DEV Community: timo teppo

Hermes AI available !

timo teppo — Sun, 24 May 2026 15:56:35 +0000

This is a submission for the Hermes Agent Challenge

Hermes AI: A Practical Look at an Open‑Source Agentic Framework

Introduction

Hermes AI (often referred to as the “Hermes Agent”) has been gaining traction as an open‑source, model‑agnostic framework for building autonomous AI agents. Unlike tightly coupled proprietary solutions, Hermes emphasizes modularity, extensibility, and seamless integration with existing tooling—making it a compelling choice for developers who want to experiment with agentic workflows without locking into a single vendor.

In this post I’ll walk through a quick local setup, demonstrate a simple tool‑integration experiment, compare Hermes to a couple of popular alternatives, and share some thoughts on what frameworks like this mean for the future of software development.

1. Getting Hermes AI Running Locally

Prerequisites

Python ≥ 3.9
Git
(Optional) Docker for isolated tool containers
An LLM endpoint (e.g., a local Ollama model, HuggingFace Inference API, or OpenAI‑compatible service)

Step‑by‑step

# 1️⃣ Clone the repo
git clone https://github.com/hermes-ai/hermes-agent.git
cd hermes-agent

# 2️⃣ Create a virtual environment
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

# 3️⃣ Install dependencies
pip install -r requirements.txt

# 4️⃣ Configure the LLM backend
cp config/example.yaml config/local.yaml
# Edit config/local.yaml to point at your model, e.g.:
# llm:
#   provider: ollama
#   model: llama3
#   base_url: http://localhost:11434

# 5️⃣ Run the agent in interactive mode
python -m hermes run --config config/local.yaml

You should see a REPL‑like prompt where you can issue natural‑language commands. The agent will parse the intent, plan a sequence of tool calls, execute them, and return the result—all while logging its reasoning trace.

2. Tool‑Integration Experiment: Adding a Custom “File‑Summarizer”

One of Hermes’s strengths is its plug‑in system. Let’s add a tiny tool that summarizes the contents of a text file.

2.1 Define the Tool

Create tools/file_summarizer.py:

from hermes.tools.base import Tool, ToolResult

class FileSummarizer(Tool):
    name = "file_summarizer"
    description = "Returns a concise summary of a text file."

    async def run(self, filepath: str) -> ToolResult:
        try:
            with open(filepath, "r", encoding="utf-8") as f:
                content = f.read()
            # Very naive summarization: first two sentences
            summary = ". ".join(content.split(". ")[:2]) + "."
            return ToolResult.success(summary)
        except Exception as e:
            return ToolResult.failure(str(e))

2.2 Register the Tool

Edit config/local.yaml (or create a new config) and add:

tools:
  - file_summarizer.FileSummarizer

2.3 Test It

Back in the Hermes REPL:

> summarize the file ./README.md

The agent will:

Recognize the intent “summarize the file”.
Match it to the file_summarizer tool.
Pass ./README.md as the filepath argument.
Return a short summary (e.g., “Hermes AI is an open‑source agentic framework….”).

You can see the full reasoning trace with --verbose, which is invaluable for debugging complex agents.

3. How Hermes Stacks Up Against Other Agentic Frameworks

Feature	Hermes AI	LangChain Agents	AutoGPT (via babyagi)
Model Agnostic	✅ (plug‑in LLM providers)	✅ (but tightly coupled to LLMs)	✅ (requires OpenAI‑compatible)
Tool System	Simple Python class registry	AgentExecutor + Tool wrappers	Custom JSON‑defined tools
Planning	LLM‑driven (ReAct‑style)	LLM‑driven (Zero‑Shot/Agent)	LLM‑driven (prompt chaining)
Memory	Short‑term (session) + optional vector store	Multiple memory modules	Limited (short‑term)
Extensibility	High (add tools, planners, memory)	Moderate (custom chains)	Low (mostly prompt tweaks)
Community & Docs	Growing, clear examples	Extensive, mature	Active but fragmented
License	MIT	MIT	MIT

Takeaway: Hermes offers a sweet spot—more structured than the free‑form prompting of AutoGPT, yet less boilerplate‑heavy than LangChain’s chain‑centric approach. Its explicit separation of planning, tool execution, and memory makes it easier to swap components or inject custom logic (like our file summarizer) without rewriting the agent core.

4. Why Open Agentic Systems Matter

Transparency: With Hermes, the agent’s thought process is exposed as a series of tool calls and observations. This makes it far easier to audit, debug, and trust compared to black‑box proprietary agents.
Composability: Developers can compose agents from reusable building blocks—think of them as the “functions” of the AI world. A file‑summarizer tool, a web‑search tool, a code‑execution sandbox—all can be mixed and matched per task.
Lower Barrier to Experimentation: Because the framework is lightweight and MIT‑licensed, hobbyists, researchers, and startups can prototype novel workflows (e.g., automated bug triage, dynamic documentation generation) without negotiating licensing terms.
Community‑Driven Evolution: As more contributors add tools (database connectors, CI/CD triggers, UI automation), the ecosystem expands organically, much like the early days of npm or PyPI.

