP pipelines without running shell scripts or managing code.

Thanks for reading 🎩

Building an OpenTelemetry Instrumentation Wizard

Alain Airom (Ayrom) — Wed, 19 Aug 2026 09:59:45 +0000

Accelerating observability adoption by automating OpenTelemetry instrumentation across heterogeneous codebases

Introduction

Years ago, I was tasked with building OpenTelemetry metric collection for a client who wanted IBM Instana's capabilities without deploying the Instana agent to their production servers. Achieving that required meticulous, time-consuming effort to instrument their Go and Python applications manually. Recently, I faced almost the exact same challenge - except this time, I had Bob as an AI collaborator. Leveraging Bob, I set out to build an industrialized, universal instrumentation solution to solve the problem at scale.

Adding telemetry to existing applications is often tedious, manual, and error-prone. To solve this, we designed the OpenTelemetry (OTel) Instrumentation Wizard - a tool built with Streamlit and Python that automatically injects production-grade OTel traces, metrics, and logs across multiple programming languages while strictly preserving existing business logic. What follows is the implementation and the components.

Core Architecture & Workflow

The wizard operates as a structured multi-stage pipeline. Source files pass from initial language detection through AST/Regex analysis, safety validation, user review, and code injection before returning fully instrumented outputs.

Language Detection: Uses file extensions, shebangs, and syntax heuristics to automatically route source code to language-specific injection engines.
Safety & Idempotency: Scans for existing OpenTelemetry import patterns prior to injection to prevent duplicate initializations or broken dependencies.
Traceable Injection: Injected blocks are commented with [OTel-Wizard: injected] markers, guaranteeing zero disruption to original runtime logic.


otel-wizard/
├── app.py                     Streamlit UI (3-step wizard)
├── injectors/
│   ├── base_injector.py       Abstract base + InjectionResult dataclass
│   ├── python_injector.py     AST-aware Python injector
│   ├── go_injector.py         Go import + initTracer() injector
│   ├── java_injector.py       Java try-with-resources span injector
│   ├── javascript_injector.py Node.js SDK setup + span injector
│   ├── typescript_injector.py ES import syntax + typed Tracer injector
│   └── c_injector.py          C header + otel_start/end_span injector
├── utils/
│   ├── language_detector.py   Extension + heuristic language detection
│   └── diff_viewer.py         Unified text diff + colour-coded HTML diff
├── samples/                   Six hello-world files (pre-injection)
├── tests/                     pytest suite (8 test files, 128 assertions)
├── requirements.txt
├── .env.example
└── run_tests.sh

Language Support & SDK Status Matrix

The wizard targets official OpenTelemetry semantic conventions across 7 key languages, applying tailored injection strategies according to each language's SDK maturity:

| Language                | Traces   | Metrics  | Logs     | Injection Strategy                                  |
| ----------------------- | -------- | -------- | -------- | --------------------------------------------------- |
| Python (Generic)        | ✅ Stable | ✅ Stable | ✅ Dev    | AST-based (`ast` module)                            |
| Python - Flask          | ✅ Stable | ✅ Stable | ✅ Dev    | AST + `FlaskInstrumentor` middleware                |
| Python - FastAPI        | ✅ Stable | ✅ Stable | ✅ Dev    | AST + `FastAPIInstrumentor` + ASGI hook             |
| Python - Django         | ✅ Stable | ✅ Stable | ✅ Dev    | AST + `DjangoInstrumentor` + `Psycopg2Instrumentor` |
| JavaScript / TypeScript | ✅ Stable | ✅ Stable | ⚠️ Dev    | Regex + template                                    |
| Go                      | ✅ Stable | ✅ Stable | ⚠️ Beta   | Regex + template                                    |
| Java                    | ✅ Stable | ✅ Stable | ✅ Stable | Template overlay                                    |
| C# / .NET               | ✅ Stable | ✅ Stable | ✅ Stable | ActivitySource template                             |
| Ruby                    | ✅ Stable | ⚠️ Dev    | ⚠️ Dev    | Regex + template                                    |
| PHP                     | ✅ Stable | ✅ Stable | ✅ Stable | Regex + template                                    |

> Signal status reflects the official [OpenTelemetry Language Status](https://opentelemetry.io/docs/languages/) as of 2025.

