Building a Multi-Agent Research Pipeline with Claude Code: Complete Guide

Modern research workflows rarely fit into a single prompt-response cycle. When you need to gather information from multiple sources, synthesize findings, fact-check claims, and produce a polished report, a single agent often struggles to maintain focus across all those tasks. This is where a multi-agent research pipeline built on Claude Code shines. By decomposing a complex research goal into specialized roles—each handled by a dedicated Claude Code agent—you gain better accuracy, parallelism, and auditability.

In this guide, you'll learn what a multi-agent research pipeline is, why it matters, how to architect one with Claude Code, and the best practices that separate toy demos from production-grade systems.

What Is a Multi-Agent Research Pipeline?

A multi-agent research pipeline is a coordinated system where several AI agents, each with a narrow responsibility, collaborate to complete a research task. Instead of asking one model to "research X and write a report," you assign distinct roles such as:

Planner Agent — breaks the research question into sub-questions and a search plan.
Searcher Agents — execute parallel searches across the web, local files, or databases.
Analyst Agent — synthesizes raw findings into structured insights.
Fact-Checker Agent — verifies claims against source citations.
Writer Agent — produces the final deliverable in the requested format.

Claude Code is particularly well-suited for this pattern because it can run as a CLI tool, invoke subagents, read and write files, and chain tool calls programmatically. Each agent becomes a subprocess with its own context window, system prompt, and tool access.

Why It Matters

Single-agent approaches hit several walls when research grows complex:

Context pollution — stuffing search results, notes, and drafts into one conversation degrades reasoning quality.
No parallelism — one agent must search sources sequentially.
Hard to debug — when the output is wrong, you can't tell which step failed.
Role drift — an agent told to "research and write" often rushes to conclusions.

A multi-agent pipeline addresses each of these. Each agent gets a clean context, searchers run concurrently, intermediate artifacts are saved to disk for inspection, and role-specific system prompts keep each agent focused.

Architecture Overview

The pipeline follows a directed acyclic graph (DAG). The planner produces a plan file, searchers consume the plan and emit raw findings, the analyst consumes findings and emits structured notes, the fact-checker validates the notes, and the writer turns validated notes into the final report. All inter-agent communication happens through files on disk, which makes the system stateless, resumable, and easy to inspect.

research-pipeline/
├── plan.json
├── findings/
│   ├── source_1.md
│   ├── source_2.md
│   └── source_3.md
├── synthesis.md
├── fact_check.md
└── final_report.md

Prerequisites and Setup

You'll need Node.js 18+, the Claude Code CLI installed and authenticated, and a working directory for the pipeline. Install Claude Code globally if you haven't:

npm install -g @anthropic-ai/claude-code
claude --version


Create the project scaffold:

mkdir research-pipeline && cd research-pipeline
mkdir -p findings
touch plan.json synthesis.md fact_check.md final_report.md


Step 1: The Planner Agent

The planner's job is to take a research question and produce a structured JSON plan listing sub-questions and suggested search queries. We'll invoke Claude Code in non-interactive mode using the -p flag and a system prompt file.

Create prompts/planner.md:

You are a research planner. Given a research question, decompose it into
3-6 sub-questions. For each sub-question, provide 1-2 search queries.

Output ONLY valid JSON matching this schema:
{
  "question": string,
  "subquestions": [
    {
      "id": string,
      "text": string,
      "queries": [string]
    }
  ]
}

Do not include markdown fences or commentary.


Now create the runner script run_planner.sh:

#!/usr/bin/env bash
set -euo pipefail

QUESTION="${1:?Usage: run_planner.sh \"your research question\"}"

claude -p \
  --system-prompt "$(cat prompts/planner.md)" \
  "$QUESTION" > plan.json

echo "Plan written to plan.json"
cat plan.json | jq . >/dev/null && echo "Valid JSON" || echo "WARNING: invalid JSON"


Run it:

chmod +x run_planner.sh
./run_planner.sh "What are the tradeoffs of vector databases vs keyword search for RAG systems?"


