RAG · Search

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

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

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

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

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.

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

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

It replaces flat vector dumps with a four-tier semantic pyramid and Mermaid symbol graphs so agents remember workflows without drowning in their own tool logs.

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

It turns your document pile into a persistent, interlinked wiki so the LLM doesn't have to re-read everything every time you ask a question.

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

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

GBrain gives AI agents a persistent memory, synthesizing cited answers from your notes and wiring a knowledge graph autonomously while you sleep.

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

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

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.

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

It exists because AI context windows reset between conversations; Supermemory extracts facts, tracks changes, and recalls them later so agents don't start from scratch every time.

MemPalace is a local-first AI memory system that stores conversations verbatim and retrieves them with semantic search backed by published, reproducible benchmarks.