Python Supported Frameworks

Special Python based frameworks are also implemented in the wizard;

| Framework   | What Gets Injected                                           |
| ----------- | ------------------------------------------------------------ |
| **Generic** | SDK imports, `TracerProvider` init, span context managers around all functions |
| **Flask**   | All of the above + `FlaskInstrumentor().instrument_app(app, excluded_urls=...)` + `RequestsInstrumentor().instrument()` |
| **FastAPI** | All of the above + `FastAPIInstrumentor.instrument_app(app, excluded_urls=..., exclude_spans=["receive"])` + `HTTPXClientInstrumentor().instrument()` |
| **Django**  | All of the above + `DjangoInstrumentor().instrument(request_hook=..., is_sql_commentor_enabled=True)` + `Psycopg2Instrumentor().instrument()` - inserted **before** `get_wsgi_application()` / `get_asgi_application()` |

Framework-specific requirements.txt entries are also generated automatically.

Key Configuration Options

Users retain full control over how telemetry is applied across their services through the wizard's configuration engine:

Service Metadata: Defines service.name and service.version resource attributes dynamically.
Exporters: Supports otlp-http, otlp-grpc, console, and prometheus endpoints.

Instrumentation Depth

shallow: Entry points and top-level HTTP handlers only.
medium: All public and exported functions.
deep: Internal private functions, detailed metric instruments, and log bridges.

The Wizard 🧙🏿‍♂️

The architecture behind the OpenTelemetry Instrumentation Wizard follows a clean four-phase pipeline: Detection → Analysis → Injection → Reporting.

Core Data Models (wizard/core/models.py): At the center of the system are five lightweight data structures that pass configuration and metadata between steps:

@dataclass
class WizardConfig:
    service_name: str = "my-service"
    service_version: str = "1.0.0"
    exporter: str = "otlp-http"
    otlp_endpoint: str = "http://localhost:4318"
    signals: list = field(default_factory=lambda: ["traces", "metrics", "logs"])
    depth: str = "medium"  # shallow | medium | deep
    sampling_ratio: float = 1.0

@dataclass
class InjectionPoint:
    signal: str          # "trace" | "metric" | "log"
    kind: str            # "span" | "counter" | "provider_init" ...
    location: str        # "function:<name>" | "file_top"
    reason: str
    code_preview: str
    enabled: bool = True

Language Detection & Extension Resolution (wizard/core/detector.py): The detector combines file extension mappings, shebang header inspection, and multiline regex heuristics to evaluate source snippets:

def detect_language(filename: str, source: str = "") -> dict:
    _, ext = os.path.splitext(filename.lower())
    if ext in EXTENSION_MAP:
        lang = EXTENSION_MAP[ext]
        return {"language": "javascript" if lang == "typescript" else lang, "confidence": "high"}

    if source:
        first_line = source.splitlines()[0] if source.splitlines() else ""
        if first_line.startswith("#!"):
            for pattern, lang in SHEBANG_PATTERNS:
                if re.search(pattern, first_line, re.IGNORECASE):
                    return {"language": lang, "confidence": "high"}

- AST & Pattern Analysis (wizard/core/analyzer.py)
: The analyzer creates an InstrumentationPlan without altering the input code. It checks idempotency, determines required OTel packages, and matches structural function points based on the target depth.

def generate_plan(source: str, language: str, filename: str, config: WizardConfig) -> InstrumentationPlan:
    plan = InstrumentationPlan(language=language, config=config)

    check = check_existing_instrumentation(source, language)
    if check["already_instrumented"]:
        plan.already_instrumented = True
        return plan

    plan.dependencies = _get_dependencies(language, config.signals)
    functions = _detect_functions(source, language, config.depth)

    for line_no, func_name, kind in functions:
        if "traces" in config.signals:
            plan.injection_points.append(InjectionPoint(
                signal="trace",
                kind="span",
                location=f"function:{func_name}",
                reason=f"Wrap '{func_name}' with a trace span for distributed tracing",
                code_preview=f"span '{func_name}' (line {line_no})"
            ))
    return plan

Dispatch Injection Strategy (wizard/core/injector.py): The injection orchestrator delegates the transformation to language-specific strategy implementations:

def inject(source: str, plan: InstrumentationPlan, filename: str = "") -> InjectionResult:
    language = plan.language

    if language == "python":
        from wizard.languages.python_injector import PythonInjector
        injector = PythonInjector(plan)
    elif language == "javascript":
        from wizard.languages.js_injector import JSInjector
        injector = JSInjector(plan)
    # ... remaining target language dispatchers

    return injector.inject(source)

Streamlit Application Loop (`wizard/app.py`)

The main entry point ties the interactive user workflow together across four clear user steps:

Upload / Paste: Captures source code and triggers detect_language().
Plan Review: Executes generate_plan() to output an itemized data frame of target span hooks and missing dependencies.
Inject: Calls inject() upon user confirmation.
Download: Calls generate_report() and packs the modified source, package metadata (requirements.txt, package.json, etc.), and markdown report into an in-memory ZIP archive.

