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MIT License
Copyright (c) 2026 Michael Freno
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# Deep Research
Multi-round deep web research powered by Firecrawl with iterative query refinement.
Deep Research is a local omp extension under `~/.omp/agent/extensions/deepi-research/`.
Omp loads it via `omp.extensions` in `package.json` (entry `./index.ts`).
## Features
- **Multi-round iteration**: Each round generates follow-up queries based on previous findings (depth 1-3)
- **Parallel query expansion**: Multiple diverse search queries per round (breadth 1-5) covering technical, practical, comparative, critical, and forward-looking angles
- **Sub-question decomposition**: Broad questions are broken into focused sub-topics before query generation (depth > 1)
- **Round-robin parallel execution**: Searches and analyses run concurrently within each round using bounded-concurrency worker pools, dramatically reducing total research time
- **LLM-driven analysis**: Each query's results are analyzed by its own agent session (per-query provenance) to extract structured findings with confidence ratings
- **Source authority scoring**: Every source is scored by domain authority; low-quality SEO domains are penalized with a hard floor; findings are ranked by authority × confidence before synthesis
- **Cross-query corroboration**: A finding is corroborated only when its sources were independently surfaced by multiple different search queries
- **Citation integrity**: References are rebuilt from the authoritative bibliography (never the LLM's), and hallucinated inline citation numbers are stripped
- **Near-duplicate detection**: Syndicated copies of the same article are removed by title similarity, and duplicate findings across rounds are merged
- **Automatic deduplication**: Search results are deduplicated by URL across all queries
- **Robust LLM output parsing**: JSON output with code fences, prose prefixes, or trailing commas is parsed reliably
- **Graceful degradation**: Individual search or analysis failures don't crash the full research — partial results are preserved, with retry-with-backoff for transient Firecrawl errors
- **Progress streaming**: Real-time progress widget with spinner, phase indicators, and progress bar
- **Abort support**: Research can be cancelled mid-flight via `AbortSignal`
- **Rich TUI rendering**: Compact collapsed view and detailed expanded view in the terminal UI
- **Fallback resilience**: Built-in fallback query generation and report synthesis when LLM calls fail
## Usage
### Tool (LLM-callable)
Registers the `deep_research` tool for AI agent use:
```
deep_research — multi-round deep web research via Firecrawl with iterative query refinement
```
Parameters:
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `question` | string | — | The research question to investigate |
| `depth` | integer (1-3) | 2 | Number of research rounds |
| `breadth` | integer (1-5) | 3 | Search queries per round |
| `format` | "markdown" \| "structured" | "markdown" | Output format for the report |
| `audience` | "general" \| "expert" \| "executive" | "general" | Tone and depth for the report audience |
| `details.showRoundDetails` | boolean | false | Include per-round search metadata (incl. failed searches) in output |
### Command (interactive)
```
/deepi-research <your research question>
```
Prompts for depth (1-3 rounds) and breadth (1-5 queries) interactively, then runs the research and sends the final report as a user message.
### Recommended usage
- Use `deep_research` for complex, multi-faceted questions that benefit from multiple search angles and iterative refinement.
- The tool handles query generation, web search, result analysis, and report synthesis automatically.
- For simple fact-finding questions, use `firecrawl_search` directly instead.
## Architecture
```
Research Flow:
Question
↓
┌─ Round 1 ───────────────────────────┐
│ LLM → generate queries (N angles) │
│ Firecrawl → search each query │
│ LLM → analyze results → findings │
└──────────────┬───────────────────────┘
↓ (follow-up queries)
┌─ Round 2 ───────────────────────────┐
│ LLM → identify knowledge gaps │
│ Firecrawl → search follow-ups │
│ LLM → analyze → new findings │
└──────────────┬───────────────────────┘
↓ (iterate depth times)
┌─ Synthesis ─────────────────────────┐
│ LLM → synthesize all findings │
│ → comprehensive research report │
└─────────────────────────────────────┘
```
## Configuration
Deep Research reads Firecrawl configuration from omp's config.yml files, with the following resolution order (later wins):
1. Environment variables (`FIRECRAWL_BASE_URL`, `FIRECRAWL_API_KEY`)
2. Global config (`$agentDir/config.yml`) → `firecrawl.*`
3. Project config (`.omp/config.yml`) → `firecrawl.*`
4. Default `http://localhost:3002` (if nothing else sets baseUrl)
The agent directory (`$agentDir`) defaults to `~/.omp/agent`.
**Global config** (`~/.omp/agent/config.yml`):
```yaml
firecrawl:
baseUrl: http://localhost:3002
```
**Project config** (`.omp/config.yml` — overrides global):
```yaml
firecrawl:
baseUrl: https://firecrawl.team.internal
apiKey: your-api-key
```
### Session startup check
On `session_start`, the extension checks whether the Firecrawl endpoint is reachable. If not, it shows a warning notification so you know searches will fail before you try to use it.

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}

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index.ts Normal file
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/**
* deep-research — Multi-round deep web research powered by Firecrawl
*
* Registers:
* - `deep_research` tool — callable by the LLM to conduct deep research
* - `/deepi-research` command — interactive session invocation
*
* Architecture:
* Each research round generates queries, searches in parallel via
* Firecrawl, analyzes results with agent sessions, then generates
* follow-up queries. A final synthesis step produces the report.
*
* Patterns borrowed from:
* - firecrawl.ts extension (direct Firecrawl HTTP calls)
* - ralpi executor (agent sessions, widget updates, progress UX)
* - subagent extension (structured tool rendering)
*/
import type {
ExtensionAPI,
ExtensionCommandContext,
ExtensionContext,
} from "@oh-my-pi/pi-coding-agent";
import type { TSchema } from "@oh-my-pi/pi-ai";
import { Type } from "typebox";
import {
Box,
Text,
truncateToWidth,
visibleWidth,
} from "@oh-my-pi/pi-tui";
import { runDeepResearch, type ResearchProgress } from "./src/research";
import { isFirecrawlReachable } from "./src/firecrawl";
import type { ResearchConfig, ResearchReport, Audience } from "./src/types";
/* ── Constants ────────────────────────────────────────────────────── */
const SPINNER_FRAMES = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"];
const PHASE_ICONS: Record<string, string> = {
decomposing: "🧩",
generating_queries: "🔍",
searching: "🌐",
analyzing: "📊",
synthesizing: "📝",
complete: "✅",
};
type ResearchPhase = Parameters<ResearchProgress>[0]["phase"];
/* ── Helpers ──────────────────────────────────────────────────────── */
function formatDuration(ms: number): string {
const seconds = Math.floor(ms / 1000);
const minutes = Math.floor(seconds / 60);
if (minutes > 0) return `${minutes}m ${seconds % 60}s`;
return `${seconds}s`;
}
function truncate(s: string, max: number): string {
if (s.length <= max) return s;
return s.slice(0, max - 3) + "...";
}
/* ── Tool Definition ──────────────────────────────────────────────── */
const DeepResearchParams = Type.Object({
question: Type.String({
description: "The research question to investigate",
}),
depth: Type.Optional(
Type.Integer({
description:
"Number of research rounds (1-3). Each round builds on findings from the previous for deeper analysis. Default: 2",
minimum: 1,
maximum: 3,
default: 2,
}),
),
breadth: Type.Optional(
Type.Integer({
description:
"Number of search queries per round (1-5). More queries = broader coverage but slower. Default: 3",
minimum: 1,
maximum: 5,
default: 3,
}),
),
format: Type.Optional(
Type.Union([Type.Literal("markdown"), Type.Literal("structured")], {
description:
'Output format for the research report. "markdown" for prose with headings, "structured" for detailed hierarchical sections. Default: "markdown"',
default: "markdown",
}),
),
audience: Type.Optional(
Type.Union(
[
Type.Literal("general"),
Type.Literal("expert"),
Type.Literal("executive"),
],
{
description:
"Target audience for the report. 'general' (accessible), 'expert' (technical depth), 'executive' (concise, action-oriented). Default: 'general'",
default: "general",
},
),
),
details: Type.Optional(
Type.Object({
showRoundDetails: Type.Optional(
Type.Boolean({
description:
"Include per-round search methodology in the output. Default: false",
}),
),
}),
),
});
interface ResearchDetails {
rounds: Array<{
round: number;
queries: string[];
findingsCount: number;
resultsCount: number;
failedSearches: number;
}>;
totalSearches: number;
totalPagesScraped: number;
durationMs: number;
}
/* ── Widget Helper ────────────────────────────────────────────────── */
/**
* Create a widget state that drives a spinner-based progress widget.
* Returns the state object, the timer, and cleanup function.
*/
function createProgressWidget(
ctx: any,
initialPhase: ResearchPhase = "generating_queries",
) {
const state: {
phase: ResearchPhase;
message: string;
detail: string | undefined;
fraction: number;
round: number | undefined;
totalRounds: number | undefined;
} = {
phase: initialPhase,
message: "Starting...",
detail: undefined,
fraction: 0,
round: undefined,
totalRounds: undefined,
};
let widgetTui: { requestRender(): void } | null = null;
let spinnerIdx = 0;
ctx.ui.setWidget(
"deep-research",
(tui: { requestRender(): void }, _theme: any) => {
widgetTui = tui;
return {
render: (width: number) => {
const spinner = SPINNER_FRAMES[spinnerIdx];
const icon = PHASE_ICONS[state.phase] ?? "";
const roundInfo =
state.round && state.totalRounds
? ` Round ${state.round}/${state.totalRounds}`
: "";
const firstLine = `${spinner} ${icon} ${state.message}${roundInfo}`;
const lines: string[] = [truncateToWidth(firstLine, width)];
if (state.detail) {
lines.push(truncateToWidth(` ${state.detail}`, width));
}
if (state.fraction > 0) {
const barLen = Math.min(15, Math.max(3, width - 4));
const filled = Math.round(barLen * state.fraction);
const bar = "█".repeat(filled) + "░".repeat(barLen - filled);
lines.push(` ${bar}`);
}
return lines;
},
invalidate: () => {},
};
},
);
const spinnerTimer = setInterval(() => {
spinnerIdx = (spinnerIdx + 1) % SPINNER_FRAMES.length;
widgetTui?.requestRender();
}, 100);
const onProgress: ResearchProgress = (update) => {
state.phase = update.phase;
state.message = update.message;
state.detail = update.detail;
state.fraction = update.fraction ?? 0;
state.round = update.round;
state.totalRounds = update.totalRounds;
};
const cleanup = () => {
clearInterval(spinnerTimer);
ctx.ui.setWidget("deep-research", undefined);
};
return { state, onProgress, cleanup, spinnerTimer };
}
/* ── Extension Entry ───────────────────────────────────────────────── */
export default function (pi: ExtensionAPI) {
pi.registerTool({
name: "deep_research",
label: "Deep Research",
description: [
"Conduct multi-round deep web research on any topic using Firecrawl.",
"Generates diverse search queries, searches the web in parallel, analyzes results,",
"and produces a comprehensive report with numbered citations and a bibliography.",
"Supports iterative refinement and sub-question decomposition for deeper analysis.",
"Parameters: question (required), depth, breadth, format, audience, details.",
"Use for complex, multi-faceted questions that benefit from multiple search angles;",
"for simple fact-finding questions use firecrawl_search directly instead.",
"Set audience to 'executive' for concise, action-oriented reports; 'expert' for technical depth;",
"'general' (default) for accessible reports.",
].join(" "),
parameters: DeepResearchParams as unknown as TSchema,
async execute(
_toolCallId: string,
params: {
question: string;
depth?: number;
breadth?: number;
format?: "markdown" | "structured";
audience?: Audience;
details?: { showRoundDetails?: boolean };
},
signal: AbortSignal | undefined,
onUpdate: ((partial: any) => void) | undefined,
ctx: any,
) {
const config: ResearchConfig = {
question: params.question,
