Mimir

Persistent Memory for AI Agents — MCP-Native. Local-First. Zero Dependencies.

Mimir is a single Rust binary that gives AI agents durable memory across sessions. One binary. One file. No Docker. No Postgres. No cloud. Just persistent memory that works with any MCP host.

One-Line Install

curl -sSf https://raw.githubusercontent.com/Perseus-Computing-LLC/mimir/main/scripts/install.sh | sh

That's it. Mimir is installed to ~/.local/bin/mimir. Start it:

mimir serve --db ~/.mimir/data/mimir.db

Connect any MCP host (Claude Desktop, Cursor, Hermes Agent, Perseus, etc.):

{
  "mcpServers": {
    "mimir": {
      "command": "mimir",
      "args": ["serve", "--db", "~/.mimir/data/mimir.db"]
    }
  }
}

30-Second Quickstart

# Start Mimir
mimir serve --db memory.db &
sleep 1

# Remember a fact (via MCP JSON-RPC on stdio)
echo '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"mimir_remember","arguments":{"category":"demo","key":"hello","body_json":"{\"text\":\"Hello from Mimir!\"}"}}}' | mimir serve --db memory.db

# Search for it
echo '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"mimir_recall","arguments":{"query":"Hello"}}}' | mimir serve --db memory.db

Why Mimir

Mimir is the only memory engine that is simultaneously MCP-native, local-first, zero-dependency, AND agent-first.

Comparison Matrix

	Mimir	Mem0	Letta	Zep
Deployment	Single binary (~8MB)	Cloud + self-host	Docker/Postgres	Docker/Postgres
Dependencies	None (SQLite embedded)	Python + vector DB	Postgres + Python	Postgres + Go
MCP-Native	✅ 44 tools	❌ Not MCP-native	❌ Not MCP-native	❌ Not MCP-native
Offline/Local	✅ Fully local	Cloud-dependent	Docker needed	Docker needed
Encryption	AES-256-GCM ✅	❌	❌	❌
Hybrid Search	BM25 + Dense + RRF	Vector only	Vector only	Vector + Graph
Entity Lifecycle	Decay + Promote + Archive	❌	❌	❌
Entity Graph	Link + Traverse	❌	❌	✅
Journal Audit Trail	✅ Immutable	❌	❌	❌
State Management	✅ Key-value + TTL	❌	❌	❌
MCP Tools	44	5	8	0
GitHub Stars	~20	~55K	~15K	~3K
License	MIT	Apache 2.0	Apache 2.0	Apache 2.0

Full comparison: Mimir vs Mem0 → vs Letta → vs Zep →

Stress Test: 100K Entities

Mimir handles production workloads on modest hardware:

Metric	Result
100K entity insert	1.01s (98,732 entities/s)
FTS5 recall (10 results)	0.022s
Decay tick (100K entities)	1.317s (batched, transactional)
Memory (100K entities)	~85MB RSS
DB file size (100K)	~45MB (with FTS5 index)

Run it yourself: cargo test stress_100k --release -- --ignored --nocapture

Framework Integrations

Ready-to-use adapters that make Mimir the default memory backend for popular AI agent frameworks:

Framework	Integration	Type
LangGraph	MimirStore	BaseStore implementation
CrewAI	MimirMemoryTool	Agent tool
AutoGen	MimirMemory	Memory implementation

Each adapter:

• Connects via MCP stdio subprocess (persistent session)
• Maps the framework's memory interface to Mimir tools
• Comes with a README quickstart (5 minutes to working)
• Has passing tests with mocked MCP transport

Any MCP-compatible framework works with Mimir directly. See Awesome Mimir for the full list.

