timo teppo

Posted on May 24

Hermes AI available !

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Hermes Agent Challenge Submission: Write About Hermes Agent

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

DEV Community