LightAgent
LightAgent: Lightweight AI agent framework with memory, tools & tree-of-thought. Supports multi-agent collaboration, self-learning, and major LLMs (OpenAI/DeepSeek/Qwen). Open-source with MCP/SSE protocol integration.
Install / Use
/learn @wanxingai/LightAgentREADME
LightAgent is an extremely lightweight active Agentic Framework with memory (mem0), tools (Tools), and a tree of thought (ToT), and it is completely open source. It supports simpler multi-agent collaboration than OpenAI Swarm, allowing you to build self-learning agents in just one step, and supports connecting to the MCP protocol via stdio and sse. The underlying models support OpenAI, Zhiyuan ChatGLM, DeepSeek, Jieyue Xingchen, Qwen Tongyi Qianwen large models, and more. At the same time, LightAgent supports OpenAI streaming format API service output, seamlessly integrating with major mainstream Chat frameworks. 🌟
✨ Features
- Lightweight and Efficient 🚀: Minimalist design, quick deployment, suitable for various application scenarios. (No LangChain, No LlamaIndex) 100% Python implementation, no additional dependencies, core code is only 1000 lines, fully open source.
- Memory Support 🧠: Supports custom long-term memory for each user, natively supporting the
mem0memory module, automatically managing user personalized memory during conversations, making agents smarter. - Autonomous Learning 📚️: Each agent possesses autonomous learning capabilities, and admins with permissions can manage each agent.
- Tool Integration 🛠️: Support for custom tools (
Tools) and MCP tool integration, flexible expansion to meet diverse needs. - Complex Goals 🌳: Built-in Tree of Thought (
ToT) module with reflection, supporting complex task decomposition and multi-step reasoning, enhancing task processing capabilities. - Multi-Agent Collaboration 🤖: Simpler to implement multi-agent collaboration than Swarm, with built-in LightSwarm for intent recognition and task delegation, enabling smarter handling of user input and delegating tasks to other agents as needed.
- Independent Execution 🤖: Tasks and tool calls are completed autonomously without human intervention.
- Multi-Model Support 🔄: Compatible with OpenAI, Zhipu ChatGLM, Baichuan Large Model, StepFun, DeepSeek, Qwen series large models.
- Streaming API 🌊: Supports OpenAI streaming format API service output, seamlessly integrates with mainstream chat frameworks, enhancing user experience.
- Tool Generator 🚀: Just provide your API documentation to the [Tool Generator], which will automatically create exclusive tools for you, allowing you to quickly build hundreds of personalized custom tools in just 1 hour to improve efficiency and unleash your creative potential.
- Agent Self-Learning 🧠️: Each agent has its own scene memory capabilities and the ability to self-learn from user conversations.
- Adaptive Tool Mechanism 🛠️: Supports adding an unlimited number of tools, allowing the large model to first select a candidate tool set from thousands of tools, filtering irrelevant tools before submitting context to the large model, significantly reducing token consumption.
🧩 Multi-agent troubleshooting (failure map)
If you are using LightSwarm or other multi-agent patterns and start seeing role drift, cross-agent memory issues or confusing logs, you can check the
Multi-agent failure map for a small symptom → mode → debug checklist.
This page is docs-only and does not change any framework code.
News
- <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[2026-02-21] LightAgent v0.5.0 Released: Adds session-level toolset constraints for granular control, fixes tool call history in multi-turn conversations, and improves LightSwarm stability.
- [2026-01-20] LightAgent v0.4.8 Released: Introduces runtime toolset constraints for session-level control and enhanced debug settings.
- [2025-11-15] LightAgent v0.4.7 Released: Improved debug configuration and fixes for LightSwarm-related bugs.
- [2025-10-28] LightAgent v0.4.6 Released: Adds support for model extension parameters (e.g., Qwen3 thinking mode) and enhanced metadata handling.
- [2025-09-16] Our paper is now available as a preprint on arXiv: https://arxiv.org/pdf/2509.09292. We invite the research community to read and cite our work.
- [2025-06-12] We are pleased to announce the official release of LightAgent v0.4.0! This version upgrade brings architectural improvements, with significant enhancements in performance, stability, and maintainability.
- [2025-05-05] LightAgent v0.3.3 Released: Deep Langfuse Logging Integration, Enhanced Context Management and Tool Invocation Stability View
- [2025-04-21] LightAgent v0.3.2 adds an adaptive Tools mechanism, supports unlimited intelligent tool filtering, reduces Token consumption by 80%, and improves response speed by 52%! View
- [2025-04-01] LightAgent v0.3.0 Support browser interaction browser_use and fully supports the MCP protocol, enabling collaborative work with multiple models and tools to achieve more efficient handling of complex tasks.<a href="mcp_release.md">View MCP release introduction.>></a>
- [2025-02-19] LightAgent v0.2.7 supports deepseek-r1 model for tot now.Significantly enhances the multi-tool planning capability for complex tasks.
- [2025-02-06] LightAgent version 0.2.5 is released now.
- [2025-01-20] LightAgent version 0.2.0 is released now.
- [2025-01-05] LightAgent version 0.1.0 is released now.
🚧 Coming Soon
- Agent Collaborative Communication 🛠️: Agents can also share information and transmit messages, achieving complex information communication and task collaboration.
- Agent Assessment 📊: Built-in agent assessment tool for conveniently evaluating and optimizing the agents you build, aligning with business scenarios, and continuously improving intelligence levels.
- Support for Agent Skills ✨: Provides plug-inable skill modules for intelligent agents, enabling rapid expansion of the agent's capability boundaries and supporting the development of custom skills as well as community sharing.
Built-in "Thought Flow"
ToT now supports DeepSeek-R1.
The Thought Flow method effectively addresses challenges in complex scenarios through systematic, structured, and flexible thinking processes. Here are the specific implementation steps:
Problem Definition: Clarify the core problems and objectives.
Information Collection: Systematically gather relevant information and data.
Problem Decomposition: Break down complex problems into multiple subproblems or modules.
Multi-dimensional Analysis: Analyze each subproblem from different angles and levels.
Establish Connections: Identify the relationships and dependencies between subproblems.
Generate Solutions: Propose possible solutions for each subproblem.
Evaluation and Selection: Assess the feasibility and impact of each solution, choosing the best one.
Implementation and Feedback: Implement the selected solution and adjust based on feedback.
🌟 Why Choose LightAgent?
- Open Source and Free 💖: Fully open source, community-driven, continuously updated, contributions are welcome!
- Easy to Get Started 🎯: Detailed documentation, rich examples, quick to get started, easy integration into your project.
- Community Support 👥: An active developer community ready to assist and provide answers at any time.
- High Performance ⚡: Optimized design, efficient operation, meeting high concurrency requirements.
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