The core wizard module includes these language-specific injector implementations:

Core Dispatcher Integration: The central dispatcher (wizard/core/injector.py) maps source languages to these concrete injector classes:

| Language                        | Injector Class       | Key Strategy & Tracing Mechanism                             |
| ------------------------------- | -------------------- | ------------------------------------------------------------ |
| **Python**  PY                  | `PythonInjector`  PY | Uses standard `ast` parsing to inject OTel imports, provider initializers, and wrap function bodies using `with _otel_tracer.start_as_current_span(...)` context managers.  PY |
| **JavaScript / TypeScript**  PY | `JSInjector`  PY     | Prepends tracer setup headers, generates an accompanying `instrumentation.js` file, and wraps functions with `_otelTracer.startActiveSpan(...)`.  PY |
| **Go**  PY                      | `GoInjector`  PY     | Injects `go.opentelemetry.io/otel` imports, adds an `initOtelProvider` initialization function, and inserts `ctx, _otelSpan := _otelTracer.Start(...)` with `defer _otelSpan.End()`.  PY |
| **Java**  PY                    | `JavaInjector`  PY   | Appends static `OpenTelemetry` class initializers and wraps public/protected method bodies using `try (Scope _ = ...)` and `finally { _otelSpan.end(); }` blocks.  PY |
| **C# / .NET**  PY               | `DotNetInjector`  PY | Injects native `System.Diagnostics.ActivitySource` fields and wraps methods using `using var _otelActivity = _otelSource.StartActivity(...)` with `try/catch` exception recording.  PY |
| **C**  PY                       | `CInjector`  PY      | Scans line-by-line using regex for C function signatures, injects `otel_init()` inside `main()`, and inserts macro/pointer span tracking blocks at function entry points.  PY |
| **PHP**  PY                     | `PHPInjector`  PY    | Prepends a PHP OTel bootstrap block and wraps function bodies in `startSpan()`, `activate()`, and `try/catch/finally` blocks.  PY |
| **Ruby**  PY                    | `RubyInjector`  PY   | Prepends `require 'opentelemetry/sdk'`, configures the global SDK, and wraps method bodies inside `_OTEL_TRACER.in_span(...) do ... end` blocks.  PY |

Standard Features Across Injectors

Injection Watermarking: Every injected code block, import, or helper macro across all language implementations is tagged with the line comment identifier // [OTel-Wizard: injected] (or # [OTel-Wizard: injected]) for easy auditing and tracing.
Dependency Reporting: Each injector builds language-native dependency manifests or installation commands alongside the modified source code - such as requirements.txt, package.json, go.mod, pom.xml, CMakeLists.txt, Gemfile, or CLI package commands.

🧩 Role of Injectors in the OTel Instrumentation Wizard

In the OTel Instrumentation Wizard, injectors are specialized components responsible for parsing source code in a target programming language, inserting required OpenTelemetry SDK setup/imports, and wrapping key execution blocks (such as entry points or main functions) with span tracking logic.

Key Responsibilities

Base Abstraction & Lifecycle Control (BaseInjector): Establishes a common inject(source_code, filename) contract and returns an InjectionResult dataclass containing the modified source code, language metadata, and a human-readable list of changes. It provides shared helpers to enforce non-empty source checks (_require_non_empty), detect pre-existing OTel instrumentation (_already_has_otel), and scan for common entry points across languages (_find_main_function).
Tracer Provider Initialisation: Prepends or injects global tracer configuration, SDK setups, or SDK bootstrap calls suitable for the target runtime environment.
Span Wrapping & Lifecycle Management: Identifies entry functions (e.g., main(), if __name__ == "__main__":) and injects code to start spans at entry and guarantee proper span finalisation/teardown via native language constructs (such as try-with-resources, defer, try/finally, or with blocks).
Idempotency Safeguards: Checks for pre-existing OTel markers (e.g., opentelemetry, go.opentelemetry.io/otel) prior to modifying files, skipping injection if the source code is already instrumented.

