Context Management Context Restore

This handles semantic context retrieval across long-running AI workflows, basically letting you pause and resume complex projects without losing your place. It uses vector search with configurable relevance thresholds (default 0.75) to reconstruct project state, architectural decisions, and agent work history within a token budget. You get full, incremental, or diff restoration modes, plus ranking strategies that weigh semantic similarity against temporal decay and historical impact. The approach is solid if you're coordinating multi-agent systems or building RAG pipelines where context continuity matters. Token budget management means you won't blow your context window on stale information, though the cryptographic signatures and three-way merge talk suggests complexity you'll want to test thoroughly before production use.