fcht-agent 0.1.0

First-Class Hermes Tool Agent — Offline ReAct agent with dual memory (episodic + semantic), skill learning, and daemon mode

FCHT Agent — First-Class Hermes Tool Agent

Offline ReAct agent with persistent dual-memory (episodic + semantic) — runs entirely on your hardware.

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🎯 What Is This?

FCHT Agent is a local-first AI agent that runs 100% offline on your machine using Ollama and your choice of local LLM (default: qwen2.5:7b). It features:

Capability	Description
ReAct Loop	Think → Act → Observe reasoning with tool use
Episodic Memory	JSONL few-shot history for context-aware responses
Semantic Memory	ChromaDB + sentence-transformers for RAG
Skill System	Agent writes executable Python tools to memory/skills/
7 Built-in Tools	shell, read/write files, python_exec, list_files, remember, retrieve
Daemon Mode	JSON-RPC over stdin/stdout for low-latency reuse
CLI + Config	Full argparse interface, YAML/env config, health checks

Zero cloud calls after initial model pull. Your data never leaves your machine.

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🚀 Quick Start

Prerequisites

• Python 3.10+
• Ollama installed and running
• Pull a model: ollama pull qwen2.5:7b

Install

pip install -e .
# or from PyPI (when published)
pip install fcht-agent

Verify Installation

fcht-agent doctor
# ✓ Ollama: http://localhost:11434
# ✓ Target model 'qwen2.5:7b' available
# ✓ All memory dirs exist
# All checks passed ✓

Run a Task

# One-shot
fcht-agent run "What is the project name? Use retrieve."

# JSON output for scripting
fcht-agent --json run "List skills in memory/skills"
# {"result": "...", "episodes_added": 1, "steps_taken": 2, "success": true}

Daemon Mode (Fast, Model Stays Loaded)

# Terminal 1: start daemon
fcht-agent-daemon
# {"jsonrpc": "2.0", "method": "ready", "params": {"model": "qwen2.5:7b"}}

# Terminal 2: send requests
echo '{"task": "What is the project name?", "id": "req-1"}' | fcht-agent-daemon
# {"id": "req-1", "result": {"output": "...", "episodes_added": 1, "steps_taken": 2, "success": true}}

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🧠 Memory Architecture

memory/
├── episodes.jsonl          # Episodic: few-shot conversation history
├── chroma/                 # Semantic: ChromaDB vector store
│   ├── chroma.sqlite3
│   └── ...
└── skills/                 # Procedural: learned Python tools
    ├── hello.py
    ├── greeting.py
    └── fibonacci.py

Memory Type	Storage	Use Case
Episodic	JSONL (append-only)	Few-shot prompting, conversation continuity
Semantic	ChromaDB + embeddings	Fact retrieval, RAG, similarity search
Procedural	Python files in skills/	Learned procedures, reusable tools

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🛠 Built-in Tools

Tool	Description	Example
shell	Run shell commands	{"tool": "shell", "args": {"cmd": "ls -la"}}
read_file	Read file contents	{"tool": "read_file", "args": {"path": "config.yaml"}}
write_file	Write file contents	{"tool": "write_file", "args": {"path": "out.py", "content": "print(1)"}}
python_exec	Execute Python code	{"tool": "python_exec", "args": {"code": "print(2+2)"}}
list_files	List directory	{"tool": "list_files", "args": {"path": "memory/skills"}}
remember	Store in semantic memory	{"tool": "remember", "args": {"text": "API key is xyz", "metadata": {"type": "secret"}}}
retrieve	Search semantic memory	{"tool": "retrieve", "args": {"query": "API key", "n_results": 3}}

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⚙️ Configuration

YAML Config (config.yaml)

ollama:
  host: "http://localhost:11434"
  model: "qwen2.5:7b"
  temperature: 0.1
  timeout: 120

memory:
  episodes_path: "memory/episodes.jsonl"
  max_few_shot: 3
  chroma_path: "memory/chroma"
  embed_model: "all-MiniLM-L6-v2"
  embed_cache_dir: "~/.cache/fcht-agent/embeddings"

agent:
  max_steps: 10
  system_prompt: |
    You are a precise, helpful assistant. Use tools to accomplish tasks.
    Output ONLY JSON tool calls. When done, respond with 'DONE: <result>'.

daemon:
  enabled: true
  host: "127.0.0.1"
  port: 8765

Environment Variables (override any setting)

FCHT_OLLAMA__MODEL=llama3.1
FCHT_MEMORY__MAX_FEW_SHOT=5
FCHT_AGENT__MAX_STEPS=15

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🧪 Testing

# Run all tests
pytest tests/ -v

# Memory tests
pytest tests/test_memory.py -v

# Agent tests
pytest tests/test_agent.py -v

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📦 Project Structure

fcht_agent/
├── __init__.py              # Exports: FCHTAgent, AgentConfig, __version__
├── config/schema.py         # Pydantic settings (YAML/env/file)
├── core/agent.py            # ReAct loop + Ollama client + tools
├── memory/
│   ├── episodic.py          # JSONL few-shot memory
│   ├── semantic.py          # ChromaDB + cached embeddings
│   └── embeddings.py        # Singleton embedding model
├── tools/registry.py        # 7 built-in tools
├── cli/main.py              # argparse CLI: run, config, doctor, version
├── daemon/server.py         # JSON-RPC stdin/stdout daemon
tests/
├── test_memory.py           # Memory module tests
├── test_agent.py            # Agent integration tests
setup.py                     # Setuptools config

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📄 License

MIT License — see LICENSE for details.

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🤝 Contributing

1. Fork the repo
2. Create a feature branch
3. Add tests for new functionality
4. Ensure pytest tests/ passes
5. Submit PR

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🙋 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Security: [e-mail] (PGP key available)

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Built with ❤️ for local-first AI. Runs on a GTX 1080 Ti (11GB) with qwen2.5:7b.