pip install robotmem

from robotmem import learn, recall, save_perception, start_session, end_session

# Start an episode
session = start_session(context='{"robot_id": "arm-01", "task": "push"}')

# Record experience
learn(
    insight="grip_force=12.5N yields highest grasp success rate",
    context='{"params": {"grip_force": {"value": 12.5, "unit": "N"}}, "task": {"success": true}}'
)

# Retrieve experiences (structured filtering + spatial nearest-neighbor)
memories = recall(
    query="grip force parameters",
    context_filter='{"task.success": true}',
    spatial_sort='{"field": "spatial.position", "target": [1.3, 0.7, 0.42]}'
)

# Store perception data
save_perception(
    description="Grasp trajectory: 30 steps, success",
    perception_type="procedural",
    data='{"sampled_actions": [[0.1, -0.3, 0.05, 0.8], ...]}'
)

# End episode (auto-consolidation + proactive recall)
end_session(session_id=session["session_id"])

# Retrieve only successful experiences
recall(query="push to target", context_filter='{"task.success": true}')

# Find spatially nearest scenarios
recall(query="grasp object", spatial_sort='{"field": "spatial.object_position", "target": [1.3, 0.7, 0.42]}')

# Combine: success + distance < 0.05m
recall(
    query="push",
    context_filter='{"task.success": true, "params.final_distance.value": {"$lt": 0.05}}'
)

{
    "params":  {"grip_force": {"value": 12.5, "unit": "N", "type": "scalar"}},
    "spatial": {"object_position": [1.3, 0.7, 0.42], "target_position": [1.25, 0.6, 0.42]},
    "robot":   {"id": "fetch-001", "type": "Fetch", "dof": 7},
    "task":    {"name": "push_to_target", "success": true, "steps": 38}
}

cd examples/fetch_push
pip install gymnasium-robotics
PYTHONPATH=../../src python demo.py  # 90 episodes, ~2 min

SQLite + FTS5 + vec0
├── BM25 full-text search (jieba CJK tokenizer)
├── Vector search (FastEmbed ONNX, CPU-only)
├── RRF fusion ranking
├── Structured filtering (context_filter)
└── Spatial nearest-neighbor sorting (spatial_sort)