Smrti

smrti

[](https://pypi.org/project/smrti/) [](https://pypi.org/project/smrti/) [](LICENSE) [](https://github.com/cyqlelabs/smrti/actions/workflows/publish.yml) [](https://codecov.io/gh/cyqlelabs/smrti)

AtomSpace-inspired memory engine for AI agents. Stores beliefs as graph nodes with Bayesian truth values, emotional valence, and attention weights in a single SQLite file with vector indexing. No extra infra to maintain. Just Plug & Play.

Not just vector search. Embedding similarity is only an entry point — a fast index to seed graph traversal. What gets returned and why is governed by graph topology (typed relation edges), Bayesian truth values (PLN), attentional economics (STI/LTI), and emotional valence. Similarity is one signal among five, not the ranking.

How It Works

> [Pipeline diagram →](docs/pipeline.md)

remember() — Embeds and stores text as a typed atom (concept, belief, episode, or goal) with a Bayesian truth value, attention weight, and valence score. Evidence is append-only; truth values update via PLN revision. A hybrid GLiNER2 + LLM pipeline auto-extracts entities and relation edges; the LLM is only called when ≥2 entities are found (~40–60% fewer LLM calls). Pronouns are resolved against the live graph using persisted entity context, not raw conversation history.

recall() — Embeds the query → KNN seeds (top-50) → 1-hop graph expansion → salience re-ranking: w_sim × similarity + w_sti × STI + w_conf × confidence + w_lti × LTI + w_val × |valence| × intensity. When valence remember · recall · believe · reflect · forget · status"] end

subgraph Servers MCP["mcp.pyMCP stdio"] REST["rest.pyFastAPI :8420"] PROXY["proxy.pyOpenAI proxy :8421"] end

subgraph Core AS["AtomSpace"] DB["Database"] EMB["Embedder"] MOD["Models"] end

subgraph Retrieval FAN["fan_out"] SAL["salience"] CLS["classify"] end

subgraph Evolution EPO["epoch"] TRU["truth"] CON["connections"] HEA["healing"] end

subgraph Spaces SOP["set_ops"] EMG["emergence"] end

subgraph Extraction EXT["extract"] RES["resolve"] ALI["aliases"] end

subgraph Storage SQL["SQLite + sqlite-vecmultilingual-MiniLM-L12-v2 · 384d · ONNX CPU"] end

MCP & REST & PROXY --> S S --> Core & Retrieval & Evolution & Extraction & Spaces Core & Retrieval & Evolution & Extraction & Spaces --> SQL

**Retrieval pipeline:** Embed query → KNN over tenant partition → filter to read spaces → 1-hop graph expansion → salience scoring → top-k

**Salience formula:**

S = wsim × similarity + wsti × sti + wconf × confidence + wlti × lti + w_val × |valence| × intensity

When valence < -0.5, weight shifts dynamically from wsti to wval so critical errors outrank recent trivia.

**Consolidation epoch** (runs automatically every `SMRTI_REFLECT_INTERVAL` seconds, or manually via `reflect()`):

1. Process pending evidence via Bayesian update
2. Decay STI and confidence
3. Propagate STI and valence to 1-hop neighbors
4. Heal orphaned episodes (link to most salient person)
5. Promote high-STI atoms to LTI
6. Resolve contradictions (weaken less confident belief)
7. Discover cross-domain connections (every 10th epoch)
8. Materialize cross-space bridge atoms (every 10th epoch)
9. Prune atoms below confidence/LTI floors

## Data Model

| Atom Type  | Purpose                  | Example                          |
| ---------- | ------------------------ | -------------------------------- |
| `concept`  | Reusable entities        | "Alice", "Python", "OpenAI"      |
| `belief`   | Probabilistic facts      | "Alice prefers TypeScript"       |
| `episode`  | Timestamped observations | "User asked about deployment"    |
| `goal`     | Desired states           | "Finish the migration by Friday" |
| `relation` | Edges between atoms      | Alice → works_at → Acme Corp     |

Each atom carries:

- **TruthValue** — `probability` [0,1] and `confidence` [0,1], merged via PLN revision
- **AttentionValue** — `sti` (short-term importance, decays fast) and `lti` (long-term, accumulates)
- **Valence** — emotional tone [-1,1] and intensity [0,1]

## Testing

```bash
pytest tests/ -v

License

MIT

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: cyqlelabs
• Source: cyqlelabs/smrti
• License: MIT
• Homepage: https://smrti.im

Install and usage instructions live in the source repository linked above.