cogito-mem (Cogi)

io.github.meridian21labPublished by GitHub account meridian21labv0.3.0Updated Nov 3, 2026

Local long-term memory for Claude Code and other agents: every message kept word for word, no cloud.

VerifiedSTDIODesktop onlyKnowledge & MemoryDeveloper Tools

Overview

cogito-mem (Cogi)'s local MCP server; no sign-in required; STDIO; 3 tools.

No AI overview yet: these facts come from the registry entry and the server itself.

Installation

In SourceWeft

  1. Open cogito-mem (Cogi) in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

cogito-mem

Long-term memory for Claude Code, on your own machine. Cogi the squirrel keeps every message you and Claude exchange, and finds the right one again, even in over a million tokens of history. No API key, no cloud, no AI calls to store.

[Cogi the squirrel: Claude forgets. Cogi doesn't.]

Why try it

  • Claude can look back. Every message and reply is kept word for word, with its date. When something has dropped out of the context window, or happened last month, Claude searches for it and gets your exact words back.
  • It remembers what you did, too. The commands you ran (and whether they failed), the files you touched, what each sub-agent reported, and the lines of output that say how a command went: errors, metrics, how a long run ended.
  • Private and free. One folder on your computer. No account, no API key, no AI calls to save a message. Nothing leaves your machine.
  • Tested. In LongMemEval's long test, each question hides its answer in about 480 past sessions, some 1.2 million tokens. The past session holding the answer is among the first five Cogi finds for 93.0% of 500 questions, with no AI model involved, ahead of four other local memory plugins we ran on the same questions (86.6–89.8% at their best settings). On the short test, with about 50 sessions, it is 99.0%, level with the best. On LoCoMo, with GPT-5 mini answering, the 1,000 tokens Cogi hands back answer 89.4% of questions right, against 94.1% from rereading the whole conversation: 95% of the quality while the model reads about 9 times fewer tokens. A search over 10 million tokens takes 26 ms on a laptop CPU. See the results.

Install

You need Claude Code and uv, on macOS or Linux (Windows is experimental). Then, in a terminal:

sh
claude plugin install cogito-mem --marketplace meridian21lab/cogito-mem

The first start downloads the model (310 MB, about a minute). To get updates, turn on auto-update once: /plugin → Marketplaces → cogito-mem → Enable auto-update.

Step-by-step guide · Codex, Claude Desktop, Docker

Try it

  1. Tell Claude: Let's call the staging server "acorn".
  2. Clear its short-term memory: /clear
  3. Ask: What did we call the staging server? Claude answers "acorn": Cogi brought it back.

What Cogi runs, downloads and stores

  • Runs: four Claude Code hooks and one MCP server, cogi. Each runs the released cogito-mem package from PyPI at the exact version the plugin pins (uvx cogito-mem==<version>); the plugin's uv.lock lists that release and numpy with their checksums. After each reply and at the end of a session, the hooks save the new messages from the session's transcript. At the start of a session and with each prompt, they add the most relevant memories (about 400 tokens per prompt, each line with an id Claude can open). The MCP server gives Claude three tools, memory_query, memory_open and memory_append, answered by a small service that listens on your computer only (127.0.0.1:7327).
  • Downloads, once, over HTTPS: cogito-mem and its one dependency, numpy, from PyPI (by uv); llama.cpp's llama-server from llama.cpp's official GitHub releases (skipped if you already have it); and the EmbeddingGemma 2 model (310 MB) from Hugging Face. After that, Cogi makes no network calls.
  • Stores: everything in ~/.cogito-mem/ on your computer: your messages, a short note of what each turn did, what each sub-agent reported back, and at most 800 characters of each command's output (errors, metrics, how a long run ended), in one SQLite file. Common forms of API keys, tokens, private keys and passwords are replaced with [redacted] before anything is saved; the rest of tool output and what Claude's Read tool returns are never saved. Redaction goes by patterns, so others can get through. Sessions from before the install are read only if you ask (cogito-mem capture claude-code --all), and old sessions may hold secrets the redaction misses. Text inside <private>…</private> is skipped, and COGITO_MEM_CAPTURE=off pauses saving.
  • Sends: nothing. No account, no analytics, no telemetry. (The optional benchmark command, cogito-mem eval locomo-qa, which only you would run, sends benchmark questions to the LLM API you set up for it, with your own key.)
  • Removes cleanly: claude plugin uninstall cogito-mem@cogito-mem, then delete ~/.cogito-mem.

Every file, download and connection, with links to the source: What you install.

How it compares

cogito-memclaude-memMem0 plugincognee pluginSupermemory plugin
AI calls to store a messagenonean observer model per tool call (Haiku on your Claude plan by default)extraction on Mem0's serversextraction with your LLM key or claude -pin Supermemory's service
Account or API keynoneoptional sign-inMem0 Platform API keyan LLM key or the Claude CLIAPI key or login
Where your memory livesyour computeryour computer (cloud sync optional)Mem0's cloudyour computer (cloud optional)Supermemory's cloud (local mode available)
What comes backyour exact words, with datesAI-written observationsextracted memoriesgraph facts and summariesextracted memories
Searchable afterthe same turnthe observer's runa batch (every 5 exchanges)the next graph runthe background save

Keep your project's rules in CLAUDE.md, and let Cogi remember what happened.

Learn more

How it worksCollect, keep, find: what Cogi stores and how it finds it
Results · BenchmarksThe numbers, and how to reproduce them
API & MCP · ReferenceHTTP, Python, terminal, settings, FAQ, roadmap
What you install · Privacy · TermsEvery file, download and connection; privacy and terms
DevelopmentRunning the tests and the evaluations

Early access. v0.2 is new, built and tested (over 200 tests), and still being measured. Feedback and issues are very welcome.

License

MIT

Source: README.md at commit 12a720a

Tools

3

memory_append

Save to memory

Save a note to the user's long-term memory: an idea, a decision, an open question or a fact the user stated. Write it so it makes sense on its own later. It is stored word for word with the current time and cannot be edited.

Parameterstextrequiredtitle

memory_query

Search memory

Search the user's long-term memory of earlier chats and sessions. Returns the few stored messages that best match, each with valid_at (when it was said) and a source pointing to the original message. Turn time phrases into valid_after (inclusive) and valid_before (exclusive): for 'a week ago' use a window of a few days around that date, for 'last month' that month. Today is Tuesday 2026-11-03 (UTC); the response's as_of is the current time.

Parameterstextrequiredmax_tokensvalid_afterthis_sessionvalid_before

memory_open

Open a remembered message

Read one stored message whole, word for word, with the messages around it in its session. Give the id from a memory_query result or a cogito-mem pointer line.

Parametersidrequiredafterbeforemax_tokens

Version history

3
  1. v0.3.0LatestNov 3, 2026
  2. v0.2.1Oct 21, 2026
  3. v0.2.0Oct 20, 2026

Score

90% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts
  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts