AlekseiMarchenko/central-intelligence

v0.3.0 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 2mo AI Agents & Assistants

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✓ No known CVEs patched

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✓ No known CVEs patched in this version

Summary

AI summary

Hybrid retrieval engine adds vector, BM25, trigram search with RRF and temporal decay.

Full changelog

What's New

Hybrid Retrieval Engine

The core memory recall system has been upgraded from single-strategy vector search to a multi-strategy hybrid pipeline:

Vector search — semantic similarity via OpenAI embeddings
BM25 full-text search — exact keyword matching via PostgreSQL tsvector
Trigram fuzzy search — typo tolerance via pg_trgm (handles "PostgrSQL", "Typscript", etc.)
Reciprocal Rank Fusion — merges results from all three strategies
Temporal decay — recent memories score higher (90-day half-life)
Context compression — large result sets compressed via GPT-4o-mini
Relevance threshold — irrelevant queries return 0 results instead of noise

Benchmark Results (94% overall)

| Category | Score |
|---|---|
| Semantic (paraphrased queries) | 75% |
| Keyword (exact technical terms) | 100% |
| Typo tolerance (misspelled queries) | 100% |
| Cross-domain (business + meetings) | 100% |
| Preference (coding style) | 100% |
| Negative (irrelevant queries filtered) | 100% |
| Overall | 94% |

New SDKs & Integrations

Node.js SDK on npm: central-intelligence-sdk
Python SDK updated on PyPI: central-intelligence
OpenAPI spec at /docs/openapi.json for ChatGPT Custom GPTs
Demo endpoint at /demo/recall — try without signup
"When to use" metadata in .well-known/mcp and .well-known/agent.json
Integration cards: Lovable, Perplexity, Antigravity, GitHub Copilot
ClawHub skill published

View diff on GitHub

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About AlekseiMarchenko/central-intelligence

Persistent memory for AI agents. Five tools (remember, recall, context, forget, share) with semantic search via vector embeddings and agent/user/org scoping. Works with Claude Code, Cursor, Windsurf, and any MCP client.

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