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README.md

MemoryLayer.ai Server

API-first memory infrastructure for LLM-powered agents.

MemoryLayer provides cognitive memory capabilities for AI agents, including episodic, semantic, procedural, and working memory with vector-based retrieval, graph-based associations, and server-side computation sandboxes.

Features

  • Cognitive Memory Architecture — Episodic, semantic, procedural, and working memory types
  • Vector Search — SQLite with sqlite-vec for efficient similarity search
  • Knowledge Graph — 60+ relationship types organized into 11 categories for memory associations
  • Context Environment — Server-side Python sandboxes for memory analysis and computation
  • Session Management — Working memory with TTL and commit to long-term storage
  • REST API — Full-featured HTTP API for all memory operations
  • Multiple Embedding Providers — OpenAI, Google GenAI, embed-server (self-hosted GPU via memorylayer-embed-server), and mock (testing)
  • Health Endpoints — /health and /health/ready for monitoring and readiness checks

Installation

# Basic installation
pip install memorylayer-server

# With OpenAI embeddings
pip install memorylayer-server[openai]

# With Google GenAI embeddings
pip install memorylayer-server[google]

# Self-hosted embeddings: install + run memorylayer-embed-server separately
# (no extras here — the main server only speaks HTTP to embed-server)
# pip install memorylayer-embed-server[gpu]

# All cloud embedding providers + LLM + document parsers
pip install memorylayer-server[all]

Package name: memorylayer-server (PyPI) Import name: memorylayer_server

Quick Start

Start the HTTP Server

# Start on default port (61001)
memorylayer serve

# Custom port
memorylayer serve --port 8080

# Bind to all interfaces
memorylayer serve --host 0.0.0.0

# Debug mode
memorylayer serve --verbose

Docker

The official Docker image comes with all optional dependencies pre-installed and pins MEMORYLAYER_EMBEDDING_PROVIDER=embed_server, which delegates all GPU/ML work to a peer memorylayer-embed-server container — set MEMORYLAYER_EMBED_SERVER_URL accordingly, or override the provider entirely (openai/google for cloud, hash for a dependency-free lexical default):

docker run -d \
  --name memorylayer \
  -p 61001:61001 \
  -v memorylayer-data:/data \
  scitrera/memorylayer-server

With OpenAI embeddings:

docker run -d \
  --name memorylayer \
  -p 61001:61001 \
  -v memorylayer-data:/data \
  -e MEMORYLAYER_EMBEDDING_PROVIDER=openai \
  -e MEMORYLAYER_EMBEDDING_OPENAI_API_KEY=sk-... \
  scitrera/memorylayer-server

API Usage

The server exposes a REST API. Use any HTTP client, or install the Python SDK (pip install memorylayer-client) for a typed client:

from memorylayer import MemoryLayerClient

async with MemoryLayerClient(base_url="http://localhost:61001") as client:
    # Store a memory
    memory = await client.remember(
        content="User prefers Python for backend development",
        type="semantic",
        importance=0.8,
        tags=["preferences", "programming"]
    )

    # Recall memories
    results = await client.recall(
        query="What programming languages does the user like?",
        limit=5
    )

    # Create associations
    await client.associate(
        source_id=memory.id,
        target_id=other_memory.id,
        relationship="related_to",
        strength=0.9
    )

