Entire Agentic Search is a retrieval API for AI coding agents that need to know not just what the code is, but why a change landed. Released by Entire on September 3, 2026, it lets an agent query every repository it can access through one call that returns matching code plus the session, transcript, and prompt behind each change.
Core Features
- Semantic search: finds the conversation where a feature was defined even if the word never appears in the code, using commits and session transcripts.
- Code search: a symbol-aware, literal lookup for exact definitions and references across one repo or the whole org.
- Hybrid ranking: each query runs BM25 and a vector search together, then tiers full-phrase, keyword, and semantic matches.
- Fast and regional: median query latency is about 100 ms across thousands of indexed repos, with data stored in-region via turbopuffer.
- Incremental indexing: pushes become searchable within seconds, and code indexes update without a full rebuild.
Use Cases
- Agents answering "why is the code like this?" across a team's engineering history.
- Developers onboarding to a large codebase who need the reasoning behind past decisions.
- Teams using Claude or other headless agents that want fewer tokens and steps per question.
Pricing
Agentic Search is available through the Entire CLI and integrates with major coding agents, returning results as JSON. Entire's benchmark showed search cut average cost per question from $0.38 to $0.23 and steps from 14 to 7. Compare with other tools in our AI programming development directory.
Our Take
Best for teams running many AI coding agents against shared, history-rich repositories. The trade-off is that it is most useful when commit messages are detailed; with thin history the gap over plain Git tooling shrinks.