depth: params.depth ?? 2,
breadth: params.breadth ?? 3,
format: params.format ?? "markdown",
audience: params.audience ?? "general",
};
const abortSignal = signal;
// eslint-disable-next-line @typescript-eslint/no-unused-vars
const { state: _state, onProgress, cleanup } = createProgressWidget(ctx);
let researchResult: ResearchReport | null = null;
let lastError: string | null = null;
try {
ctx.ui.setStatus(
"deep-research",
`🌐 Researching: ${truncate(config.question, 40)}`,
);
onProgress({
phase: "generating_queries",
message: "Starting deep research...",
fraction: 0,
});
researchResult = await runDeepResearch(
config,
ctx,
onProgress,
abortSignal,
);
// ── Build the tool result ──────────────────────────────────
const details: ResearchDetails = {
rounds: researchResult.rounds.map((r) => ({
round: r.round,
queries: r.queries.map((q) => q.query),
findingsCount: r.findings.length,
resultsCount: r.results.length,
failedSearches: r.failedSearches,
})),
totalSearches: researchResult.totalSearches,
totalPagesScraped: researchResult.totalPagesScraped,
durationMs: researchResult.durationMs,
};
// Stream final content via onUpdate before returning
if (onUpdate) {
onUpdate({
content: [{ type: "text", text: researchResult.finalReport }],
details: {
phase: "complete",
duration: researchResult.durationMs,
rounds: researchResult.rounds.length,
findings: researchResult.rounds.reduce(
(s, r) => s + r.findings.length,
0,
),
references: researchResult.references.length,
},
});
}
cleanup();
ctx.ui.setStatus("deep-research", undefined);
let output = researchResult.finalReport;
// Append methodology section if requested
if (params.details?.showRoundDetails) {
output += `\n\n---\n\n## Research Methodology\n\n`;
for (const round of researchResult.rounds) {
output += `### Round ${round.round}\n\n`;
output += `**Queries:**\n`;
for (const q of round.queries) {
output += `- "${q.query}" (${q.angle}) — ${q.rationale}\n`;
}
output += `\n**Results scraped:** ${round.results.length}\n`;
output += `**Findings extracted:** ${round.findings.length}\n`;
if (round.failedSearches > 0) {
output += `**Failed searches:** ${round.failedSearches}\n`;
}
output += `\n`;
}
output += `**Total searches:** ${researchResult.totalSearches}\n`;
output += `**Total pages scraped:** ${researchResult.totalPagesScraped}\n`;
output += `**Sources in bibliography:** ${researchResult.references.length}\n`;
output += `**Duration:** ${formatDuration(researchResult.durationMs)}\n`;
}
return {
content: [{ type: "text", text: output }],
details,
};
} catch (error) {
cleanup();
ctx.ui.setStatus("deep-research", undefined);
lastError = error instanceof Error ? error.message : String(error);
return {
content: [
{
type: "text",
text: `Research failed: ${lastError}`,
},
],
details: {
rounds: [],
totalSearches: 0,
totalPagesScraped: 0,
durationMs: 0,
error: lastError,
} as ResearchDetails & { error: string },
isError: true,
};
}
},
// ── TUI: Render the tool call (collapsed view) ──────────────────
renderCall(
args: any,
_options: any,
theme: any,
) {
const question = truncate(args.question ?? "?", 70);
const depth = args.depth ?? 2;
const breadth = args.breadth ?? 3;
const format = args.format ?? "markdown";
const audience = args.audience ?? "general";
const text =
theme.fg("toolTitle", theme.bold("deep_research ")) +
theme.fg("accent", `"${question}"`) +
theme.fg(
"muted",
` [depth:${depth} breadth:${breadth} ${format} ${audience}]`,
);
return new Text(text, 0, 0);
},
// ── TUI: Render the tool result (expanded/collapsed) ─────────────
renderResult(
result: any,
{ expanded }: { expanded: boolean },
theme: any,
_context: any,
) {
const details = result.details as ResearchDetails | undefined;
if (!details) {
const text = result.content?.[0]?.text ?? "(no output)";
return new Text(text, 0, 0);
}
const container = new Box();
// ── Collapsed view ────────────────────────────────────────────
if (!expanded) {
const totalRounds = details.rounds.length;
const totalFindings = details.rounds.reduce(
(s, r) => s + r.findingsCount,
0,
);
const duration = formatDuration(details.durationMs);
let text = "";
text +=
theme.fg("success", "✓ ") +
theme.fg("toolTitle", theme.bold("deep research"));
text += theme.fg(
"muted",
` — ${totalRounds} rounds, ${totalFindings} findings`,
);
text += theme.fg("dim", ` (${duration})`);
text += "\n";
for (const round of details.rounds) {
const icon =
round.findingsCount > 0
? theme.fg("success", "✓")
: theme.fg("muted", "·");
text += ` ${icon} ${theme.fg("accent", `Round ${round.round}:`)} `;
text += theme.fg(
"dim",
`${round.queries.length} queries, ${round.resultsCount} pages, ${round.findingsCount} findings`,
);
text += "\n";
}
text += theme.fg("muted", "(Ctrl+O to expand)");
container.addChild(new Text(text, 0, 0));
return container;
}
// ── Expanded view ─────────────────────────────────────────────
const headerText =
theme.fg("toolTitle", theme.bold("Deep Research Results")) +
"\n" +
theme.fg("dim", `Duration: ${formatDuration(details.durationMs)} | `) +
theme.fg("dim", `Searches: ${details.totalSearches} | `) +
theme.fg("dim", `Pages scraped: ${details.totalPagesScraped}`);
container.addChild(new Text(headerText, 0, 0));
for (const round of details.rounds) {
container.addChild(new Text("", 0, 0)); // Spacer
const roundHeader = `Round ${round.round}`;
container.addChild(
new Text(theme.fg("toolTitle", theme.bold(roundHeader)), 0, 0),
);
container.addChild(
new Text(
theme.fg(
"dim",
`${round.queries.length} queries → ${round.resultsCount} pages → ${round.findingsCount} findings`,
),
0,
0,
),
);
for (const q of round.queries) {
container.addChild(
new Text(
theme.fg("muted", " · ") + theme.fg("accent", truncate(q, 70)),
0,
0,
),
);
}
}
return container;
},
});
// ── Command ───────────────────────────────────────────────────────
pi.registerCommand("deepi-research", {
description:
"Conduct multi-round deep web research on any topic via Firecrawl. Usage: /deepi-research <question>",
handler: async (args: string, ctx: ExtensionCommandContext) => {
if (!args || args.trim().length === 0) {
ctx.ui.notify(
"Usage: /deepi-research <your research question>",
"error",
);
return;
}
// Ask about depth
const depthStr = await ctx.ui.select("Research depth?", [
"1 round (quick survey)",
"2 rounds (standard)",
"3 rounds (deep dive)",
]);
const depth = depthStr?.startsWith("1")
? 1
: depthStr?.startsWith("3")
? 3
: 2;
// Ask about breadth
const breadthStr = await ctx.ui.select("Research breadth?", [
"1 query/round (narrow)",
"3 queries/round (balanced)",
"5 queries/round (broad)",
]);
const breadth = breadthStr?.startsWith("1")
? 1
: breadthStr?.startsWith("5")
? 5
: 3;
// Ask about audience
const audienceStr = await ctx.ui.select("Report audience?", [
"General (accessible, explains terms)",
"Expert (technical depth, assumes domain knowledge)",
"Executive (concise, action-oriented)",
]);
const audience: Audience = audienceStr?.startsWith("Expert")
? "expert"
: audienceStr?.startsWith("Executive")
? "executive"
: "general";
ctx.ui.setStatus(
"deep-research",
`🌐 Researching: ${truncate(args, 40)}`,
);
const config: ResearchConfig = {
question: args,
depth,
breadth,
format: "markdown",
audience,
};
const { onProgress, cleanup } = createProgressWidget(ctx);
try {
const report = await runDeepResearch(config, ctx, onProgress);
cleanup();
ctx.ui.setStatus("deep-research", undefined);
// Show notification
ctx.ui.notify(
`Research complete: ${report.rounds.length} rounds, ${report.totalSearches} searches, ${report.totalPagesScraped} pages, ${report.references.length} sources in ${formatDuration(report.durationMs)}`,
"info",
);
// Send the report as a user message
pi.sendUserMessage(
`## Deep Research: ${args}\n\n${report.finalReport}\n\n---\n*${report.rounds.length} rounds · ${report.totalSearches} searches · ${report.totalPagesScraped} pages · ${report.references.length} sources · ${formatDuration(report.durationMs)}*`,
);
} catch (error) {
cleanup();
ctx.ui.setStatus("deep-research", undefined);
const msg = error instanceof Error ? error.message : String(error);
ctx.ui.notify(`Research failed: ${msg}`, "error");
}
},
});
// ── Startup check ─────────────────────────────────────────────────
pi.on("session_start", async (_event: unknown, ctx: ExtensionContext) => {
const reachable = await isFirecrawlReachable();
if (!reachable) {
ctx.ui.notify(
"Deep Research: Firecrawl endpoint unreachable — searches will fail. Set firecrawl.baseUrl in config.yml (global ~/.omp/agent or project .omp) or the FIRECRAWL_BASE_URL env var.",
"warning",
);
}
});
}

43
package.json Normal file
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{
"name": "@mikefreno/omp-deepi-research",
"version": "0.2.0",
"description": "Deep research extension for pi — parallel web research via Firecrawl with iterative query refinement",
"keywords": [
"pi-package",
"pi-extension",
"research",
"firecrawl",
"deep-research",
"web-search",
"ai"
],
"license": "MIT",
"files": [
"index.ts",
"src/",
"README.md",
"LICENSE"
],
"scripts": {
"typecheck": "tsc --noEmit",
"prepublishOnly": "tsc --noEmit"
},
"engines": {
"bun": ">=1.3.14"
},
"dependencies": {
"yaml": "^2.4.0"
},
"omp": {
"extensions": [
"./index.ts"
]
},
"devDependencies": {
"@oh-my-pi/pi-coding-agent": "17.2.12",
"@oh-my-pi/pi-tui": "17.2.12",
"@types/node": "^20.0.0",
"typebox": "^1.1.0",
"typescript": "^5.3.0"
}
}

115
scripts/run-harness.ts Normal file
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/**
* Deep Research — standalone end-to-end test harness
*
* Imports the extension's REAL source (fresh, uncached) and runs the full
* research pipeline with real Firecrawl + real pi agent sessions.
*
* WHY: pi caches extension modules per session (keyed by path + cwd +
* generation) and only invalidates on /reload or cwd change. Tool calls in
* a live session therefore run the version loaded at session start. This
* harness bypasses that cache so you can iterate on src/ without reloading
* pi, and it prints verification stats (round stats, angle provenance,
* corroboration distribution, citation integrity, authority stats).
*
* Usage (from repo root):
* NODE_PATH=/opt/homebrew/lib/node_modules bun scripts/run-harness.ts \
* "<question>" [depth] [breadth] [audience]
*
* Requires the pi SDK to be resolvable (NODE_PATH above points at the
* global pi install) and Firecrawl reachable (settings.json firecrawl.baseUrl).
* Report is written to /tmp/deepi-harness/report.md.
*/
import { runDeepResearch } from "../src/research.ts";
import type { ResearchReport } from "../src/types.ts";
import { mkdirSync, writeFileSync } from "node:fs";
const question =
process.argv[2] ?? "Compare Rust and Go for backend services in 2025";
const depth = Number(process.argv[3] ?? 2);
const breadth = Number(process.argv[4] ?? 3);
const audience = (process.argv[5] ?? "expert") as
| "expert"
| "general"
| "executive";
const started = Date.now();
const report: ResearchReport = await runDeepResearch(
{
question,
depth,
breadth,
format: "markdown",
audience,
},
{ cwd: process.cwd() } as any,
(update) => {
const round = update.round
? ` [r${update.round}/${update.totalRounds}]`
: "";
console.log(` [${update.phase}${round}] ${update.message}`);
},
);
console.log("\n" + "=".repeat(80));
console.log(`DURATION: ${((Date.now() - started) / 1000).toFixed(1)}s`);
console.log(`TOTAL SEARCHES: ${report.totalSearches}`);
console.log(`TOTAL PAGES: ${report.totalPagesScraped}`);
console.log(`REFERENCES: ${report.references.length}`);
console.log(`ROUNDS: ${report.rounds.length}`);
for (const round of report.rounds) {
console.log(
` Round ${round.round}: ${round.queries.length} queries (${round.successfulSearches} ok, ${round.failedSearches} failed) → ${round.results.length} unique pages → ${round.findings.length} findings`,
);
const angles = new Map<string, number>();
for (const f of round.findings) {
const a = f.angle ?? "none";
angles.set(a, (angles.get(a) ?? 0) + 1);
}
console.log(
` finding angles: ${Array.from(angles.entries())
.map(([a, n]) => `${a}(${n})`)
.join(", ")}`,
);
const corr = round.findings.map((f) => f.corroborationScore ?? 0);
const strong = corr.filter((c) => c >= 0.5).length;
const partial = corr.filter((c) => c > 0 && c < 0.5).length;
const none = corr.filter((c) => c === 0).length;
console.log(
` corroboration: ${strong} strong(>=0.5), ${partial} partial, ${none} none`,
);
}
// Citation integrity: citations used in the report BODY vs reference list.