Configuration

Environment Variables

Variable Default Description
MEMORYLAYER_SERVER_HOST 127.0.0.1 Server bind address
MEMORYLAYER_SERVER_PORT 61001 Server port
MEMORYLAYER_DATA_DIR ~ State root, not the data directory itself. Data is written to <root>/.config/memorylayer-server/, so the default resolves to ~/.config/memorylayer-server and MEMORYLAYER_DATA_DIR=/data resolves to /data/.config/memorylayer-server.
MEMORYLAYER_SQLITE_STORAGE_PATH memorylayer.db SQLite database path (relative to the resolved data directory)
MEMORYLAYER_EMBEDDING_PROVIDER hash Embedding provider (hash, openai, google, embed_server, mock). The default hash is lexical and dependency-free so the server runs with no setup — set a real provider for production (see below).
MEMORYLAYER_SESSION_SERVICE persistent persistent (survives restarts, uses the storage backend) or in-memory (ephemeral)
MEMORYLAYER_EMBEDDING_OPENAI_API_KEY — OpenAI API key
MEMORYLAYER_EMBEDDING_GOOGLE_API_KEY — Google API key
MEMORYLAYER_EMBEDDING_DIMENSIONS 384 (hash / embed_server) Vector width. Must match the model actually producing the embeddings — see Embedding dimensions
MEMORYLAYER_EMBED_SERVER_URL http://localhost:61051 Base URL for memorylayer-embed-server (used by embed_server provider)
MEMORYLAYER_EMBED_TRANSPORT http http for direct calls or aether for cross-DC mTLS via Aether

Deterministic memory and generation policy

MEMORYLAYER_ENRICHMENT_POLICY controls generative model use at the central LLM service boundary. Its compatibility default is generative. deterministic rejects every completion before provider selection while remember, RAG recall, checkpoint capture, context packs, and context deltas continue to work. adaptive allows only activities named in MEMORYLAYER_ADAPTIVE_GENERATION_ACTIVITIES (default: reflection,synthesis). Explicit LLM/agentic recall modes return generation_not_allowed when policy does not authorize their required activity.

Variable Default Rollback value / effect
MEMORYLAYER_ENRICHMENT_POLICY generative generative restores compatibility behavior; deterministic is the zero-generation guarantee
MEMORYLAYER_ADAPTIVE_GENERATION_ACTIVITIES reflection,synthesis Empty disables all adaptive generation
MEMORYLAYER_GENERATION_MAX_CALLS 8 Per-operation call ceiling
MEMORYLAYER_GENERATION_MAX_INPUT_TOKENS 100000 Per-operation estimated input ceiling
MEMORYLAYER_GENERATION_MAX_OUTPUT_TOKENS 16384 Per-operation reserved/actual output ceiling
MEMORYLAYER_EXTRACTIVE_TIERS_ENABLED true false disables new extractive tier writes
MEMORYLAYER_SESSION_CHECKPOINT_CAPTURE_ENABLED true false disables the checkpoint endpoint without deleting captures
MEMORYLAYER_SESSION_CHECKPOINT_MAX_BYTES 1048576 Maximum UTF-8 raw segment size
MEMORYLAYER_CONTEXT_PACK_ENABLED true false disables pack/delta reads without deleting events
MEMORYLAYER_CONTEXT_CURSOR_SECRET built-in local default Set a stable private value shared by all server replicas
MEMORYLAYER_CONTEXT_EVENT_RETENTION_DAYS 30 Retained delta window; expired cursors require a full pack
MEMORYLAYER_RELATIONAL_RECALL_ENABLED true false leaves stored relation/evidence rows intact but disables the recall arm
MEMORYLAYER_RETRIEVAL_CONFIDENCE_ENABLED true false returns compatibility confidence fields
MEMORYLAYER_RECALL_TOKEN_BUDGET_DEFAULT 0 0 preserves count/detail behavior unless a caller supplies a budget

Session recovery APIs are additive:

  • POST /v1/sessions/{session_id}/checkpoints durably stores the exact raw transcript segment before deterministic indexing. Idempotency is scoped to the session and key.
  • POST /v1/sessions/{session_id}/context-pack returns a ready-to-inject, deterministic rendering and opaque delta cursor under a hard token budget.
  • POST /v1/sessions/{session_id}/context-delta returns at-least-once changes, including tombstones, and advances only through delivered events.

Recall accepts budget_tokens, include_confidence, and include_relations. Remember accepts explicit typed entity relations with source spans. Structural relations are stored separately from similarity associations and remain backed by active source-memory evidence.

