limen-memory 1.2.6

Personal memory system with knowledge graph, novelty-gated learning, and context-influenced adaptation

Limen-memory

Personal memory system for Claude Code. Persistent memory with a unified knowledge graph, novelty-gated learning, and context-influenced adaptation.

Limen-memory observes how you work, identifies patterns, and adapts behavior over time — without being told to. It integrates with Claude Code through hooks (automatic session-boundary behavior) and an MCP server (on-demand tools during conversations).

How It Works

Limen-memory runs a continuous learning loop:

1. Session start — A hook loads relevant context (session bridge, reflections, user facts, strategies, predictions) and injects it into the conversation.
2. During conversation — MCP tools let Claude record user facts (limen_learn), search memories (limen_query), load topic-specific context (limen_context), and more.
3. Session end — A hook analyzes the conversation transcript through the reflection pipeline: LLM-driven insight extraction, novelty filtering, embedding generation, knowledge graph connections, fact extraction, and strategy observations.
4. Background maintenance — A scheduler handles consolidation, aging, pruning, and backup on a priority-based schedule, triggered opportunistically at session boundaries.

Over time, limen-memory builds an increasingly accurate model of who you are and how to work with you.

Installation

# Clone and install
git clone https://github.com/Taderich73/limen-memory.git
cd limen-memory
poetry install

# Initialize database, config, skill, hooks, and MCP server
limen-memory init

limen-memory init does the following:

• Creates the data directory (~/.limen-memory/) and SQLite database
• Writes default config at ~/.limen-memory/config.yaml
• Installs the SKILL.md to ~/.claude/skills/limen-memory/
• Copies hook scripts to ~/.claude/hooks/ and registers them in ~/.claude/settings.json
• Registers the MCP server in ~/.claude/settings.json under mcpServers.limen-memory

Requirements

• Python >=3.11, <3.14
• Poetry >=2.0
• An embedding provider configured (OpenAI API key or Ollama running locally) for the reflection pipeline
• Claude Code CLI (for hook and MCP integration)

Configuration

Limen-memory is configured via environment variables, ~/.limen-memory/config.yaml, or constructor arguments (in that priority order).

Variable	Description	Default
LIMEN_EMBEDDING_PROVIDER	Embedding provider (openai or ollama)	(required for reflection)
LIMEN_EMBEDDING_API_KEY	API key for OpenAI embeddings	(required for openai provider)
LIMEN_DATA_DIR	Data directory	~/.limen-memory/
LIMEN_CLAUDE_MODEL	Claude model for LLM operations	sonnet
LIMEN_CLAUDE_TIMEOUT	LLM timeout in seconds	120

Claude Code Integration

MCP Server

The MCP server exposes limen-memory's memory system as native tools. Claude Code and Claude.ai can call these tools directly during conversations.

13 tools:

Tool	Description
limen_context	Load tiered session context, optionally focused on a topic
limen_learn	Record a user fact (idempotent on category+key)
limen_reflect	Add a single reflection with novelty gating
limen_query	Hybrid search over reflections (semantic + keyword + recency + confidence + graph)
limen_status	System health check and database statistics
limen_user_profile	View user facts, optionally filtered by category
limen_interaction_profile	View behavioral model dimensions (-1.0 to 1.0 scale)
limen_search_conversations	Full-text search over conversation summaries
limen_graph_inspect	Inspect knowledge graph — global diagnostics or specific node edges
limen_deprecate	Soft-delete a reflection
limen_consolidate	Run LLM-driven memory consolidation (dedup, merge, validate)
limen_scheduler_tick	Execute one scheduler maintenance task
limen_reflect_transcript	Reflect on a full conversation transcript

4 read-only resources: limen://profile, limen://strategies, limen://predictions, limen://status.

The MCP server is registered automatically by limen-memory init and can also be started manually:

limen-memory mcp                          # stdio transport (default)
limen-memory mcp --transport streamable-http --port 8000  # HTTP transport

Hooks

Limen-memory uses Claude Code hooks so the memory loop runs without manual invocation:

SessionStart hook (limen-memory-session-start.py) — Fires on startup and resume. Calls limen-memory context and prints the output to stdout, which Claude Code injects as conversation context. Also spawns a background scheduler-tick for opportunistic maintenance.

Stop hook (limen-memory-stop.py) — Fires at session end. Skips trivial conversations (fewer than 10 transcript lines). Spawns limen-memory reflect-transcript as a background process with lock-file concurrency protection. Also spawns scheduler-tick.

Hooks are designed to never crash a session — all exceptions are caught and the process exits cleanly.

Skill

The SKILL.md file (installed to ~/.claude/skills/limen-memory/) teaches Claude when and how to use each MCP tool during conversations.

Uninstalling

limen-memory uninstall          # or: limen-memory uninstall-hooks

Removes hook scripts from ~/.claude/hooks/, hook entries from ~/.claude/settings.json, and the MCP server entry from ~/.claude/settings.json.

CLI Reference

All commands support --json for machine-readable output.

