Rekal

rekal

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

rekal is an MCP server that gives AI coding agents 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.

[How it works](#how-it-works) · [Quickstart](#quickstart-claude-code) · [Install](#install) · [Setup](#setup--claude-code) · [Updating](#updating) · [Tools](#tools) · [Under the hood](#under-the-hood) · [Troubleshooting](#troubleshooting--claude-code)

Works with any MCP-capable agent: [Claude Code](#setup--claude-code), [Codex CLI](#setup--codex-cli), [OpenCode](#setup--opencode).

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.

How it works

1. Store — the agent saves a durable fact with memory_store: a preference, a decision, a non-obvious discovery.
2. Index — rekal writes it to SQLite and builds two indexes over it: a BM25 keyword index and a 384-dimensional vector embedding, both computed locally with no network calls.
3. Recall — in a later session the agent calls memory_search (or memory_build_context). rekal blends keyword match, semantic similarity, and recency into a single score and returns the top hits.

All state is a single file: ~/.rekal/memory.db. No daemon, no cloud, no API keys. For the scoring formula, schema, and embedding model, see [Under the hood](#under-the-hood).

Quickstart (Claude Code)

uv tool install rekal                            # 1. install rekal (or: pip install rekal)
claude mcp add --scope user rekal -- rekal       # 2. register the MCP server (all projects)
claude plugin marketplace add janbjorge/rekal    # 3. add the plugin marketplace
claude plugin install rekal-skills@rekal         # 4. install the plugin

Then add "autoMemoryEnabled": false to ~/.claude/settings.json so Claude Code's built-in memory doesn't compete with rekal.

Restart Claude Code and the agent has persistent memory. For what each step does, the other agents (Codex CLI, OpenCode), and the rationale behind disabling built-in memory, read on.

Install

pip install rekal
# or
uv tool install rekal

Requires Python 3.11+. On first run, rekal creates ~/.rekal/memory.db. To upgrade an existing install later, see [Updating](#updating).

Setup — Claude Code

Three steps: add the MCP server, install the plugin, and disable built-in memory.

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

claude mcp add --scope user rekal -- rekal

--scope user registers rekal for all your projects. Without it, claude mcp add defaults to local scope and the server loads only in the project where you ran it (MCP scopes) — memory should follow you everywhere. The -- separates Claude Code's own flags from the command that launches the server; stdio is the default transport.

2. Install the plugin — teaches Claude Code when to use those tools, and prevents conflicts with built-in memory:

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

3. Disable built-in auto memory — add "autoMemoryEnabled": false to ~/.claude/settings.json:

{
  "autoMemoryEnabled": false
}

Why disable built-in memory, and what if I forget?

Why is this required? Left enabled, Claude Code's built-in auto memory competes with rekal. It loads its own memory into the agent's context (context layout) and the agent favors it, writing to a flat file with no search, no deduplication, no ranking. Disabling it (autoMemoryEnabled: false, settings docs) removes the competitor. The plugin's hooks then re-assert rekal: SessionStart restores the context injection auto memory normally provided, and UserPromptSubmit reinforces it every turn.

What if I forget? The plugin's block-memory-writes and redirect-memory-reads hooks catch flat-file memory access (MEMORY.md/.txt, memories.*) and redirect the agent to rekal as a safety net, but it wastes turns hitting them. Disabling auto memory is cleaner.

Can the plugin do this automatically? No — Claude Code only lets a plugin's settings.json set the agent and subagentStatusLine keys (plugin settings); it cannot touch autoMemoryEnabled. This manual step is the only way.

What the plugin provides — hooks and skills

Hooks (automatic, no user action needed):

| Hook | Event | What it does | |------|-------|-------------| | session-start | SessionStart | Reminds agent to call memory_build_context before doing anything | | user-prompt-submit | UserPromptSubmit | Re-asserts rekal as the memory system every turn — stops the agent drifting back to file-based memory as context grows | | block-memory-writes | PreToolUse on Edit/Write | Denies writes to flat-file memory (MEMORY.md/.txt, memories.*) with a reason redirecting to rekal tools | | redirect-memory-reads | PreToolUse on Read | Denies reads of flat-file memory and tells the agent to call memory_build_context instead — a missing file no longer reads as "no memory exists" |

Skills (user-invocable):

| Skill | Trigger | What it does | |-------|---------|-------------| | rekal-init | /rekal-init | Scans codebase and bootstraps rekal with project knowledge | | rekal-save | /rekal-save or 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 |

Setup — Codex CLI

One step. rekal is a standard MCP stdio server — no plugin system, no competing memory to disable (Codex memories are off by default).

Add to ~/.codex/config.toml (Codex MCP docs):

[mcp_servers.rekal]
command = "rekal"

# optional: scope all memories to a project automatically
[mcp_servers.rekal.env]
REKAL_PROJECT = "my-project"

Instruct the agent to call memory_build_context at session start. Add to your project's AGENTS.md:

Call memory_build_context with your current task before exploring the codebase.

If you've enabled Codex memories

(memories = true in ~/.codex/config.toml): disable them to avoid competing memory instructions.

[features]
memories = false

Setup — OpenCode

One step. OpenCode has no built-in memory system, so rekal plugs in cleanly with no conflicts.

Add to opencode.jsonc in your project root (OpenCode MCP docs):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "rekal": {
      "type": "local",
      "command": ["rekal"],
      "enabled": true,
      "environment": {
        "REKAL_PROJECT": "my-project"
      }
    }
  }
}

OpenCode does not auto-read AGENTS.md; you must list instruction files explicitly (OpenCode config docs). Add to your opencode.jsonc:

{
  "$schema": "https://opencode.ai/config.json",
  "instructions": ["AGENTS.md"]
}

Updating

Update rekal (the MCP server)

pip install -U rekal
# or
uv tool upgrade rekal

Restart your agent so it relaunches the server. The SQLite schema migrates automatically on the next start: new columns are added in place and existing memories are preserved. No manual migration step, no data loss. To start fresh instead, delete ~/.rekal/memory.db (rekal recreates it on next run).