44 MCP Tools

Entity CRUD

Tool	Description
mimir_remember	Store/update entity. Idempotent by (category, key); a content change snapshots the prior version into history.
mimir_recall	Search with FTS5/dense/hybrid modes, filters, stemming expansion.
mimir_recall_when	Proactive just-in-time recall: surface entities whose recall_when triggers match.
mimir_get_entity	Fetch one entity by ID with full body_json.
mimir_as_of	Bi-temporal time-travel: the version of a fact (category + key) that was live at a past instant.
mimir_forget	Soft-delete (archived=1).

Search & RAG

Tool	Description
mimir_ask	RAG: recall context, query LLM, return grounded answer with sources.
mimir_embed	Generate dense vectors via the bundled model, Ollama, or OpenAI-compatible endpoint.
mimir_semantic_search	Dense-only semantic search shortcut — find entities by meaning, ranked purely by embedding similarity (no keyword fallback).
mimir_context	Pre-formatted markdown block for session injection.
mimir_ingest	Trigger connector syncs (GitHub, file watcher).
mimir_ingest_file	Locally extract a document's text (plaintext/markdown always; DOCX/PDF with the multimodal feature) and store it as a recallable entity.
mimir_extract	Local, deterministic, rule-based knowledge extraction (facts / preferences / temporal events / episodes) from text or a stored entity. Read-only.

Graph

Tool	Description
mimir_link	Create typed relationship links between entities.
mimir_unlink	Remove entity links.
mimir_traverse	Walk entity link graph up to configurable depth.

Journal

Tool	Description
mimir_journal	Append structured event with actor attribution.
mimir_timeline	Query journal by time range with filters.

State

Tool	Description
mimir_state_set	Set key-value state with optional TTL.
mimir_state_get	Get state value. Returns null if expired.
mimir_state_delete	Delete state entry.
mimir_state_list	List state keys, optionally filtered by prefix.

Lifecycle

Tool	Description
mimir_decay	Recalculate Ebbinghaus decay scores (batched 1000-entity transactions).
mimir_prune	Bulk archive by category, decay threshold, or age.
mimir_purge	Permanently delete archived entities + VACUUM. Destructive.
mimir_cohere	Autonomous coherence grooming pass — promote, decay, link, archive.
mimir_autocohere	Full atomic grooming: cohere → decay → compact in one pass (supports dry-run).
mimir_compact	Archive entities below decay threshold.
mimir_reindex	Rebuild FTS5 search index from entities table.

Quality

Tool	Description
mimir_score	Assign quality score (0.0-1.0).
mimir_conflicts	Detect conflicting entities via trigram similarity; opt-in resolve=true invalidates the lower-certainty side into history (reversible, dry-run by default).
mimir_correct	Structured correction capture for learning from errors.
mimir_supersede	Mark a new fact as superseding an old one (sets the old entity to deprecated).

Vault & Federation

Tool	Description
mimir_vault_export	Export entities to .md files with YAML frontmatter.
mimir_vault_import	Import from .md vault directory (idempotent).
mimir_federate	Copy entities between workspaces.
mimir_share	Share one entity (by category + key) into another workspace, preserving content.
mimir_workspace_list	List all distinct entity categories.

Metrics & Ops

Tool	Description
mimir_stats	Full DB statistics across all tables.
mimir_health	Server and DB health check.
mimir_bench	Performance benchmark tracking.
mimir_maintenance	DB maintenance: dedup, orphan detection, VACUUM, FTS5 reindex (supports dry-run).
mimir_synthesize	LLM session synthesis — extract lessons from transcripts.
mimir_migrate	Migrate v0.1.x DB to current schema.

