Membral

Membral

A local memory store for coding agents. Every decision and learning it extracts keeps its verbatim quote and links back to the session turn it came from, and your agents search it over MCP.

Membral runs in Docker on your machine. It saves your coding-agent sessions (Claude Code, Codex, opencode, Copilot, Antigravity) in one store, extracts the decisions, learnings and outcomes, and keeps each one tied to its source: the exact words said, and the turn to open (get_code_session with a turn) to read what was around them. An agent can check a recalled fact against the transcript instead of trusting a summary.

How it fits together

flowchart LR
  you([You])

  subgraph inputs["Where your work happens"]
    direction TB
    subgraph agents["Coding agents"]
      direction TB
      cc[Claude Code]
      cx[Codex CLI]
      oc[opencode]
      cp["GitHub Copilot<br/>CLI and VS Code"]
      ag[Antigravity]
    end
    subgraph chats["Chat apps"]
      direction TB
      gpt[ChatGPT]
      cai[Claude.ai]
    end
  end

  cli["membral CLI<br/>hooks · 20-min sweep · import"]
  ext["Chrome extension"]

  subgraph stack["Your machine · membral local up (Docker)"]
    direction TB
    api["Membral app<br/>API · dashboard"]
    mcp["MCP server<br/>memory_search · get_code_session · …"]
    extract["Extraction<br/>runs on a timer"]
    db[("Postgres + pgvector<br/>sessions · turns · memories")]
  end

  llm["Extraction model<br/>Claude login · Ollama 7B+ · API key"]
  emb["Ollama on your machine<br/>nomic-embed-text"]

  you -->|prompts| agents
  you -->|chats| chats
  agents -->|transcripts on disk| cli
  chats -->|export file| cli
  chats -->|open chat pages| ext
  cli -->|upload sessions| api
  ext -->|sync chats| api
  api -->|store| db
  db -->|new sessions| extract
  extract <-->|turns in, records out| llm
  extract -->|decisions · learnings · outcomes| db
  api <-->|text in, vectors out| emb
  agents <-->|MCP tool calls| mcp
  mcp <-->|search| db
  api -->|preamble · prompt lookup| cli
  cli -->|added to the agent's context| agents
  api -->|dashboard| you

  style stack stroke-dasharray: 6 4

In. The membral CLI reads each agent's transcripts from disk (end-of-session hooks and a 20-minute sweep) and uploads them. Chats come in through the Chrome extension or an export file.

Stored and processed. The app keeps sessions, turns and memories in Postgres with pgvector. A timed job sends new turns to an extraction model and stores the decisions, learnings and outcomes that come back. A local embedding model turns text into vectors so search can compare meaning.

Back to your agent. Agents search over MCP. The CLI adds a preamble at session start and, in Claude Code, matching records at each prompt. The dashboard shows everything stored.

What it does

• One store for sessions from every agent, in one shape: turns, models, token usage, tools, files and sub-agents.
• Extracts decisions, learnings and outcomes, each with its quote and a link to the session.
• Searches by meaning, with keyword search as the fallback.
• MCP tools (memory_search, memory_recall, get_code_session) for any MCP-capable agent. Clients that only speak stdio can run membral mcp.
• A session-start preamble and, in Claude Code only, prompt-time lookup.
• ChatGPT and Claude.ai chats saved next to your coding sessions, and a dashboard to browse it all.

It is not live state: whether a PR is merged or what is deployed is checked in your forge. One store per person; no team or admin controls yet.

Run it locally

You need Node 22.5+ with Git, Docker with Compose 2.23+ (40 GiB of VM disk), and one extraction model (step 2). up starts three containers: Postgres with pgvector, PostgREST, and the Membral app, which serves sign-in itself and runs extraction on a timer. Embeddings use a bundled model that downloads on first use (about 137 MB); Ollama is optional.

