Research Agent

04 —

How the score was earned

• —A coverage plan with breadth classification, so broad questions can't stop after a single supported claim.
• —Atomic evidence storage instead of a repeatedly-rewritten running summary — detail survives many search rounds instead of degrading through re-compression.
• —One write-once synthesis pass after research finishes, not a summary-of-summaries.
• —A deterministic citation-compliance postcondition: unsupported or unknown-URL citations get one bounded repair pass, then a fallback — never another unchecked model guess.

05 —

Architecture

• —No framework — a plain, readable Python loop, with iterative and deep research modes behind the same model and tool interfaces.
• —Provider-agnostic via one model string (litellm) — runs on hosted models or fully local through Ollama, no API key required.
• —A minimal Tool abstraction over search and page fetching.
• —Defensive and hardened: bounded retries, fail-loud, skip paths.
• —Every architecture change measured against DeepResearch Bench, an external benchmark, not graded by vibes.

06 —

Engineering proof

• —127 tests passing, all network-mocked (~2s to run).
• —50/50 English benchmark tasks completed, zero failed agent reports.
• —2× the valid citations per report versus the pre-planner baseline (32.76 vs. 16.38), with citation validity held within 0.3 points (90.80% vs. 91.1%).
• —Dockerized single-process deploy; bring-your-own-key web UI — keys live only in localStorage, never server-side.

07 —

Stack

08 —

Screenshots