rekal 1.6.0

Long-term memory MCP server for LLMs — hybrid search in a single SQLite file

rekal

Long-term memory for LLMs. One SQLite file, no cloud, no API keys.

rekal is an MCP server that gives Claude Code persistent memory across sessions. Memories are stored locally in SQLite and retrieved with hybrid search (BM25 keywords + vector semantics + recency decay). Nothing leaves your machine.

Session 1:   "I prefer Ruff over Black"  → memory_store(...)
Session 47:  "Set up linting"            → memory_search("formatting preferences")
                                          ← "User prefers Ruff over Black" (0.92)
                                          Sets up Ruff without asking.

Install

pip install rekal

or with uv:

uv tool install rekal

Requires Python 3.11+. On first run, rekal creates ~/.rekal/memory.db — a single file that holds everything.

Setup

Two steps: add the MCP server, then install the skills plugin.

1. Add the MCP server — gives Claude Code the memory tools:

claude mcp add rekal rekal

2. Install the skills plugin — teaches Claude Code when and how to use those tools:

claude plugin marketplace add janbjorge/rekal
claude plugin install rekal-skills@rekal

The MCP server provides the tools. The skills drive the behavior — session capture, deduplication, hygiene. Both are required.

Skills

Skill	Trigger	What it does
rekal-init	/rekal-init	Scans codebase and bootstraps rekal with project knowledge
rekal-save	Auto on session end	Deduplicates and stores durable knowledge from the conversation
rekal-usage	/rekal-usage	Teaches agents how to use rekal effectively
rekal-hygiene	/rekal-hygiene	Finds conflicts, duplicates, stale data — proposes fixes

Tools

rekal exposes 16 MCP tools grouped into four categories.

Core — read and write memories:

Tool	Purpose
memory_store	Store a memory with type, project, and tags
memory_search	Hybrid search across all memories
memory_update	Edit content, tags, or type of an existing memory
memory_delete	Remove a memory by ID

Smart write — manage knowledge over time:

Tool	Purpose
memory_supersede	Replace a memory while linking the old one as history
memory_link	Connect memories: supersedes, contradicts, or related_to
memory_build_context	One call that returns relevant memories + conflicts + timeline

Introspection — explore what's stored:

Tool	Purpose
memory_similar	Find memories similar to a given one
memory_topics	Topic summary grouped by type
memory_timeline	Chronological view with optional date range
memory_related	All links to and from a memory
memory_health	Database stats: counts by type, project, date range
memory_conflicts	Find memories that contradict each other

Conversations — track session threads:

Tool	Purpose
conversation_start	Start a conversation, optionally linked to a previous one
conversation_tree	Get the full conversation DAG
conversation_threads	List recent conversations with memory counts
conversation_stale	Find inactive conversations

How it works

Storage

Everything lives in a single SQLite file (~/.rekal/memory.db). Three subsystems share it:

• memories table — content, type, project, tags, timestamps, access counts
• FTS5 virtual table — full-text index over content+tags+project, auto-synced via triggers on insert/update/delete
• sqlite-vec virtual table — 384-dimensional vector index for semantic search

When you store a memory, rekal writes the row, updates the FTS5 index (automatically), and inserts a vector embedding. When you update content, it re-embeds automatically.

Memory links (supersedes, contradicts, related_to) are stored in a separate table. memory_supersede writes the new memory and creates a supersedes link to the old one in a single operation — old knowledge stays queryable but the link makes the lineage explicit.

Embeddings

rekal uses fastembed with the BAAI/bge-small-en-v1.5 model (384 dimensions). It runs locally via ONNX — no API calls, no network, no tokens billed. The model is downloaded once on first use (~50MB) and cached.

Vectors are stored as packed floats in sqlite-vec and queried with approximate nearest-neighbor search.

Search

Every memory_search runs two parallel lookups, merges candidates, then scores them:

1. Vector search   → top 3×limit candidates by cosine distance
2. FTS5 search     → top 3×limit candidates by BM25 rank
3. Union the candidate sets
4. For each candidate, compute:

   score = w_fts × sigmoid(-BM25)        ← keyword relevance     (default 0.4)
         + w_vec × (1 - cosine_distance)  ← semantic similarity  (default 0.4)
         + w_recency × exp(-0.693 × days/half_life)  ← recency  (default 0.2, 30-day half-life)

5. Sort by score, return top limit

Why three signals? Keywords alone miss synonyms ("deploy" vs "ship to prod"). Vectors alone miss exact identifiers (BAAI/bge-small-en-v1.5 needs exact match). Recency alone buries important old knowledge. The blend covers all three failure modes.

Why 0.4/0.4/0.2 defaults? Keywords and semantics contribute equally — neither dominates. Recency is a tiebreaker at 0.2: a one-day-old memory scores ~0.195, a 90-day-old memory still scores ~0.025. Old memories surface when keyword or semantic match is strong enough.

Configurable weights. All weights and the half-life are configurable at two levels:

• Per project — memory_set_config(key="w_recency", value="0.5", project="my-app") persists in the database across sessions. Searches scoped to that project automatically use its config.
• Per search — pass w_fts, w_vec, w_recency, or half_life directly to memory_search or memory_build_context to override for a single query.

Lookup order: per-search params > project config > hardcoded defaults (0.4/0.4/0.2, 30-day half-life).

Why over-fetch 3x? Filtering by project/type/conversation happens after scoring (no dynamic SQL injection). Over-fetching ensures enough candidates survive filtering to fill the requested limit.

Why SQLite?

• Single file — copy it, back it up, version-control it, delete it to start fresh
• Zero config — no daemon, no port, no connection string
• FTS5 built-in — BM25 ranking with no external search engine
• sqlite-vec extension — vector search in the same process, no separate vector DB
• Sub-millisecond — everything is local disk I/O, no network round-trips
• Portable — works on macOS, Linux, Windows without different backends

CLI

rekal serve    # Run as MCP server (default)
rekal health   # Database health report
rekal export   # Export all memories as JSON

License

MIT