Open Source AI Memory Apps
Self-hostable AI memory layers that give agents and LLM apps persistent context across sessions. Compare open source options by repo health and license.
Apps in AI Memory
Language
License
supermemory
AI Development · AI Memory · Note Taking
The state-of-the-art memory and context engine for AI agents — ranked #1 on all three major AI memory benchmarks.
claude-mem
AI Agents · AI Development · AI Memory
Give your AI coding agents persistent memory — claude-mem captures everything that happens during sessions, compresses it with AI, and surfaces the right context in every future session automatically.
MemPalace
AI Agents · AI Development · AI Memory
Local-first AI memory with verbatim storage, pluggable backends, and 96.6% retrieval recall on LongMemEval — no API key required.
OpenViking
AI Development · AI Memory · Databases
An open-source context database that gives AI agents a unified filesystem for memory, resources, and skills with hierarchical tiered retrieval.
knowhere
AI Development · AI Memory · Developer Tools
Transform messy, unstructured documents into persistent, navigable memory that AI agents can actually use.
NornicDB
AI Development · AI Memory · Databases
A single graph+vector+temporal database for AI workloads — Neo4j-compatible, sub-millisecond hybrid search, and built-in memory decay.
agentmemory
AI Agents · AI Memory
Persistent memory for AI coding agents — built on the iii engine, it works across Claude Code, GitHub Copilot CLI, Cursor, Gemini CLI, Codex CLI, and any MCP client, so agents stop needing everything re-explained every session.
Hippo
AI Agents · AI Memory
A biologically-inspired memory layer for AI coding agents — memories decay by default and strengthen through use, modeled on the hippocampus, with zero runtime dependencies and a SQLite backbone.
byterover-cli
AI Agents · AI Code Assistants · AI Memory
A portable memory layer for AI coding agents — curate structured project knowledge into a version-controlled context tree that syncs across tools, machines, and teammates.
mnemo
AI Agents · AI Development · AI Memory
A local-first AI memory sidecar that extracts entities, builds a persistent knowledge graph, and injects ranked context into any LLM pipeline — no cloud required.
About AI Memory
AI Memory tools give AI agents and LLM applications a place to keep what they learn. A language model starts every session with an empty context window. A memory layer stores facts, preferences, decisions and past conversations, then retrieves the relevant pieces when the agent needs them. Without one, every session begins by re-explaining the project.
Apps in this category take a few different approaches. Memory layers sit beside any agent through an API or an MCP server, extract facts from conversations and inject ranked context back into prompts. Context databases organize memory, documents and skills into a structure an agent can browse, with tiered retrieval to keep token usage down. A third group targets AI coding agents specifically, capturing what happened in each session so Claude Code, Cursor or Codex pick up where they left off. Storage ranges from a single SQLite file to graph and vector databases with built-in memory decay.
When choosing, look at where the data lives (local-first or a hosted service), how memories are retrieved (vector search, a knowledge graph, or both), whether it connects to your agent through MCP or an SDK, and how it handles information that goes stale. Self-hosting a memory layer keeps conversation history and project knowledge on infrastructure you control, which matters when that history includes source code, customer data or internal decisions.