// (Reference titles may legitimately contain "[2026]" etc. — those live in
// the ## References section which was rebuilt authoritatively.)
const refIds = new Set(report.references.map((r) => r.id));
const body = report.finalReport.replace(/^## References[\s\S]*$/m, "");
const cited = new Set(
[...body.matchAll(/\[(\d+)\]/g)].map((m) => Number(m[1])),
);
const dangling = [...cited].filter((id) => !refIds.has(id));
console.log(
`CITATIONS (body only): ${cited.size} unique numbers used, ${dangling.length} dangling (${dangling.join(",")})`,
);
console.log(
`REFERENCES SECTION PRESENT: ${/^## References/m.test(report.finalReport)}`,
);
const iconCount = (report.finalReport.match(/[⭐✓○]/g) ?? []).length;
console.log(`AUTHORITY ICONS IN REFERENCES: ${iconCount}`);
const authorities = report.references.map((r) => r.authorityScore);
const avgAuth =
authorities.reduce((a, b) => a + b, 0) / Math.max(1, authorities.length);
console.log(
`AVG SOURCE AUTHORITY: ${(avgAuth * 100).toFixed(0)}% (max ${(Math.max(...authorities) * 100).toFixed(0)}%, min ${(Math.min(...authorities) * 100).toFixed(0)}%)`,
);
console.log(
"DOMAINS:",
[...new Set(report.references.map((r) => r.domain))].join(", "),
);
mkdirSync("/tmp/deepi-harness", { recursive: true });
writeFileSync("/tmp/deepi-harness/report.md", report.finalReport);
console.log("\nReport saved to /tmp/deepi-harness/report.md");

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/**
* Deep Research — Agent Session helper
*
* Uses omp's in-process `createAgentSession` for LLM subtasks
* (query generation, result analysis, report synthesis).
* Pattern borrowed from ralpi's runAgentSession().
*/
import {
createAgentSession,
AgentRegistry,
SessionManager,
} from "@oh-my-pi/pi-coding-agent";
import type { AgentSessionEvent } from "@oh-my-pi/pi-coding-agent";
/** Aggregate tool usage stats */
export interface ToolUsage {
read: number;
write: number;
edit: number;
bash: number;
other: number;
}
export interface AgentResult {
success: boolean;
text: string;
error?: string;
toolUsage: ToolUsage;
}
/**
* Run a prompt through an in-process omp agent session.
* Non-blocking — the event loop stays responsive.
*/
export async function runAnalysisAgent(
systemPrompt: string,
taskPrompt: string,
cwd: string,
timeoutMs: number = 120_000,
onEvent?: (event: AgentSessionEvent) => void,
signal?: AbortSignal,
): Promise<AgentResult> {
const toolUsage: ToolUsage = {
read: 0,
write: 0,
edit: 0,
bash: 0,
other: 0,
};
let timeoutHandle: ReturnType<typeof setTimeout> | null = null;
if (timeoutMs > 0) {
timeoutHandle = setTimeout(() => {
sessionRef.session?.agent.abort();
}, timeoutMs);
}
const sessionRef: {
session?: Awaited<ReturnType<typeof createAgentSession>>["session"];
} = {};
try {
const result = await createAgentSession({
cwd,
sessionManager: SessionManager.inMemory(cwd),
toolNames: ["read", "grep", "glob"],
restrictToolNames: true,
disableExtensionDiscovery: true,
skills: [],
promptTemplates: [],
rules: [],
contextFiles: [],
enableMCP: false,
enableLsp: false,
agentRegistry: new AgentRegistry(),
});
sessionRef.session = result.session;
const abortHandler = () => result.session.agent.abort();
signal?.addEventListener("abort", abortHandler, { once: true });
let finalText = "";
let errorMessage: string | undefined;
const unsubscribe = result.session.subscribe((event: AgentSessionEvent) => {
onEvent?.(event);
if (event.type === "message_end") {
const message = event.message as {
role?: string;
content?: unknown;
errorMessage?: string;
};
if (message.role !== "assistant") return;
if (message.errorMessage) errorMessage = message.errorMessage;
const text = extractAssistantText(message.content);
if (text) finalText = text;
}
if (event.type === "tool_execution_start") {
const name = event.toolName;
if (name in toolUsage) {
(toolUsage as unknown as Record<string, number>)[name]++;
} else {
toolUsage.other++;
}
}
});
if (signal?.aborted) throw new Error("Aborted");
await result.session.prompt(`${systemPrompt}\n\n${taskPrompt}`);
await result.session.agent.waitForIdle();
unsubscribe();
result.session.dispose();
signal?.removeEventListener("abort", abortHandler);
if (timeoutHandle) clearTimeout(timeoutHandle);
if (errorMessage && !finalText) {
return { success: false, text: "", error: errorMessage, toolUsage };
}
return { success: true, text: finalText.trim(), toolUsage };
} catch (error) {
if (timeoutHandle) clearTimeout(timeoutHandle);
return {
success: false,
text: "",
error: error instanceof Error ? error.message : String(error),
toolUsage,
};
} finally {
sessionRef.session?.dispose();
}
}
function extractAssistantText(content: unknown): string {
if (typeof content === "string") return content;
if (!Array.isArray(content)) return "";
return content
.filter(
(c): c is { type: string; text?: string } =>
!!c &&
typeof c === "object" &&
(c as { type?: string }).type === "text",
)
.map((c) => (c as { text?: string }).text ?? "")
.join("");
}

525
src/firecrawl.ts Normal file
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/**
* Deep Research — direct Firecrawl HTTP client
*
* Calls the self-hosted Firecrawl API directly (same approach as the
* firecrawl.ts extension)
*/
import * as fs from "node:fs";
import * as path from "node:path";
import { parse as parseYaml } from "yaml";
import type { SearchResult, EnrichedSearchResult, ContentType } from "./types";
import { getAgentDir } from "@oh-my-pi/pi-coding-agent";
/* ── Config ──────────────────────────────────────────────────────── */
/**
* Read and merge Firecrawl settings from omp's config.yml files.
*
* Resolution order (later wins):
* 1. env vars FIRECRAWL_BASE_URL / FIRECRAWL_API_KEY
* 2. global ~/.omp/agent/config.yml → firecrawl.*
* 3. project .omp/config.yml → firecrawl.*
* 4. default http://localhost:3002 (if no baseUrl configured)
*/
function loadFirecrawlConfig() {
// Start with env var defaults
let baseUrl = process.env.FIRECRAWL_BASE_URL ?? "http://localhost:3002";
let apiKey = process.env.FIRECRAWL_API_KEY;
const agentDir = getAgentDir();
// Helper: read a config.yml and merge its firecrawl.* keys
const tryReadConfig = (configPath: string): void => {
try {
const raw = parseYaml(fs.readFileSync(configPath, "utf-8")) as Record<
string,
unknown
>;
const fc = (raw?.firecrawl ?? {}) as Record<string, unknown>;
if (typeof fc.baseUrl === "string" && fc.baseUrl.length > 0) {
baseUrl = fc.baseUrl;
}
if (typeof fc.apiKey === "string" && fc.apiKey.length > 0) {
apiKey = fc.apiKey;
}
} catch {
// File missing or unparseable — skip
}
};
// 1. Global config
tryReadConfig(path.join(agentDir, "config.yml"));
// 2. Project config (override global)
tryReadConfig(path.join(process.cwd(), ".omp", "config.yml"));
return {
baseUrl: baseUrl.replace(/\/+$/, ""),
apiKey,
};
}
const { baseUrl: BASE_URL, apiKey: API_KEY } = loadFirecrawlConfig();
/* ── Domain Authority Heuristics ─────────────────────────────────── */
/**
* Known high-authority domains and their authority scores (0.0 – 1.0).
* Academic, official, and established technical sources score highest.
*/
const AUTHORITY_DOMAINS: Record<string, number> = {
// Academic & scholarly
"arxiv.org": 0.95,
"scholar.google.com": 0.95,
"pubmed.ncbi.nlm.nih.gov": 0.95,
"semanticscholar.org": 0.9,
"ieee.org": 0.95,
"acm.org": 0.95,
"springer.com": 0.9,
"sciencedirect.com": 0.9,
"wiley.com": 0.85,
"nature.com": 0.95,
"science.org": 0.95,
"plos.org": 0.85,
// Official documentation
"docs.python.org": 0.9,
"developer.mozilla.org": 0.9,
"learn.microsoft.com": 0.85,
"developer.apple.com": 0.85,
"kubernetes.io": 0.85,
"react.dev": 0.85,
"nextjs.org": 0.8,
// Official language/platform docs
"go.dev": 0.9,
"golang.org": 0.9,
"rust-lang.org": 0.9,
"nodejs.org": 0.85,
"python.org": 0.85,
"typescriptlang.org": 0.85,
"openai.com": 0.8,
"anthropic.com": 0.8,
"cloud.google.com": 0.8,
"aws.amazon.com": 0.8,
"azure.microsoft.com": 0.8,
"postgresql.org": 0.85,
"sqlite.org": 0.85,
"redis.io": 0.85,
"docker.com": 0.75,
"elastic.co": 0.75,
"grafana.com": 0.75,
"datadoghq.com": 0.75,
"cloudflare.com": 0.8,
"blog.cloudflare.com": 0.8,
"techempower.com": 0.8,
"goframe.org": 0.75,
"corrode.dev": 0.6,
"evrone.com": 0.4,
"rustify.rs": 0.4,
"core.cz": 0.4,
// Medical / clinical
"mayoclinic.org": 0.9,
"heart.org": 0.85,
"researchgate.net": 0.6,
"healthline.com": 0.5,
"medicalnewstoday.com": 0.5,
"webmd.com": 0.45,
"verywellhealth.com": 0.5,
// Databases & dev tools
"mysql.com": 0.85,
"mariadb.org": 0.85,
"cockroachlabs.com": 0.7,
"timescale.com": 0.7,
"mongodb.com": 0.8,
"liquibase.com": 0.6,
"sqlpipe.com": 0.5,
"data-tune.com": 0.4,
"binaryigor.com": 0.4,
// Government & non-profits
".gov": 0.9,
".edu": 0.85,
"who.int": 0.9,
"worldbank.org": 0.85,
"oecd.org": 0.85,
// Established tech & news
"github.com": 0.8,
"stackoverflow.com": 0.7,
"medium.com": 0.4,
"dev.to": 0.5,
"wikipedia.org": 0.7,
"reuters.com": 0.8,
"apnews.com": 0.8,
"bbc.com": 0.75,
"nytimes.com": 0.75,
"theguardian.com": 0.7,
"techcrunch.com": 0.6,
"arstechnica.com": 0.65,
"wired.com": 0.65,
"infoworld.com": 0.55,
// Practitioner/aggregator content with measurable quality
"github.io": 0.6,
"crates.io": 0.7,
"docs.rs": 0.75,
"digitalocean.com": 0.6,
"freecodecamp.org": 0.6,
"geeksforgeeks.org": 0.35,
"stackexchange.com": 0.65,
"huggingface.co": 0.65,
"nasa.gov": 0.9,
"mit.edu": 0.9,
"stanford.edu": 0.9,
"harvard.edu": 0.9,
"ox.ac.uk": 0.9,
"cam.ac.uk": 0.9,
// Low-authority: personal social / SEO content
"linkedin.com": 0.25,
"reddit.com": 0.25,
"x.com": 0.3,
"twitter.com": 0.3,
"youtube.com": 0.3,
"blogspot.com": 0.25,
"substack.com": 0.3,
"hashnode.dev": 0.35,
"quora.com": 0.3,
"netguru.com": 0.3,
"relisoftware.com": 0.3,
"dasroot.net": 0.3,
"devgenius.io": 0.3,
"devnewsletter.com": 0.3,
};
/**
* Known low-quality SEO/comparison-spam domains. Content is often
* auto-generated, republished from other sites, or thin on substance.
* These get a hard authority floor so they never rank above real content.