5. Next Steps & Ideas to Try

Multi‑Agent Collaboration: Spin up two Hermes agents—one as a “planner” and another as an “executor”—and have them negotiate via a shared message bus.
Persistent Memory: Integrate a vector store (FAISS, Chroma) to give the agent long‑term recall of past interactions.
Sandboxed Code Execution: Add a tool that runs Python snippets in a Docker container, enabling the agent to write, test, and iteratively improve code.
UI Layer: Build a simple Gradio or Streamlit front‑end that visualizes the agent’s reasoning trace in real time.

Closing Thoughts

Hermes AI illustrates how open‑source agentic frameworks can democratize access to sophisticated AI workflows. By focusing on clean abstractions, extensible tooling, and transparent reasoning, it empowers developers to treat agents not as mystical black boxes but as programmable components—just like any library or service we already use daily.

If you’re curious about building your own AI‑augmented workflows, give Hermes a spin. Start small (like the file summarizer above), observe how the agent reasons, and then gradually layer in more complex tools. The journey from a simple REPL prompt to a fully autonomous agent is both educational and genuinely fun.

Happy agent building! 🚀

Timo Teppo
System Specialist

Reasoning Studio

timo teppo — Sun, 24 May 2026 10:31:16 +0000

This is a submission for the Gemma 4 Challenge: Build with Gemma 4

What I Built

Reasoning Studio is for coding, writing, planning, strategies, visioning ... everything that a productive team can offer . AI powered . Has also agent properties .

Demo

https://ai.studio/apps/b8143b9d-2287-40af-b320-f205b9aa304b

Code

https://ai.studio/apps/b8143b9d-2287-40af-b320-f205b9aa304b

How I Used Gemma 4

Reasoning Studio is built with Gemma 4 31b . I wanted to use the latest model available .

Timo Teppo
System Specialist

Gemini AI Flash: low-latency, multimodal model accelerating coding, agents and real-time apps while demanding strong governance.

timo teppo — Sun, 24 May 2026 07:22:02 +0000

This is a submission for the Google I/O Writing Challenge

Gemini AI Flash

Introduction

Gemini AI Flash represents a deliberate step toward making advanced generative artificial intelligence both faster and more practical for real‑world use. It is positioned as a low‑latency variant of frontier models, optimized to deliver rapid responses while retaining strong reasoning capabilities. The design philosophy behind Flash models emphasizes a balance between raw capability and operational efficiency, aiming to bring high‑quality AI assistance into contexts where speed and cost matter as much as accuracy.

Speed is the current currency of usefulness; Gemini Flash trades latency for immediacy without surrendering depth.

Practical advances and performance

At the heart of Gemini Flash is an engineering focus on latency reduction and throughput improvement. Compared with larger, slower frontier models, Flash variants are tuned to produce answers more quickly and with lower computational cost per token. This makes them attractive for interactive applications—chat interfaces, coding assistants, and agent frameworks—where users expect near‑instant feedback. The performance gains are not merely about raw speed; they also enable new patterns of interaction, such as iterative, multi‑turn problem solving and real‑time collaboration between humans and models.

When milliseconds matter, the machine learns to speak in the tempo of human thought.

Multimodal capabilities

Gemini Flash is built to be multimodal, able to process and combine inputs such as text, images, audio, and short video clips. This multimodality expands the range of tasks the model can handle: from document analysis that fuses scanned diagrams with explanatory text, to visual question answering and multimodal summarization. The ability to reason across modalities allows the model to form richer internal representations of problems, which in turn supports more nuanced outputs—whether that means generating a concise textual summary of a complex infographic or suggesting edits to a short video based on a written brief.

A single conversation can carry images, sounds, and sentences; the model learns to translate between senses.

Coding, agents, and developer workflows

One of the most prominent use cases for Gemini Flash is software development and agent orchestration. The model’s architecture and training emphasize code understanding, generation, and debugging, making it a powerful assistant for developers. In agentic settings, Flash models can coordinate multiple subagents or tools to accomplish multi‑step tasks: fetching data, transforming it, invoking APIs, and synthesizing results into coherent outputs. This capability is particularly valuable for automating workflows that previously required human orchestration across disparate systems.

When code becomes conversation, agents become the craftsmen of automated work.

Availability, integration, and ecosystem fit

Gemini Flash is designed to be integrated across a broad ecosystem of products and developer platforms. Its low latency and cost profile make it suitable for embedding in consumer apps, enterprise services, and cloud‑based development tools. For organizations, this means the possibility of deploying advanced AI features at scale without prohibitive expense. For individual users, it means access to more responsive assistants that can help with everyday tasks—drafting messages, summarizing content, or generating quick prototypes—without long waits or heavy compute bills.

Intelligence that fits in your pocket and scales in the cloud changes how we expect tools to behave.

Risks, governance, and responsible use

Faster and more accessible AI also amplifies the need for careful governance. Lower latency and broader availability can increase token consumption and operational scale, which in turn raises questions about cost management, data privacy, and misuse. Models that coordinate agents or execute code introduce additional safety considerations: automated actions must be constrained by robust guardrails, auditing, and human oversight. Ethical deployment requires not only technical controls—rate limits, input sanitization, and monitoring—but also organizational policies that define acceptable use, accountability, and remediation pathways.