Language-Specific Implementations & Code Excerpts

1. Python (`PythonInjector`)

Inserts OTel imports, sets up a TracerProvider and ConsoleSpanExporter, and wraps the body of def main(): using Python's with tracer.start_as_current_span(...) context manager while handling indentation.

Code Excerpt (injectors/python_injector.py):

_OTEL_IMPORTS = """\
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
    BatchSpanProcessor,
    ConsoleSpanExporter,
)
from opentelemetry.sdk.resources import Resource, SERVICE_NAME
"""

_OTEL_PROVIDER_INIT = """\
# [OTel] Initialise TracerProvider
_otel_resource = Resource(attributes={SERVICE_NAME: __name__})
_otel_provider = TracerProvider(resource=_otel_resource)
_otel_provider.add_span_processor(
    BatchSpanProcessor(ConsoleSpanExporter())
)
trace.set_tracer_provider(_otel_provider)
tracer = trace.get_tracer(__name__)
"""

2. Go (`GoInjector`)

Injects go.opentelemetry.io/otel packages into import (...) blocks, appends an initTracer() helper function, and modifies func main() to initialize the provider with defer tp.Shutdown(ctx) and defer span completion via defer span.End().

Code Excerpt (injectors/go_injector.py):

_MAIN_PREAMBLE = """\
    // [OTel] Initialise tracer
    ctx := context.Background()
    tp, err := initTracer()
    if err != nil {
        log.Fatalf("failed to initialise tracer: %v", err)
    }
    defer func() {
        if shutdownErr := tp.Shutdown(ctx); shutdownErr != nil {
            log.Printf("tracer shutdown error: %v", shutdownErr)
        }
    }()
    tracer := otel.Tracer("main")
    ctx, span := tracer.Start(ctx, "main") // [OTel] root span
    defer span.End()
    _ = ctx // use ctx in downstream calls
"""

3. Java (`JavaInjector`)

Inserts io.opentelemetry.api imports after package or existing import statements and wraps public static void main(String[] args) with a try-with-resources block using Scope scope = span.makeCurrent() and explicit error reporting in a catch block.

Code Excerpt (injectors/java_injector.py):

otel_preamble = (
    f"\n{body_indent}// [OTel] Initialise tracer\n"
    f"{body_indent}OpenTelemetry openTelemetry = GlobalOpenTelemetry.get();\n"
    f"{body_indent}Tracer tracer = openTelemetry.getTracer(\"main\");\n"
    f"{body_indent}Span span = tracer.spanBuilder(\"main\").startSpan();\n"
    f"{body_indent}try (Scope scope = span.makeCurrent()) {{\n"
)
otel_postamble = (
    f"{body_indent}}} catch (Exception e) {{\n"
    f"{body_indent}    span.setStatus(StatusCode.ERROR, e.getMessage());\n"
    f"{body_indent}    throw e;\n"
    f"{body_indent}}} finally {{\n"
    f"{body_indent}    span.end();\n"
    f"{body_indent}}}\n"
)

4. JavaScript & TypeScript (`JavaScriptInjector` / `TypeScriptInjector`)

Prepends a Node.js NodeSDK configuration block and tracer acquisition (_otelTracer.startSpan). TypeScript uses ES module imports and type declarations (const _otelTracer: Tracer), wrapping function bodies with standard try/catch/finally blocks.

Code Excerpt (injectors/typescript_injector.py):

_OTEL_SETUP_BLOCK = """\
// [OTel] ── OpenTelemetry setup ──────────────────────────────────────────────
import { NodeSDK } from '@opentelemetry/sdk-node';
import { ConsoleSpanExporter } from '@opentelemetry/sdk-trace-node';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
import { Resource } from '@opentelemetry/resources';
import { SemanticResourceAttributes } from '@opentelemetry/semantic-conventions';
import { trace, Tracer, context, SpanStatusCode } from '@opentelemetry/api';

const _otelSdk: NodeSDK = new NodeSDK({
  resource: new Resource({
    [SemanticResourceAttributes.SERVICE_NAME]: 'otel-wizard-service',
  }),
  traceExporter: new ConsoleSpanExporter(),
  instrumentations: [getNodeAutoInstrumentations()],
});
_otelSdk.start();
process.on('SIGTERM', (): void => { _otelSdk.shutdown(); });

const _otelTracer: Tracer = trace.getTracer('main');
// [OTel] ─────────────────────────────────────────────────────────────────────
"""

5. C (`CInjector`)

Appends C OTel headers (#include "opentelemetry_c/otel_api.h"), inserts otel_init() and otel_start_span("main") at the beginning of int main(), injects otel_end_span() prior to any return statement inside main(), and appends otel_shutdown() before closing the function.