*/
const LOW_AUTHORITY_DOMAINS: Record<string, number> = {
"markaicode.com": 0.15,
"bytegoblin.io": 0.2,
"towardsdev.com": 0.2,
"rustvsgo.com": 0.3,
"seekingalpha.com": 0.3,
"investopedia.com": 0.55,
"devops-daily.com": 0.3,
};
/** Content-type hints based on domain patterns */
const CONTENT_TYPE_HINTS: [RegExp, ContentType][] = [
[
/arxiv\.org|semanticscholar|ieee\.org|acm\.org|springer|sciencedirect|pubmed\.ncbi/,
"paper",
],
[
/docs\.|learn\.|developer\.|kubernetes\.io|react\.dev|nextjs\.org/,
"documentation",
],
[/wikipedia\.org|stackoverflow\.com|medium\.com|dev\.to/, "forum"],
[
/reuters\.com|apnews\.com|bbc\.com|nytimes\.com|techcrunch|arstechnica|wired/,
"news",
],
[/\.gov|\.edu|who\.int|worldbank|oecd\.org/, "official"],
[/github\.com/, "documentation"],
];
/* ── Source enrichment helpers ───────────────────────────────────── */
/**
* Extract the registered domain from a URL (e.g., "blog.example.com" → "example.com").
* Uses a simple 2-part TLD heuristic. For common cases like .co.uk this is approximate.
*/
function extractDomain(url: string): string {
try {
const hostname = new URL(url).hostname.toLowerCase();
// Special-case common multi-part TLDs
const multiPartTlds =
/\.(co\.uk|org\.uk|ac\.uk|gov\.uk|com\.au|co\.jp|co\.kr|com\.br)$/;
const parts = hostname.split(".");
if (multiPartTlds.test(hostname) && parts.length >= 3) {
return parts.slice(-3).join(".");
}
return parts.slice(-2).join(".");
} catch {
return url.replace(/^https?:\/\//, "").split("/")[0] ?? url;
}
}
function computeAuthorityScore(domain: string): number {
// Hard floor for known low-authority domains first
if (LOW_AUTHORITY_DOMAINS[domain] !== undefined)
return LOW_AUTHORITY_DOMAINS[domain];
// Direct match first
if (AUTHORITY_DOMAINS[domain]) return AUTHORITY_DOMAINS[domain];
// Suffix matches (.gov, .edu, etc.)
for (const [key, score] of Object.entries(AUTHORITY_DOMAINS)) {
if (key.startsWith(".") && domain.endsWith(key)) return score;
}
// Subdomain matches (e.g., blog.example.com matches example.com)
const parent = domain.split(".").slice(-2).join(".");
if (parent !== domain && AUTHORITY_DOMAINS[parent]) {
return AUTHORITY_DOMAINS[parent] * 0.9;
}
// github.io personal sites: treat as practitioner content (medium)
if (domain.endsWith(".github.io")) return 0.55;
return 0.3; // Unknown / low-authority default
}
function detectContentType(url: string, description: string): ContentType {
const lowerUrl = url.toLowerCase();
const lowerDesc = description.toLowerCase();
for (const [pattern, type] of CONTENT_TYPE_HINTS) {
if (pattern.test(lowerUrl)) return type;
}
// Heuristics from description text
if (/paper|research|study|experiment|analysis\b/.test(lowerDesc))
return "paper";
if (/documentation|guide|tutorial|api|reference/.test(lowerDesc))
return "documentation";
if (/blog|post|article|opinion/.test(lowerDesc)) return "blog";
if (/news|report|announce|release/.test(lowerDesc)) return "news";
if (/forum|discussion|question|answer|thread/.test(lowerDesc)) return "forum";
return "other";
}
function tryParseDate(dateStr: string | undefined | null): Date | null {
if (!dateStr) return null;
const d = new Date(dateStr);
return isNaN(d.getTime()) ? null : d;
}
/**
* Normalize a title for near-duplicate detection: lowercase, strip
* punctuation, collapse whitespace, drop common filler words.
* Two syndicated copies of the same article normalize identically.
*/
export function normalizeTitle(title: string): string {
return title
.toLowerCase()
.replace(/[^a-z0-9\s]/g, " ")
.replace(
/\b(?:the|a|an|of|for|and|or|in|on|with|vs|versus|to|how|what|why|2024|2025|2026)\b/g,
" ",
)
.replace(/\s+/g, " ")
.trim();
}
/**
* Near-duplicate check between two titles: normalized forms must share
* a substantial token overlap (same core words in the same order).
*/
export function isNearDuplicateTitle(a: string, b: string): boolean {
const normA = normalizeTitle(a);
const normB = normalizeTitle(b);
if (!normA || !normB) return false;
if (normA === normB) return true;
const tokensA = normA.split(" ");
const tokensB = normB.split(" ");
if (tokensA.length < 3 || tokensB.length < 3) return normA === normB;
// Check if one title is a substring of the other (after normalization)
if (normA.includes(normB) || normB.includes(normA)) return true;
// Jaccard-ish overlap on the shorter token set
const [short, long] =
tokensA.length <= tokensB.length ? [tokensA, tokensB] : [tokensB, tokensA];
const overlap = short.filter((t) => long.includes(t)).length;
return overlap / short.length >= 0.75;
}
/**
* Enrich a raw search result with source authority metadata.
* Accepts extra fields (e.g. date) from the Firecrawl API response.
*/
export function enrichResult(
result: SearchResult & Record<string, unknown>,
): EnrichedSearchResult {
const domain = extractDomain(result.url);
return {
...result,
domain,
authorityScore: computeAuthorityScore(domain),
publishedDate: tryParseDate(result.date as string | undefined),
contentType: detectContentType(result.url, result.description),
};
}
/* ── Helpers ──────────────────────────────────────────────────────── */
async function firecrawlRequest(
endpoint: string,
body: Record<string, unknown>,
signal?: AbortSignal,
): Promise<unknown> {
const headers: Record<string, string> = {
"Content-Type": "application/json",
};
if (API_KEY) {
headers["Authorization"] = `Bearer ${API_KEY}`;
}
const res = await fetch(`${BASE_URL}/v1/${endpoint}`, {
method: "POST",
headers,
body: JSON.stringify(body),
signal,
});
if (!res.ok) {
const text = await res.text();
throw new Error(
`Firecrawl ${endpoint} failed (${res.status}): ${text.slice(0, 500)}`,
);
}
return res.json();
}
/**
* firecrawlRequest with retry-with-backoff for transient failures
* (429 rate limits, 5xx server errors, network blips). Does NOT retry
* 4xx client errors (invalid requests) or aborts.
*/
async function firecrawlRequestWithRetry(
endpoint: string,
body: Record<string, unknown>,
signal?: AbortSignal,
retries: number = 2,
): Promise<unknown> {
let lastError: unknown;
for (let attempt = 0; attempt <= retries; attempt++) {
if (signal?.aborted) throw new Error("Aborted");
try {
return await firecrawlRequest(endpoint, body, signal);
} catch (error) {
lastError = error;
const status =
error instanceof Error
? Number(/failed \((\d+)\)/.exec(error.message)?.[1] ?? 0)
: 0;
// Don't retry aborts or 4xx client errors (other than 429)
if (
signal?.aborted ||
(status >= 400 && status < 500 && status !== 429)
) {
throw error;
}
if (attempt < retries) {
const delayMs = 400 * 2 ** attempt + Math.random() * 200;
await new Promise((r) => setTimeout(r, delayMs));
}
}
}
throw lastError;
}
export async function isFirecrawlReachable(): Promise<boolean> {
try {
const res = await fetch(`${BASE_URL}/v1/scrape`, {
method: "POST",
headers: {
"Content-Type": "application/json",
...(API_KEY ? { Authorization: `Bearer ${API_KEY}` } : {}),
},
body: JSON.stringify({ url: "https://example.com", formats: ["links"] }),
signal: AbortSignal.timeout(10_000),
});
return res.ok;
} catch {
return false;
}
}
/* ── Search ───────────────────────────────────────────────────────── */
/**
* Search the web and return structured, enriched results.
* Uses Firecrawl's search endpoint with scrape to get full page content.
*/
export async function searchWeb(
query: string,
limit: number = 5,
signal?: AbortSignal,
): Promise<EnrichedSearchResult[]> {
const body: Record<string, unknown> = {
query,
limit: Math.min(limit, 10),
scrapeOptions: {
formats: ["markdown"],
onlyMainContent: true,
},
};
const result = await firecrawlRequestWithRetry("search", body, signal);
if (!result || typeof result !== "object") return [];
const res = result as {
success?: boolean;
data?: Record<string, unknown>[];
error?: string;
};
if (!res.success || !res.data) return [];
const rawResults: (SearchResult & Record<string, unknown>)[] = res.data
.map((doc) => ({
title: (doc.title as string) ?? "",
url: (doc.url as string) ?? "",
description: (doc.description as string) ?? "",
markdown: (doc.markdown as string) ?? "",
// Preserve extra fields for date extraction
...doc,
}))
.filter((r) => {
// Keep results with a meaningful body OR a substantive description.
// Filters out stub pages / pure navigation results that would
// waste analysis tokens.
const hasBody = (r.markdown ?? "").trim().length >= 150;
const hasSubstantiveDesc = (r.description ?? "").trim().length >= 40;
return hasBody || hasSubstantiveDesc;
});
// Enrich each result with source metadata
return rawResults.map(enrichResult);
}
/* ── Scrape ───────────────────────────────────────────────────────── */
/**
* Scrape a single URL and return its markdown content.
*/
export async function scrapeUrl(
url: string,
signal?: AbortSignal,
): Promise<{ title: string; markdown: string; links: string[] } | null> {
const result = await firecrawlRequestWithRetry(
"scrape",
{ url, formats: ["markdown"] },
signal,
);
if (!result || typeof result !== "object") return null;
const res = result as {
success?: boolean;
data?: Record<string, unknown>;
error?: string;
};
if (!res.success || !res.data) return null;
return {
title: (res.data.title as string) ?? "",
markdown: (res.data.markdown as string) ?? "",
links: (res.data.links as string[]) ?? [],
};
}

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/**
* Deep Research — Search query generation & refinement
*
* Uses an LLM agent to generate search queries from different research
* angles, then analyzes results to produce follow-up queries.
*/
import type {
SearchQuery,
Finding,
ResearchRound,
EnrichedSearchResult,
} from "./types";
import { runAnalysisAgent } from "./agent";
/* ── System Prompts ──────────────────────────────────────────────── */
const DECOMPOSE_SYSTEM = `You are a research methodology expert. Given a broad research question, your job is to break it down into 4-7 focused sub-questions that, when answered, collectively provide a complete answer to the original question.
Guidelines:
- Each sub-question should tackle ONE specific facet of the research question
- Cover different dimensions: what, how, why, who, comparison, evidence, implications
- Sub-questions should be independently researchable via web search
- Avoid overlap between sub-questions
- Prioritize questions that will surface concrete evidence over speculative ones
Output ONLY a JSON array of sub-question strings.
Example:
Input: "What are the benefits and risks of artificial intelligence in healthcare?"
Output: ["What specific AI technologies are currently deployed in clinical healthcare settings?", "What peer-reviewed evidence exists for AI improving diagnostic accuracy?", "What are the documented risks and failure cases of AI in healthcare?", "How do regulatory frameworks (FDA, EMA) address AI-based medical devices?", "What do healthcare practitioners report as barriers to AI adoption?"]
`;
const GENERATE_QUERIES_SYSTEM = `You are a research methodology expert. Your role is to generate effective web search queries that will yield high-quality, diverse information about a research topic.
Guidelines:
- Create queries from DIFFERENT angles (technical, practical, comparative, critical, forward-looking, authoritative)
- Each query should target a specific facet of the question
- Queries should use keywords that search engines rank well (avoid overly long questions)
- Cover contrasting viewpoints and alternative approaches
- Include queries for finding authoritative sources (docs, papers, official sites)
- Prioritize recent information where relevant
Output ONLY a JSON array of objects with fields:
- "query": the search query string
- "rationale": why this query will help answer the research question
- "angle": one of "technical" | "practical" | "comparative" | "critical" | "forward-looking" | "authoritative" | "historical" | "case-study" | "data-statistics" | "ethical"
Example:
[
{"query": "Rust async/await performance benchmarks 2024", "rationale": "Understanding current performance characteristics", "angle": "technical"},
{"query": "Rust vs Go concurrency patterns comparison", "rationale": "Comparative analysis helps contextualize trade-offs", "angle": "comparative"}
]
`;
const FOLLOWUP_SYSTEM = `You are a research analyst. Given the research question, sub-questions, and findings so far, your job is to identify what's still unknown and generate follow-up search queries to fill those gaps.
