Local-first knowledge-graph memory layer for AI agents and humans, exposed entirely via MCP. Conversations and notes are stored as plain Markdown files; observations and wikilinks compound into a semantic graph over time. Designed to work with any AI client or IDE that speaks MCP — Claude, Copilot, Cursor, and others. A team cloud tier (basicmemory.com) provides shared workspaces.
- Storage
- Markdown files on local disk are the source of truth — every note, observation, and entity is a human-readable file. Wikilinks between notes build a knowledge graph that accumulates automatically as agents write memories. All writes go through MCP tools so no direct file-system access is required from the agent.
- Retrieval
- Semantic search and graph traversal via MCP tools. Agents query the knowledge graph through a standardised MCP server; the server resolves wikilinks to expand context and uses embedded similarity for fuzzy recall. The local setup requires no external embedding API (uses a bundled model).
- Self-host
- Self-host: trivial
- License
- AGPL-3.0
- Pricing
- Open-source AGPL-3.0, free to self-host (`uv tool install basic-memory`). A cloud-hosted team workspace (basicmemory.com) is available with a free trial; cloud pricing not publicly listed. · Free + paid
- GitHub stars
- 3,330
- Last release
- 2026-06-13
- Last commit
- 2026-06-25
- First catalogued
- 2026-06-28
Strengths
- Zero-infra local-first install (single `uv` command); runs entirely on local disk with no external API keys required
- MCP-native: works with any agent or IDE that supports the Model Context Protocol out of the box
- Human-readable Markdown + wikilink graph — memories are inspectable and editable by both humans and AI
- Active development with frequent releases; 3330+ stars; AGPL-3.0 open-source core
Watch out
- AGPL-3.0 license may require open-sourcing application code in commercial products that incorporate it
- Team/cloud tier (basicmemory.com) pricing is not publicly listed — evaluate licensing before committing to the hosted path
- Knowledge graph quality depends on consistent MCP-tool usage; agents that bypass MCP and write files directly will miss graph updates
Best for
- Individual developers and small teams wanting persistent cross-session memory for AI coding assistants with zero infrastructure
- Projects where human-readable memory files and direct editing are a design requirement
Benchmark results
No sourced results yet.
Sources
- Basic Memory README (vendor)
- GitHub API repo metadata (stars, AGPL-3.0, v0.22.1 release) (third-party)
Last verified 2026-06-28 · updated by discover-frameworks