Code Excerpt (injectors/c_injector.py):

_OTEL_MAIN_INIT = """\
    /* [OTel] Initialise tracer */
    otel_init("otel-wizard-service");
    otel_span_t *_otel_span = otel_start_span("main");
"""

_OTEL_MAIN_CLOSE = """\
    /* [OTel] End root span and shutdown */
    otel_end_span(_otel_span);
    otel_shutdown();
"""

🛠️ Utility Modules Overview

The utils directory provides critical supporting functionality for the OTel Instrumentation Wizard, focusing on file analysis and difference visualization:

Language Detection (utils/language_detector.py): The LanguageDetector class identifies programming languages through explicit extension maps and keyword content heuristics. It offers two primary mechanisms: detect(), which strictly validates whether a file has an active injector available (raising UnsupportedLanguageError otherwise), and detect_any(), a non-raising alternative designed for fluid UI workflows that gracefully flags recognized but non-instrumentable languages.
Diff Generation & Visualisation (utils/diff_viewer.py): Leverages Python's native difflib module to compute line-by-line differences between original and instrumented source code. It provides generate_unified_diff() to output standard text diffs (diff -u) and generate_html_diff() to build sanitised, color-coded HTML views with styled line highlights (green for additions, red for deletions, and blue for hunk headers).

🧪 Testing Samples Overview

To validate the code injection process across all supported target environments, the project includes clean, minimal "Hello World" benchmark samples free of any initial OpenTelemetry instrumentation:

Language Coverage: Standardized samples are provided for C (helloworld.c), Go (helloworld.go), Java (HelloWorld.java), JavaScript (helloworld.js), Python (helloworld.py), and TypeScript (helloworld.ts).
Canonical Code Structure: Each file implements a simple main entry point and helper greeting function (such as greet() / main()). This standardized pattern allows unit and integration tests to reliably verify header/import additions, tracer setup placements, and entry-point span wrapping across every supported language.

A Simple Test and Implementation for Go applications

Step 1: Uploading or Copy/Pasting a Go code (or any other code)

  // tests/samples/go/sample_app.go
  // A plain Go HTTP server WITHOUT OTel instrumentation.
  // Used to validate the wizard's Go injection.

  package main

  import (
    "encoding/json"
    "fmt"
    "log"
    "math/rand"
    "net/http"
    "time"
  )

  type Item struct {
    Name     string  `json:"name"`
    Price    float64 `json:"price"`
    Quantity int     `json:"quantity"`
  }

  type OrderRequest struct {
    UserID int    `json:"user_id"`
    Items  []Item `json:"items"`
  }

  type OrderResponse struct {
    User   string  `json:"user"`
    Total  float64 `json:"total"`
    Status string  `json:"status"`
  }

  func calculateTotal(items []Item) float64 {
    total := 0.0
    for _, item := range items {
        qty := item.Quantity
        if qty == 0 {
            qty = 1
        }
        total += item.Price * float64(qty)
    }
    return total
  }

  func fetchUser(userID int) (map[string]interface{}, error) {
    time.Sleep(time.Duration(rand.Intn(50)) * time.Millisecond)
    if userID <= 0 {
        return nil, fmt.Errorf("invalid userID: %d", userID)
    }
    return map[string]interface{}{
        "id":   userID,
        "name": fmt.Sprintf("User_%d", userID),
    }, nil
  }

  func processOrder(userID int, items []Item) (*OrderResponse, error) {
    user, err := fetchUser(userID)
    if err != nil {
        return nil, err
    }
    total := calculateTotal(items)
    log.Printf("Order processed for %v: $%.2f", user["name"], total)
    return &OrderResponse{
        User:   user["name"].(string),
        Total:  total,
        Status: "confirmed",
    }, nil
  }

  func orderHandler(w http.ResponseWriter, r *http.Request) {
    if r.Method != http.MethodPost {
        http.Error(w, "method not allowed", http.StatusMethodNotAllowed)
        return
    }
    var req OrderRequest
    if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
        http.Error(w, err.Error(), http.StatusBadRequest)
        return
    }
    resp, err := processOrder(req.UserID, req.Items)
    if err != nil {
        http.Error(w, err.Error(), http.StatusBadRequest)
        return
    }
    w.Header().Set("Content-Type", "application/json")
    json.NewEncoder(w).Encode(resp)
  }

  func healthHandler(w http.ResponseWriter, r *http.Request) {
    w.Header().Set("Content-Type", "application/json")
    fmt.Fprintf(w, `{"status":"ok"}`)
  }

  func main() {
    http.HandleFunc("/order", orderHandler)
    http.HandleFunc("/health", healthHandler)
    log.Println("Server listening on :8080")
    log.Fatal(http.ListenAndServe(":8080", nil))
  }

