MemMesh

Persistent, self-improving memory for AI agents. A local-first memory engine in Rust: one binary that runs as a desktop component (SQLite) or a server backend (Postgres), speaks the Model Context Protocol, and wires into your AI tools with a single command.

memmesh.ai · docs · Apache-2.0

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Why

LLM agents forget everything between sessions. MemMesh gives them a durable, typed, scoped memory: facts, contacts, and relationships that persist, are searchable, and improve as they're used — without shipping your data to a third party. It runs on your machine, in your infrastructure, or both with sync between them.

Install

Build from source (Rust 1.78+):

cargo build --release --bin memmesh
# binary at ./target/release/memmesh

Wire it into every AI tool on your machine in one command:

memmesh install

That detects each supported tool, writes its MCP server config block, and drops the agent teaching skill in the right place. Existing MCP servers in your configs are preserved — configs are merged, never replaced.

Tool	MCP config	Skill location
Claude Code	~/.claude.json	~/.claude/skills/memmesh/SKILL.md
Cursor	~/.cursor/mcp.json	~/.cursor/rules/memmesh/SKILL.md
Windsurf	~/.codeium/windsurf/mcp_config.json	(MCP tool descriptions)
Codex CLI	~/.codex/config.toml	(MCP tool descriptions)

Restart the host tool afterward so it picks up the new config. Useful flags: --dry-run, --tool <id> (repeatable), --force, --skill-only, --mcp-only.

Usage

The binary opens ~/.memmesh/memory.db by default (override with --db <path>):

# Init / re-apply migrations (safe to repeat)
memmesh migrate

# Save a memory item
memmesh save \
    --platform plat_test --project proj_alpha \
    --type fact --scope project \
    --content "Sarah prefers email over phone"

# Fetch by id
memmesh get mem_demo_1

# Search (scope/project-filtered)
memmesh search --query "Sarah" --project proj_alpha --limit 10

# Run as an MCP stdio server
memmesh mcp

Tools exposed over MCP

Underscore names are canonical; dot names are accepted as legacy aliases.

Tool	What it does
memory_observe	Feed raw text; the engine decides what to save (primary write path)
memory_save	Upsert a memory item with scope, type, content, importance (rare)
memory_recall	Fetch by id (reinforces the item on access)
memory_search	Filter by scope/project/agent/user/session + content match
memory_list	Most-recent items in a scope
memory_delete	Forget an item — soft-reject (default, sync-safe) or hard delete
memory_supersede	Record a correction (old item kept for provenance)
memory_stats	Counts by type/scope/status + age span
memory_extract_pending / memory_commit_extraction	Client-LLM knowledge-graph extraction
memory_graph_reason	Multi-hop reasoning over the knowledge graph
memory_query_graph	Point-in-time (bi-temporal) edge query
memory_prefetch_related	Anticipatory retrieval via spreading activation
memory_build_context	Full subject context bundle (profile + patterns + predictions)
memory_predict	Forecast a subject's next events, calibrated + with provenance

Architecture

A single Rust workspace:

Crate	Purpose
core	Domain types (MemoryItem, MemoryScope, Contact, …) + algorithms
storage	Storage trait + SqliteStore + PostgresStore
embed / embed-server	Embedding generation + a standalone embedding service
mcp	MCP stdio protocol layer
server	Long-running services (MCP + HTTP)
sync	Bi-temporal sync between local and server stores
audit	Append-only audit log
license	Offline license-token verification (Ed25519)
cli	The memmesh binary
eval	Retrieval-quality evaluation harness

Mode is chosen at runtime, not compile time — the same binary runs local (SQLite) or server (Postgres).

License

Apache License 2.0. © 2026 ThinkFleet, Inc. and MemMesh contributors.