OpenMembrane

OpenMembrane is the intelligent membrane for AI coding memory. It autonomously reads and learns from your coding sessions — you never have to tell it what to save. It selectively absorbs project knowledge, blocks secrets, filters noise, resolves conflicts, and persists only what matters.

No manual effort. No data leaves your machine unless you choose it. Safe, private, and trustworthy by design.

Table of Contents

• Installation

• Configuring Your AI Tool

  • Claude Desktop

  • Claude Code

  • Codex

  • VS Code / GitHub Copilot

  • Cursor

  • OpenCode

• Automatic Memory Capture

• Deployment Modes

• Environment Variables

• MCP Tools

• Architecture

• Diagnostics And Errors

• Static Fallback Files

• Development

• Documentation

• 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 Private by default — memory is stored locally in git-ignored .openmembrane/

• Tool-agnostic — dedicated adapters for OpenCode, Claude Code, Codex and VS Code/Copilot, plus MCP for Cursor, Claude Desktop and others

Related MCP server: total-agent-memory

Installation

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

npx openmembrane

Or install globally:

npm install -g openmembrane
openmembrane

No cloud account is required for the default Local Private mode, which stores memory locally in git-ignored .openmembrane/.

The local daemon has explicit lifecycle commands:

openmembrane serve   # foreground
openmembrane start   # detached
openmembrane status
openmembrane stop

Bare openmembrane and openmembrane mcp both launch MCP stdio. CLI data commands and the review UI use the authenticated local daemon. Run openmembrane start first; these surfaces report an error if the daemon is unavailable and never fall back to opening stores themselves. MCP uses the same client and auto-starts the daemon on the first tool call, then reuses it across calls and sessions. Listing tools does not start it. See Daemon Protocol for transport, authentication, and per-project request settings.

CLI and local review

Run these commands from your project directory. --project <id> overrides OPENMEMBRANE_PROJECT_ID; OPENMEMBRANE_HOME selects project storage, not the per-user daemon endpoint.

openmembrane ingest --file session.txt --tool codex
openmembrane ingest --stdin --tool claude-code --session-id session-123
openmembrane context --query "deployment rules" --output markdown
openmembrane candidates list --output json
openmembrane candidates approve <id>
openmembrane candidates reject <id> --reason "Obsolete"
openmembrane candidates approve-all
openmembrane candidates reject-all
openmembrane memory update <id> --content "Updated project rule"
openmembrane memory supersede <id> --reason "Replaced" --replacement-id <new-id>
openmembrane memory stale --stale-after-months 6
openmembrane export --targets agents,claude --preview
openmembrane export --targets agents,claude --output-dir generated
openmembrane diagnostics --severity warning --limit 100
openmembrane audit --limit 100
openmembrane team configure --owner my-team --repo private-memory
openmembrane team status

All data commands support --output text|json|markdown. Ingest defaults to JSON for compatibility; the others default to text. openmembrane --help lists command-specific filters and options. Bulk approval skips candidates that cannot be approved, including secrets, and reports each skipped ID. GitHub Team mutations report publication/retry state rather than implying that an unmerged proposal is already active.

From this repository, npm run review-ui -- --no-open starts the loopback review UI. It provides memory search, editing and superseding, candidate review (individual and bulk), audit, diagnostics, stale-memory review, and static-export preview/write. Preview never writes files. Export excludes confidential memories unless you explicitly opt in.

Configuring Your AI Tool

Pick the integration for your tool. OpenCode, Claude Code and Codex adapters capture sessions through the local daemon; retrieval and stored-memory egress to the model provider require the opt-ins described in each guide (Claude Code and Codex also need separate MCP setup for retrieval). VS Code cannot capture ordinary Copilot chats automatically: use explicit export capture, or opt-in auto-save for the extension's own participant exchanges. MCP is the fallback for tools without an adapter. The CLI is required for every integration except VS Code, whose extension bundles it (npm install -g openmembrane).

The MCP server runs over stdio and auto-starts the daemon on its first tool call. Add it to your AI tool's MCP configuration:

Claude Desktop

Edit claude_desktop_config.json:

{
  "mcpServers": {
    "openmembrane": {
      "command": "npx",
      "args": ["openmembrane"]
    }
  }
}

Claude Code

claude mcp add openmembrane -- npx openmembrane

Codex

For automatic session capture with native hooks and startup retrieval instructions, follow Codex setup:

npm install -g openmembrane
npx @openmembrane/adapter-codex install

Restart Codex and review/trust the hook with /hooks. MCP retrieval is configured separately and requires explicit permission for stored-memory egress.

VS Code / GitHub Copilot

The VS Code extension bundles the CLI, starts the local daemon and registers MCP retrieval without manual configuration. It also adds @openmembrane /rules, /recall, /review, candidate review, export and diagnostics commands, and explicit chat export capture. Stable VS Code APIs do not expose all Copilot sessions; opt-in auto-save covers only the extension's own participant exchanges. See VS Code setup for VSIX installation.

