Sovereign Brain
Local-first persistent memory with semantic search. sqlite-vec powered. No cloud dependency. Built for AI agents that need to remember.
Features
| Feature | Description |
|---|---|
| Key-value store | Read, write, delete brain entries with categories and priorities |
| Semantic search | Vector similarity via sqlite-vec + Ollama embeddings |
| Knowledge graph | Entries linked with typed, weighted edges |
| Hybrid retrieval | 3-signal search: keyword + semantic + graph, re-ranked |
| Temporal awareness | Auto-detects event dates, tracks lifecycle (upcoming/today/past) |
| Anti-repetition | Penalizes recently-shown results per session (up to 90%) |
| Freshness decay | Exponential decay scoring with configurable half-life |
| Time-window queries | Filter by date range |
| Auto-archive | Completed events archived after N days |
| Chat history | Per-session conversation tracking |
| Zero cloud | Everything runs locally. Your data stays yours. |
Install
pip install sovereign-brain
# For semantic search (recommended):
pip install sovereign-brain[vec]
ollama pull mxbai-embed-large
Quick Start
from sovereign_brain import BrainAPI
brain = BrainAPI()
# Write
brain.write("project.api", {"status": "active", "date": "2026-06-05"},
category="projects", description="API redesign", priority=8)
# Read
data = brain.read("project.api") # {"status": "active", ...}
# Search (keyword)
results = brain.search("api redesign", limit=5)
# Hybrid search (keyword + semantic + graph + freshness)
results = brain.hybrid_search("what's the api status", k=5, chat_id="session1")
# Knowledge graph
brain.add_edge("project.api", "project.frontend", "blocks", weight=0.9)
neighbors = brain.graph_neighbors("project.api", depth=2)
# Temporal awareness
temporal = brain.temporal_extract(data) # {"event_date": "2026-06-05", "status": "active"}
status = brain.temporal_status(temporal) # "today"
# Chat history
brain.save_chat("session1", "user", "what's the api status?")
brain.save_chat("session1", "assistant", "The API redesign is active.")
history = brain.chat_history("session1")
# Stats
brain.stats() # {"entries": 42, "graph_edges": 15, "vectors": 200, "chats": 3}
brain.close()
Anti-Repetition
When you pass chat_id to hybrid_search, the brain tracks what was shown and penalizes repeated results:
# First call: normal results
r1 = brain.hybrid_search("deployment", k=3, chat_id="s1")
# Second call: previously-shown entries get 30% penalty per occurrence
r2 = brain.hybrid_search("deployment", k=3, chat_id="s1")
# Different results surface to avoid repetition
Freshness Decay
Results are scored with exponential freshness decay (48h half-life by default):
brain.freshness_score(datetime.now().isoformat()) # 1.0 (just updated)
brain.freshness_score("2026-06-01T00:00:00") # ~0.25 (4 days old)
brain.freshness_score("2026-05-01T00:00:00") # ~0.0 (very old)
Architecture
sovereign_brain/
brain.py # BrainAPI class — all features in one module
__init__.py # Exports BrainAPI, get_brain
Storage: SQLite (brain_context + brain_graph + brain_chat_history + brain_retrieval_log)
Vectors: sqlite-vec extension (optional, graceful fallback to keyword search)
Embeddings: Ollama local API (mxbai-embed-large, 1024 dimensions)
Configuration
brain = BrainAPI(
db_path="./my_brain.db", # Custom DB location
ollama_url="http://localhost:11434/api/embed", # Ollama endpoint
embed_model="mxbai-embed-large", # Embedding model
vec_extension_path="/path/to/vec0", # sqlite-vec extension
)
License
MIT. Built by Like One Foundation.
Metadata
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