Graphify-Labs/graphify
Graphify turns any codebase, including documentation and other files, into a queryable knowledge graph using local AST parsing, without relying on vector stores or embeddings.
Awesome AI Agents › Knowledge Graph Orchestration
zjunlp/AutoAct
AutoAct is an innovative framework designed for automatic agent learning specifically tailored for question answering (QA) tasks through a self-planning approach. The project addresses the challenges faced by existing language agent systems, such as reliance on costly, non-reproducible annotated data and the difficulty of using a single model for multiple functions. AutoAct overcomes these limitations by not depending on large-scale annotated datasets or synthetic trajectories generated by closed-source models like GPT-4. Instead, it uses a tool library and limited data to automatically synthesize planning trajectories without human intervention or assistance from strong closed-source models. The framework employs a division-of-labor strategy to differentiate the Meta-Agent into multiple sub-agents, each specializing in distinct functionalities to collaboratively complete complex tasks. These sub-agents include the Plan-Agent, which handles task decomposition and tool invocation planning; the Tool-Agent, responsible for executing tool invocations with appropriate parameters; and the Reflect-Agent, which reflects on historical trajectories to improve performance. AutoAct supports self-instruction to generate sufficient task data for training and uses automatic tool selection to identify relevant tools for each task. The system is compatible with various large language models (LLMs) and demonstrates competitive or superior performance compared to strong baseline methods. The project provides comprehensive installation instructions, usage examples for self-instruct data generation, self-planning modules including tool selection, trajectory synthesis, self-differentiation, and group planning for evaluation. It also offers pre-synthesized trajectories and prompts for reproducibility and further research. AutoAct is a significant contribution to the field of language agents, enabling more efficient, scalable, and autonomous learning for QA tasks without heavy reliance on external annotated data or proprietary models.
https://github.com/zjunlp/AutoAct
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