OpenMemBrain

Zero-effort— learns from sessions automatically, no commands or prompts needed

Secure by default— secrets are detected and rejected before they ever reach storage

Self-managing— deduplicates, resolves conflicts, and filters noise on its own

Local-first— all memory stays on your machine; optional EU/CH-hosted cloud sync

Tool-agnostic— works with any AI coding tool via MCP (Claude, Copilot, Cursor, OpenCode, and more)

Install and run the MCP server with npx (requires Node.js >= 18):

npm install -g openmembrane openmembrane

No cloud accounts required. All memory is stored locally.

OpenMembrane runs as an MCP server over stdio. Add it to your AI tool's MCP configuration:

Adding the MCP server gives your AI tool access to OpenMembrane's tools. To ensure the AI uses them automatically — loading project memory at session start and saving durable knowledge as it's discovered — add a global instruction file.

Create~/.config/openmembrane/instructions.mdwith instructions for the AI to:

- Callget_project_rules,get_relevant_context, andlist_memory_candidatesat the start of each session.
- Callrememberproactively when durable knowledge is discovered, providing structured content and a type (e.g.,coding_rule,known_gotcha,architecture_decision). No API key needed.

Then wire the file into your tool's global configuration:

Alternatively, runexport_static_memory_filesin any project to generate per-project instruction files (AGENTS.md, CLAUDE.md, etc.) that include both usage instructions and stored memories.

By default, local memory is stored in.openmembraneunder the current working directory. Override this with:

- OPENMEMBRANE_HOME: directory for local JSON memory stores.
- OPENMEMBRANE_PROJECT_ID: default project id when a tool call does not passprojectId.

- remember— save structured memory directly. Provide content, type, and optional scope/tags. No API key needed. Supports single and batch mode.
- propose_memory_from_session— submit a session transcript or summary for server-side LLM extraction. Requires a configured extractor. Useful for automation adapters.
- get_project_rules— retrieve project rules and conventions for the current scope.
- get_relevant_context— find memories relevant to a natural language query.
- search_memory— search saved memories by query, scope, type, or tags.
- list_memory_candidates— list pending memory candidates awaiting approval.
- approve_memory_candidate— approve a pending candidate to save it as memory.
- approve_all_candidates— approve all pending candidates at once.
- reject_memory_candidate— reject a pending candidate with an optional reason.
- reject_all_candidates— reject all pending candidates at once.
- update_memory— update the content, type, scope, or tags of a saved memory.
- supersede_memory— mark a memory as superseded, optionally linking a replacement.
- review_stale_memories— list memories older than a threshold (default: 6 months).
- export_static_memory_files— generate static instruction files (AGENTS.md, CLAUDE.md, etc.).
- get_diagnostics— retrieve diagnostic events filtered by severity or code.
- list_audit_log— retrieve recent audit events.

OpenMembrane supports two paths for saving memory:
-

remember(primary):The AI tool callsrememberdirectly with structured content and type. No server-side LLM needed. Memories go through the full pipeline (secret detection, policy filtering, deduplication) and are auto-saved.

propose_memory_from_session(secondary):An adapter or AI tool submits a full session transcript for server-side LLM extraction. Requires a configured extractor (OpenAI or compatible provider).

remember tool propose_memory_from_session | | v v processStructured() SessionIngestor | -> SecretDetector redaction v -> MemoryExtractor interface MemoryClassifier -> MemoryClassifier -> PolicyEngine -> PolicyEngine -> Deduplicator -> Deduplicator -> ConflictDetector -> ConflictDetector -> ActionRecommender -> ActionRecommender -> MemoryStore or PendingCandidateStore

- packages/core: domain types, extraction interface, policy checks, classification, deduplication, conflict detection, and pipeline orchestration.
- packages/storage: local JSON persistence for saved memory, pending approvals, and audit events.
- packages/exporters: static fallback file generation for AI tools that read project instruction files.
- packages/shared: small runtime helpers for IDs, time, and result types.
- apps/mcp-server: local MCP server exposing saved memory and approval workflows to AI tools.

Provider-specific LLM calls are intentionally kept out of the core. The boundary is:

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