agent-memory
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agent-memorysearch my memory for notes about the payment integration"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Quickstart
pip install agents-memory && agents-memory sync --initScaffolds ~/.agents/memory/, autowires MCP into installed IDEs, and registers assistant skills.
π€ Agent-Driven Setup: Give your coding agent this repo (clone or URL), then tell it to "install and set up agents-memory."
Source checkouts can also be installed and managed with vand.
Related MCP server: RepoRecall
What it does
Vendors keep chat in product graves (Cursor jsonl, Claude sessions, Antigravity brains, Open AI exports). agents-memory is the portable layer on top: identity, project map, typed facts. Markdown on disk is the source of truth. MCP is a clerk, not a second store. The search index is disposable FTS5 β delete it, rebuild, same results.
Layer | Where | What lives there |
Global |
|
|
Per repo |
| facts, ADRs, in-progress work (gitignored) |
Always-on | host | short inject; agents |
Search. search_memory tries exact substring first, then FTS5 fill. get_related follows explicit frontmatter relations (refs, supersedes, same_as), not cosine similarity. Known project slug β get_project_memories.
Ingest β staging β distill. Catalog writes titles and paths to chats-index.md. Extract filters user lines into staging/ (PII, how-tos, dumps dropped). You (or distill_batch / memory-distill) promote durable facts into typed files. Chat bodies never become memory. Conversation logs belong to agents-traces.
IDE injection. One sync splices always-on context into hosts it knows (AGENTS.md / rules) and merges MCP where a config file already lives. Text outside <!-- agents-memory-sync --> stays. Details: Where it runs.
Cloud sync (new in 1.1.0). Several machines, one vault β see below.
Where it runs
Floor: anywhere with a terminal or an MCP client. Markdown vault + python -m agents_memory mcp is enough. No IDE lock-in.
Deeper support is layered β sync --init autowires what it finds on disk; ingest only covers graves we actually parse.
Layer | What you get | Who |
Vault + MCP/CLI | Full tools ( | Any MCP host / any shell |
Autowire on sync | Merge | Cursor, Claude Code, Claude Desktop, Zed ( |
Always-on / rules | Marked inject block + bound rules |
|
Chat ingest |
| Cursor, Claude Code, Antigravity, VS Code Copilot, Windsurf, Roo, Cline, Pi, Open AI GDPR export |
Ingest β βsupports the product.β Titles/paths + filtered user bullets only β same contract for every source (abi/INGEST.md). Distill is still agent/human judgment.
MCP without autowire: Aider, Continue, Goose, stock Copilot Chat, β¦ β point the host at our stdio server yourself. Vault works; we just do not invent their config path.
Not ingested yet: live Codex rollouts, ChatGPT desktop LevelDB, vendor /memory clouds. Add a source when a parser exists β do not wholesale-import foreign memory.
Cloud sync
Mirror the personal store across laptops, a VPS, and other workstations. Each device keeps local files as the working copy. The server holds a merged bundle. MCP tools still run locally; push/pull keeps devices aligned.
1. Host (VPS / always-on box):
agents-memory remote serve --port 8443 --token <YOUR_SECRET_TOKEN>2. Clients (laptops / workstations):
agents-memory connect https://memory.your-domain.com --token <YOUR_SECRET_TOKEN>New slugs append. Same-slug edits: incoming wins. Conflicts land in
staging/sync-conflicts.md.Project trees sync as
mirror/projects/<slug>/in the bundle, then merge back into registered clones.Ingest still reads local chat folders, then pushes the distilled markdown.
agents-memory disconnectpulls a last snapshot and restores stdio MCP.
Layout and merge rules: abi/REMOTE.md.
MCP tools
Primary surface. Agents talk to the vault here β not via scraping CLI help.
Tool | What it does |
| Exact substring, then FTS5 fill. Not chat graves. Known slug β |
| Follow frontmatter |
| File a typed fact; auto-syncs inject |
| Raw file by id ( |
| One slugβs in-tree memory (call when opening a repo) |
| Project map |
| Staging β typed memory |
| Drop a search hit by id |
| Rewrite always-on inject |
Fifteen tools. Full contract: abi/MCP.md. Session snap/grep/tail live on agents-traces.
CLI
Ops / install / batch. Humans and agents rarely need the vault CRUD verbs β those mirror MCP for scripts. Machine-readable catalog: python -m agents_memory --help-json (do not scrape --help).
Command | Purpose |
| Always-on inject, first-run scaffold, optional mirror push |
| Disk vs |
| MCP vault mirrors (scripts / no-MCP hosts) |
| Chat catalog and staging extract |
| Staging inbox / noise pass |
| Mechanical store health (no LLM) |
| Rebuild disposable FTS5 cache (MCP start already rebuilds) |
| Cloud mirror ( |
| Local viewer / static HTML export |
| Clear local caches / temp state |
| stdio MCP clerk |
extract-openai is deprecated β ingest extract (openai-export source).
ABI
Implementation-agnostic layout in abi/:
WHY.mdβ why markdown wins over RAG-as-memoryLAYOUT.mdβ directory contractKINDS.mdβ typed taxonomyHYGIENE.mdβ lifetimes, write boundariesMCP.mdβ tool surfaceINGEST.mdβ catalog β extract β distillINJECTION.mdβ host injectREMOTE.mdβ mirror bundle (and extra project roots)
Tests
python tests/run_all_tests.pyLicense
MIT. See LICENSE.
This server cannot be deployed
Maintenance
Related MCP Connectors
Hosted MCP memory for coding agents: persistent across sessions, editable markdown, team sharing.
One memory, every AI. A shared, user-owned markdown memory your AI clients read and write over MCP.
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- ContexelOAuthai.contexel
Shared AI memory. ChatGPT, Claude, Cursor and any MCP app read and write the same memory.
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