Best AI Agent Memory & RAG Tools (2026)
Memory layers, knowledge graphs, vector databases and RAG frameworks that give AI agents long-term memory and access to your documents. Below are the top 20 of 40 matching tools in the AgentScout directory, ranked.
How we ranked: Ranked by GitHub stars as recorded on each AgentScout listing (link checks 2026-10-18). Tools without a tracked public repo come after, in alphabetical order. Stars measure developer popularity, not quality or fit for your job, so read each listing before you choose. No paid placements.

1. MarkItDown
Microsoft's Python tool and MCP server that converts PDFs, Office documents, images, audio and web pages to Markdown for LLM and agent pipelines.
Pricing (as stated): Open source · Mature

2. Claude-Mem
Gives coding agents memory across sessions by capturing what they do, compressing it and feeding the relevant context back in later.
Pricing (as stated): Open source · Mature

3. RAGFlow
Open-source RAG engine with deep document parsing and agent features for building grounded question-answering over your files.
Pricing (as stated): Open source + cloud · Mature

4. Headroom
Library, proxy and MCP server that compresses tool outputs, logs, files and RAG chunks locally before they reach the LLM, cutting tokens for coding agents.
Pricing (as stated): Open source · Mature

5. Mem0
Memory layer for AI agents.
Pricing (as stated): Open source + paid cloud · Mature

6. MemPalace
Local-first AI memory system that stores conversations verbatim with a pluggable backend and serves recall to agents without API calls.
Pricing (as stated): Open source · Mature

7. Hindsight
Agent memory system that stores experiences and learns from them so agents improve over time.
Pricing (as stated): Open source + cloud · Mature

8. OpenViking
Context database for AI agents that keeps memory, knowledge and skills in one inspectable virtual filesystem agents can navigate.
Pricing (as stated): Open source · Mature

9. PageIndex
Document index for reasoning-based, vectorless RAG that lets agents navigate long documents like a table of contents.
Pricing (as stated): Open source + cloud · Mature

10. Cognee
Open-source memory platform that builds knowledge graphs so agents keep long-term memory.
Pricing (as stated): Open source + cloud · Mature

11. Graphiti
Builds real-time, temporal knowledge graphs that serve as memory for AI agents.
Pricing (as stated): Open source · Mature

12. Supermemory
Memory and context engine for agents with an API, app and MCP server, which can also run fully locally.
Pricing (as stated): Open source + cloud · Mature

13. AgentMemory
Persistent memory layer for AI coding agents.
Pricing (as stated): Open source · Mature

14. Chroma
Open-source search and vector database widely used for agent memory and RAG.
Pricing (as stated): Open source + cloud · Mature

15. TencentDB-Agent-Memory
Team-level memory hub that turns conversations, docs and code into shared memory for agents.
Pricing (as stated): Open source · Mature

16. llmware
Framework for building private, local RAG and agent applications with small specialized models on AI PCs, edge and self-hosted setups.
Pricing (as stated): Open source · Mature

17. memU
Lightweight memory system that gives Codex, Claude Code, Cursor and other agents a shared memory across sessions and devices and distills reusable skills.
Pricing (as stated): Open source · Mature

18. EverOS
Local-first, portable memory layer for AI agents stored as Markdown.
Pricing (as stated): Open source · Mature

19. txtai
All-in-one Python framework for semantic search, LLM orchestration and agents built on an embeddings database of vector, graph and relational indexes.
Pricing (as stated): Open source · Mature

20. MemOS
Memory operating system for LLMs and agents with persistent memory and retrieval.
Pricing (as stated): Open source · Mature
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Related: Memory for agents · RAG and documents for agent memory · AI agent memory tools
Related searches: AI agent memory tools · Free AI document and RAG tools
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Facts come from our directory listings; pricing is as each maker states it and GitHub stars are as recorded on each listing. Check official sites before you choose.