exomem

Durable memory with sources, proof, history, and review for MCP-capable agents.

exomem turns an owned Markdown/Obsidian vault into a local knowledge substrate for Codex, Claude Code, Cursor, chatbots, CLI agents, and any client that can call MCP tools. Your files stay plain, local, portable, and editable outside the server.

agent -> MCP tools -> exomem -> your Markdown / Obsidian vault

Prove it in 30 seconds

uvx exomem demo

One command, no install, no config, no vault of your own needed:

exomem demo — bundled sample vault, keyword mode, fully local
vault: /tmp/exomem-demo-XXXXXX

1. doctor: PASS (0.8s)
2. find "retrieval": PASS (0.1s)
   - Knowledge Base/Sources/Sessions/2026-06-30-sample-session.md
   - Knowledge Base/Notes/Insights/retrieval-needs-owned-files.md
3. get retrieval insight: PASS (0.0s)
   - title: Retrieval needs owned files
   - type: insight
   - excerpt: Local-first knowledge tools should retrieve from files the user already owns.
4. audit: PASS (0.0s)

demo PASS — total 1.0s. This is your proof: agents search files you own.
Next: connect your own vault with `exomem setup`

Runs fully local and read-only against a sample vault bundled in the package. Add --keep to leave that copy on disk afterward and open it in Obsidian.

Install on Mac (one line)

Not comfortable with a terminal? Paste this into the macOS Terminal app — it installs uv, installs exomem, and walks you through exomem setup:

curl -fsSL https://raw.githubusercontent.com/Artexis10/exomem/main/scripts/install.sh | sh

Also works on Linux. Safe to run again later — it skips whatever's already done. If it can't prompt you interactively (e.g. run from another script), it prints the exact command to run next instead of guessing. Prefer to run each step yourself? See below or the full manual walkthrough in QUICKSTART.md.

Set it up in 5 minutes

uv tool install exomem   # or: pip install exomem
exomem setup --vault "/path/to/your/Obsidian"

One command does the whole local setup: the wizard scans your vault and shows what's already there, initializes Knowledge Base/, runs the doctor preflight, registers the server with Claude Code, and installs the skill.

Already have a vault full of notes? That's the normal case: adopt gives a scan-first, read-only report of what's there, suggested knowledge packs, and safe copy/compile-planning next actions. Exomem only ever writes under Knowledge Base/ — your existing files stay untouched unless you explicitly copy or compile selected material. See QUICKSTART.md § Already have a vault full of notes? for the full contract, including daily-notes vaults. Re-running setup is safe; completed steps report [skipped]. Non-interactive: exomem setup --yes --vault "/path" --lean.

The individual steps (exomem init / doctor / install-skill / install-hook, plus claude mcp add) still exist as the manual path — see QUICKSTART.md.

The skill installs under the Claude Code name exomem — the same name as the connector, so skill, server, and tools all read as one product. The skill is recommended for Claude Code — the server gives Claude the tools, the skill is what makes it use them. Hooks are local-client reliability nudges for Claude Code and Codex: a read-side reminder before answers and a write-side reminder at natural stopping points. The read-side hook suppresses obvious control/status prompts like continue, merge it, and are you done?, and can optionally upgrade that reminder to real retrieved KB content (EXOMEM_RETRIEVE_INJECT=1, opt-in; the legacy KB_RETRIEVE_INJECT name still works). For Codex, run exomem install-hook --client codex; for Claude Code, exomem install-hook — see QUICKSTART.md § 7. Other MCP clients can still use the server. If they do not support Skills, have them call bootstrap() once at the start of the session; it returns the same compact operating contract through MCP, including when to search, when to save, workflow-skill discovery, upload guidance, and performance profiles. It also teaches the authoring loop: search first, draft the typed note, run suggest_links, write with the right tool, inspect warnings/suggestions, then report the path.

For client-specific assistant instructions, see docs/ai-assistant-guide.md. For the boundary between Exomem and a chat product's built-in memory, see docs/vs-built-in-memory.md.

Full local setup is in QUICKSTART.md. Remote/mobile setup is in docs/remote-quickstart.md and docs/deployment.md.

The product model is intentionally simple: built-in AI memory remembers preferences and routing, while Exomem stores durable governed knowledge with sources, proof, history, decisions, records, and review. See docs/product-model.md for the full mental model, docs/review-studio.md for the packaged browser review loop, docs/epistemic-inbox.md for daily review and relation repair, docs/knowledge-packs.md for pack/admin details, and docs/workflow-skills.md for the named agent workflows.

For development, or to run the sample vault from a checkout instead of a package install:

git clone https://github.com/Artexis10/exomem.git
cd exomem
uv sync
uv run exomem demo

Connect your agent

Client	How
Claude Code	exomem setup registers it for you (see above)
Codex CLI	codex mcp add plus optional exomem install-hook --client codex - see docs/ai-assistant-guide.md#codex-cli
claude.ai or hosted chat	Remote MCP/connector - see docs/remote-quickstart.md and docs/ai-assistant-guide.md#hosted-chat-clients
Any MCP client	Generic stdio config - see below and docs/ai-assistant-guide.md#generic-stdio-mcp-clients; call bootstrap() first
Docker (no Python)	One docker run line — see below and docs/docker.md

Codex CLI

codex mcp add exomem --env EXOMEM_VAULT_PATH="/path/to/vault" -- exomem --transport stdio

Optional local hooks, using the same Exomem scripts as Claude Code:

exomem install-hook --client codex

Verify deployed Claude Code/Codex hooks without changing anything:

exomem install-hook --check

For the AGENTS.md instruction block and the "do not search every tiny prompt" policy, see docs/ai-assistant-guide.md.

Or add it directly to ~/.codex/config.toml:

[mcp_servers.exomem]
command = "exomem"
args = ["--transport", "stdio"]
env = { EXOMEM_VAULT_PATH = "/path/to/vault" }

Any MCP client (generic stdio)

{"mcpServers": {"exomem": {"command": "exomem", "args": ["--transport", "stdio"], "env": {"EXOMEM_VAULT_PATH": "/path/to/vault"}}}}

After connecting, ask the agent to call bootstrap() before using the KB. Claude Skills are still the best UX where available, but bootstrap() lets generic MCP clients learn Exomem's search/save/upload contract without a separate skill file, including the write loop for compiled notes. See docs/ai-assistant-guide.md for the copyable standing instruction.

Docker (no Python on the host)

claude mcp add exomem -- docker run -i --rm -v "/path/to/vault:/vault" -e EXOMEM_VAULT_PATH=/vault ghcr.io/artexis10/exomem:latest --transport stdio

Use :latest for the lean keyword/BM25 image, :ml for CPU hybrid search, or :cuda for NVIDIA/Linux CUDA capability. CUDA images still boot CPU-default at idle; opt into GPU residency with EXOMEM_MODE=performance when you want it. The image also runs as an always-on remote server via docker compose with a tunnel sidecar — see docs/docker.md. Windows users with a live vault should usually prefer the native install (WSL2 bind mounts miss live file-watch events); macOS Apple Silicon users need native install for MPS/MLX.

The first start downloads search models in the background — find works immediately with keyword ranking and upgrades to semantic search automatically once the models land. Run exomem warm to pre-download them ahead of time.

Resource modes

Exomem is CPU-first by default so an idle server does not quietly occupy GPU memory. Control the machine footprint without editing code:

exomem mode quiet          # low-resource: no heavy warm-up, evict caches, defer semantic reindex
exomem mode normal         # default: CPU steady-state, warm CPU caches allowed
exomem mode performance    # explicit opt-in for GPU-capable bulk/model work
exomem status --resources --json

Use quiet before gaming or other foreground workloads. Keyword/BM25 freshness, file-change freshness, inbound links, and resolver state stay live; expensive semantic/CLIP reindex work can be deferred and is reported in resource status. Run exomem index or kb reconcile later to heal deferred semantic work.

What it does

• Searches the vault you already own. Markdown stays in place; exomem does not import copies into a proprietary note store.
• Adopts messy vaults safely. adopt starts with a read-only report and explicit copy and compile-planning options, so originals remain archival until you choose.
• Retrieves across text and media. Markdown, PDFs, Office docs, images, screenshots, audio, and video can become searchable through local extraction.
• Keeps sources separate from conclusions. Raw captures, compiled notes, entities, evidence, and superseded conclusions live in typed folders.
• Surfaces review work. Audit and attention queues can show unprocessed sources, stale notes, broken links, and close-by claims worth reviewing.
• Measures, never judges. The server does deterministic work: search, extraction, ranking, embeddings, file writes, and graph checks. Reasoning stays in the client model.

Why use it

Most AI note tools make you move into their app or ingest your files into their store. exomem works the other way around: agents come to your vault.

Compared with	Difference
Doc-chat / RAG apps	exomem works over live files instead of imported copies.
Basic MCP note servers	exomem adds typed knowledge operations, multimodal extraction, audit queues, and CLI/REST parity.
Memory hidden inside one assistant	exomem is client-agnostic: use the same vault from Claude Code, Codex, Cursor, scripts, or a custom chatbot.

For a deeper point-in-time comparison, see docs/comparison-engraph.md. The reproducible graph-only comparison with Basic Memory is in docs/comparison-basic-memory-graph.md. For the practical boundary with chat products' own memory features, see docs/vs-built-in-memory.md.

Measured retrieval quality — and speed. Retrieval is graded by a reproducible golden-set eval harness, not asserted, and latency is measured per lane at corpus scale: hybrid find() runs sub-second end-to-end at 50,000 notes (864 ms on the reference desktop, hot cache off), with the keyword/BM25 lanes served from an FTS5 sidecar index in milliseconds — built into stdlib SQLite, so it works on the lean install too. Methodology and numbers in docs/benchmarks.md.

Simple front door

Agents should route normal user requests through simple actions first, then use the typed tools underneath.

Action	Use when the user says	Backed by
Save	"remember this", "log this", "this is a decision"	add, note, link, preserve
Adopt/import	"make this old vault usable", "import my notes safely"	adopt, overview
Ask	"what do we know about X?", "show the sources"	find, get
Prove	"save this for the warranty case", "show the evidence"	preserve, upload/download, find
Review	"what needs cleanup?", "what is stale?"	attention, audit, propose_compilation
Update	"this replaced the old conclusion", "fix that note"	edit, replace, reconcile
Connect	"link this to X", "what should this cite?"	link, suggest_links

Core tools

exomem exposes typed MCP tools for common knowledge-base work:

Tool	Purpose
find	Search notes, sources, entities, and evidence with type/project/tag filters.
get	Read a full page or frontmatter.
add	Capture a raw source page.
note	Create compiled notes: research note, insight, failure, pattern, experiment, or production log.
edit	Patch an existing compiled page.
replace	Supersede an old conclusion with a new one and preserve the link between them.
preserve	Store binary or text evidence append-only.
audit	Check graph and corpus health.
attention	Surface review queues such as stale notes, close-by claims, and unprocessed sources.
overview	Bounded, read-only structure report of the vault or a subtree — works outside Knowledge Base/ and before init.
adopt	Existing-vault adoption: scan-only by default; can save a manifest, copy selected legacy text files as Sources, or return a compile plan while preserving originals.

Tier-2 filesystem tools exist for escape hatches such as listing directories, creating files, moving pages, trashing files, and recovering from trash. Set EXOMEM_DISABLE_TIER2=1 if you want a smaller tool surface.

Every write records durable history in Knowledge Base/log.md. Service calls also go to logs/exomem.log.

One operation, three doors

Every operation is declared once and exposed through:

• MCP for agents.
• CLI for terminal and scripts.
• REST for personal HTTP integrations when EXOMEM_REST_API_KEY is set.

Examples:

kb find "project handoff" --mode keyword
kb find "stale decision" --json
kb get "Notes/Insights/retrieval-needs-owned-files" --json
kb note --note-type insight --title "Agents need durable context" \
  --content "# Agents need durable context"

curl -s -X POST http://127.0.0.1:8765/api/find \
  -H "Authorization: Bearer $EXOMEM_REST_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "project handoff", "mode": "keyword"}'

CLI and REST share the same JSON envelope:

{"success": true, "data": []}

Multimodal by default, resident only while working

The native release-service command installs the standard profile: local embeddings, PDFs/Office documents, OCR bindings, ASR, and CLIP. Heavy models are not loaded at service startup. Media jobs enter a durable queue, run serially in one disposable child process, and the child exits after five idle minutes so its RAM and MPS/MLX/CUDA state return to the host.