Step 2: Parallel Searcher Agents

Each sub-question gets its own searcher agent. We'll use a small Node.js orchestrator to read the plan, spawn one Claude Code process per sub-question, and write findings to findings/.

Create prompts/searcher.md:

You are a research searcher. You will be given a sub-question and search
queries. Use available tools to find relevant information. For each source,
record:
- The URL or file path
- A 2-3 sentence summary
- 3-5 key facts with direct quotes where possible

Write your findings as Markdown. Be concise and factual. If you cannot
find authoritative sources, say so explicitly.


Create orchestrator.js:

import { readFile, writeFile, mkdir } from "node:fs/promises";
import { execFile } from "node:child_process";
import { promisify } from "node:util";
import { join } from "node:path";

const exec = promisify(execFile);
const PLAN_PATH = "plan.json";
const FINDINGS_DIR = "findings";

async function runSearcher(sub) {
  const prompt = `Sub-question: ${sub.text}\nSuggested queries:\n${sub.queries
    .map((q) => `- ${q}`)
    .join("\n")}`;

  const { stdout } = await exec("claude", [
    "-p",
    "--system-prompt",
    await readFile("prompts/searcher.md", "utf8"),
    prompt,
  ]);

  const outPath = join(FINDINGS_DIR, `${sub.id}.md`);
  await writeFile(outPath, stdout, "utf8");
  console.log(`Wrote ${outPath}`);
}

async function main() {
  await mkdir(FINDINGS_DIR, { recursive: true });
  const plan = JSON.parse(await readFile(PLAN_PATH, "utf8"));

  // Run searchers in parallel
  await Promise.all(plan.subquestions.map(runSearcher));
  console.log("All searchers complete.");
}

main().catch((err) => {
  console.error(err);
  process.exit(1);
});


Run the orchestrator:

node orchestrator.js


Because each searcher is an independent process, they execute concurrently. For plans with many sub-questions, you may want to add a concurrency limit using a simple pool.

Step 3: The Analyst Agent

The analyst reads all findings and produces a synthesis that identifies themes, contradictions, and gaps. Create prompts/analyst.md:

You are a research analyst. You will receive multiple findings files.
Synthesize them into a structured Markdown document with these sections:

## Key Themes
## Areas of Consensus
## Contradictions and Open Debates
## Information Gaps
## Preliminary Conclusions

Cite sources by filename. Do not invent facts not present in the findings.


Create run_analyst.sh:

#!/usr/bin/env bash
set -euo pipefail

FINDINGS=$(find findings -name '*.md' -exec echo "--- FILE: {} ---" \; -exec cat {} \;)

claude -p \
  --system-prompt "$(cat prompts/analyst.md)" \
  "$FINDINGS" > synthesis.md

echo "Synthesis written to synthesis.md"


Step 4: The Fact-Checker Agent

The fact-checker audits the synthesis for unsupported claims. This is a critical guardrail against hallucination. Create prompts/factchecker.md:

You are a fact-checker. You will receive a synthesis document and the
original findings. For each claim in the synthesis:

1. Mark it as SUPPORTED, PARTIALLY SUPPORTED, or UNSUPPORTED.
2. Quote the supporting evidence from the findings, or note its absence.
3. Flag any claim that appears fabricated.

Output a Markdown table with columns: Claim | Verdict | Evidence.
End with a section "## Required Corrections" listing changes the writer
must make before publishing.


Create run_factchecker.sh:

#!/usr/bin/env bash
set -euo pipefail

FINDINGS=$(find findings -name '*.md' -exec echo "--- FILE: {} ---" \; -exec cat {} \;)
SYNTHESIS=$(cat synthesis.md)

claude -p \
  --system-prompt "$(cat prompts/factchecker.md)" \
  "FINDINGS:\n$FINDINGS\n\nSYNTHESIS:\n$SYNTHESIS" > fact_check.md

echo "Fact-check written to fact_check.md"


Step 5: The Writer Agent

The writer takes the synthesis and the fact-check report and produces the final deliverable. Create prompts/writer.md:

You are a technical writer. Using the synthesis and fact-check report,
produce a polished research report. Rules:

- Apply every correction listed in "Required Corrections".
- Remove or hedge any UNSUPPORTED claims.
- Use clear headings, bullet points, and a short executive summary.
- Include a "Sources" section at the end.
- Target length: 800-1500 words.