Look for:
- Claims made without sufficient evidence
- Conflicting information that needs resolution
- Angles that haven't been explored yet
- Missing authoritative sources (papers, official docs, primary data)
- Practical implications that need more detail
- Recent developments that might have updated findings
Guidelines:
- Do NOT repeat or paraphrase queries already explored — aim for genuinely new angles
- Prefer querying for authoritative/primary sources over more blog posts when evidence is weak
- When findings conflict, craft a query designed to resolve the contradiction
- Keep queries concise and keyword-rich
Output ONLY a JSON array of objects with fields:
- "query": the search query string
- "rationale": what gap this query fills or what angle it explores
- "angle": one of "technical" | "practical" | "comparative" | "critical" | "forward-looking" | "authoritative" | "historical" | "case-study" | "data-statistics" | "ethical"
`;
/* ── JSON parsing helpers ────────────────────────────────────────── */
/**
* Robustly parse a JSON array from LLM output.
*
* LLMs frequently wrap JSON in ```json fences, prepend prose like
* "Here are the queries:", or emit trailing punctuation. This strips
* fences and extracts the first bracketed array before parsing.
*
* Returns null when no array can be extracted.
*/
function parseJsonArray(text: string): unknown[] | null {
if (!text) return null;
// Strip markdown code fences
const withoutFences = text
.replace(/```(?:json|javascript)?\s*/gi, "")
.replace(/```/g, "");
// Find the first '[' ... ']' block (arrays are our target shape)
const start = withoutFences.indexOf("[");
const end = withoutFences.lastIndexOf("]");
if (start === -1 || end === -1 || end <= start) return null;
const candidate = withoutFences.slice(start, end + 1);
try {
const parsed = JSON.parse(candidate);
return Array.isArray(parsed) ? parsed : null;
} catch {
// Try to salvage: strip trailing commas (common LLM artifact)
try {
const fixed = candidate.replace(/,\s*([}\]])/g, "$1");
const parsed = JSON.parse(fixed);
return Array.isArray(parsed) ? parsed : null;
} catch {
return null;
}
}
}
/** Map a parsed array entry to a SearchQuery, tolerating missing fields. */
function toSearchQuery(q: Record<string, unknown>): SearchQuery | null {
const query = String(q.query ?? "").trim();
if (!query) return null;
return {
query,
rationale: String(q.rationale ?? "").trim(),
angle: String(q.angle ?? "technical").trim() || "technical",
};
}
/* ── Sub-Question Decomposition ───────────────────────────────────── */
/**
* Decompose a broad research question into focused, independently
* researchable sub-questions. Returns the sub-questions or an empty
* array if the LLM call fails.
*/
export async function decomposeQuestion(
question: string,
cwd: string,
signal?: AbortSignal,
): Promise<string[]> {
const taskPrompt = `Break down this research question into 4-7 focused sub-questions:\n\n${question}`;
const result = await runAnalysisAgent(
DECOMPOSE_SYSTEM,
taskPrompt,
cwd,
60_000,
undefined,
signal,
);
if (!result.success || !result.text) return [];
const parsed = parseJsonArray(result.text);
if (parsed) {
const subQuestions = parsed
.map(String)
.map((s: string) => s.trim())
.filter((s: string) => s.length > 10);
if (subQuestions.length > 0) return subQuestions;
}
return [];
}
/* ── Query Generation ────────────────────────────────────────────── */
/**
* Generate initial search queries for a research question.
* When sub-questions are available, generates queries per sub-question
* for better depth and diversity.
*/
export async function generateQueries(
question: string,
count: number,
cwd: string,
signal?: AbortSignal,
subQuestions?: string[],
): Promise<SearchQuery[]> {
// If we have sub-questions, generate queries distributed across them
if (subQuestions && subQuestions.length > 0) {
const queriesPerSub = Math.max(1, Math.ceil(count / subQuestions.length));
const allQueries: SearchQuery[] = [];
for (const subQ of subQuestions) {
if (allQueries.length >= count) break;
const taskPrompt = `Research question: ${question}\nSub-question: ${subQ}\n\nGenerate ${queriesPerSub} search query(ies) to answer this sub-question specifically.`;
const result = await runAnalysisAgent(
GENERATE_QUERIES_SYSTEM,
taskPrompt,
cwd,
60_000,
undefined,
signal,
);
if (!result.success || !result.text) continue;
const parsed = parseJsonArray(result.text);
if (parsed) {
const queries = parsed
.slice(0, queriesPerSub)
.map((q) => toSearchQuery(q as Record<string, unknown>))
.filter((q): q is SearchQuery => q !== null);
allQueries.push(...queries);
}
}
if (allQueries.length > 0) {
return allQueries.slice(0, count);
}
}
// Fall through to standard query generation
const taskPrompt = `Research question: ${question}
Generate ${count} diverse search queries to research this topic effectively. Cover different angles.`;
const result = await runAnalysisAgent(
GENERATE_QUERIES_SYSTEM,
taskPrompt,
cwd,
60_000,
undefined,
signal,
);
if (!result.success || !result.text) {
return generateFallbackQueries(question, count);
}
try {
const parsed = parseJsonArray(result.text);
if (parsed && parsed.length > 0) {
return parsed
.slice(0, count)
.map((q) => toSearchQuery(q as Record<string, unknown>))
.filter((q): q is SearchQuery => q !== null);
}
} catch {
// JSON parse failed, fall back
}
return generateFallbackQueries(question, count);
}
/* ── Follow-up Query Generation ──────────────────────────────────── */
/**
* Generate follow-up queries based on findings from previous rounds.
*/
export async function generateFollowUpQueries(
question: string,
rounds: ResearchRound[],
count: number,
cwd: string,
signal?: AbortSignal,
): Promise<SearchQuery[]> {
// Build a summary of findings so far
const allFindings = rounds.flatMap((r) => r.findings);
const findingsSummary = allFindings
.map((f) => {
const corr =
f.corroborationScore !== undefined
? ` [corroboration: ${(f.corroborationScore * 100).toFixed(0)}%]`
: "";
return `- ${f.title}: ${f.summary} (confidence: ${f.confidence}${corr})`;
})
.join("\n");
const exploredAngles = rounds
.flatMap((r) => r.queries)
.map((q) => `[${q.angle}] ${q.query} — ${q.rationale}`)
.join("\n");
// Find low-corroboration or low-confidence topics
const gaps = allFindings
.filter((f) => f.confidence === "low" || (f.corroborationScore ?? 1) < 0.5)
.map((f) => `Gap: ${f.title} — ${f.summary}`)
.join("\n");
const taskPrompt = `Research question: ${question}
Queries already explored:
${exploredAngles}
Findings so far:
${findingsSummary}
${gaps ? `Remaining knowledge gaps:\n${gaps}` : ""}
Generate ${count} follow-up search queries to fill remaining gaps and deepen the research. Do not repeat or paraphrase the queries already explored.`;
const result = await runAnalysisAgent(
FOLLOWUP_SYSTEM,
taskPrompt,
cwd,
60_000,
undefined,
signal,
);
if (!result.success || !result.text) {
return [];
}
const exploredNormalized = new Set(
rounds.flatMap((r) => r.queries).map((q) => normalizeQueryText(q.query)),
);
const parsed = parseJsonArray(result.text);
if (parsed && parsed.length > 0) {
const fresh: SearchQuery[] = [];
for (const q of parsed.slice(0, count)) {
const sq = toSearchQuery(q as Record<string, unknown>);
if (!sq) continue;
const normalized = normalizeQueryText(sq.query);
// Skip queries that are near-duplicates of already-explored ones
if (exploredNormalized.has(normalized)) continue;
if (fresh.some((fq) => normalizeQueryText(fq.query) === normalized))
continue;
exploredNormalized.add(normalized);
fresh.push(sq);
}
return fresh;
}
return [];
}
/**
* Lightweight query-text normalization for duplicate detection.
*/
function normalizeQueryText(query: string): string {
return query
.toLowerCase()
.replace(/[^a-z0-9\s]/g, " ")
.replace(/\s+/g, " ")
.trim();
}
/* ── Fallback Query Generation ────────────────────────────────────── */
/**
* Fallback query generation when the LLM call fails.
*/
function generateFallbackQueries(
question: string,
count: number,
): SearchQuery[] {
const queries: SearchQuery[] = [];
const angles = [
{ angle: "technical", desc: "technical details and specifications" },
{
angle: "practical",
desc: "practical examples, tutorials, and best practices",
},
{ angle: "comparative", desc: "comparisons with alternatives" },
{ angle: "critical", desc: "limitations, challenges, and criticisms" },
{ angle: "forward-looking", desc: "future trends and developments" },
];
for (let i = 0; i < Math.min(count, angles.length); i++) {
queries.push({
query: `${question} ${angles[i].desc}`,
rationale: `Exploring ${angles[i].desc} related to the research question`,
angle: angles[i].angle as SearchQuery["angle"],
});
}
return queries;
}
/* ── Analysis ────────────────────────────────────────────────────── */
const ANALYZE_SYSTEM = `You are a research analyst. Given search results for a specific query, extract key findings.
For each finding:
- Give it a concise, specific title (a claim, not a topic)
- Summarize what was found in 1-3 sentences, focused on evidence
- List which source URLs support this finding
- Include 1-2 key quotes from the sources
- Rate your confidence (high/medium/low) based on source authority and consistency
Guidelines:
- Extract 3-6 findings maximum, prioritizing the most decision-relevant
- Prefer findings with concrete evidence over generic observations
- Ignore boilerplate, navigation text, and irrelevant tangents in the content
- Do NOT invent quotes — only use text that appears in the provided content
- When sources conflict, note the conflict in the summary
Output ONLY a JSON array of objects with fields:
- "title": concise finding title
- "summary": 1-3 sentence summary
- "sources": array of source URLs
- "keyQuotes": array of 1-2 key quotes
- "confidence": "high" | "medium" | "low"`;
/**
* Analyze search results for a specific query and extract findings.
*/
export async function analyzeResults(
query: string,
results: EnrichedSearchResult[],
cwd: string,
signal?: AbortSignal,
angle?: string,
): Promise<Finding[]> {
// Include authority metadata in the prompt so the LLM can consider source quality.
// Token budget: give high-authority sources generous space, truncate
// low-authority/SEO content aggressively so junk doesn't dominate the prompt.
const MAX_CHARS_HIGH_AUTH = 3500;
const MAX_CHARS_LOW_AUTH = 1200;
const resultsText = results
.map((r, i) => {
const maxChars =
r.authorityScore >= 0.6 ? MAX_CHARS_HIGH_AUTH : MAX_CHARS_LOW_AUTH;
const content = r.markdown.slice(0, maxChars).trim();
const body =
content.length > 0
? content
: `(no body content; description only)\n${r.description}`;
return `--- Result ${i + 1} ---\nTitle: ${r.title}\nURL: ${r.url}\nDomain: ${r.domain}\nAuthority Score: ${(r.authorityScore * 100).toFixed(0)}%\nContent Type: ${r.contentType}\nDescription: ${r.description}\nContent:\n${body}`;
})
.join("\n\n");
const taskPrompt = `Search query: "${query}"${angle ? ` (angle: ${angle})` : ""}
Search results:
${resultsText}
Extract key findings from these results. Consider source authority when rating confidence.`;
const result = await runAnalysisAgent(
ANALYZE_SYSTEM,
taskPrompt,
cwd,
90_000,
undefined,
signal,
);
if (!result.success || !result.text) return [];
const parsed = parseJsonArray(result.text);
if (parsed) {
return parsed
.map((f) => {
const entry = f as Record<string, unknown>;
return {
title: String(entry.title ?? "").trim(),
summary: String(entry.summary ?? "").trim(),
sources: Array.isArray(entry.sources)
? entry.sources.map(String)
: [],
keyQuotes: Array.isArray(entry.keyQuotes)
? entry.keyQuotes.map(String)
: [],
confidence: (["high", "medium", "low"].includes(
String(entry.confidence),
)
? String(entry.confidence)
: "medium") as Finding["confidence"],
// Provenance: which query and angle produced this finding
query,
angle,
};
})
.filter((f) => f.title && f.summary);
}
return [];
}
/* ── Corroboration Tracking ──────────────────────────────────────── */
/**
* Cross-reference all findings to compute corroboration scores.
