Evidence memory for coding agents
Zephr is the evidence and currentness layer underneath AI coding workflows. Recover what is known, why it is believed, when it was true, and what contradicts it — across context window collapses and tool switches.
The four questions
A recalled belief must answer four questions
Zephr prefers abstention over fluency when evidence is insufficient. Inspectable memory that survives a context window collapse and travels between agents without losing provenance.
Session, commit, and file-line provenance for every belief.
Bi-temporal recording — know if context is current or stale.
Structured contradictions with review state, not silent conflicts.
Full score decomposition: lexical, semantic, and RRF fusion.
What makes it different
Trust over fluency
Not another note store. Inspectable memory with deterministic recall, provenance anchors, and structured abstention.
Provenance chains
Every recalled belief carries a full chain: session → commit → file:line. No mystery sources, no hallucinated context.
Bi-temporal evidence
Beliefs track when they were recorded and when they were true. Stale context is flagged, not silently served.
Contradiction detection
When two beliefs conflict, Zephr surfaces the contradiction with both sides — it abstains rather than guessing.
Hybrid retrieval
FTS5 lexical + sqlite-vec semantic, fused via reciprocal rank. Score decomposition is inspectable per result.
Local-first
Your evidence graph lives in a SQLite file on your machine. No cloud, no telemetry, no account required.
8 tools, forever
A frozen MCP surface: remember, recall, why, verify, session, rules, status, admin. No ninth tool. No drift.
Start in 30 seconds
Add Zephr to your Claude Code or Cursor MCP config. No account, no cloud.
Read the setup guide →