Deep research agentic loop with rig AgentRunner + Gemma models #2

Merged
schaefera merged 4 commits from worktree-deep-research-agent into master 2026-08-14 17:08:01 +00:00

4 commits

Author SHA1 Message Date
Austin Schaefer
34b93eae3d feat: add current-date context and footnote-style citations to research loop
Interpolates today's date into the researcher's preamble so it can judge
source freshness instead of relying on training-cutoff knowledge, and
asks it to cite facts with bracketed footnote numbers backed by a
Sources list, which the writer agent is now instructed to preserve
through to the final report.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-08-14 19:06:15 +02:00
Austin Schaefer
3fd18e6a7c feat: add a reviewer agent that gates and redirects the research loop
Adds a reviewer step (gemma4-e4b, fresh context) between gathering and
writing: it uses rig's typed Extractor to judge whether the findings'
conclusions actually follow from their cited sources, rather than
relying on free-text parsing. research() is now a plain bounded loop —
"the least agentic design that solves the problem", per rig's own
workflow guidance — that reruns the researcher with the reviewer's
solid_findings/gaps feedback folded into the next round's task until
it approves or MAX_RESEARCH_ROUNDS runs out.
2026-08-14 12:58:39 +02:00
Austin Schaefer
2f24dc1b50 feat: name agents and add custom tracing spans for the research/writing phases
Agents were showing up as "Unnamed Agent" in rig's built-in gen_ai.*
spans. Naming them via .name(...) fixes that, and splitting the two
phases into #[tracing::instrument]-annotated functions wraps rig's
per-turn chat/execute_tool spans in a parent span per phase, making
the trace tree legible instead of a flat stream of chat calls.
2026-08-14 12:46:01 +02:00
Austin Schaefer
9212914282 feat: deep research agentic loop with rig AgentRunner + Gemma models
Pins rig to the newest published crates.io release (0.41.0) instead of
the git main branch, and adapts swear_cleanup's revise.rs to that
release's API (OneOrMany::first() returns T directly, raw_completion
folded into CompletionResponse::raw_response).

The research agent (gemma4:26b) drives rig's AgentRunner tool-calling
loop with two lean #[rig::tool_macro] tools — a DuckDuckGo HTML search
and a page-text fetcher — to gather and cross-check findings. A second
agent (gemma4-e4b) turns those findings into a structured report; the
smaller/faster model suffices there since it's reformatting already-
digested notes rather than doing multi-step research reasoning.
2026-08-14 12:40:09 +02:00