RAG · Search

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

It builds a persistent structural map of your codebase so AI assistants read only the files in a change's blast radius instead of the whole repo.

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

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

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

Official Jupyter notebooks demonstrating how to wire Claude into production tasks like RAG, SQL queries, and multimodal pipelines.

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

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

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

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

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

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

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

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

It exists because pure vector search often misses the big picture, so LightRAG auto-maps entities and relationships into a knowledge graph for richer retrieval.

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

Graphiti continuously ingests structured and unstructured data into temporal context graphs so agents can query what is true now—or was true at any specific moment—without rebuilding the graph from scratch.

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

Langflow is a visual schematic editor for AI workflows that deploys your graphs as APIs or MCP servers, saving you from writing yet another Python script to chain LLM calls together.

OpenDataLoader PDF exists to extract structured data from PDFs for AI pipelines while auto-tagging untagged documents for screen readers, all without proprietary dependencies.