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LightAgent

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Multi-Agent Platforms & Frameworks
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Basic Information

LightAgent is an open source, production-oriented agent development framework designed to let developers build lightweight, autonomous AI agents with memory, tool integration, and structured reasoning. It provides a compact Python implementation that supports detachable memory modules such as mem0, an adaptive tools mechanism, a Tree of Thought planning module, and a simple multi-agent collaboration layer called LightSwarm. The project targets integration with many large models and protocols, including OpenAI, ChatGLM, DeepSeek, Qwen series, and MCP via stdio and SSE. It also supports OpenAI-style streaming outputs for seamless use with chat frameworks. The repository includes examples, quick start instructions, and a tool generator for automatically creating Python tools from API descriptions. The emphasis is on minimal dependencies, fast deployment, and extensibility for building self-learning, tool-enabled agents.

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App URL
https://github.com/wxai-space/LightAgent
Github Repository
https://github.com/wxai-space/LightAgent/blob/main/README.md

App Details

Features
Lightweight core: compact pure-Python codebase intended for quick deployment without heavy external frameworks. Memory support: detachable mem0 integration for automated long-term context and per-user memory management. Tools: unlimited custom Python tools with parameter typing and an AI-driven tool generator to produce API call tools automatically. Tree of Thought: built-in ToT module for task decomposition, multi-step reasoning, and planning. Multi-agent collaboration: LightSwarm for simple swarm-like agent coordination and task delegation. Adaptive tool mechanism: candidate tool selection to filter irrelevant tools and reduce token usage. Multi-model support: compatibility with OpenAI, ChatGLM, DeepSeek, Qwen and others. Streaming API: supports OpenAI streaming format output. Observability: optional Langfuse log tracking for monitoring calls and metrics.
Use Cases
LightAgent helps developers and teams build intelligent, tool-enabled agents faster by combining memory, planning, and tool orchestration in a lightweight package. It automates context memory and retrieval so agents can maintain multi-turn dialogue consistency and self-learn from interactions. The tool system and generator speed integration of external APIs and services, while the adaptive tool filter lowers token costs and improves response relevance. Tree of Thought and multi-agent features enable handling of complex, multi-step tasks and distributing work across agents. Streaming output and broad model compatibility make it easy to plug agents into chat systems and production services. The project includes examples, quick start pip installation, and integration hooks for monitoring, making it practical for prototypes and production deployments.

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