*
* For each finding, we check:
* 1. How many other findings reference the same or similar source URLs
* 2. The authority scores of the supporting sources
* 3. Whether independent domains support the same claim
*
* Returns the findings with added corroborationScore, bestSourceAuthority,
* and avgSourceAuthority.
*/
export function computeCorroboration(
findings: Finding[],
urlQueryCounts?: Map<string, number>,
): Finding[] {
if (findings.length === 0) return [];
// Collect all unique source URLs and their authority scores
// In a real implementation, we'd map URLs to EnrichedSearchResult authority scores
// For now, extract domain-level patterns
// Build a map of domain -> authority scores from source URLs
const domainAuthority = new Map<string, number>();
for (const finding of findings) {
for (const url of finding.sources) {
try {
const domain = extractDomainSimple(url);
if (!domainAuthority.has(domain)) {
domainAuthority.set(domain, heuristicDomainScore(domain));
}
} catch {
// skip invalid URLs
}
}
}
return findings.map((finding) => {
if (finding.sources.length === 0) {
return {
...finding,
corroborationScore: 0,
bestSourceAuthority: 0,
avgSourceAuthority: 0,
};
}
// Compute source authority stats
const authorities: number[] = finding.sources.map((url) => {
try {
const domain = extractDomainSimple(url);
return domainAuthority.get(domain) ?? 0.3;
} catch {
return 0.3;
}
});
const bestAuthority = Math.max(...authorities);
const avgAuthority =
authorities.reduce((a, b) => a + b, 0) / authorities.length;
// Compute corroboration.
//
// PRIMARY signal (when urlQueryCounts is provided): what fraction of
// this finding's sources were independently surfaced by multiple
// DIFFERENT search queries? A source found by several independent
// searches is genuinely corroborated; same-query duplicates do not
// count (findings from one query analyzed the same result set).
//
// FALLBACK signal (no map): domain-level agreement across findings
// from different queries.
let corroborationScore: number;
if (urlQueryCounts && urlQueryCounts.size > 0) {
const multiQuerySources = finding.sources.filter(
(url) => (urlQueryCounts.get(url) ?? 1) > 1,
).length;
corroborationScore =
finding.sources.length > 0
? multiQuerySources / finding.sources.length
: 0;
} else {
// Fallback: cross-query agreement by shared domain
const myDomains = new Set(
finding.sources.map((u) => extractDomainSimple(u)),
);
let corroboratingFindings = 0;
let independentOthers = 0;
for (const other of findings) {
if (other === finding) continue;
// Same query provenance = same analyzed result set = not independent
if (other.query && finding.query && other.query === finding.query) {
continue;
}
independentOthers++;
const otherDomains = new Set(
other.sources.map((u) => extractDomainSimple(u)),
);
const shared = [...myDomains].some((d) => otherDomains.has(d));
if (shared) corroboratingFindings++;
}
corroborationScore =
independentOthers > 0
? Math.min(1, corroboratingFindings / independentOthers)
: 0;
}
return {
...finding,
corroborationScore: Math.round(corroborationScore * 100) / 100,
bestSourceAuthority: Math.round(bestAuthority * 100) / 100,
avgSourceAuthority: Math.round(avgAuthority * 100) / 100,
};
});
}
/**
* Simple domain extraction (avoids URL constructor for compatibility).
*/
function extractDomainSimple(url: string): string {
const match = url.match(/https?:\/\/([^/]+)/);
if (!match) return url;
const hostname = match[1].toLowerCase();
const parts = hostname.split(".");
const multiPartTlds =
/\.(co\.uk|org\.uk|ac\.uk|gov\.uk|com\.au|co\.jp|co\.kr|com\.br)$/;
if (multiPartTlds.test(hostname) && parts.length >= 3) {
return parts.slice(-3).join(".");
}
return parts.slice(-2).join(".");
}
/**
* Very basic domain score heuristic without the full domain list.
*/
function heuristicDomainScore(domain: string): number {
if (/\.gov$|\.edu$/.test(domain)) return 0.85;
if (/arxiv|scholar|pubmed|ieee|acm|springer|nature|science/.test(domain))
return 0.9;
if (/github|gitlab|bitbucket/.test(domain)) return 0.75;
if (/wikipedia|stackoverflow|medium|dev\.to/.test(domain)) return 0.55;
if (/docs\.|learn\.|developer\./.test(domain)) return 0.8;
if (/reuters|apnews|bbc|nytimes|bloomberg/.test(domain)) return 0.75;
if (/blog|forum|reddit/.test(domain)) return 0.3;
return 0.4;
}

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/**
* Deep Research — Report synthesis
*
* Takes all research rounds and synthesizes a comprehensive report
* using an LLM agent. Produces:
* - Numbered inline citations with a bibliography
* - Layered report: TL;DR → Executive Summary → Key Findings
* → Detailed Analysis → Limitations/Gaps → References
* - Audience-aware tone adjustment
*/
import type {
ResearchRound,
ResearchConfig,
Reference,
Finding,
} from "./types";
import { runAnalysisAgent } from "./agent";
import { isNearDuplicateTitle } from "./firecrawl";
/** Return shape from synthesizeReport */
export interface SynthesisResult {
report: string;
references: Reference[];
}
/**
* Maximum findings included in the synthesis prompt.
* Keeps token usage bounded and forces the synthesizer to focus on
* the highest-quality evidence.
*/
const MAX_SYNTHESIS_FINDINGS = 30;
/**
* Rank a finding for inclusion in synthesis: authority-weighted,
* confidence-weighted, with a corroboration bonus.
*/
function findingQualityScore(f: Finding): number {
const authority =
(f.bestSourceAuthority ?? f.avgSourceAuthority ?? 0.5) || 0.5;
const confidenceWeight =
f.confidence === "high" ? 1.0 : f.confidence === "medium" ? 0.7 : 0.45;
const corroborationBonus = (f.corroborationScore ?? 0) * 0.3;
return authority * confidenceWeight + corroborationBonus;
}
/* ── System Prompts ──────────────────────────────────────────────── */
function buildSynthesisSystem(audience: string): string {
const audienceGuidance: Record<string, string> = {
expert:
"Assume expert-level domain knowledge. Use precise technical terminology, reference specific methodologies and standards, and prioritize depth over hand-holding. The reader understands the field.",
general:
"Write for an informed general audience. Define technical terms on first use, explain context, and keep the tone accessible but not simplistic. Avoid jargon without explanation.",
executive:
"Write for a busy executive or decision-maker. Lead with actionable conclusions and recommendations. Be concise — use bold for key takeaways. Minimize technical detail; focus on implications, trade-offs, and decisions. Target 2-3 pages.",
};
const guidance = audienceGuidance[audience] ?? audienceGuidance.general;
return `You are a senior research analyst synthesizing findings from multiple web searches into a comprehensive, well-structured report.
Audience: ${guidance}
Report structure (use ## headings):
1. **TL;DR** — One paragraph (2-3 sentences) giving the single most important answer
2. **Executive Summary** — 2-3 paragraphs covering what was found, how confident we are, and key implications
3. **Key Findings** — Tiered by importance/confidence. Bullet points with inline citations
4. **Detailed Analysis** — Organized by theme. Each section covers one aspect with evidence
5. **Limitations & Knowledge Gaps** — What evidence is weak, missing, or contradictory
6. **Conclusion** — Wrap up with actionable takeaways
Citation rules:
- Use numbered references like [1], [2] etc. throughout the text
- At the end, include a ## References section listing each citation
- Format references as: [1] Title — Domain (URL)
- Cite specific evidence, not vague associations
- When multiple sources support a claim, cite all of them: [1][3][5]
Style guidelines:
- Write in an objective, authoritative tone
- Use bullet points for listing evidence
- Note the confidence level for key claims
- Be thorough but concise — every paragraph should add value
- Use > for notable direct quotes with citations`;
}
/* ── Evidence Builder ────────────────────────────────────────────── */
function buildEvidenceText(
question: string,
rounds: ResearchRound[],
): { evidenceText: string; referenceMap: Map<string, Reference> } {
const allFindings = rounds.flatMap((r) => r.findings);
const totalSearches = rounds.reduce((sum, r) => sum + r.queries.length, 0);
const totalPages = rounds.reduce((sum, r) => sum + r.results.length, 0);
// Build a bibliography map (url -> Reference)
const seenUrls = new Map<string, Reference>();
let refId = 0;
for (const round of rounds) {
for (const result of round.results) {
if (!seenUrls.has(result.url)) {
refId++;
seenUrls.set(result.url, {
id: refId,
url: result.url,
title: result.title,
domain: result.domain,
authorityScore: result.authorityScore,
accessedAt: new Date().toISOString().split("T")[0],
});
}
}
}
// ── Deduplicate findings across rounds ────────────────────────────
// The same claim often surfaces in multiple rounds under slightly
// different titles. Merge them (union of sources/quotes, keep the
// highest-quality version) so the synthesizer isn't double-counting.
const deduped: Finding[] = [];
for (const finding of allFindings) {
const dupIndex = deduped.findIndex(
(f) =>
f.title !== finding.title &&
isNearDuplicateTitle(f.title, finding.title),
);
if (dupIndex === -1) {
deduped.push({ ...finding });
} else {
const existing = deduped[dupIndex];
deduped[dupIndex] = {
title: existing.title,
summary: existing.summary,
sources: Array.from(new Set([...existing.sources, ...finding.sources])),
keyQuotes: Array.from(
new Set([...existing.keyQuotes, ...finding.keyQuotes]),
).slice(0, 3),
confidence:
existing.confidence === "high" || finding.confidence === "high"
? "high"
: existing.confidence === "medium" ||
finding.confidence === "medium"
? "medium"
: "low",
query: existing.query ?? finding.query,
angle: existing.angle ?? finding.angle,
};
}
}
// ── Rank and cap findings for synthesis ───────────────────────────
const ranked = deduped
.map((f) => ({ f, score: findingQualityScore(f) }))
.sort((a, b) => b.score - a.score)
.slice(0, MAX_SYNTHESIS_FINDINGS)
.map(({ f }) => f);
// Organize findings by their own angle (provenance-aware)
const evidenceByAngle = new Map<string, Finding[]>();
for (const finding of ranked) {
const angle = finding.angle ?? "general";
if (!evidenceByAngle.has(angle)) evidenceByAngle.set(angle, []);
evidenceByAngle.get(angle)!.push(finding);
}
let evidenceText = `## Research Question\n${question}\n\n`;
evidenceText += `## Overview\n- Rounds of research: ${rounds.length}\n`;
evidenceText += `- Total searches executed: ${totalSearches}\n`;
evidenceText += `- Total pages analyzed: ${totalPages}\n`;
evidenceText += `- Key findings extracted: ${allFindings.length} (${ranked.length} passed dedup/quality filter)\n\n`;
// Build evidence grouped by angle with reference IDs
for (const [angle, findings] of Array.from(evidenceByAngle)) {
if (findings.length === 0) continue;
evidenceText += `## Angle: ${angle}\n\n`;
for (const finding of findings) {
// Get reference IDs for this finding's sources
const refs = finding.sources
.map((url) => seenUrls.get(url))
.filter((r): r is Reference => !!r)
.map((r) => `[${r.id}]`);
const avgAuth =
finding.avgSourceAuthority !== undefined
? ` | Avg Authority: ${(finding.avgSourceAuthority * 100).toFixed(0)}%`
: "";
const corr =
finding.corroborationScore !== undefined
? ` | Corroboration: ${(finding.corroborationScore * 100).toFixed(0)}%`
: "";
const bestAuthStr =
finding.bestSourceAuthority !== undefined
? ` | Best Source: ${(finding.bestSourceAuthority * 100).toFixed(0)}%`
: "";
evidenceText += `### ${finding.title}\n`;
evidenceText += `**Confidence:** ${finding.confidence}${avgAuth}${corr}${bestAuthStr}\n`;
if (refs.length > 0) {
evidenceText += `**Sources:** ${refs.join(", ")}\n`;
}
evidenceText += `${finding.summary}\n\n`;
if (finding.keyQuotes.length > 0) {
evidenceText += `> ${finding.keyQuotes[0]}\n\n`;
}
}
}
// Include reference metadata for the LLM to build proper citations
evidenceText += `## Reference Metadata\n\n`;
for (const [, ref] of seenUrls) {
evidenceText += `[${ref.id}] ${ref.title} (${ref.domain}, authority: ${(ref.authorityScore * 100).toFixed(0)}%) — ${ref.url}\n`;
}
return { evidenceText, referenceMap: seenUrls };
}
/* ── Main Synthesis ──────────────────────────────────────────────── */
/**
* Synthesize a research report from all rounds.
