RAG · Search

Because making AI agents read Twitter, Reddit, or Bilibili usually means wrestling with a dozen scrapers, API keys, and cookie jars.

RocketRide exists because wiring LLM workflows shouldn't require leaving your IDE or surrendering your data to a cloud dashboard.

Odysseus is a self-hosted AI workspace that replicates the ChatGPT experience on your own hardware while also handling email, calendars, documents, and deep research without shipping your data to third-party clouds.

Persistent memory for coding agents that currently forget everything when the session ends.

Hindsight is an agent memory system that tries to make AI agents learn from experience rather than just retrieving conversation logs.

Graphify builds a queryable knowledge graph from your whole project so AI assistants can answer architecture questions without grepping raw files.

Builds interactive knowledge graphs that teach you a codebase instead of just flexing its complexity.

This repo open-sources a full Chinese textbook on AI agent engineering—complete with Markdown source, compiled PDF, and runnable Python demos for every chapter.

WeKnora exists to turn scattered enterprise documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining wiki.

Cognee gives AI agents persistent memory by treating documents as a living knowledge graph rather than a vector dump.

PageIndex replaces vector similarity search with hierarchical tree reasoning to retrieve complex documents more accurately.

Ruflo turns Claude Code from a solo assistant into a coordinated swarm with shared memory, plugins, and cross-machine federation.

Why rebuild the same RAG pipeline or agent loop from scratch when you can fork a working template instead?

Mem0 gives AI agents a persistent memory layer that accumulates user context across sessions instead of overwriting it every time.

OpenViking replaces fragmented vector-database storage with a filesystem paradigm that unifies agent memory, knowledge RAG, and skills in one browsable hierarchy.

To wrap every LLM backend, RAG pipeline, and enterprise auth scheme into a single self-hosted interface.

It turns images and PDFs into structured JSON and Markdown so your RAG pipeline doesn't have to squint.

RAGFlow fuses deep document parsing with agentic workflows so LLMs can answer from messy corporate documents without making things up.

Docling turns PDFs, Office files, images, and even audio into structured AI-ready formats, entirely on your own hardware.

Because stitching together LLM workflows, RAG, agents, and observability by hand is a full-time job.