Update the Claude Code plugin

Third-party marketplaces have auto-update off by default (auto-update docs), so refresh manually, then reload:

claude plugin marketplace update rekal     # refresh the catalog
claude plugin install rekal-skills@rekal   # reinstall to pull the update

If hooks or skills are still missing afterward, Claude Code is serving a stale plugin cache. Clear it, restart Claude Code, then reinstall (official remedy):

rm -rf ~/.claude/plugins/cache

Tools

rekal exposes 21 MCP tools across four categories. The three you'll use most:

| Tool | Purpose | |------|---------| | memory_store | Store a durable memory with type, project, and tags | | memory_search | Hybrid search across memories; filter by tier (durable/scratch) | | memory_build_context | One call returning durable + scratch memories, conflicts, and timeline |

All 21 tools — core, smart write, introspection, conversations

Core — read and write memories:

| Tool | Purpose | |------|---------| | memory_store | Store a durable memory with type, project, and tags | | memory_store_scratch | Store a transient note that auto-expires after ttl_hours (default 24h) | | memory_search | Hybrid search across memories; filter by tier (durable/scratch) | | memory_update | Edit content, tags, or type of an existing memory | | memory_delete | Remove a memory by ID | | memory_prune | Bulk-delete by scope (project / type / age); dry-run by default | | memory_set_project | Set the default project for the current session | | memory_set_config | Persist per-project scoring weights (w_fts, w_vec, w_recency, half_life) |

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 returning durable + scratch memories (per-tier budgets), conflicts, and 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 |

Under the hood

Storage

Everything lives in ~/.rekal/memory.db. Three subsystems share it:

• memories table — content, type, project, tags, timestamps, access counts, plus tier (durable or scratch) and optional expires_at
• FTS5 virtual table — full-text index over content+tags+project, auto-synced via triggers
• sqlite-vec virtual table — 384-dimensional vector index for semantic search

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

Tiers. Durable memories live forever; scratch memories carry an expires_at and are hard-deleted on server start once past their TTL. Search, timeline, and topics hide expired scratch entries automatically. Use scratch for in-flight hypotheses and working notes that should not pollute the durable store.

Data model

One table does the work; everything else hangs off it.

| Table | Holds | |---|---| | memories | the atomic unit: content + memory_type (semantic) + tier (lifecycle) + scope, provenance, tags | | memories_fts | FTS5 keyword index, trigger-synced to memories | | memory_vec | sqlite-vec 384-dim embedding, 1:1 with memories (synced in Python, no trigger) | | memory_links | memory→memory graph: supersedes / contradicts / related_to | | conversations + conversation_links | session threads and their graph | | project_config | per-project scoring-weight overrides |

A memory has three orthogonal axes: type (fact / preference / procedure / context / episode), tier (durable, or scratch with a TTL), and links (the graph). The full schema — every column, trigger, foreign-key note, and query lifecycle — lives in [docs/data-model.md](docs/data-model.md).

Embeddings

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

Search

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

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)

Why three signals? Keywords miss synonyms ("deploy" vs "ship to prod"). Vectors miss exact identifiers. Recency alone buries important old knowledge. The blend covers all three failure modes.

Configurable weights — four resolution layers + .rekal/config.yml

All weights and half-life are configurable at four levels:

| Priority | Source | Set by | Persists? | |----------|--------|--------|-----------| | 1 (highest) | Per-search params | memory_search(..., w_fts=0.8) | No — single query only | | 2 | Database project config | memory_set_config(key, value, project) | Yes — SQLite, across sessions | | 3 | .rekal/config.yml | Checked into version control | Yes — shared with team | | 4 (lowest) | Hardcoded defaults | Built into rekal | Always: 0.4 / 0.4 / 0.2, 30-day half-life |

Layers resolve per-key independently. A .rekal/config.yml setting w_fts and a DB override for half_life combine, and each key uses its highest-priority source.

# .rekal/config.yml
scoring:
  w_fts: 0.6
  w_vec: 0.3
  w_recency: 0.1
  half_life: 14.0

Full ranking reference — normalization, candidate retrieval, weight resolution, and a tuning guide — in [docs/scoring.md](docs/scoring.md).

Why SQLite?

• Single file — copy, back up, version-control, or delete to start fresh
• Zero config — no daemon, no port, no connection string
• FTS5 built-in — BM25 ranking without an external search engine
• sqlite-vec extension — vector search in the same process, no separate vector DB
• Sub-millisecond — local disk I/O, no network round-trips

Troubleshooting — Claude Code

Agent still writes to MEMORY.md

1. Check autoMemoryEnabled is false in ~/.claude/settings.json
2. Check the plugin is installed: claude plugin list should show rekal-skills

Agent doesn't call memorybuildcontext at session start

The SessionStart and UserPromptSubmit hooks inject a reminder at session start and on every turn. If the agent still ignores it, add to your project's CLAUDE.md:

Call memory_build_context before exploring the codebase.

Memories not being stored

Check the MCP server is running: claude mcp list should show rekal. If missing:

claude mcp add --scope user rekal -- rekal

Hooks or skills missing after a plugin update

Claude Code may serve a stale plugin

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: janbjorge
• Source: janbjorge/rekal
• License: MIT
• Homepage: https://pypi.org/project/rekal/

Install and usage instructions live in the source repository linked above.