rekal 1.0.0

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

rekal

Long-term memory for LLMs, as an MCP server.

Every conversation starts from scratch — your LLM forgets what you told it yesterday. rekal is a small MCP server that stores memories in a single SQLite file and retrieves them with hybrid search. Install it, point your MCP client at it, and your LLM starts remembering things between sessions.

pip install rekal

Why I built this

I got tired of repeating myself. "I prefer Ruff." "We deploy with tags." "The auth service lives in services/auth." Every new conversation, same explanations.

Existing memory tools either do keyword search (which misses anything phrased differently) or vector search (which misses exact terms). I wanted both, plus a bias toward recent memories so stale stuff sinks naturally. And I wanted it in a single file I could back up by copying.

How search works

rekal runs three searches and blends the results:

score = 0.4 · BM25(keyword match)
      + 0.4 · cosine(semantic similarity)
      + 0.2 · exp(-t/half_life)

So a memory about "deploying the auth service to staging" shows up whether you search for "deploy auth" or ask about "shipping the login system to pre-prod". Recent memories rank higher, but old ones still surface if they're relevant.

Embeddings run locally with fastembed — no API keys, no network calls.

Quick start

Add to your MCP client config (Claude Desktop, Cursor, Claude Code, etc.):

{
  "mcpServers": {
    "rekal": {
      "command": "rekal"
    }
  }
}

rekal creates ~/.rekal/memory.db on first run. That file is your entire memory — portable, backupable, yours.

Requires Python 3.14+.

What it looks like in practice

Your LLM picks up on things worth remembering and stores them:

User: "I prefer Ruff over Black for formatting"
LLM:  → memory_store("User prefers Ruff over Black", type="preference")

Weeks later, in a completely different conversation:

User: "Set up linting for my new project"
LLM:  → memory_search("formatting linting preferences")
      ← "User prefers Ruff over Black" (score: 0.92)

When knowledge changes, it doesn't just pile up — old versions stay linked:

LLM: → memory_supersede(old_id="mem_abc", new_content="API moved from v2 to v3")

And when things contradict each other, you can ask:

LLM: → memory_conflicts(project="backend")
     ← "use PostgreSQL for everything" contradicts "migrate analytics to ClickHouse"

Tools

rekal exposes 16 tools over MCP.

Core

Tool	Description
memory_store	Store a memory with type, project, tags, and conversation scope
memory_search	Hybrid search — BM25 + vector + recency in one query
memory_update	Update content, tags, or type (re-embeds automatically)
memory_delete	Delete a memory by ID

Smart write

Tool	Description
memory_supersede	Replace a memory while preserving the old one as history
memory_link	Link memories: supersedes, contradicts, related_to
memory_build_context	Relevant memories + conflicts + timeline for a query, in one call

Introspection

Tool	Description
memory_similar	Find memories similar to a given one
memory_topics	Topic summary grouped by type
memory_timeline	Chronological view with optional date range filters
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

Tool	Description
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

Memory types

Memories are tagged with a type so search can be scoped:

Type	For	Example
fact	Things that are true	"The API rate limit is 1000 req/min"
preference	How the user likes things	"Prefers dataclasses over hand-written __init__"
procedure	Steps to do something	"Deploy: git tag vX.Y.Z && git push --tags"
context	What's going on right now	"Currently rewriting the payment service"
episode	Things that happened	"Debugged the OOM — root cause was unbounded cache"

Architecture

Everything lives in one SQLite file:

rekal
  │
  SQLite ──┬── FTS5 index ──── keyword relevance (BM25)
           ├── sqlite-vec ──── semantic similarity (384d vectors)
           ├── recency ─────── exponential decay (30-day half-life)
           └── memory links ── supersedes / contradicts / related_to

Conversations form a DAG — follow-ups, branches, merges — so you can navigate interaction history the way you'd navigate a Git log.

CLI

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

License

MIT