Local-first persistent memory for AI agents — store, recall, and consolidate knowledge across sessions using FAISS, SQLite, and any LLM. MCP server for coding agents and any MCP client.

consolidation-memory

Trust-calibrated working memory for coding agents.

consolidation-memory is built around a simple stance: claims are the reusable unit, episodes are the raw evidence, and code drift is a first-class reason to distrust stale memory. The system stores episodic events, consolidates them into structured knowledge and claims, and serves the same trust-aware retrieval semantics across Python, MCP, REST, and OpenAI-style tool calls.

Design Philosophy

• Treat the system as a trust layer for coding-agent memory, not a generic blob store.
• Prefer reusable claims with provenance over raw episodic recall.
• Keep uncertainty visible through temporal validity, contradiction history, and drift challenge signals.
• Preserve local-first inspectability: SQLite, FAISS, markdown, and logs should all stay understandable on disk.
• Support shared memory only when scopes and policy make reuse safe.

What It Is And Is Not

consolidation-memory is for:

• coding agents that need durable, inspectable working memory
• teams that want reusable claims without mixing unrelated contexts
• workflows where code changes should automatically reduce trust in older conclusions

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