한국어 | Install | Use it | Operations

Local memory for finding what you did with a coding AI, and why you decided to do it.

Memnest stores pi, Claude Code, and Codex conversation text on your machine. Connected AIs can search those records and read the source after a session ends. Connect several clients to the same Memnest service to use the same history.

Use the accumulated record for questions such as:

Why did we switch task managers? Find the discussion behind that decision.

What deployment approach did we last settle on for this project, and why?

What did we try the last time this error happened?

The AI searches memory, reads the relevant source, and uses it to answer. Results include source IDs so you can check the evidence. These are example requests, not an autonomous demo or a guarantee that an answer will be found.

For a few rules that should apply every time, AGENTS.md or CLAUDE.md is simpler. Memnest is for a growing history of conversations and decisions that you want to look up when needed.

Linux x86_64 and Arm64 archives are available; Rust is not required. The first write or search downloads an embedding model of about 1.1 GB. Embedding can use about 1.9 GB of RAM.

Read the downloaded script before running it. Before upgrading an existing installation, back up memory.db together with master.key.

curl -fsSL https://raw.githubusercontent.com/Blue-B/memnest/v0.3.1/core/scripts/install.sh \
  -o /tmp/memnest-install.sh
VERSION=v0.3.1 bash /tmp/memnest-install.sh --user

Setup starts the server and conversation watcher and adds missing connections to supported Claude Code, Codex, and Cursor configurations. It backs up existing configuration before changing it and prints recovery commands.

With the core running, install the matching extension:

pi install npm:[email protected]

If Memnest is already registered from another source, remove that registration first to avoid loading it twice. Use /memnest in pi to check the connection. See extension settings.

Other MCP clients connect to http://127.0.0.1:3111/mcp. See connection examples, other environments and source installation, and upgrade and recovery. macOS is source-only; native installation and removal have not been validated.

Ask your connected AI to look up a previous conversation or decision. Automatic recall is off by default; you can start by requesting a search when you need one. See extension settings to enable automatic recall.

Clients that call tools directly use this flow:

memory_search(query="reason for switching task managers")
memory_get(id="<ID returned by search>")

Five tools cover saving, searching, reading, correcting, and deleting. Use memory_remember to explicitly save an important decision. When a fact changes, save its replacement with supersedes=<old ID> to link it to the earlier record.

When the host provides the current directory, searches include that workspace and the shared playbook. Specify project=all for a deliberate cross-project search. See source reading for long records and nearby conversation entries.

Conversations from before installation are not all imported automatically. Select the original files you need with history import.

• Retrieval is not truth checking. Search can miss a relevant record. Memnest cannot determine whether an old decision still holds. A following-source hint is not a verified reply relationship.
• Capture is selective. It stores visible conversation text, not reasoning, tool traffic, images, or subagent conversations. It does not import ChatGPT's or Claude's built-in memory.
• Storage is local; your connected AI answers. Memnest makes no generative LLM calls. A cloud AI provider receives retrieved text that you send to its model.
• Regular memories are not encrypted. Redaction handles known credential patterns, not every possible secret. Do not expose port 3111 directly to the internet.
• Deletion is not immediate erasure. Records remain in trash for 30 days and may remain in archive JSONL. See security and deletion limits.

One Rust service stores source records in SQLite and searches with BM25 plus local multilingual embeddings. Search indexes can be rebuilt from the source records.

• Real-client validation and reproducible shared-store demo
• Following-source checks and limits, retrieval evaluation
• Design decisions, operations and backups, contributing
• Latest release and changelog

MIT © Blue-B