Manual/source installs can select the same capabilities directly:

uv sync --extra embeddings
uv sync --extra media

• embeddings: local text embeddings plus CLIP image search.
• media: OCR for images, PDF extraction, Office document extraction, and faster-whisper ASR for audio/video.

lean and hybrid remain available for constrained machines. Generated image captioning and speaker diarization are advanced opt-ins, not requirements for the standard multimodal path.

System tools: Tesseract is required for image OCR. On Windows:

winget install --id UB-Mannheim.TesseractOCR -e

GPU acceleration is useful but not required. Steady-state torch models default to CPU in normal and quiet; performance is the explicit opt-in for capable NVIDIA CUDA or Apple Silicon MPS/Metal paths. See docs/deployment.md for CUDA, Blackwell, Apple Silicon, diarization, and remote-service details.

Configuration

The server reads environment variables or a .env file. The main ones are:

Variable	Purpose
EXOMEM_VAULT_PATH	Vault root containing the governed folder (default Knowledge Base/).
EXOMEM_KB_DIRNAME	Name of the governed folder inside the vault (default Knowledge Base).
EXOMEM_DISABLE_EMBEDDINGS	1 forces keyword/BM25-only search.
EXOMEM_DISABLE_TIER2	1 hides Tier-2 filesystem tools.
EXOMEM_REST_API_KEY	Enables authenticated REST routes.
EXOMEM_DISABLE_MEDIA_EXTRACTION	1 skips server-side OCR/ASR/PDF/Office extraction.
EXOMEM_DISABLE_CLIP	1 disables CLIP image search.
EXOMEM_MODE	Hard-pin resource mode: quiet, normal, or performance. Env wins over config.
EXOMEM_QUIET_MODE	Legacy truthy alias for quiet when EXOMEM_MODE is unset.
EXOMEM_AUTO_QUIET	1 enables optional non-torch GPU-pressure auto-quiet switching (default off).
EXOMEM_DEVICE / EXOMEM_TORCH_DEVICE	Force all torch models to cuda, mps, or cpu. Normally leave unset and use exomem mode.
EXOMEM_MPS_FP16	On Apple Silicon, run bge/CLIP in fp16 on the Metal GPU — ~half the memory, faster encodes (default on; set 0 to keep fp32).
EXOMEM_VIDEO_SCENE_FRAMES	Set to enable video scene detection + persisted, OCR'd scene-frame JPEGs (default off).
EXOMEM_VIDEO_SCENE_THRESHOLD	Scene-boundary hash threshold in bits of 64 (default 10).
EXOMEM_VIDEO_SCENE_MIN_SECS	Minimum scene duration in seconds; closer boundaries merge (default 4).
EXOMEM_SEMANTIC_SEGMENTS	Set to enable timed transcripts + semantic segment retrieval for audio/video (default off).
EXOMEM_WHISPER_MODEL	faster-whisper model size for ASR, such as base or small.
EXOMEM_ASR_BACKEND	ASR engine: mlx (Apple Silicon Metal GPU, needs the media-mlx extra) or faster-whisper (CUDA/CPU). Default auto-selects MLX on Apple Silicon, else faster-whisper.
EXOMEM_MLX_WHISPER_MODEL	HF repo for the MLX ASR model (default mlx-community/whisper-large-v3-mlx; use mlx-community/whisper-large-v3-turbo for speed).
EXOMEM_TESSERACT_CMD	Path to the tesseract binary if not auto-discovered.

Legacy EXOMEM_* names (from the project's former working name, exomem) remain honored: each is promoted to its EXOMEM_* equivalent at startup, with an explicitly set EXOMEM_* value winning on conflict. import exomem and python -m exomem likewise keep working as deprecated aliases.

Remote-only variables and full deployment notes are in docs/deployment.md.

Project status

exomem is packaged on PyPI, uses Release Please for versioning, and follows the lightweight SemVer policy in docs/release.md. The public CLI entry point is exomem; kb is the short daily-driver alias for knowledge-base operations.

License

AGPL-3.0-or-later. See LICENSE.