Skip to main content

Local memory server for AI agents — offline, private, fast

Project description

memra-local

Local-first memory server for AI agents. Offline, private, fast.

memra-local gives your coding agent persistent memory that lives entirely on your machine — no account, no network, no data leaves the laptop. Works with Claude Code, Cursor, Zed, Droid, Hermes Agent, OpenClaw, and any MCP-compatible client.

When you're ready to sync across devices or share with a team, a single command pushes your local namespace to Memra Cloud. Same tools, same API, your choice.

Install

pip install memra-local
memra mcp          # start the MCP server

Requires Python 3.10+.

Wire it into your editor

Claude Code / Cursor

{
  "mcpServers": {
    "memra": {
      "command": "memra",
      "args": ["mcp"]
    }
  }
}

Zed

{
  "context_servers": {
    "memra": {
      "command": { "path": "memra", "args": ["mcp"] }
    }
  }
}

Droid (Factory.ai) / Hermes Agent / OpenClaw

See usememra.com/install for client-specific snippets.

What you get

  • Flat-file memory in ~/.memra/ — plain YAML, inspectable, greppable, diff-able
  • MCP server exposing memra_add, memra_recall, memra_get, memra_list, memra_supersede, memra_history, and more
  • Local embeddings via fastembed (ONNX all-MiniLM-L6-v2) — no OpenAI key, no PyTorch
  • Sync to cloud optional: memra sync enable <namespace> --api-key memra_live_...

Commands

memra mcp          # MCP server over stdio
memra status       # verify server + list namespaces
memra hooks install  # optional — auto-capture decisions/patterns as you work
memra --help       # full CLI reference

Docs + source

License

BUSL-1.1. Change Date 2030-04-17 — on that date the license auto-converts to Apache-2.0. Until then, personal and non-production use are unrestricted; commercial production use requires a separate license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

memra_local-0.3.2.tar.gz (43.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

memra_local-0.3.2-py3-none-any.whl (51.2 kB view details)

Uploaded Python 3

File details

Details for the file memra_local-0.3.2.tar.gz.

File metadata

  • Download URL: memra_local-0.3.2.tar.gz
  • Upload date:
  • Size: 43.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for memra_local-0.3.2.tar.gz
Algorithm Hash digest
SHA256 e86bedcfce7a2fc1db59d63d868c0022caf63ac0c148deff1949bf15eea1e6f1
MD5 da5bce3ef206c295a94fc5e602c4394d
BLAKE2b-256 75b83a37d11765636e4b9a7ba09d0edb64867db570ed8be36822da3563b5f7ec

See more details on using hashes here.

File details

Details for the file memra_local-0.3.2-py3-none-any.whl.

File metadata

  • Download URL: memra_local-0.3.2-py3-none-any.whl
  • Upload date:
  • Size: 51.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for memra_local-0.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 b5d98ba37eb9fc8b5c17fb57945640e69f633b932045921a1b38ec6578aeaf2e
MD5 0c93f7b90ca1dca2d7566dcc93d10286
BLAKE2b-256 0e157e46a8281c1532d2d3d916700cb03a2b9075a206bae258a11e53e2987490

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page