Step 2: Validate the instrumentation plan

Step 3: Confirm the changes and Inject

Step 4: Download the modified Code

// tests/samples/go/sample_app.go
// A plain Go HTTP server WITHOUT OTel instrumentation.
// Used to validate the wizard's Go injection.

package main

import (
    "context"  // [OTel-Wizard: injected]
    "os"  // [OTel-Wizard: injected]
    "go.opentelemetry.io/otel"  // [OTel-Wizard: injected]
    "go.opentelemetry.io/otel/attribute"  // [OTel-Wizard: injected]
    "go.opentelemetry.io/otel/codes"  // [OTel-Wizard: injected]
    "go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp"  // [OTel-Wizard: injected]
    "go.opentelemetry.io/otel/sdk/resource"  // [OTel-Wizard: injected]
    sdktrace "go.opentelemetry.io/otel/sdk/trace"  // [OTel-Wizard: injected]
    semconv "go.opentelemetry.io/otel/semconv/v1.26.0"  // [OTel-Wizard: injected]
    "go.opentelemetry.io/otel/trace"  // [OTel-Wizard: injected]

// initOtelProvider initialises the TracerProvider. // [OTel-Wizard: injected]
func initOtelProvider(ctx context.Context) (func(context.Context) error, error) {
    exporter, err := otlptracehttp.New(ctx,
        otlptracehttp.WithEndpoint(getEnv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4318")),
        otlptracehttp.WithInsecure(),
    )
    if err != nil {
        return nil, err
    }
    res, err := resource.Merge(
        resource.Default(),
        resource.NewWithAttributes(
            semconv.SchemaURL,
            semconv.ServiceName(getEnv("OTEL_SERVICE_NAME", "my-service")),
            semconv.ServiceVersion(getEnv("OTEL_SERVICE_VERSION", "1.0.0")),
        ),
    )
    if err != nil {
        return nil, err
    }
    tp := sdktrace.NewTracerProvider(
        sdktrace.WithBatcher(exporter),
        sdktrace.WithResource(res),
    )
    otel.SetTracerProvider(tp)
    return tp.Shutdown, nil
}

// getEnv returns env var value or fallback. // [OTel-Wizard: injected]
func getEnv(key, fallback string) string {
    if v := os.Getenv(key); v != "" {
        return v
    }
    return fallback
}

var _otelTracer = otel.Tracer("my-service")  // [OTel-Wizard: injected]

    "encoding/json"
    "fmt"
    "log"
    "math/rand"
    "net/http"
    "time"
)

type Item struct {
    Name     string  `json:"name"`
    Price    float64 `json:"price"`
    Quantity int     `json:"quantity"`
}

type OrderRequest struct {
    UserID int    `json:"user_id"`
    Items  []Item `json:"items"`
}

type OrderResponse struct {
    User   string  `json:"user"`
    Total  float64 `json:"total"`
    Status string  `json:"status"`
}

func calculateTotal(items []Item) float64 {
    // [OTel-Wizard: injected]
    ctx, _otelSpan := _otelTracer.Start(ctx, "calculateTotal",
        trace.WithAttributes(attribute.String("code.function", "calculateTotal")))
    defer func() {
        if r := recover(); r != nil {
            _otelSpan.SetStatus(codes.Error, "panic")
            _otelSpan.End()
            panic(r)
        }
        _otelSpan.End()
    }()  // [OTel-Wizard: injected]
    _ = ctx  // [OTel-Wizard: injected]
    total := 0.0
    for _, item := range items {
        qty := item.Quantity
        if qty == 0 {
            qty = 1
        }
        total += item.Price * float64(qty)
    }
    return total
}