Alternatively, add to .vscode/mcp.json in your project:

{
  "servers": {
    "openmembrane": {
      "command": "npx",
      "args": ["openmembrane"]
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project:

{
  "mcpServers": {
    "openmembrane": {
      "command": "npx",
      "args": ["openmembrane"]
    }
  }
}

OpenCode

For automatic session capture and opt-in context injection, follow OpenCode plugin setup. It covers installation, explicit consent to send stored memory to OpenCode's model provider, extraction configuration, and uninstall instructions.

For optional MCP tools, add to ~/.config/opencode/opencode.json:

{
  "mcp": {
    "openmembrane": {
      "type": "local",
      "command": ["npx", "-y", "openmembrane"]
    }
  }
}

See .opencode/INSTALL.md for detailed setup including global instructions and development-from-source configuration.

Automatic Memory Capture

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.md with instructions for the AI to:

• Call get_project_rules, get_relevant_context, and list_memory_candidates at the start of each session.

• Use approve_memory_candidate or reject_memory_candidate after the developer decides what to retain.

• Call remember proactively 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:

See the platform-specific setup guides in docs/setup/ for detailed instructions.

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

Deployment Modes

OpenMembrane ships Local Private mode by default and GitHub Team mode for shared memory through a dedicated private repository. Self-Hosted Team and Managed Team are planned. Static exported files are separate from team sync; see Deployment Modes for the approved-memory boundary and GitHub pull-request workflow.

Environment Variables

By default, local memory is stored in .openmembrane under 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 pass projectId.

MCP Tools

• remember — save structured memory directly. Provide content, type, and optional scope/tags. No API key needed. Supports single and batch mode.

• 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.

• reject_memory_candidate — reject a pending candidate with an optional reason.

These are the seven mid-session MCP tools. Transcript ingestion, bulk review, memory maintenance, export, diagnostics, audit, and team configuration are user operations available through the CLI and review UI. See MCP migration for each removed tool's replacement.

Architecture

OpenMembrane supports two paths for saving memory:

1. remember (primary): The AI tool calls remember directly 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.

2. openmembrane ingest (secondary): An adapter submits a full session transcript through the CLI or daemon ingestion endpoint for server-side LLM extraction. Requires an explicitly configured daemon extractor (OpenAI or compatible provider).

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

Package responsibilities:

• 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/service: project-scoped operations, storage lifecycle, and GitHub Team synchronization owned by the daemon.

• packages/protocol: versioned daemon HTTP schemas and shared endpoint resolution.

• packages/client: authenticated local-daemon client used by MCP, CLI, review UI, and adapters.

• 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.

• apps/daemon: authenticated per-user HTTP daemon with project-scoped stores and CLI lifecycle management.

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

interface MemoryExtractor {
  extract(input: SessionInput): Promise<MemoryCandidate[]>;
}

The MockMemoryExtractor is used for deterministic testing. The LlmMemoryExtractor supports OpenAI and any compatible API endpoint (via baseUrl).

Diagnostics And Errors

OpenMembrane distinguishes audit history from diagnostics:

• Audit events describe normal memory activity, such as session ingestion, candidate extraction, saved memory, queued candidates, and rejected candidates.

• Diagnostics describe operational problems, such as validation errors, missing candidates, invalid local JSON stores, unsafe approval attempts, and export failures.

MCP tools return safe user-facing error payloads with a diagnosticId, preserving the daemon's ID and safe message. Inspect project diagnostics with openmembrane diagnostics or the review UI, and daemon failures in the local daemon log. Client-side failures are correlated on MCP stderr, never stdout.

Static Fallback Files

Static exporters can generate:

• AGENTS.md

• CLAUDE.md

• .github/copilot-instructions.md

• .cursor/rules/openmembrane.mdc

• docs/ai/project-memory.md

These files are compatibility fallbacks for tools that cannot retrieve memory through MCP. By default, exporters omit confidential memories because these files may be committed to source control. Callers must explicitly opt in to include confidential memory.

Development

git clone https://github.com/mohamadalhusseinie/openmembrane.git
cd openmembrane
npm install

Run the MCP server locally (from source via tsx):

npm run mcp:stdio

Run tests and type checking:

npm test          # vitest
npm run typecheck # tsc --noEmit
npm run check     # both

Build the publishable bundle:

npm run build

Documentation

• Setup guides — per-tool installation for OpenCode, Claude Code, Codex, VS Code/Copilot and Cursor

• Daemon Protocol — transport, authentication and lifecycle

• Architecture — pipeline design, type schemas, MCP tool surface, package dependencies

• Security and Privacy — secret handling, data storage rules, LLM usage policy

• Product Vision — product thesis, UX workflow, memory quality criteria

• Deployment Modes — current Local Private and GitHub Team modes, plus planned team modes

• Roadmap — phased delivery plan for Local Private, GitHub Team, and planned team modes

• Contributing — setup, development workflow, PR guidelines