CLI

# Server
mimir serve --db /data/mimir.db
mimir serve --web --port 8767 --encryption-key ~/.mimir/secret.key
mimir serve --llm-endpoint http://localhost:11434/api/generate --llm-model llama3
mimir serve --transport sse --port 8787 --mcp-token my-secret-token

# Maintenance (operate directly on DB, no server needed)
mimir stats          --db /data/mimir.db
mimir forget         --db /data/mimir.db --category decision --key stale-choice --reason "superseded"
mimir prune          --db /data/mimir.db --category junk --min-decay 0.1 --dry-run
mimir purge          --db /data/mimir.db --dry-run
mimir decay          --db /data/mimir.db
mimir reindex        --db /data/mimir.db
mimir vault-export   --db /data/mimir.db --vault-dir ./export/
mimir vault-import   --db /data/mimir.db --vault-dir ./export/
mimir obsidian-sync  ~/obsidian-vault/Mimir/          # one-shot export to an Obsidian vault
mimir obsidian-sync  ~/obsidian-vault/Mimir/ --watch  # continuous sync on every memory change

# Key management
mimir keygen --key-file ~/.mimir/secret.key

Flags

Flag	Description
--db	SQLite database path (default: ~/.mimir/data/mimir.db)
--web	Start web dashboard
--port	Dashboard port (default: 8767)
--web-bind	Dashboard bind address (default: 127.0.0.1)
--transport	MCP transport: stdio (default), sse, or http
--mcp-token	Bearer token for SSE/HTTP transport auth
--encryption-key	AES-256-GCM key file path
--llm-endpoint	LLM API endpoint for mimir_ask and embeddings
--llm-model	LLM model name (default: llama3)
--llm-api-key	API key for LLM endpoints (OpenAI, Azure, etc.)
--embedding-endpoint	OpenAI-compatible embedding endpoint
--connectors-config	Path to connectors.yaml

Your AI Memory in Obsidian

Mimir is your AI agent's long-term memory — and it doubles as your second brain. Every entity your agent remembers exports to a plain Markdown note with YAML frontmatter, so your AI's memory becomes a navigable personal knowledge base inside the tools you already use: Obsidian, Logseq, or Notion.

# Export your entire memory to an Obsidian vault as linked Markdown notes
mimir obsidian-sync ~/obsidian-vault/Mimir/

# Keep it live — re-export automatically on every memory change
mimir obsidian-sync ~/obsidian-vault/Mimir/ --watch

Open the vault in Obsidian and you get a graph of your agent's knowledge.

WikiLink backlinks. When one entity links to another (via mimir_link or a depends_on / implements / references relationship), the exported note gets a ## Links section with [[WikiLink]] backlinks that resolve natively in Obsidian's graph view:

---
id: cli-de8dfb8364b6
category: architecture
key: api
type: insight
decay_score: 0.5000
---

{"content":"axum service"}

## Links

- [[cli-99756b494c7d|database]] (depends_on)

Links resolve by entity id (notes are written as <id>.md) so they never break, and Obsidian shows the human-readable key as the link label. Open the graph view and your agent's architecture, decisions, and insights become a clickable knowledge map.

--watch polls Mimir's cheap, deterministic state digest on an interval and re-exports only when memory actually changes. It naturally catches every mimir_remember write with no filesystem-watcher dependency and no coupling to the server. Tune the interval with MIMIR_SYNC_INTERVAL_SECS (default: 2s).

Other PKM tools

Tool	How
Obsidian	mimir obsidian-sync <vault> — WikiLinks resolve in the graph view out of the box.
Logseq	Point obsidian-sync at your Logseq graph directory. Logseq reads the same [[WikiLink]] syntax and Markdown frontmatter.
Notion	Run mimir vault-export, then use Notion's Import → Markdown & CSV to pull the notes in.

Unlike cloud-only "second brain" tools, Mimir runs 100% local, is written in Rust, encrypts at rest with AES-256-GCM, and applies decay scoring so stale memories fade — your knowledge base stays yours and stays fresh.

Features

Semantic Search (on by default)

• Bundled, in-process embeddings — a quantized all-MiniLM-L6-v2 model (384-dim) is compiled into the binary, so dense/semantic search works with zero config and zero network: no Ollama, no API key, no model download. This is the default build (bundled-embeddings feature).
• Auto-embed on write (#271) — mimir_remember embeds each new (or content-changed) entity synchronously as it is written, using the bundled model. Single-entity embedding is deterministic and LRU-cached, so it is cheap and adds no background tasks. Embedding failures are non-fatal (logged to stderr); the write always succeeds.
• Hybrid is the default recall mode (#271) — mimir_recall(query=...) with no mode flag automatically selects hybrid (dense + keyword fused via RRF) whenever embeddings exist, and transparently falls back to fts5 keyword search when none do. No manual mimir_embed step, no flags to remember.
• mimir_semantic_search(query, limit) — a one-tool shortcut for pure dense, meaning-based search (no keyword fallback) when you just want "find things like this".
• Optional alternate embedder — to use Ollama or any OpenAI-compatible /v1/embeddings endpoint instead of the bundled model, set --llm-endpoint (and --embedding-endpoint / --llm-api-key as needed). This is entirely optional; the bundled model is used by default.
• Build a lean binary without bundled embeddings via cargo build --no-default-features — recall then defaults to keyword search unless a remote embedder is configured.

Hybrid Search internals

• FTS5 keyword search with LIKE fallback and Porter stemming expansion
• Dense vector search via cosine similarity on stored embeddings
• Reciprocal Rank Fusion (RRF) — combine keyword + vector results
• Query expansion — automatic stemming variants for broader recall

Memory Lifecycle

• Ebbinghaus decay — memories naturally fade unless retrieved (refresh on access)
• Layer promotion — buffer → working → core based on access frequency
• Automatic archival — stale entities archive; purge to permanently delete + VACUUM
• Always-on entities — pin critical memories for unconditional session injection

RAG & Embeddings

• mimir_ask — natural language Q&A over stored memories via any LLM (Ollama, OpenAI, etc.)
• mimir_embed — generate and store dense vectors via Ollama or OpenAI-compatible /v1/embeddings
• Supports single-entity and batch-category embedding

Encryption

• AES-256-GCM transparent encryption for entity body_json
• Opt-in via --encryption-key flag
• mimir keygen subcommand for key generation
• FTS5 index stays plaintext for search

Web Dashboard

• Built-in Axum HTTP server (mimir serve --web --port 8767)
• Dark-themed dashboard with search, entity table, vis.js graph, timeline
• Default bind: 127.0.0.1 (use --web-bind 0.0.0.0 to expose)
• Separate SQLite connection in WAL mode for concurrent reads

External Connectors

• GitHub issues connector — ingest issues/PRs by repo, rate-limit aware
• File watcher — scan directories for .md/.txt/.json files with content-hash dedup
• YAML-based connector config via --connectors-config

Multi-Transport

• stdio (default) — zero-config, works with any MCP host
• SSE — Server-Sent Events for HTTP-based MCP clients
• HTTP — REST-style MCP endpoint
• Bearer token auth — for SSE/HTTP transports

Perseus Integration

Mimir is the default memory backend for Perseus:

mimir:
  enabled: true
  transport: "stdio"
  command: ["mimir", "serve", "--db", "~/.mimir/data/mimir.db"]
  timeout_s: 30.0
  merge_strategy: "local_first"
  fallback_to_local: true
  context_categories: ["decision", "architecture", "convention"]
  context_limit: 10

Government & Federal Procurement

Mimir is built for government deployment from the ground up.

Capability	Status
License	MIT — no copyleft, no GPL/AGPL
SBOM	Published — NTIA minimum elements
Air-gapped	Fully offline — no telemetry, no API calls, no network by default
Encryption at rest	AES-256-GCM, transparent, opt-in
Audit trail	Immutable journal with chain-of-custody
Supply chain	SLSA attestation in progress

For federal buyers: See docs/federal-buyers.md for procurement information, compliance status, and deployment models (air-gapped, on-premises, classified environments).

Perseus Computing LLC is a US-owned small business. SAM.gov registration in progress. NAICS: 541715, 541511, 541512.

License

MIT — see LICENSE.