Local-first persistent memory MCP server for AI coding agents.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
One-click editor setup isnβt available for this listing yet β we donβt have a confirmed install command, and weβd rather show nothing than point your editor at the wrong package or host. Follow the projectβs own setup instructions, linked above.
Phase 0.5 MVP: a small Rust MCP server that gives Claude Code persistent, project-scoped memory using SQLite FTS5.
AI coding agents lose important context when a session ends. Architecture decisions, failed approaches, project conventions, and bug-fix details often have to be explained again.
AgentOS provides a small local memory layer that coding agents can access through MCP. Memories are stored in SQLite and retrieved with FTS5 keyword searchβwithout a cloud account, embedding API, or external database.
Phase 0.5 is intentionally narrow: save explicit project memories and retrieve them in future Claude Code sessions.
memory.remember and memory.searchSQLite is the source of truth. The FTS5 index is kept synchronized with the memories table through SQLite triggers.
AgentOS currently uses a Windows-first development workflow.
Install:
A separate SQLite installation is not required. AgentOS compiles and links bundled SQLite through rusqlite.
Open PowerShell:
The active host should normally be:
If required, select it explicitly:
The release binary will be created at:
Verify it:
Resolve the release binary to an absolute path:
Register it as a project-scoped stdio MCP server:
Inspect the configuration and connection status:
The expected status is:
If the server is waiting for project approval, start Claude Code and approve the MCP configuration:
Inside Claude Code, ask:
Ask:
Exit Claude Code, start a new session from the same project directory, and repeat the search. The saved memory should remain available.
On Windows, the default database is:
Inspect it with PowerShell:
Override the location for the current PowerShell session:
Remove the override:
| Tool | Purpose | Required arguments |
|---|---|---|
memory.remember | Store a durable memory for the current project | title, body |
memory.search | Search current-project memories with SQLite FTS5 | query |
memory.rememberUse memory.remember for information that should survive future coding sessions:
Example arguments:
Example result:
Arguments:
| Name | Type | Required | Description |
|---|---|---|---|
title | string | Yes | Short, searchable memory title |
body | string | Yes | Full memory content |
tags | string array | No | Searchable labels |
memory.searchmemory.search performs local FTS5 keyword search. Titles receive a higher BM25 ranking weight than memory bodies and tags.
Example arguments:
Example result:
Arguments:
| Name | Type | Required | Description |
|---|---|---|---|
query | string | Yes | Keywords to search for |
limit | integer | No | Number of results, from 1 to 50; defaults to 10 |
Search terms are quoted and joined with OR. This keeps the FTS query safe and favors useful partial matches, but it is not semantic search.
Claude Code starts AgentOS as a child process and communicates with it using newline-delimited JSON-RPC over stdin and stdout.
AgentOS implements the MCP methods required for this MVP:
Protocol responses are written only to stdout. Startup information and diagnostic messages are written to stderr so they do not corrupt MCP framing.
When the process starts, AgentOS:
The default project key is the canonical working-directory path with Windows path separators converted to forward slashes. Every search filters by this key.
Because project isolation depends on the working directory, Claude Code should be started from the same project root when memories are saved and retrieved.
Phase 0.5 does not include:
These limitations are intentional. Phase 0.5 tests the smallest useful version of persistent coding-agent memory before introducing additional storage and retrieval systems.
The current tests cover:
Generate a SHA-256 checksum:
Create a JSONL request file:
Run it through AgentOS:
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