Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
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Open Source AI Platform - AI Chat with advanced features that works with every LLM
A powerful MCP toolkit for coding, providing semantic retrieval and editing capabilities - the IDE for your agent
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
- ai
- python
- natural-language-processing
- react
[MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.
Auto Ontology is a Python framework for building semantic, graph-based reasoning layers over data. It enables deterministic query generation, better context retrieval, and more explainable AI for LLM and agent workflows.
Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read
Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 14 runtimes: Claude Code, Codex CLI, Gemini CLI, OpenCode, Copilot, Kiro and more.
- ai-agent
- agent-os
- llm-orchestration
- agentic-ai
Your Personal Librarian Agent. Ask anything on your computer, organize files, remember important info and files.
- document-parser
- embeddings
- llm
- open-source
The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs
- ai
- ast
- claude-code
- code-analysis
OpenRAG is a comprehensive, single package Retrieval-Augmented Generation platform built on Langflow, Docling, and Opensearch.
Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.
LeanCTX is the Context Gateway for AI Systems. Select task-relevant context, apply supported controls, connect local knowledge and inspect recorded evidence. Open-source Engine: CLI, MCP, hooks and supported proxy paths.
[ICML'26] SimpleMem: Efficient Lifelong Memory for LLM Agents — Text & Multimodal
Universal memory layer for AI Agents. It provides scalable, extensible, and interoperable memory storage and retrieval to streamline AI agent state management for next-generation autonomous systems.
- ai
- memory
- memory-management
- python
Symbol-level code retrieval for AI agents: fetch the function you need, not the whole file. Runs locally. 96.2% fewer tokens per lookup on average than grep-and-read on our 15-lookup benchmark. Works with Claude Code, Cursor, Codex and any MCP client. Free for non-commercial use; commercial use needs a one-time license.
AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scenarios, 5 specialist agents (IoT, FMSR, TSFM, Work Order,...), and multi-agent orchestration blueprints (MetaAgent, AgentHive) over MCP.
- llm-agents
- model-context-protocol
- time-series-forecasting
- condition-based-maintenance
Multi-engine MCP server, CLI, and local daemon for agent web search and content retrieval — skill-guided workflows, no API keys.
Build agentic systems. Run them with confidence. Orchestrate agents, automate business processes, inspect every execution, and keep humans in control. Deploy Heym on your own infrastructure.
Bitemporal AI memory system: six-variable decay, time-graded recall, TTL expiry, dedupe, competitive retrieval (Neo4j + PostgreSQL + Redis)
- agent-memory
- ai
- bitemporal
- graphiti
Committed to building the world’s most cutting-edge, CJK-first memory module for all mainstream agents. Any issues and PRs are welcome.
- agent-memory
- agent-skills
- mcp
- memory
Security-hardened MCP server for Docker with read-only defaults, restart deny-lists, and secret redaction
Auto Ontology is a Python framework for building semantic, graph-based reasoning layers over data. It enables deterministic query generation, better context retrieval, and more explainable AI for LLM and agent workflows.