Create run_writer.sh:

#!/usr/bin/env bash
set -euo pipefail

claude -p \
  --system-prompt "$(cat prompts/writer.md)" \
  "SYNTHESIS:\n$(cat synthesis.md)\n\nFACT CHECK:\n$(cat fact_check.md)" \
  > final_report.md

echo "Final report written to final_report.md"


Step 6: Tying It All Together

Create a top-level run_pipeline.sh that executes every stage in order and stops on failure:

#!/usr/bin/env bash
set -euo pipefail

QUESTION="${1:?Usage: run_pipeline.sh \"research question\"}"

echo "[1/5] Planning..."
./run_planner.sh "$QUESTION"

echo "[2/5] Searching..."
node orchestrator.js

echo "[3/5] Synthesizing..."
./run_analyst.sh

echo "[4/5] Fact-checking..."
./run_factchecker.sh

echo "[5/5] Writing report..."
./run_writer.sh

echo "Done. See final_report.md"


Run the entire pipeline with one command:

chmod +x *.sh
./run_pipeline.sh "What are the tradeoffs of vector databases vs keyword search for RAG systems?"


Best Practices


  Keep system prompts narrow. Each agent should have one job. Resist the urge to add "and also write the report" to the analyst's prompt.
  Use files as the communication bus. Passing large payloads through CLI arguments hits shell limits. Disk-based handoffs are inspectable and resumable.
  Validate intermediate outputs. After the planner writes plan.json, run it through jq. After searchers write findings, check file sizes are non-zero. Fail fast.
  Cap concurrency. Spawning 20 parallel Claude Code processes can exhaust rate limits. A pool of 3-5 concurrent searchers is a sane default.
  Log every invocation. Wrap each claude call with timestamps and save raw stdout to a logs/ directory for debugging.
  Make agents cite sources. The fact-checker can only do its job if searchers record where each fact came from. Treat unsourced facts as unreliable.
  Iterate on prompts, not code. Most quality improvements come from refining the system prompt files, not the orchestration logic.
  Cache findings. If you re-run the pipeline after tweaking the writer prompt, skip the search stage by checking whether findings/ already contains fresh files.
  Set explicit output formats. JSON schemas for the planner and Markdown templates for downstream agents reduce parsing failures.
  Handle failures gracefully. If a searcher returns empty output, log a warning and continue with the remaining sources rather than aborting the whole run.


Extending the Pipeline

Once the core pipeline is stable, you can extend it in several directions. Add a critic agent that reviews the final report and suggests revisions before publishing. Integrate a translation agent for multilingual output. Replace the generic searcher with domain-specific agents—one for academic papers via Semantic Scholar, one for code via GitHub search, one for news via an RSS aggregator. Each domain agent can use Claude Code's tool-use capabilities to call specific APIs.

You can also add a feedback loop: after the fact-checker flags issues, route corrections back to the relevant searcher to gather additional evidence, then re-run synthesis. This iterative refinement often produces noticeably higher-quality reports than a single pass.

Conclusion

Building a multi-agent research pipeline with Claude Code turns an unwieldy, error-prone single-prompt workflow into a structured, inspectable, and parallelizable system. By assigning each stage—planning, searching, synthesizing, fact-checking, and writing—to a dedicated agent with a focused system prompt, you get cleaner context windows, faster execution through concurrency, and a clear audit trail on disk. The architecture presented here is intentionally simple: shell scripts and a small Node orchestrator, with files as the communication layer. That simplicity is the point. Start with this baseline, measure where quality breaks down, and iterate on the prompts and tool access for the specific agent that needs improvement. With disciplined prompt engineering and the best practices above, you can produce research reports that are more accurate, more transparent, and far more scalable than what a single agent can deliver alone.