Connectors can also acquire general professional-work relations without an LLM by placing a typed profile under metadata.knowledge_work. The profile maps owners, assignees, authors, contributors, reviewers, approvers, projects, dependencies, decisions, references, evidence, impacts, and topics onto the canonical entity registry and relation store. See docs/DESIGN_knowledge_work_relations.md. Connector-shaped requests may instead declare metadata.connector_type and provide ordinary source fields such as title, assignee, creator, reviewers, project, dependencies, or their supported vendor forms. The single-memory API, batch API, email adapter, and document pipeline normalize that explicit envelope into the same auditable profile. Arbitrary metadata without a connector declaration is never interpreted.

Prometheus metrics separate memorylayer_generation_*, memorylayer_embedding_*, and memorylayer_reranker_* work. Generation metric labels are limited to activity, policy, and outcome; provider/profile attribution is emitted in logs.

Embedding dimensions

Choose your embedding model before ingesting data. The vector width it produces is written into every stored memory, so it is a property of your data, not just of configuration — and nothing in the schema stops you from mixing widths.

Provider Default model Dimensions
hash (default) — (lexical feature hashing) 384
embed_server sentence-transformers/all-MiniLM-L6-v2 384
embed_server (GPU/vLLM) Qwen/Qwen3-VL-Embedding-2B 2048
openai text-embedding-3-small 1536
google gemini-embedding-001 768

Set MEMORYLAYER_EMBEDDING_DIMENSIONS to match whatever model you actually run — and set it on the memorylayer-embed-server peer too, if you use one.

What goes wrong if you don't. Two memories embedded at different widths cannot be compared:

  • On SQLite with sqlite-vec (the default), vec_distance_cosine() raises on a width mismatch and the entire query returns no results — not just the mismatched rows. A few wrong-width memories break recall for the whole workspace.
  • If the sqlite-vec extension is unavailable, the pure-Python fallback scores mismatched vectors as 0.0, so older memories silently stop matching.

No schema migration is involved — memories.embedding is a plain BLOB with no dimension in the DDL — but changing dimensions on a populated deployment means re-embedding the existing memories, or starting a fresh workspace.

Matching widths is necessary but not sufficient: the default hash provider is also 384-d, so switching from hash to MiniLM produces vectors that compare without error while being semantically unrelated to the ones already stored. Re-embed when you change the model, not only when you change the width.

Embedding Providers

The default (hash) works offline with zero setup, but is not a semantic model. It hashes tokens into a vector, so it matches on shared words rather than meaning — fine for local development, tests, and the retrieval-eval harness; not for production recall quality. The server logs a warning at startup while it is in use. Pick one of the real providers below for anything beyond trying it out.

The legacy in-process providers local (sentence-transformers), colpali (colpali-engine), and qwen3-vl (qwen-vl-utils) were removed. All self-hosted/multi-vector embedding now routes through the embed_server provider, which delegates to the standalone memorylayer-embed-server package. Setting any of those legacy values for MEMORYLAYER_EMBEDDING_PROVIDER raises a startup error with migration guidance.

Embed-server (self-hosted) — Run memorylayer-embed-server as a peer process or container; the main server only speaks HTTP to it. This is what the published Docker image is pinned to:

# In a GPU-equipped peer:
pip install memorylayer-embed-server[gpu]
memorylayer-embed-server serve --port 61051

# In the main server process:
export MEMORYLAYER_EMBEDDING_PROVIDER=embed_server
export MEMORYLAYER_EMBED_SERVER_URL=http://embed-host:61051
memorylayer serve

OpenAI:

pip install memorylayer-server[openai]
export MEMORYLAYER_EMBEDDING_PROVIDER=openai
export MEMORYLAYER_EMBEDDING_OPENAI_API_KEY=sk-...
memorylayer serve

Google GenAI:

pip install memorylayer-server[google]
export MEMORYLAYER_EMBEDDING_PROVIDER=google
export MEMORYLAYER_EMBEDDING_GOOGLE_API_KEY=...
memorylayer serve