Core Commands

Command	Description
limen-memory init	Initialize database, config, skill, hooks, and MCP server
limen-memory status	Database stats and system health
limen-memory query <topic>	Search reflections (hybrid semantic + keyword scoring)
limen-memory context <topic>	Load tiered context with rule-engine processing
limen-memory mcp	Start the MCP server (stdio or streamable-http transport)

Learning & Reflection

Command	Description
limen-memory learn -c CAT -k KEY -v VAL	Record a user fact
limen-memory add-reflection -s SECTION -c CONTENT	Add a reflection with novelty filtering
limen-memory reflect <session-file>	Run full reflection pipeline on a conversation
limen-memory reflect-transcript <path>	Parse JSONL transcript and run reflection (used by hooks)

Querying

Command	Description
limen-memory reflections	List active reflections
limen-memory user-profile	Show user facts by category
limen-memory interaction-profile	Interaction profile dimensions (-1.0 to +1.0)
limen-memory search <query>	Full-text search over conversation summaries
limen-memory graph-inspect	Knowledge graph diagnostics
limen-memory session-history	Recent session contexts
limen-memory run-rules <query>	Run rule engine on a query

Maintenance

Command	Description
limen-memory scheduler-tick	Execute highest-priority overdue task
limen-memory scheduler-status	Show scheduler state and next-due tasks
limen-memory consolidate	Memory consolidation (dedup, merge, validate)
limen-memory review-strategies	Strategy review (promote, decay, merge)
limen-memory cross-analyze	Detect patterns across conversations
limen-memory validate-predictions	Check prediction edges against evidence
limen-memory synthesize-profile	Rebuild interaction profile from evidence
limen-memory age	Age stale reflections and edges
limen-memory prune	Deprecate orphaned graph relationships
limen-memory backup	Backup database (keeps last 5)

Data Management

Command	Description
limen-memory deprecate <id>	Soft-delete a reflection
limen-memory create-edge --source ID --target ID --type T	Create a knowledge graph edge
limen-memory save-session --id ID --topic T	Save a session context
limen-memory uninstall	Remove hooks and MCP server from Claude Code

Architecture

CLI (cli.py)     ──┐
                    ├──→ Services → Stores → SQLite (WAL mode)
MCP Server (mcp/)──┘        ↕
                       Rule Engine

Both CLI and MCP server share the same service layer via service_factory.create_services().

Data stores: MemoryStore (reflections, facts, sessions), GraphStore (knowledge graph), EmbeddingStore (vectors), ConversationStore (FTS5 search), StrategyStore, ProfileStore.

Services: ReflectionService (full pipeline), ContextLoader (tiered retrieval with 6000-char budget), NoveltyFilter (embedding similarity gates with continuous temporal decay), ConsolidationService (LLM-driven dedup), Scheduler (background tasks), CrossAnalyzer, ProfileService, StrategyService.

Rule engine: Priority-based execution across 4 phases (PRE_RETRIEVAL → POST_RETRIEVAL → PRE_RESPONSE → POST_RESPONSE). Five rules: ContradictionDetection, HighConfidenceInjection, BehavioralDirective, PreferenceApplication, PredictionSurfacing.

Key Design Patterns

• Dual-write — Every entity save registers a GraphNode in the knowledge graph
• Novelty gating — New reflections pass through embedding + LLM hybrid filtering
• Soft deletes only — Deprecation via deprecated_at, never hard deletion
• Evidence accumulation — Confidence grows with momentum-based updates (consistent evidence accelerates, oscillating stalls); decays via time-continuous exponential aging
• Hybrid search — Semantic (0.35) + keyword (0.20) + recency (0.20, exponential with 90-day half-life) + confidence (0.15) + graph connectivity (0.10)
• Activity-pressure aging — Reflections in active topic areas age faster; high-confidence reflections can begin aging when superseded

Theoretical Foundation

The memory architecture is informed by computational neuroscience research on dual memory systems — the discovery that biological working memory and episodic memory solve the same problem through fundamentally different mechanisms. Limen implements this as a four-stage pipeline: fast approximate linking at write time, novelty-gated persistence, background LLM validation (consolidation), and relevance-gated retrieval that only surfaces knowledge when contextually relevant.

The decay, confidence, and forgetting mechanisms are inspired by the Titans paper ("Learning to Memorize at Test Time", arXiv:2501.00663). While the neural architecture (MAC/MAG/MAL) is specific to attention-based sequence models, the underlying update equations — surprise-as-gradient, momentum-based accumulation, and data-dependent forgetting — map onto limen-memory's novelty filtering, confidence evolution, and aging systems as continuous exponential functions replacing discrete step functions.

Development

make dev          # Full dev environment (poetry + pre-commit hooks)
make test         # Run tests (tox -e py311)
make lint         # Lint (tox -e lint)
make mypy         # Type check (tox -e typecheck)
make typecheck    # Type check (alias for make mypy)
make format       # Auto-format (tox -e format)
make all          # All quality checks
make clean        # Remove build artifacts
tox -e coverage   # Tests with coverage (80% threshold)
tox -e bandit     # Security scan

Tests use in-memory SQLite. Shared fixtures in tests/conftest.py provide pre-built model instances and store instances. Test organization mirrors source structure, with tests/test_mcp/ covering server lifecycle, tool handlers, and service factory.

License

MIT