---
title: "Using Claude Code to Run Deep Research"
url: https://daily.dev/posts/using-claude-code-to-run-deep-research-anmpeatvb
source_url: https://blog.promptlayer.com/using-claude-code-to-run-deep-research
type: article
source: "PromptLayer Blog"
published: 2026-07-06T03:22:06.742Z
updated: 2026-07-06T03:22:30.346Z
tags: ["ai-agents", "anthropic", "claude-code"]
reading_time: 11
upvotes: 0
comments: 0
language: en
---

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# Using Claude Code to Run Deep Research

**[PromptLayer Blog](https://daily.dev/sources/promptlayer)** · 11 min read · 0 upvotes · 0 comments

## Summary

A walkthrough of building a multi-agent deep research system using Claude Code and the Claude Agent SDK. The architecture uses a manager-worker pattern with three agent types: a Lead Agent that decomposes queries and delegates via a Task tool, parallel Researcher agents that perform web searches and write findings to disk, and a Report Writer that synthesizes all notes into a final report. Agents coordinate through a shared file system rather than direct messaging, enabling context isolation and persistent memory across subagents. The post includes a Python code example, compares the approach to LangChain and OpenAI function-calling, and claims a 90% performance improvement over single-agent systems through parallelism and task specialization.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.promptlayer.com/using-claude-code-to-run-deep-research>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#anthropic](https://daily.dev/tags/anthropic), [#claude-code](https://daily.dev/tags/claude-code)

[View this post on daily.dev](https://daily.dev/posts/using-claude-code-to-run-deep-research-anmpeatvb)