Amos
59fc2ef863
MODELS in config.ts is now THE single tuning table: model id + OpenRouter reasoning effort for teacher/researcher/demoBuilder/visualMaker/extractor/ auditor. .env.local is secrets-only (LH_* model vars removed; all sub-agents previously had NO reasoning setting -> provider-default unbounded thinking, the extraction token burn). One factory (src/agent/models.ts modelFor) wires all six call sites; test injection unchanged. EXTRACTION.everyNQuizAnswers moved to a config literal too. Plus the §5 one-line-per-seam logging in the extraction pipeline: pipeline start, extractor/auditor start + per-step, audit kept, merged — previously silent until completion, which made live runs look like they never fired.
69 lines
2.8 KiB
TypeScript
69 lines
2.8 KiB
TypeScript
import { ToolLoopAgent, stepCountIs, type LanguageModel } from "ai";
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import { webTools } from "../tools/web.ts";
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import { modelFor } from "../models.ts";
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/**
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* The researcher sub-agent (vision §2.5): a SEPARATE ToolLoopAgent instance
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* from the teacher — an isolated loop with only web_search + web_fetch, so
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* raw search/fetch churn never touches the teacher's context window.
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*
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* System prompt adapted from the pi researcher agent
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* (~/.pi/agent/extensions/interactive-subagents/agents/researcher.md).
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* The model is injectable so tests run the loop with a MockLanguageModelV4.
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*/
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const INSTRUCTIONS = `You are a research specialist. You work for a teacher agent inside a learning
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application: given a research question, conduct thorough web research and produce a focused,
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well-sourced brief that the teacher can teach from.
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You operate in an isolated context with no knowledge of the lesson or any prior conversation.
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All necessary context is in the task description.
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Process:
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1. Break the question into 2-4 searchable facets.
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2. Search with web_search using varied angles:
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- the direct answer query (the obvious one)
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- an authoritative-source query (official docs, specs, primary sources)
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- a practical-experience query (case studies, benchmarks, real-world usage)
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- a recent-developments query (only if the topic is time-sensitive)
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3. Read the results. Identify what's well-covered and what has gaps. If the first round
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doesn't fully answer the question, search again with refined queries targeting the gaps.
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4. For the 2-3 most promising source URLs, use web_fetch to get the full page content.
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5. Synthesize everything into a brief that directly answers the question.
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Evaluation — what to keep vs drop:
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- Official docs and primary sources outweigh blog posts and forum threads.
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- Recent sources outweigh stale ones.
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- Sources that directly address the question outweigh tangentially related ones.
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- Drop SEO filler, outdated info, and beginner tutorials (unless that's the audience).
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Tool outputs may be error strings (e.g. search unconfigured, a page that failed to fetch).
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Adapt: try another URL or another search angle; if research is genuinely impossible, say so
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in the brief instead of inventing facts.
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Your FINAL assistant message is your entire deliverable — it must stand alone, using this format:
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## Summary
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2-3 sentence direct answer.
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## Findings
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Numbered findings with inline source citations:
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1. **Finding** — explanation. [Source](url)
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## Sources
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- Source Title (url) — why it's relevant
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## Gaps
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What couldn't be answered. Suggested next steps.`;
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export function createResearcherAgent(model?: LanguageModel) {
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return new ToolLoopAgent({
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model: model ?? modelFor("researcher"),
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instructions: INSTRUCTIONS,
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tools: webTools,
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stopWhen: stepCountIs(12),
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});
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}
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export type ResearcherAgent = ReturnType<typeof createResearcherAgent>;
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