cellin 0.3.0.dev2399760517601

Build long-lived multimodal memory, dream over it, and retrieve context with transparent weighting.

cellin

Cellin builds long-lived multimodal memory, dreams over it to consolidate ideas, and retrieves context with transparent weighted ranking.

Install

From source today:

git clone https://github.com/ben-ranford/cellin.git
cd cellin
make bootstrap

Install from PyPI:

python3 -m pip install cellin

Quickstart

From the repository root:

WORKSPACE=.cellin-workspace
python3 -m uv run cellin init --workspace "$WORKSPACE"
python3 -m uv run cellin ingest --config "$WORKSPACE/cellin.json" --input examples/starter/seed_envelopes.json
python3 -m uv run cellin retrieve --config "$WORKSPACE/cellin.json" --query "memory graph retrieval" --top-k 2
python3 -m uv run cellin dream --config "$WORKSPACE/cellin.json" --strategy abstraction
python3 -m uv run cellin eval run --suite smoke --config "$WORKSPACE/cellin.json" --output "$WORKSPACE/smoke.json"
python3 -m uv run cellin trace inspect --config "$WORKSPACE/cellin.json" --limit 5

See examples/starter/README.md for the same flow in a shorter checklist form.

Workspace config now supports role-specific storage backends:

{
  "runtime_id": "cellin-cli",
  "trace_path": "traces.jsonl",
  "profile_name": "balanced",
  "storage": {
    "memory": { "backend": "in_memory" },
    "graph": { "backend": "in_memory" },
    "vector": { "backend": "in_memory_vector_index" },
    "representation": { "backend": "in_memory_vector_index" }
  }
}

cellin init now writes this in-memory-first preset by default.

Legacy workspaces that only define database_path continue to work and are migrated to this shape behind the scenes.

For an explicit SQLite preset, set:

{
  "memory": { "backend": "sqlite", "database_path": "cellin.sqlite" },
  "graph": { "backend": "sqlite", "database_path": "cellin.sqlite" }
}

For additional SQL-backed presets, install optional dependencies as needed:

python3 -m pip install cellin[duckdb]
python3 -m pip install cellin[postgresql]
python3 -m pip install cellin[mysql]
python3 -m pip install cellin[sql-backends]

Use duckdb to point both roles at a local DB file:

{
  "memory": { "backend": "duckdb", "database_path": "cellin.duckdb" },
  "graph": { "backend": "duckdb", "database_path": "cellin.duckdb" }
}

Use postgresql and mysql with connection strings:

{
  "memory": { "backend": "postgresql", "database_path": "postgresql://user:pass@host:5432/db" },
  "graph": { "backend": "postgresql", "database_path": "postgresql://user:pass@host:5432/db" }
}

{
  "memory": { "backend": "mysql", "database_path": "mysql://user:pass@host:3306/db" },
  "graph": { "backend": "mysql", "database_path": "mysql://user:pass@host:3306/db" }
}

For document and cache-oriented presets, install optional dependencies as needed:

python3 -m pip install cellin[mongodb]
python3 -m pip install cellin[redis]
python3 -m pip install cellin[document-cache-backends]

Use mongodb when you want durable document storage for both memories and edges:

{
  "memory": { "backend": "mongodb", "database_path": "mongodb://user:pass@host:27017/cellin" },
  "graph": { "backend": "mongodb", "database_path": "mongodb://user:pass@host:27017/cellin" }
}

Use redis for low-latency cache-oriented deployments where operators control TTL or eviction:

{
  "memory": { "backend": "redis", "database_path": "redis://host:6379/0" },
  "graph": { "backend": "redis", "database_path": "redis://host:6379/0" }
}

MongoDB stores whole memory and edge documents and preserves archived entries as tombstones in the document payloads. Redis stores JSON payloads per key and also preserves archived entries as tombstones, filtering them from neighbor and edge listing reads rather than hard-deleting them.

For graph-native backends, install the optional dependencies you need:

python3 -m pip install cellin[neo4j]
python3 -m pip install cellin[memgraph]
python3 -m pip install cellin[arangodb]
python3 -m pip install cellin[graph-backends]

Use neo4j, memgraph, or arangodb for the graph role while keeping memory storage separate if you prefer:

{
  "memory": { "backend": "sqlite", "database_path": "cellin.sqlite" },
  "graph": { "backend": "neo4j", "database_path": "bolt://user:pass@host:7687" }
}

Graph-native stores persist edge relationships plus graph-local memory payload snapshots. When GraphStore.get_memory() is asked for a node that only exists as a placeholder created during edge upserts, it returns None and retrieval falls back to the configured memory store as the source of truth. That keeps mixed deployments working without caller changes while making the graph-local snapshot behavior explicit.

Primary surfaces

• CLI: cellin init, ingest, retrieve, dream, plugin list, eval run, trace inspect
• Ingestion: cellin.ingest.ArtifactEnvelope and cellin.ingest.CanonicalIngestor
• Retrieval: cellin.retrieval.WeightedRetriever, cellin.retrieval.RetrievalCandidateGenerator, and cellin.ranking.WeightedRanker
• Dreaming: cellin.dreaming.DreamRunner plus the built-in deduplication, abstraction, and contradiction-repair strategies
• Evals: cellin.evals.run_evaluation_suite and cellin.evals.run_smoke_eval
• Extensibility: cellin.runtime.PluginRegistry and the contracts exported from cellin.core