- src/agent/lh-tool.ts deleted: the zod-wrapped LhTool + toPiTool bridge (z.toJSONSchema per call, clientQuestion flag, hand-rolled result mapping) is gone. Tools are pi-agent-core native AgentTools now. - New src/agent/tool-execute.ts owns what the wrapper added: the abort guard (runWithAbortGuard: run signal + 30s per-call timeout + 250ms grace, 'Operation aborted' / timeout wording preserved), ToolInputError (the retryable '- problem at path' format), result mapping (string verbatim / object JSON / image parts, details rides the record's tool part output), and questionResult() — placeholder + terminate:true (owner Option A: belt of terminate + slim makeQuestionStopper with the clean-question rule; maxTurns deleted from the teaching turn entirely per owner). - All 11 tool files + tools.ts registries converted to TypeBox parameters (typebox@1.3.7, pinned to pi-agent-core's copy). zod refinements (quizInputProblems, askOptionProblems, live-nodeId) moved from schemas into execute — the model sees the same retryable error shape. - question-core + contracts/probe TypeBox-authored (single source shared with the UI); tool-parts parse seam runs the boundary's full chain (Convert → Check → Clean → refinement), fixing the live-smoke lockout: pi's boundary Value.Converts correctAnswer: 17 → '17' but the record keeps raw args, so a parse without Convert rejected a posed quiz (invisible pending question, turn ended). Pinned with the real smoke emission shape. - Subagents converted: demo-builder/researcher AgentTool-native, visual-maker/pdf-reader declaration-only pi-ai Tools with local execute fns, extraction submit schemas re-authored in TypeBox (owner option 2: zod defaults → Type.Optional + fill at the submit seam). - Accepted delta, flagged: TypeBox-native schemas skip pi's JSON-Schema primitive coercion fast path... (boundary still Value.Converts), and prober-quiz unknown keys ride through (Value.Clean strips at display). - Tests: 988 passing (was 967 at the recon baseline); tool-abort and read-tool rewritten against the native seams; question-core/question- tools/tool-parts/demos/web-search/graph-tools/plan-tree/probe-done/ tool-toggles converted to Value.Check/execute pins. Live smoke (PORT=3004, real model): native ls turn streams end-to-end; a real quiz pose (first attempt missing correctAnswer → tool-input-error → model retry → posed) terminates the turn cleanly and persists pending.
96 lines
4.0 KiB
TypeScript
96 lines
4.0 KiB
TypeScript
import type { AgentTool } from "@earendil-works/pi-agent-core";
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import type { Usage } from "@earendil-works/pi-ai";
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import { createWebTools } from "../tools/web.ts";
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import type { SearchProvider } from "../../core/search.ts";
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import type { PiEngine } from "../models.ts";
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import { runSubagent } from "./pi-run.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 engine is injectable so tests run the loop with a scripted faux engine.
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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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- Kept: Source Title (url) — why relevant
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- Dropped: Source Title — why excluded
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## Gaps
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What couldn't be answered. Suggested next steps.`;
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export interface ResearcherRunOptions {
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engine?: PiEngine;
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/** The search seam (search port wave, 2026-09-20): resolves the LIVE
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* provider per call, null = honestly unavailable (the web_search tool
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* reports that in its output — no crash, no silent fallback). Absent
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* seam reads as unavailable: the server wiring always injects one; the
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* dispatch-level drop (jobTools) keeps doomed jobs from even starting. */
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search?: () => SearchProvider | null;
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abortSignal?: AbortSignal;
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onUsage?: (usage: Usage) => void;
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onToolStart?: (toolName: string, args: unknown) => void;
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}
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/**
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* Run the researcher to completion: isolated context, web tools only, final
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* text = the brief. The engine and search seam are injectable so tests run
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* the loop scripted.
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*/
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export async function runResearcherAgent(prompt: string, opts: ResearcherRunOptions = {}): Promise<string> {
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const { text } = await runSubagent(Object.values(createWebTools(opts.search ?? (() => null))), {
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role: "researcher",
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prompt,
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engine: opts.engine,
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systemPrompt: INSTRUCTIONS,
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maxTurns: 12,
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abortSignal: opts.abortSignal,
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onUsage: opts.onUsage,
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onToolStart: opts.onToolStart,
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});
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return text;
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}
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export type ResearcherAgent = ReturnType<typeof runResearcherAgent>;
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