Summary

AgentSilex provides a transparent, minimal, and hackable agent framework designed for developers and researchers seeking deep control over AI agent internals. It addresses the complexity and "black box" nature of larger frameworks by offering a ~300-line codebase, enabling easy understanding, customization, and debugging.

How It Works

The framework's core design prioritizes readability and simplicity, allowing the entire codebase to be grasped quickly. It leverages LiteLLM for universal LLM support, enabling seamless switching between over 100 models (including OpenAI, Anthropic, Google Gemini, and local LLMs) with a single line of code. This approach facilitates experimentation and adaptation to various model providers without significant architectural changes.

Quick Start & Requirements

• Installation: pip install agentsilex or uv add agentsilex.
• Prerequisites: Python environment. Specific LLM models may require additional hardware (e.g., GPU) or dependencies not managed by the framework itself.
• Demo: A real-time streaming response demonstration is available.

Highlighted Details

• Universal LLM Support: Integrates with LiteLLM, supporting 100+ LLM providers and models.
• Multi-Agent Handoffs: Enables routing requests between specialized agents for complex workflows.
• OpenTelemetry Tracing: Built-in support for visualizing agent execution, tool calls, and handoffs via compatible backends like Phoenix.
• Hackable Architecture: Minimal ~300-line codebase designed for modification and deep customization.

Maintenance & Community

No specific details on maintainers, community channels (like Discord/Slack), or sponsorship were provided in the README excerpt.

Licensing & Compatibility

The license type and compatibility notes for commercial use were not specified in the provided README excerpt.

Limitations & Caveats

The current roadmap indicates planned features such as asynchronous execution, robust tool call error handling, parallel tool execution, and state persistence, suggesting these are not yet implemented. The framework's minimal nature may require significant development effort for production-ready applications.