* Returns both the formatted report and the full bibliography.
*/
export async function synthesizeReport(
question: string,
rounds: ResearchRound[],
config: ResearchConfig,
cwd: string,
signal?: AbortSignal,
): Promise<SynthesisResult> {
const audience = config.audience ?? "general";
const { evidenceText, referenceMap } = buildEvidenceText(question, rounds);
const formatInstruction =
config.format === "structured"
? "Structured report with numbered sections, clear hierarchies, and data tables where appropriate."
: "Well-formatted markdown report with ## headings, bullet points, and inline numbered citations like [1].";
const taskPrompt = `Synthesize the following research findings into a comprehensive, well-structured report.
${evidenceText}
Write a thorough report that answers the original question: "${question}"
Format: ${formatInstruction}
Audience: ${audience}
Remember to use numbered citations like [1], [2] and include a ## References section at the end.`;
const result = await runAnalysisAgent(
buildSynthesisSystem(audience),
taskPrompt,
cwd,
120_000,
undefined,
signal,
);
if (result.success && result.text) {
// Build bibliography section
const bibSection = buildBibliography(referenceMap);
let report = result.text;
// ── Citation integrity ─────────────────────────────────────────
// 1. Strip any references section the LLM wrote and replace it with
// the authoritative bibliography (built from real scraped sources).
// 2. Remove inline [n] citations that point at IDs outside the
// bibliography (hallucinated numbers), so every citation resolves.
report = report.replace(
/^#+\s*references\s*$/gim,
"\n## END_OF_REPORT_MARKER",
);
const markerIdx = report.indexOf("## END_OF_REPORT_MARKER");
if (markerIdx !== -1) {
report = report.slice(0, markerIdx).trimEnd();
}
const maxRefId = Math.max(
0,
...Array.from(referenceMap.values()).map((r) => r.id),
);
report = report.replace(/\[(\d+)\]/g, (match, id: string) => {
const num = parseInt(id, 10);
return num >= 1 && num <= maxRefId ? match : "";
});
report = report.trimEnd() + `\n\n${bibSection}`;
return { report, references: Array.from(referenceMap.values()) };
}
// Fallback: generate a simple structured report
const fallbackReport = generateFallbackReport(
question,
rounds,
referenceMap,
audience,
);
return {
report: fallbackReport + `\n\n${buildBibliography(referenceMap)}`,
references: Array.from(referenceMap.values()),
};
}
/* ── Bibliography Builder ────────────────────────────────────────── */
/**
* Build a structured ## References section from the reference map.
*/
function buildBibliography(referenceMap: Map<string, Reference>): string {
if (referenceMap.size === 0) return "## References\n\nNo sources cited.";
const refs = Array.from(referenceMap.values()).sort((a, b) => a.id - b.id);
const lines: string[] = ["## References\n"];
for (const ref of refs) {
const authIcon =
ref.authorityScore >= 0.8 ? "⭐" : ref.authorityScore >= 0.5 ? "✓" : "○";
lines.push(
`[${ref.id}] ${authIcon} **${ref.title}** — ${ref.domain} (${ref.url}) — accessed ${ref.accessedAt}`,
);
}
return lines.join("\n");
}
/* ── Fallback Report ─────────────────────────────────────────────── */
/**
* Fallback report when the LLM synthesis fails.
* Produces a clean, structured report from the evidence.
*/
function generateFallbackReport(
question: string,
rounds: ResearchRound[],
referenceMap: Map<string, Reference>,
_audience: string,
): string {
const lines: string[] = [];
const allFindings = rounds.flatMap((r) => r.findings);
// ── TL;DR ──
lines.push(`# Research Report: ${question}`);
lines.push("");
const highConfFindings = allFindings.filter((f) => f.confidence === "high");
const totalHigh = highConfFindings.length;
const total = allFindings.length;
lines.push("## TL;DR");
lines.push("");
if (highConfFindings.length > 0) {
lines.push(
`Based on analysis of ${total} findings across ${rounds.length} research round(s), ` +
`${totalHigh} high-confidence conclusions were identified. ` +
`${highConfFindings[0].title}: ${highConfFindings[0].summary}`,
);
} else {
lines.push(
`This report covers findings from ${rounds.length} research round(s) exploring "${question}". ` +
`${total} findings were extracted, with varying levels of confidence.`,
);
}
lines.push("");
// ── Executive Summary ──
lines.push("## Executive Summary");
lines.push("");
lines.push(
`This report synthesizes findings from ${rounds.length} research round(s), ` +
`${rounds.reduce((s, r) => s + r.queries.length, 0)} search queries, ` +
`and ${rounds.reduce((s, r) => s + r.results.length, 0)} sources.`,
);
lines.push("");
// ── Key Findings (tiered) ──
if (allFindings.length > 0) {
lines.push("## Key Findings");
lines.push("");
// High confidence first
const highConf = allFindings.filter((f) => f.confidence === "high");
if (highConf.length > 0) {
lines.push("### High Confidence");
for (const finding of highConf) {
const refs = finding.sources
.map((url) => referenceMap.get(url))
.filter((r): r is Reference => !!r)
.map((r) => `[${r.id}]`);
lines.push(
`- **${finding.title}** ${refs.length > 0 ? refs.join("") : ""}`,
);
lines.push(` - ${finding.summary}`);
}
lines.push("");
}
// Medium confidence
const medConf = allFindings.filter((f) => f.confidence === "medium");
if (medConf.length > 0) {
lines.push("### Moderate Confidence");
for (const finding of medConf) {
const refs = finding.sources
.map((url) => referenceMap.get(url))
.filter((r): r is Reference => !!r)
.map((r) => `[${r.id}]`);
lines.push(
`- **${finding.title}** ${refs.length > 0 ? refs.join("") : ""}`,
);
lines.push(` - ${finding.summary}`);
}
lines.push("");
}
// Low confidence
const lowConf = allFindings.filter((f) => f.confidence === "low");
if (lowConf.length > 0) {
lines.push("### Lower Confidence (Needs Further Research)");
for (const finding of lowConf) {
const refs = finding.sources
.map((url) => referenceMap.get(url))
.filter((r): r is Reference => !!r)
.map((r) => `[${r.id}]`);
lines.push(
`- **${finding.title}** ${refs.length > 0 ? refs.join("") : ""}`,
);
lines.push(` - ${finding.summary}`);
}
lines.push("");
}
// ── Detailed Analysis ──
lines.push("## Detailed Analysis");
lines.push("");
const byAngle = new Map<string, Finding[]>();
for (const round of rounds) {
for (const f of round.findings) {
const angle = f.angle ?? round.queries[0]?.angle ?? "general";
if (!byAngle.has(angle)) byAngle.set(angle, []);
byAngle.get(angle)!.push(f);
}
}
for (const [angle, findings] of byAngle) {
lines.push(`### ${angle.charAt(0).toUpperCase() + angle.slice(1)}`);
lines.push("");
for (const f of findings) {
const corrStr =
f.corroborationScore !== undefined
? ` (corroboration: ${(f.corroborationScore * 100).toFixed(0)}%)`
: "";
lines.push(`**${f.title}** — *${f.confidence} confidence${corrStr}*`);
lines.push("");
lines.push(f.summary);
lines.push("");
if (f.keyQuotes.length > 0) {
lines.push(`> ${f.keyQuotes[0]}`);
lines.push("");
}
}
}
// ── Limitations ──
const lowConfCount = allFindings.filter(
(f) => f.confidence === "low",
).length;
const noCorr = allFindings.filter(
(f) => (f.corroborationScore ?? 0) < 0.3,
).length;
lines.push("## Limitations & Knowledge Gaps");
lines.push("");
if (lowConfCount > 0) {
lines.push(
`- **${lowConfCount} of ${allFindings.length} findings** have low confidence, indicating limited or conflicting evidence.`,
);
}
if (noCorr > 0) {
lines.push(
`- **${noCorr} findings** lack corroboration from multiple independent sources.`,
);
}
lines.push(
"- This research relied on web search results; some relevant sources may not be indexed or accessible.",
);
lines.push(
"- Findings are dependent on search engine ranking and the quality of indexed content.",
);
lines.push("");
// ── Conclusion ──
lines.push("## Conclusion");
lines.push("");
if (highConf.length > 0) {
lines.push(
`The research identified ${highConf.length} high-confidence finding(s) and ${medConf.length} moderately-supported finding(s). ` +
`The strongest evidence relates to: ${highConf.map((f) => f.title).join(", ")}.`,
);
} else {
lines.push(
"The research surfaced relevant information but with limited high-confidence evidence. Further investigation is recommended for the identified knowledge gaps.",
);
}
lines.push("");
}
// ── Methodology ──
lines.push(`*Report prepared for: ${_audience} audience*`);
lines.push("");
lines.push("## Methodology");
lines.push("");
for (const round of rounds) {
const failedSearches =
round.failedSearches ?? round.queries.length - round.successfulSearches;
lines.push(`### Round ${round.round}`);
lines.push(
`Queries: ${round.queries.map((q) => `"${q.query}" [${q.angle}]`).join(", ")}`,
);
lines.push(`Pages scraped: ${round.results.length}`);
lines.push(`Findings extracted: ${round.findings.length}`);
if (failedSearches > 0) {
lines.push(`Searches failed: ${failedSearches}`);
}
lines.push("");
}
return lines.join("\n");
}

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/**
* Deep Research — Core research orchestration
*
* Manages the multi-round deep research process:
* 1. Decompose the question into sub-questions (when depth > 1)
* 2. Generate initial search queries (per sub-question for better diversity)
* 3. Execute all queries in parallel via Firecrawl
* 4. Analyze results and extract findings
* 5. Compute corroboration scores
* 6. Generate follow-up queries for gaps
* 7. Iterate for depth rounds
* 8. Synthesize final report with numbered references
*
* Widget and progress callback patterns borrowed from ralpi's executor.
*/
import type { ExtensionContext } from "@oh-my-pi/pi-coding-agent";
import type {
Finding,
ResearchConfig,
EnrichedSearchResult,
ResearchRound,
ResearchReport,
} from "./types";
import type { SynthesisResult } from "./report";
import { searchWeb, isNearDuplicateTitle } from "./firecrawl";
import {
generateQueries,
generateFollowUpQueries,
analyzeResults,
computeCorroboration,
decomposeQuestion,
} from "./queries";
import { synthesizeReport } from "./report";
/** Progress callback for UI updates */
export type ResearchProgress = (update: {
phase:
| "decomposing"
| "generating_queries"
| "searching"
| "analyzing"
| "synthesizing"
| "complete";
round?: number;
totalRounds?: number;
message: string;
detail?: string;
fraction?: number; // 0-1
}) => void;
// ── Round-Robin Parallel Execution ──────────────────────────────────
/**
* Maximum concurrent Firecrawl search requests.
* Prevents rate limiting while still parallelizing queries.
*/
const MAX_SEARCH_CONCURRENT = 3;
/**
* Maximum concurrent analysis agent sessions.
*/
const MAX_ANALYSIS_CONCURRENT = 2;
/**
* Minimum findings per round before we consider early stopping.
* If we're getting very few new findings, saturation is near.
*/
const SATURATION_THRESHOLD = 0.15; // < 15% new findings = likely saturated
/**
* Bounded-concurrency parallel execution with round-robin slot assignment.
*
* Similar to ralpi's ModelRoundRobin: with N concurrent slots, items are
* assigned to free slots in FIFO order. When a slot finishes, the next
* item in the queue is assigned to it.
*
* This ensures even load distribution and avoids bursty concurrency.
*/
async function boundedConcurrency<T, R>(
items: T[],
maxConcurrent: number,
mapper: (item: T, index: number) => Promise<R>,
): Promise<R[]> {
const results: R[] = new Array(items.length);
let nextIndex = 0;
async function worker(): Promise<void> {
while (true) {
const currentIndex = nextIndex++;
if (currentIndex >= items.length) return;
results[currentIndex] = await mapper(items[currentIndex], currentIndex);
}
}
const numWorkers = Math.min(maxConcurrent, items.length);
const workers = Array.from({ length: numWorkers }, () => worker());
await Promise.all(workers);
return results;
}
/**
* Assess whether the research is reaching information saturation.
