Features

This chapter covers the core features and capabilities of AI Agent Framework.

Features

• ReAct loop — Thought → Action → Observation reasoning loop (same pattern as LangChain/AutoGPT)
• Tool system — Register custom tools with parameter schemas and callable functions
• Memory manager — Sliding-window context management with intelligent trimming
• Output parser — Parses structured Thought/Action/Final Answer format from LLM responses
• Built-in tools — Search, calculator, and JSON formatter included as examples
• LLM-agnostic — Abstract LLMInterface — wire to OpenAI, Anthropic, or any API
• Safety limits — Configurable max_steps prevents infinite loops
• Full trace — Every step recorded with thought, action, observation
• Config export — Export agent configuration as JSON

Quick Start

# Run the demo with a simulated agent
python src/ai_agent_framework.py --demo

# List all registered tools
python src/ai_agent_framework.py --list-tools

# Export agent config
python src/ai_agent_framework.py --export-config my_agent.json

# Run agent on a task (uses simulated LLM)
python src/ai_agent_framework.py --task "What is the population of Helsinki?"

Project Structure

Follow this guide to get AI Agent Framework up and running in your environment.

Project Structure

ai-agent-framework/
├── README.md
├── LICENSE
├── src/
│   └── ai_agent_framework.py    # Core engine (~420 lines)
└── examples/
    ├── basic_usage.py             # Build a custom agent
    └── custom_tools.json          # Tool definition examples

CLI Reference