1. Run the CLI with npx (no install): npx membral local up in step 3 works as is. Or install it from source, from a clone. This builds it and installs it globally: git clone https://github.com/surajtripathy07/membral && cd membral && npm ci && npm run cli:install
2. Pick an extraction model by putting one choice in ~/.membral/local/.env (mkdir -p ~/.membral/local first):
   • Claude subscription: CLAUDE_CODE_OAUTH_TOKEN=<from claude setup-token>. When claude is on your PATH and no other choice is set, up selects the claude-code provider for you.
   • API key: OPENAI_API_KEY=... (or OPENROUTER_API_KEY)
   • Ollama: a 7B+ model such as qwen2.5:7b, run with OLLAMA_CONTEXT_LENGTH=16384 ollama serve
3. Start it: membral local up (add --embeddings ollama to use Ollama's nomic-embed-text instead of the bundled model).
4. Check it: membral local status (it prints an Embeddings: ... line).

up prints a sign-in URL and password and an API key; treat that output as a secret. The app is at http://localhost:3000. The image is multi-arch (amd64 and arm64); if a pull fails, build it locally and pass --image. Image source builds, extraction setup, disk, logs, Linux notes and what works without an extraction model are in docs/SELF-HOSTING.md.

Large stores (tens of GB of sessions) need Docker or Colima memory of 12 GB or more, for example colima start --memory 14. up sizes Postgres memory to the Docker VM and warms the excerpts table in the background after it starts.

To run a second stack beside it (for example to test a branch), add --project <name> to local up|down|status|logs (or set MEMBRAL_LOCAL_PROJECT) and pass a free --port value. Its files live in ~/.membral/local-<name> and its Docker volumes end in -<name>; the default stack is untouched. Both stacks write the one local CLI profile, so the last up wins.

Why embeddings

Search compares meaning, so a question worded differently from the saved text still finds it. Without an embedder, memory_search works on keywords only and says so ("SEARCH DEGRADED ... keyword-only results"), and the relevance floor that drops weak matches is off. nomic-embed-text is the model whose thresholds are calibrated in this repo (apps/cloud/src/lib/memory/similarity.ts). It runs on Ollama on your machine, so record text never leaves it. Vectors are 1,536 wide, and smaller models are zero-padded. Chat and session-text search (search_conversations) is calibrated only for text-embedding-3-small, so with nomic-embed-text it stays keyword-only and says so. The claude-code provider does no embeddings.

Chrome extension

It syncs ChatGPT and Claude.ai chats to your local stack.

1. npm run build --workspace=@membral/extension (targets http://localhost:3000; for another port build with VITE_CLOUD_URL=http://localhost:<port>, or save a Server URL in the options page, where Chrome asks permission).
2. In chrome://extensions, turn on Developer mode, choose Load unpacked, pick packages/extension/dist.
3. In the options page, paste the API key membral local up printed (it starts lls_) and Save. The popup shows the server and "Connected with an API key".

Supported agents

Agent	Capture	Sync	MCP	Preamble
Claude Code	yes	yes	yes	yes
opencode	yes	yes	yes	yes
GitHub Copilot (CLI + VS Code Copilot Chat)	yes	yes	yes	yes
Codex CLI	yes	yes	yes	yes
Antigravity	yes	yes	yes	yes

Capture reads the agent's transcripts. Sync is membral sessions plus the sweep. MCP is search by the agent. The preamble is added at session start. The per-prompt lookup is Claude Code only. A Claude Code plugin is an alternative to the CLI hooks (docs/SELF-HOSTING.md).

Where your data goes

The local stack keeps everything on your machine. The exceptions are model calls: with an API key, session text goes to that provider, and with a Claude login, to Anthropic through Claude Code. Saved: prompts, replies, tools called, files touched and text written to files (capped). Tool output is not kept. Secrets are redacted by pattern, and files such as .env keep no text. You choose which projects sync (~/.membral/sync.json). To skip throwaway scratch directories, add path patterns to that file (* matches any characters): {"excludePathPatterns":["/private/var/folders/*/membral-lab-*"]}. A project listed with enabled: true still syncs.

Commands

• membral local up | status | logs <service> | down runs the stack (down --wipe deletes the data); membral local reembed moves stored vectors to a new embedding model.
• membral setup, login (default http://localhost:3000), use, sessions, import <export.json> and status.
• membral --help lists every command. npm run smoke checks the dashboard renders the fixtures.

More

docs/SELF-HOSTING.md covers the stack in depth. CONTRIBUTING.md goes from clone to green gate to PR.

License

MIT, see LICENSE. Source: github.com/surajtripathy07/membral.