<p align="center"> <img src="assets/logo.png" alt="memkeeper logo" width="180" height="180" /> </p>

<p align="center"><em>Most software has the memory of a goldfish. This one doesn’t.</em></p>

<p align="center"> <a href="https://glama.ai/mcp/servers/teflon07/memkeeper"><img src="https://glama.ai/mcp/servers/teflon07/memkeeper/badges/score.svg" alt="memkeeper MCP server quality score on Glama" /></a> </p>

memkeeper

Local-first memory for AI agents. A fast, embeddable memory engine that stores, ranks, and retrieves an agent's durable context, entirely on your machine, with no required network or LLM calls.

ℹ️ Generated release mirror. This repo is generated from a private development repo and published as releases. The main branch may be regenerated, so pin to tagged releases (or the release artifacts) rather than to arbitrary main commits — tagged releases are stable. See CONTRIBUTING.md for how to contribute; issues, security reports, and design feedback are the best paths today.

• Local-first. A single SQLite database. No server, no cloud, no telemetry.
• Fast at prompt time. Deterministic BM25/FTS retrieval with optional ONNX semantic embeddings and a cross-encoder reranker.
• Durable by design. Atomic writes, schema-versioned storage, and a retention model that promotes recurring, high-signal memories to a durable tier.

<p align="center"> <img src="assets/hero.gif" alt="memkeeper demo: store two memories, then a semantic search whose query shares no keywords with the stored memory still surfaces the right one" width="820" /> </p>

<p align="center">_{Real CLI output, formatted for readability via <a href="scripts/mkfmt"><code>scripts/mkfmt</code></a>. The search query shares no keywords with the memory it surfaces.}</p>

Status: pre-release (v0.2). APIs and the wire protocol may change before 1.0.

Quickstart

# Install the latest release binary (macOS arm64 / Linux x86_64) to ~/.local/bin.
# It's self-contained — nothing else to install.
curl -fsSL https://raw.githubusercontent.com/teflon07/memkeeper/main/install.sh | bash

# Optional, one-time: fetch on-device semantic models. Lexical search works without it.
memkeeper pull-models

# Create a store, remember something, search it back.
memkeeper init
memkeeper remember --json '{"content":"memkeeper stores memories in a local SQLite database"}'
memkeeper search   --json '{"query":"where are memories stored","limit":3}'

That's the whole install: a self-contained binary, no runtime network/LLM/API key. Prefer not to pipe a script to your shell? Grab a binary from the releases page and verify its .sha256, or build from source. The store defaults to ~/.memkeeper/store.sqlite when --store is omitted; a --json value can also be @<file> or - (stdin) instead of an inline string, which avoids shell-quoting pitfalls (handy in Windows PowerShell).

Use it from your agent (MCP)

memkeeper speaks MCP (JSON-RPC 2.0 over stdio), so any MCP client — Claude Code, Cursor, and others — can read and write memory during a session. Point your client's MCP config at the native binary (no Python, no extra deps):

{ "mcpServers": { "memkeeper": { "command": "memkeeper", "args": ["mcp"] } } }

The agent calls remember to capture a durable fact and search to recall it later, across separate sessions, with the same retrieval as the CLI.

<p align="center"> <img src="assets/mcp.gif" alt="memkeeper over MCP: an agent connects, calls remember to store a fact, then in a later session calls search and recalls it via semantic retrieval" width="820" /> </p>

<p align="center">_{Real <code>memkeeper mcp</code> JSON-RPC round-trips, formatted for readability via <a href="scripts/mcpfmt"><code>scripts/mcpfmt</code></a>.}</p>

For native MCP details and the optional Python bridge, see adapters/mcp.

What to store

memkeeper holds self-contained memories: facts, decisions, preferences, lessons. Each remember is one memory written to stand on its own, with its context and intent intact (store "the user likes pineapple on pizza," not just "pineapple"). Atomic means one idea per memory, not a stripped keyword. Retrieval, dedup, supersession, and the entity graph all work best at that grain.

Two ends to avoid:

• Too small: a bare keyword or fragment that drops the point.
• Too large: a whole document. The curated memory tier has no chunking, and the embedder sees only the first ~512 tokens of an entry, so loading long files (for example, an entire markdown library) gives weak semantic recall on those entries (lexical BM25 still indexes the full text). To bring whole documents in, don't store them as memories — use the document store, memkeeper's separate RAG tier that chunks and embeds files into an isolated space (the memkeeper-ingest add-on imports whole folders this way). Or distill the document down to its takeaways and store those as memories.

Capturing memories

memkeeper is curated memory you populate deliberately — not an automatic transcript logger. Memories get in two ways:

• Directly — memkeeper remember --json '{"content":"…"}', from the CLI or a script.
• From an agent — the MCP bridge lets an MCP client (Claude and other agents) call remember during a session, so durable facts are captured as they come up.

On the retrieval side, memkeeper hook retrieve is a Claude Code UserPromptSubmit hook client that injects relevant memories into the prompt — so an agent recalls without an explicit search. It retrieves; capture stays a deliberate remember.

Semantic retrieval (default)

memkeeper has three retrieval modes. Local semantic is the default and the recommended, fully on-device mode. Pick one up front — the embedding backend is recorded in the store, so changing it means re-embedding (reindex --embed), not a flip.

<p align="center"> <img src="assets/retrieval.gif" alt="memkeeper retrieval: the deterministic BM25/FTS floor returns an exact-keyword match with zero models, then with the ONNX models loaded a semantic query that shares no keywords still finds the right memory" width="820" /> </p>

<p align="center">_{The deterministic floor (zero models, zero network) and semantic + rerank on top — same store, same query path. Real output via <a href="scripts/mkfmt"><code>scripts/mkfmt</code></a>.}</p>

Mode	Network	Setup
Local semantic (default)	none	install binary, then pull-models
Lexical only	none	works out of the box; just skip pull-models
Off-device semantic	embeds via an API	set MEMKEEPER_EMBED_PROVIDER=openai + base URL + key

Privacy: off-device semantic sends your memory text to the embeddings provider to be vectorized. Use it only where that is acceptable; the two on-device modes never send memory content anywhere.

Local semantic (default)

The release binary ships semantic-capable (the ONNX runtime is statically bundled), so there's no rebuild — it just needs the embed + rerank models, which aren't downloaded automatically. Fetch them once:

# Needs curl; ~2.1GB, or --quantized for ~0.6GB (slightly lower recall).
memkeeper pull-models

pull-models writes to ~/.memkeeper/models/ (override with MEMKEEPER_MODELS_DIR or --dir) — exactly where memkeeper looks by default. So semantic turns on with no env vars to set: run a search afterward and it's active.

If the models are missing, memkeeper does not degrade silently: it logs their absence and points you at pull-models, marks results semantic-unavailable (e.g. "semantic":{"attempted":false,"reason":"missing_embedding"}), and falls back to lexical (BM25/FTS) so search keeps working. Set MEMKEEPER_REQUIRE_SEMANTIC=1 to fail closed instead — refuse the request rather than serve degraded results — in any deployment that must never silently run lexical-only.

Embeddings are computed when a memory is written. Memories you stored before the models were present (for example, the one from the Quickstart above) are lexical-only until embedded. Backfill existing memories once with:

memkeeper reindex --embed

New memories written with the models in place are embedded automatically.

Lexical only

Skip pull-models and the release binary runs deterministic, model-free lexical-only (BM25/FTS) retrieval — zero network, zero models. Building from source with --no-default-features produces a leaner binary that omits the ONNX runtime entirely (see Build from source).

Off-device semantic (no model download)

Prefer not to download the ONNX models? Point memkeeper at an OpenAI-compatible embeddings API (OpenAI, OpenRouter, or any compatible proxy) instead. This mode embeds and reranks over the network rather than loading the local models, so it needs no pull-models:

# Embeddings (required for semantic): any OpenAI-compatible /embeddings endpoint.
export MEMKEEPER_EMBED_PROVIDER=openai     # "openai" = the OpenAI-compatible API dialect
export MEMKEEPER_EMBED_BASE_URL=https://api.openai.com/v1/embeddings   # or your provider, e.g. OpenRouter
export MEMKEEPER_EMBED_API_KEY=sk-...
export MEMKEEPER_EMBED_MODEL=text-embedding-3-small
export MEMKEEPER_EMBED_DIMS=1536

# Reranking (optional, recommended): Cohere /rerank dialect, which OpenRouter speaks.
export MEMKEEPER_RERANK_PROVIDER=openrouter
export MEMKEEPER_RERANK_API_KEY=sk-...
export MEMKEEPER_RERANK_MODEL=cohere/rerank-v3.5

The prebuilt release binaries support all three modes (--features semantic,api): run pull-models for fully on-device local semantic (the default and recommended mode), configure an API key for off-device semantic, or configure neither and they serve lexical (BM25/FTS). MEMKEEPER_REQUIRE_SEMANTIC=1 makes them refuse rather than serve degraded.

Prebuilt binaries are published for macOS (Apple Silicon) and Linux x86_64. Windows is experimental — there's no prebuilt binary, but it builds and runs from source; see docs/windows.md. (serve --socket is Unix-only there; the http dashboard and stdio serve are cross-platform.)

Switching the embedding model

The embedding backend is recorded per store, and memkeeper refuses to mix vectors from different models (they live in different vector spaces). To switch — local↔ off-device, or between models — change the embedding env vars, then re-embed every memory under the new model in one step:

./target/release/memkeeper reindex --embed --store ~/.memkeeper/store.sqlite

This wipes the old vectors, records the new active model, and re-embeds all active memories in one transaction. It is the supported way to change models; there is no partial mix.

Document store (RAG)

Alongside curated memories, memkeeper can hold a separate tier of ingested document chunks for retrieval-augmented use. Chunks live in their own space (default documents), isolated from the curated memory tier, so they never receive supersession, dedup, graph, or promotion treatment.

• ingest — store a document source as embedded, isolated chunks. Re-ingesting the same source_path repairs that chunk's provenance in place; identical content under a different path is kept as an independent chunk.
• document-search — hybrid (BM25 + vector) search over the chunks, with a citation back to source_path and chunk index.
• document-get — fetch a document's chunks by path, or one chunk by id.
• document-duplicates — surface exact-content duplicate chunks (the same content held under different sources) as clusters. stats also reports a document_duplicate_clusters count so you know when there are duplicates worth reviewing.
• document-prune — delete the specific chunks you choose (supports dry_run). Deletion is always explicit: review duplicates, decide which copies to keep, then prune the rest.
• promotion-candidates / mark-extracted — rank chunks that earned retrieval traffic, and mark a chunk extracted once it has been promoted into a memory.

Run memkeeper schema <command> for each command's accepted JSON fields. Over serve --http, reads (search/get/duplicates) are available on the read-only dashboard. Writes (ingest, document-prune) are disabled unless you set a write token: start the server with MEMKEEPER_HTTP_WRITE_TOKEN=<secret> in the environment, then send it on write requests as Authorization: Bearer <secret>. With no token set, the HTTP server is read-only.

The dashboard

memkeeper serve --http starts a read-only local dashboard (default http://127.0.0.1:7777) for browsing memories and the entity graph. Point it at a store with --store <path> (or MEMKEEPER_STORE); it uses the default store otherwise.

A fresh store starts empty — that's expected. Two views, populated differently:

• The memory list fills as you remember.
• The graph visualizes entities and relationships, which are a separate layer from raw memories. They accrue from memories that carry an entity, from the dream synthesis pipeline extracting them over time, or from explicit entity-upsert / relationship-upsert. So a handful of plain memories fill the list but leave the Graph tab empty until entities and links exist — add a few connected memories (or run dream) and it builds out.

Benchmarks

On LoCoMo (10 multi-session dialogues, 1,982 evidence-bearing questions), memkeeper's default semantic retrieval scores:

Metric	Score
recall@20	0.768
hit@20	0.880
MRR	0.668

Prompt-time search on a warm serve daemon (ONNX models loaded once) runs in ~25 ms p50/p95, about 32× faster than a cold per-call binary that reloads the models on every query.

Full methodology, per-config results (including the late-interaction upgrade), and a reproduction script are in docs/benchmarks.md.

Build from source

Building is optional — the Quickstart binary is self-contained. Build from source to track the latest main, produce a leaner lexical-only binary, or develop.

Prerequisites: a Rust toolchain (stable, via rustup; edition 2021, Rust 1.56+) and a C toolchain for the native deps (bundled SQLite plus the ONNX runtime for semantic search). macOS: Xcode Command Line Tools (xcode-select --install); Debian/Ubuntu: build-essential. Building fetches crates from crates.io the first time; after that a clean build is offline.

# Semantic build (default): local embeddings + cross-encoder rerank.
cargo build --release
# ...or lexical-only — omits the ONNX runtime and models entirely:
cargo build --release --no-default-features

# The binary lands at ./target/release/memkeeper (not on PATH). To install it:
cargo install --path crates/memkeeper-cli   # then a bare `memkeeper` works

Then memkeeper pull-models to enable semantic, exactly as in the Quickstart.

Workspace layout

Crate	Role
memkeeper-core	Core types and retrieval policy
memkeeper-store	SQLite storage, schema, indexing, promotion
memkeeper-embed	ONNX embeddings + cross-encoder reranker
memkeeper-protocol	Wire protocol (memkeeper.v0.1)
memkeeper-cli	The memkeeper binary (CLI + daemon)

Editor/agent integrations live under adapters/ (an MCP bridge and a thin extension client).

Further reading

Design notes and benchmarks on the memkeeper blog:

• Local-first memory for AI agents: why the default should be your own machine, not a hosted vector DB.
• Why hybrid retrieval beats pure vector search: what BM25, dense embeddings, and a cross-encoder each cover.
• A memory that says "I don't know": abstention, and the number we publish to prove it.
• Benchmarking agent memory on LoCoMo: the method and a script to reproduce the numbers.
• Where memkeeper fits: an honest comparison to mem0, Zep, and Graphiti.
• Getting started in ten minutes: from install to recall, including MCP wiring.

Memkeeper family

Warden is a companion capability broker and execution gate: it decides whether an agent's requested action (a shell command, a file read/write) is allowed by a declared, auditable policy, and logs every decision. memkeeper remembers; Warden guards.

License

Dual-licensed under either of MIT or Apache-2.0 at your option.

Contributing

See CONTRIBUTING.md. Contributions require signing the project Contributor License Agreement — the CLA bot prompts you on your first pull request. You keep the copyright to your contributions.