A powerful engine needs both a skilled driver and a clear map to avoid unintended detours.

Conclusion

Gemini AI Flash embodies a pragmatic evolution in generative AI: it narrows the gap between frontier reasoning and real‑time usability. By prioritizing latency and efficiency while preserving multimodal understanding and coding prowess, Flash models unlock new interaction patterns and practical applications. At the same time, their strengths make it imperative to invest in governance, transparency, and human‑centered design so that speed and scale do not outpace responsibility. In short, Gemini Flash offers a glimpse of AI that is not only smarter but also faster and more woven into everyday workflows—provided we steward its deployment with care.

Speed without stewardship is a promise that can outpace prudence; wisdom must travel as fast as invention.

Reflection and Future Vision

Reflection on present capabilities and responsibilities

Gemini AI Flash already signals a shift in how we conceive of practical intelligence: it is not merely a research artifact but a tool engineered for everyday interaction and complex orchestration. Its strengths—low latency, multimodality, and coding fluency—make it uniquely positioned to bridge exploratory research and production systems. Yet with capability comes responsibility: deploying such systems at scale requires careful attention to privacy, fairness, and the socio‑technical contexts in which they operate.

A single leap in speed can ripple into a thousand small decisions; stewardship must match ambition.

Near‑term trajectories and integration

In the near term, we can expect Gemini‑class Flash models to proliferate across developer tools, customer support systems, and creative applications. Their low latency will enable more interactive debugging sessions, real‑time collaborative editing, and responsive multimodal assistants that can interpret images, audio, and text in a single conversational flow. For enterprises, this means embedding intelligent layers into workflows—automated report generation, intelligent monitoring agents, and adaptive interfaces that reduce cognitive load for human operators.

When responsiveness becomes the norm, interfaces learn to listen as quickly as we think.

Democratization and new forms of creativity

As latency and cost barriers fall, access to advanced generative capabilities will broaden. This democratization can empower small teams and individual creators to prototype faster, iterate more boldly, and explore hybrid human‑AI creative processes. We may see new genres of work emerge where human intent and model suggestion co‑author products, from interactive narratives to personalized educational content. However, equitable access will depend on thoughtful pricing models, open standards for interoperability, and tools that let users understand and control model behavior.

Creativity will no longer be a solitary spark but a duet between human curiosity and machine suggestion.

Economic and labor implications

The automation of multi‑step tasks and the rise of agentic workflows will reshape certain job functions while creating new roles focused on oversight, prompt engineering, and AI orchestration. Organizations will need to invest in reskilling programs and design roles that emphasize judgment, ethics, and domain expertise—areas where human strengths complement machine speed. Economic value will increasingly accrue to teams that can integrate AI into decision loops responsibly and to workers who can translate domain knowledge into effective model prompts and guardrails.

Machines will take on the routine; humans will be called to steward the meaningful.

Governance, safety, and societal norms

Faster, more capable models intensify the urgency of governance frameworks. Technical safeguards—such as robust auditing, explainability tools, and fine‑grained access controls—must be paired with legal and organizational policies that define accountability and redress. Public discourse will need to address questions of consent, data provenance, and the acceptable scope of automated action. International cooperation and cross‑sector standards will help align incentives and reduce harms that arise from fragmented deployments.

A map without borders is a recipe for getting lost; shared rules chart safer paths.

Speculative horizons and long‑term possibilities

Looking further ahead, Flash‑class models could become the connective tissue of ambient intelligence: assistants that anticipate needs, synthesize context across devices, and act as persistent collaborators across projects and time. In education, personalized tutors could adapt curricula in real time to each learner’s pace and style. In science, agentic systems might coordinate experiments, analyze multimodal datasets, and propose hypotheses that humans then validate. These scenarios hinge on advances in model interpretability, robust long‑term memory systems, and trustworthy human‑AI collaboration paradigms.

The future is not a single destination but a network of possible worlds we can choose to build.

A call to intentional design

If Gemini AI Flash and its successors are to realize their promise, designers, engineers, policymakers, and communities must collaborate intentionally. That means building tools that are transparent by design, creating governance that is adaptive rather than reactive, and centering human dignity in every deployment decision. It also means investing in public literacy about AI so that more people can participate in shaping the norms and institutions that govern these technologies.

Speed without shared values risks widening divides; intentional design stitches speed to social good.

Closing vision

Ultimately, the most compelling vision for Gemini AI Flash is not one of machines replacing human ingenuity but of machines amplifying it—making expertise more accessible, creativity more iterative, and complex systems more navigable. When paired with strong governance and human‑centered design, Flash‑class models can help societies tackle harder problems faster, from accelerating scientific discovery to making public services more responsive and inclusive. The challenge ahead is to ensure that the acceleration of capability is matched by an acceleration in wisdom, equity, and care.

A future where intelligence is both fast and humane is possible—if we choose to build it together.

Timo Teppo
System Specialist