Mock (testing only):

export MEMORYLAYER_EMBEDDING_PROVIDER=mock
memorylayer serve

LLM Provider (Optional)

Some features (reflection, smart extraction, context environment queries) require an LLM provider configured via profiles:

# OpenAI
export MEMORYLAYER_LLM_PROFILE_DEFAULT_PROVIDER=openai
export MEMORYLAYER_LLM_PROFILE_DEFAULT_API_KEY=sk-...

# Anthropic Claude
export MEMORYLAYER_LLM_PROFILE_DEFAULT_PROVIDER=anthropic
export MEMORYLAYER_LLM_PROFILE_DEFAULT_API_KEY=sk-ant-...

# Google Gemini
export MEMORYLAYER_LLM_PROFILE_DEFAULT_PROVIDER=google
export MEMORYLAYER_LLM_PROFILE_DEFAULT_API_KEY=...

Profile configuration variables (replace DEFAULT with any profile name):

Variable Description
MEMORYLAYER_LLM_PROFILE_<NAME>_PROVIDER Provider (openai, anthropic, google)
MEMORYLAYER_LLM_PROFILE_<NAME>_API_KEY API key
MEMORYLAYER_LLM_PROFILE_<NAME>_MODEL Model name override
MEMORYLAYER_LLM_PROFILE_<NAME>_BASE_URL Custom API base URL
MEMORYLAYER_LLM_PROFILE_<NAME>_MAX_TOKENS Max response tokens
MEMORYLAYER_LLM_PROFILE_<NAME>_TEMPERATURE Sampling temperature

Without an LLM provider, core memory operations (remember, recall, forget, associate) work normally, but synthesis features will be unavailable.

Context Environment

The Context Environment provides server-side Python sandboxes for memory analysis and computation. See Context Environment documentation for details.

Configuration:

Variable Default Description
MEMORYLAYER_CONTEXT_EXECUTOR smolagents Executor backend (smolagents or restricted)
MEMORYLAYER_CONTEXT_MAX_EXEC_SECONDS 30 Timeout per code execution
MEMORYLAYER_CONTEXT_MAX_OUTPUT_CHARS 50000 Max captured stdout characters
MEMORYLAYER_CONTEXT_QUERY_MAX_TOKENS 4096 Max tokens for server-side LLM queries
MEMORYLAYER_CONTEXT_MAX_MEMORY_BYTES 268435456 Memory limit per sandbox (256 MB)
MEMORYLAYER_CONTEXT_RLM_MAX_ITERATIONS 10 Max iterations for RLM loops
MEMORYLAYER_CONTEXT_RLM_MAX_EXEC_SECONDS 120 Total timeout for RLM loops
MEMORYLAYER_CONTEXT_MAX_OPERATIONS 1000000 Max operations per sandbox execution

Storage

The default storage backend is SQLite with sqlite-vec for vector operations. The database file defaults to ~/.config/memorylayer-server/memorylayer.db and contains all memories, embeddings, associations, and session data.

Override the data directory:

export MEMORYLAYER_DATA_DIR=/var/lib/memorylayer

Override the database path:

export MEMORYLAYER_SQLITE_STORAGE_PATH=/var/lib/memorylayer/data.db

Recall Modes

The active recall mode is RAG (vector similarity + graph traversal). LLM and Hybrid modes are deprecated.

MCP Integration

The Model Context Protocol (MCP) server is a separate TypeScript package (@scitrera/memorylayer-mcp-server), not part of this Python server CLI.

To use MemoryLayer with Claude Code or Claude Desktop:

  1. Start the HTTP server: memorylayer serve
  2. Install and configure the MCP server: npm install -g @scitrera/memorylayer-mcp-server

See the MCP Server documentation for setup instructions.

Health Checks

  • GET /health — Basic health check (returns immediately)
  • GET /health/ready — Readiness check (verifies storage connectivity)

The Docker image includes a built-in health check at /health (every 30s, 10s startup grace period).

Documentation

License

Apache 2.0 License -- see LICENSE for details.