A Python-based Agentic RAG system that uses local LLMs (Ollama) for intelligent documentation crawling, summarization, and vector embeddings (Supabase). Features a self-correcting agentic loop that dynamically refines answers through tool usage and validation, providing a robust, local solution for accurate information retrieval and generation.
Cross-referenced across 55 tracked directories
#606
Popularity Rank
1 / 55
Listed In
Emerging
Adoption Stage
1/8/2026
First Seen
1
GitHub Stars
Score: 100/100
0 dependency vulnerabilities found
Run an AI-powered security scan to analyze this package's source code for vulnerabilities, prompt injection vectors, data exfiltration risks, and behavior mismatches.
Scans fetch actual source code from the GitHub repository, not just the README.
Shy2593666979
AgentChat 是一个基于 LLM 的智能体交流平台,内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作,Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、Milvus 和 ElasticSearch 等技术,实现高效的知识检索与工具调用,使用 FastAPI 构建高性能后端服务。
...morecall518
🔍Professional MCP server for PostgreSQL operations & monitoring: 30+ extension-independent tools for performance analysis, table bloat detection, autovacuum monitoring, schema introspection, and database management. Supports PostgreSQL 12-17.
...moreagentailor
Production-ready Next.js template for building AI agents with LangGraph.js. Features MCP integration for dynamic tool loading, human-in-the-loop tool approval, persistent conversation memory with PostgreSQL, and real-time streaming responses. Built with TypeScript, React, Prisma, and Tailwind CSS.
...moreCanner
Data API Framework for AI Agents and Data Apps
1/8/2026
Last Commit
Recently added to the ecosystem