*/
function assessSaturation(
previousRound: ResearchRound | undefined,
currentRound: ResearchRound,
): number {
if (!previousRound || previousRound.findings.length === 0) return 0;
const prevUrls = new Set(previousRound.results.map((r) => r.url));
const newUrls = currentRound.results.filter(
(r) => !prevUrls.has(r.url),
).length;
const totalUrls = currentRound.results.length;
const newRatio = totalUrls > 0 ? newUrls / totalUrls : 0;
// Also check finding novelty
const prevFindingTitles = new Set(
previousRound.findings.map((f) => f.title.toLowerCase()),
);
const newFindings = currentRound.findings.filter(
(f) => !prevFindingTitles.has(f.title.toLowerCase()),
).length;
const totalFindings = currentRound.findings.length;
const findingNovelty = totalFindings > 0 ? newFindings / totalFindings : 0;
// Weight: URL novelty (40%) + finding novelty (60%)
return newRatio * 0.4 + findingNovelty * 0.6;
}
/**
* Run a complete deep research session.
*/
export async function runDeepResearch(
config: ResearchConfig,
ctx: ExtensionContext,
onProgress: ResearchProgress,
signal?: AbortSignal,
): Promise<ResearchReport> {
const startTime = Date.now();
const rounds: ResearchRound[] = [];
let totalSearches = 0;
let totalPages = 0;
let subQuestions: string[] = [];
// ── Phase: Decompose question into sub-questions ────────────────
if (config.depth > 1) {
onProgress({
phase: "decomposing",
round: 1,
totalRounds: config.depth,
message: "Decomposing research question into sub-topics...",
fraction: 0,
});
if (signal?.aborted) throw new Error("Research cancelled");
subQuestions = await decomposeQuestion(config.question, ctx.cwd, signal);
}
// ── Phase: Generate initial queries ─────────────────────────────
onProgress({
phase: "generating_queries",
round: 1,
totalRounds: config.depth,
message:
subQuestions.length > 0
? `Generating queries across ${subQuestions.length} sub-topics...`
: "Generating initial search queries...",
fraction: 0.05,
});
if (signal?.aborted) throw new Error("Research cancelled");
const queries = await generateQueries(
config.question,
config.breadth,
ctx.cwd,
signal,
subQuestions.length > 0 ? subQuestions : undefined,
);
if (queries.length === 0) {
throw new Error("Failed to generate any search queries");
}
// ── Execute rounds ───────────────────────────────────────────────
for (let round = 1; round <= config.depth; round++) {
if (signal?.aborted) throw new Error("Research cancelled");
const isFirstRound = round === 1;
const currentQueries = isFirstRound
? queries
: await generateFollowUpQueries(
config.question,
rounds,
config.breadth,
ctx.cwd,
signal,
);
if (!currentQueries || currentQueries.length === 0) {
// No follow-up queries to generate — stop here
break;
}
// ── Search phase (parallel with round-robin) ────────────────────
onProgress({
phase: "searching",
round,
totalRounds: config.depth,
message: `Searching ${currentQueries.length} queries in parallel...`,
fraction: 0.25,
});
if (signal?.aborted) throw new Error("Research cancelled");
// Run searches in parallel using round-robin bounded concurrency.
// Each mapper call runs independently; failures are caught per-query.
// Results keep their originating query index for later grouping.
const searchResultsArrays: (EnrichedSearchResult[] | null)[] =
await boundedConcurrency(
currentQueries,
MAX_SEARCH_CONCURRENT,
async (q, i) => {
onProgress({
phase: "searching",
round,
totalRounds: config.depth,
message: `Searching: "${q.query.slice(0, 60)}..."`,
detail: q.rationale,
fraction: 0.25 + (i / currentQueries.length) * 0.25,
});
try {
return await searchWeb(q.query, 5, signal);
} catch (error) {
const errorMsg =
error instanceof Error ? error.message : String(error);
onProgress({
phase: "searching",
round,
totalRounds: config.depth,
message: `Search failed: ${errorMsg.slice(0, 80)}`,
fraction: 0.25 + ((i + 1) / currentQueries.length) * 0.25,
});
return null;
}
},
);
const successfulSearches = searchResultsArrays.filter(
(r): r is EnrichedSearchResult[] => r !== null,
).length;
const failedSearches = currentQueries.length - successfulSearches;
totalSearches += currentQueries.length;
// Track which URLs were independently surfaced by MULTIPLE different
// queries. This is the corroboration signal: a source found by
// several independent searches is stronger evidence than one found
// by a single query.
const urlQueryCounts = new Map<string, number>();
searchResultsArrays.forEach((results, queryIndex) => {
if (!results) return;
const queryUrls = new Set(results.map((r) => r.url));
for (const url of queryUrls) {
urlQueryCounts.set(url, (urlQueryCounts.get(url) ?? 0) + 1);
}
// (queryIndex is unused beyond the closure; kept for clarity)
void queryIndex;
});
// ── Per-query result collection ──────────────────────────────────
// Deduplicate within each query's results by URL (prefer higher
// authority) AND by near-identical title (catches syndicated copies
// of the same article under different URLs).
const resultsByQuery: EnrichedSearchResult[][] = currentQueries.map(
() => [],
);
searchResultsArrays.forEach((results, queryIndex) => {
if (!results) return;
const seenUrls = new Set<string>();
const seenTitles: string[] = [];
for (const r of results) {
if (seenUrls.has(r.url)) continue;
seenUrls.add(r.url);
// Skip syndicated duplicates (same article, different URL)
if (
r.title &&
seenTitles.some((t) => isNearDuplicateTitle(t, r.title))
) {
continue;
}
if (r.title) seenTitles.push(r.title);
resultsByQuery[queryIndex].push(r);
}
});
// Global URL dedup across queries: a URL found by multiple queries
// stays attached to the FIRST query that surfaced it (most likely
// the most relevant one).
const globalSeen = new Set<string>();
for (const list of resultsByQuery) {
for (let i = list.length - 1; i >= 0; i--) {
if (globalSeen.has(list[i].url)) {
list.splice(i, 1);
} else {
globalSeen.add(list[i].url);
}
}
}
const uniqueResults = resultsByQuery.flat();
totalPages += uniqueResults.length;
// ── Analyze phase (parallel with round-robin) ──────────────────
onProgress({
phase: "analyzing",
round,
totalRounds: config.depth,
message: `Analyzing ${uniqueResults.length} search results in parallel...`,
fraction: 0.6,
});
if (signal?.aborted) throw new Error("Research cancelled");
// Build query-result pairs for parallel analysis.
// Each query is analyzed with the results IT actually produced,
// so findings stay coherent with the query's intent and angle.
const analysisTasks: Array<{
query: (typeof currentQueries)[number];
results: EnrichedSearchResult[];
index: number;
}> = [];
for (let i = 0; i < currentQueries.length; i++) {
const queryResults = resultsByQuery[i];
if (!queryResults || queryResults.length === 0) continue;
analysisTasks.push({
query: currentQueries[i],
results: queryResults,
index: i,
});
}
// Run analyses in parallel using round-robin bounded concurrency
const findingsArrays: Finding[][] = await boundedConcurrency(
analysisTasks,
MAX_ANALYSIS_CONCURRENT,
async (task) => {
onProgress({
phase: "analyzing",
round,
totalRounds: config.depth,
message: `Analyzing: "${task.query.query.slice(0, 40)}..."`,
fraction:
0.6 + (task.index / Math.max(analysisTasks.length, 1)) * 0.2,
});
try {
return await analyzeResults(
task.query.query,
task.results,
ctx.cwd,
signal,
task.query.angle,
);
} catch {
// Analysis failure shouldn't crash the round
return [];
}
},
);
// Flatten all findings
const allFindings: ResearchRound["findings"] = findingsArrays.flat();
// ── Corroboration pass ────────────────────────────────────────
// Cross-reference findings to compute corroboration scores.
// Corroboration = fraction of a finding's sources that were
// independently surfaced by multiple different search queries.
const corroboratedFindings = computeCorroboration(
allFindings,
urlQueryCounts,
);
// Record this round
const followUpTopics = corroboratedFindings
.filter(
(f: Finding) =>
f.confidence === "low" && (f.corroborationScore ?? 0) < 0.5,
)
.map((f: Finding) => f.title);
rounds.push({
round,
queries: currentQueries,
results: uniqueResults,
findings: corroboratedFindings,
followUpTopics,
successfulSearches,
failedSearches,
});
// ── Adaptive depth: check for saturation ──────────────────────
if (round > 1 && round < config.depth) {
const saturation = assessSaturation(
rounds[rounds.length - 2],
rounds[rounds.length - 1],
);
if (saturation < SATURATION_THRESHOLD) {
onProgress({
phase: "synthesizing",
message: `Information saturation reached (${(saturation * 100).toFixed(0)}% novelty) — synthesizing early`,
fraction: 0.85,
});
break;
}
}
}
// ── Synthesis phase ───────────────────────────────────────────────
onProgress({
phase: "synthesizing",
message: "Synthesizing research into final report...",
fraction: 0.9,
});
if (signal?.aborted) throw new Error("Research cancelled");
const synthesisResult: SynthesisResult = await synthesizeReport(
config.question,
rounds,
config,
ctx.cwd,
signal,
);
const finalReport = synthesisResult.report;
const references = synthesisResult.references;
const durationMs = Date.now() - startTime;
onProgress({
phase: "complete",
message: "Research complete!",
fraction: 1.0,
});
return {
question: config.question,
rounds,
finalReport,
totalSearches,
totalPagesScraped: totalPages,
durationMs,
references,
};
}

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/**
* Deep Research — type definitions
*/
/** Content type classification for a source */
export type ContentType =
| "documentation"
| "paper"
| "news"
| "blog"
| "forum"
| "official"
| "other";
/** A single search result from Firecrawl */
export interface SearchResult {
title: string;
url: string;
description: string;
markdown: string;
}
/** Enriched search result with source authority metadata */
export interface EnrichedSearchResult extends SearchResult {
domain: string;
authorityScore: number; // 0.0 – 1.0
publishedDate: Date | null;
contentType: ContentType;
}
/** A finding extracted from search results by an analysis agent */
export interface Finding {
title: string;
summary: string;
sources: string[];
keyQuotes: string[];
confidence: "high" | "medium" | "low";
/** The search query this finding was extracted under (provenance) */
query?: string;
/** The research angle of the originating query (provenance) */
angle?: string;
/** 0.0 – 1.0: how many independent sources support this finding */
corroborationScore?: number;
/** Authority score of the best source supporting this finding */
bestSourceAuthority?: number;
/** Average authority score across all sources */
avgSourceAuthority?: number;
}
/** A numbered reference with full metadata */
export interface Reference {
id: number;
url: string;
title: string;
domain: string;
authorityScore: number;
accessedAt: string; // ISO date string
}
/** A generated search query with its intent/rationale */
export interface SearchQuery {
query: string;
rationale: string;
angle: string;
}
/** Output from one research round */
export interface ResearchRound {
round: number;
queries: SearchQuery[];
results: EnrichedSearchResult[];
findings: Finding[];
/** Any follow-up questions/angles the analysis suggests */
followUpTopics: string[];
/** Number of search queries that actually returned data (non-empty) */
successfulSearches: number;
/** Number of search queries that failed entirely */
failedSearches: number;
}
/** Target audience expertise level */
export type Audience = "expert" | "general" | "executive";
/** Configuration for a research session */
export interface ResearchConfig {
question: string;
depth: number; // 1-3 rounds
breadth: number; // queries per round (1-5)
format: "markdown" | "structured";
audience?: Audience;
/** Focus on specific research angles only (empty = all angles) */
focus?: string[];
/** Show the research methodology section in the report */
showMethodology?: boolean;
}
/** Final research report */
export interface ResearchReport {
question: string;
rounds: ResearchRound[];
finalReport: string;
totalSearches: number;
totalPagesScraped: number;
durationMs: number;
references: Reference[];
}

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tsconfig.json Normal file
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{
"compilerOptions": {
"target": "ES2022",
"module": "ES2022",
"moduleResolution": "node",
"lib": ["ES2022"],
"noEmit": true,
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true,
"resolveJsonModule": true
},
"include": ["index.ts", "src/**/*"],
"exclude": ["node_modules", "dist"]
}