func fetchUser(userID int) (map[string]interface{}, error) {
    // [OTel-Wizard: injected]
    ctx, _otelSpan := _otelTracer.Start(ctx, "fetchUser",
        trace.WithAttributes(attribute.String("code.function", "fetchUser")))
    defer func() {
        if r := recover(); r != nil {
            _otelSpan.SetStatus(codes.Error, "panic")
            _otelSpan.End()
            panic(r)
        }
        _otelSpan.End()
    }()  // [OTel-Wizard: injected]
    _ = ctx  // [OTel-Wizard: injected]
    time.Sleep(time.Duration(rand.Intn(50)) * time.Millisecond)
    if userID <= 0 {
        return nil, fmt.Errorf("invalid userID: %d", userID)
    }
    return map[string]interface{}{
        "id":   userID,
        "name": fmt.Sprintf("User_%d", userID),
    }, nil
}

func processOrder(userID int, items []Item) (*OrderResponse, error) {
    // [OTel-Wizard: injected]
    ctx, _otelSpan := _otelTracer.Start(ctx, "processOrder",
        trace.WithAttributes(attribute.String("code.function", "processOrder")))
    defer func() {
        if r := recover(); r != nil {
            _otelSpan.SetStatus(codes.Error, "panic")
            _otelSpan.End()
            panic(r)
        }
        _otelSpan.End()
    }()  // [OTel-Wizard: injected]
    _ = ctx  // [OTel-Wizard: injected]
    user, err := fetchUser(userID)
    if err != nil {
        return nil, err
    }
    total := calculateTotal(items)
    log.Printf("Order processed for %v: $%.2f", user["name"], total)
    return &OrderResponse{
        User:   user["name"].(string),
        Total:  total,
        Status: "confirmed",
    }, nil
}

func orderHandler(w http.ResponseWriter, r *http.Request) {
    // [OTel-Wizard: injected]
    ctx, _otelSpan := _otelTracer.Start(ctx, "orderHandler",
        trace.WithAttributes(attribute.String("code.function", "orderHandler")))
    defer func() {
        if r := recover(); r != nil {
            _otelSpan.SetStatus(codes.Error, "panic")
            _otelSpan.End()
            panic(r)
        }
        _otelSpan.End()
    }()  // [OTel-Wizard: injected]
    _ = ctx  // [OTel-Wizard: injected]
    if r.Method != http.MethodPost {
        http.Error(w, "method not allowed", http.StatusMethodNotAllowed)
        return
    }
    var req OrderRequest
    if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
        http.Error(w, err.Error(), http.StatusBadRequest)
        return
    }
    resp, err := processOrder(req.UserID, req.Items)
    if err != nil {
        http.Error(w, err.Error(), http.StatusBadRequest)
        return
    }
    w.Header().Set("Content-Type", "application/json")
    json.NewEncoder(w).Encode(resp)
}

func healthHandler(w http.ResponseWriter, r *http.Request) {
    // [OTel-Wizard: injected]
    ctx, _otelSpan := _otelTracer.Start(ctx, "healthHandler",
        trace.WithAttributes(attribute.String("code.function", "healthHandler")))
    defer func() {
        if r := recover(); r != nil {
            _otelSpan.SetStatus(codes.Error, "panic")
            _otelSpan.End()
            panic(r)
        }
        _otelSpan.End()
    }()  // [OTel-Wizard: injected]
    _ = ctx  // [OTel-Wizard: injected]
    w.Header().Set("Content-Type", "application/json")
    fmt.Fprintf(w, `{"status":"ok"}`)
}

func main() {
    http.HandleFunc("/order", orderHandler)
    http.HandleFunc("/health", healthHandler)
    log.Println("Server listening on :8080")
    log.Fatal(http.ListenAndServe(":8080", nil))
}

… and the code’s dependencies;

# Add to go.mod require block:
go.opentelemetry.io/otel v1.33.0
go.opentelemetry.io/otel/sdk v1.33.0
go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp v1.33.0
go.opentelemetry.io/otel/sdk/metric v1.33.0

🎯 Conclusion

The OTel Instrumentation Wizard provides an automated, end-to-end solution for introducing OpenTelemetry tracing into multi-language source code without requiring manual boilerplate construction. By leveraging a streamlined three-step workflow (Upload → Preview Diff → Download), the application abstracts the complexity of observability integration:

Intelligent Language Detection & Flexibility: Through the LanguageDetector module, the wizard automatically identifies file types via explicit extension matching and keyword-based content heuristics. It provides broad recognition across many ecosystem languages while enforcing clear guardrails for supported injection targets.
Robust Code Injection Engine: Built upon a extensible BaseInjector contract, concrete injectors for Python, Go, Java, JavaScript, TypeScript, and C automatically handle import injections, tracer provider setup, and entry-point span wrapping. Native language constructs (such as try-with-resources, defer, with blocks, or explicit return-wrapping) guarantee safe span lifecycle management and error propagation across diverse runtime models.
Safety, Transparency, & Idempotency: Built-in safeguards check for pre-existing OTel markers to prevent duplicate instrumentation. The interactive UI offers full transparency via unified and color-coded HTML diff viewers (utils/diff_viewer.py), ensuring developers can inspect and accept all injected modifications with total confidence.
Empirically Validated Architecture: Accompanied by standardized, clean test samples across all target languages, the architecture ensures reproducible and dependable AST/text transformations across real-world codebases.

Ultimately, the application bridges the gap between raw source code and modern observability practices, lowering the barrier to entry for distributed tracing across heterogeneous developer stacks.

Thanks for reading 🔮

DEV Community: Alain Airom (Ayrom)

Deep Agents in Action: Building a Multi-Agent Research System

Introduction: The Deep Agents Architecture

What is the Deep Agents Pattern?

Deep Agents overview

Core Concepts of LangChain Deep Agents

Technical Implementation

Architecture Overview

Technical Stack

Code Structure

Key Code Excerpts

Safe AST Arithmetic Evaluator (tools/calculator.py)

Sandboxed Python Code Executor (tools/code_executor.py)

Thread-Safe Streamlit UI Relay (app.py)

Example Use Case: Climate Change Analysis

Adding a New Specialist Agent

Conclusion

Links

Building a Synthetic Data Generator

Introduction

TL;DR-What is SDG Hub (Red Hat synthetic data generator hub)?

Implementation

Application Architecture

Component Stack

Application's Core

Multi-Stage Generation Orchestration

External Flow Delegation & Asynchronous Execution

Multi-Layer Quality & Safety Engine

Pipeline Summary

REST API

LLM Implementation

Flows

Specific Plugin built for Bob by Bob

Available commands

Plugin structure

Conclusion

Links

Testing NVIDIA NemoClaw in a Sandboxed Environment

Introduction

TL;DR: What is NVIDIA NemoClaw?

NVIDIA NemoClaw: Reference Stack for Sandboxed AI Agents in OpenShell (from official Github repository)

Technical Stack of the Local Implementation

Components Stack

Core Architecture Highlights

Implementation Guide And Tests

Environment Setup & Prerequisites

Architecture & Hardening Configuration

Network Policy Definition

Sandbox Onboarding & Execution

Python State Inspection Test

Test-Case Walkthrough

Normal In-Sandbox Execution (TC-01)

Scenario 2: Traversal & Egress Violation Defense (TC-02)

Scenario 3: Resource Limit Enforcement (TC-03)

Scenario 4: Inter-Agent Communication Containment (TC-04)

Scenario 5: Failure Handling & Graceful Teardown (TC-05)

Implementation Blueprint & Execution

Running the Full Test Suite

Troubleshooting & Production Best Practices

Summary Checklist for Deployment

Conclusion

Links

End-to-End Enterprise RAG Pipeline with IBM Docling Pipelines and OpenSearch

Introduction

TL;DR-What is Docling-Pipelines?

Features

Architecture Overview of the Solution

Implementation

Components Stack

Programmatic Pipeline Authoring (flow_builder.py)

Pipeline Execution Engine (pipeline_manager.py)

Dense Vector Ingestion & RAG Orchestration (rag_engine.py)

Conclusion

Links

What is Text Watermarking?

Introduction & Theoretical Foundations

The Green-List / Red-List Partitioning Algorithm (Kirchenbauer et al., 2023)

Statistical Hypothesis Testing & Detection

Didactical Application Architecture

System Flowchart

Safe AST Arithmetic Evaluator (`tools/calculator.py`)

Sandboxed Python Code Executor (`tools/code_executor.py`)

Thread-Safe Streamlit UI Relay (`app.py`)

Programmatic Pipeline Authoring (`flow_builder.py`)

Pipeline Execution Engine (`pipeline_manager.py`)

Dense Vector Ingestion & RAG Orchestration (`rag_engine.py`)

Core Orchestration (`backend/app.py`)

Output Parsing & Reasoning Traces (`backend/model.py`)

Registry Core (`tools/registry.py`)

Adding a Custom Tool (`tools/catalog/my_tool.py`)

Deploying the Sample Workload (`hello-k8s`)

Workload Manifest (`k8s/hello-k8s.yaml`)