RAG · Search

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.

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

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

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

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

It catalogs 42 advanced RAG techniques as runnable notebooks so developers can compare chunking, reranking, and graph retrieval strategies without wiring up every pipeline from scratch.

GraphRAG exists to give LLMs a structured memory layer for reasoning over messy, private narrative text.

Quivr wraps retrieval-augmented generation in a Python library with YAML-configurable workflows, letting you swap LLMs and vector stores without rewriting plumbing.

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

Minds Platform exists so teams can self-host autonomous AI agents and semantic search without shipping data to a managed cloud.

A privacy-focused AI answering engine that runs entirely on your own hardware so your searches never leave the building.

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.

Onyx bundles chat, RAG, agents, and sandboxed code into a single self-hosted interface so teams can use any LLM without shipping data to a SaaS black box.

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

It centralizes model definitions so the same architecture works across PyTorch, JAX, vLLM, and llama.cpp without rewrites.

STORM simulates expert research conversations so LLMs can write long, cited articles from scratch.

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

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

Milvus is a Go/C++ vector database built for billion-scale ANN search with Kubernetes-native horizontal scaling.