The Build - LangChain Open Deep Research

Описание: Watch as we dive deep into LangChain's powerful open-source deep research project - the same architecture behind ChatGPT and Google Gemini's research capabilities! In this hands-on demo, Cameron Rohn shows how to build, configure, and deploy multi-agent research systems that can revolutionize how you gather and synthesize information.

📊 What You'll Learn:
3 different agent architectures (workflow vs multi-agent patterns)
Smart model selection: Using Claude Haiku for summarization, GPT-4 for reasoning
Configuring search providers (Tavily, Perplexity, and more)
Real-world troubleshooting of rate limits and errors
Converting research agents for industry-specific use cases

🎯 Key Highlights:

Live demonstration of the visual LangGraph interface
Multi-agent supervisor pattern with specialized research teams
Human-in-the-loop interrupts for better control
Context optimization strategies

⏱️ Timestamps:
00:00 Introduction to LangChain's Deep Research Project
02:15 Setting up the development environment
04:30 Exploring 3 different agent architectures
07:20 Configuring search providers and models
10:45 Running the workflow with Google Gemini CLI example
15:30 Multi-agent workflow demonstration
18:00 Understanding supervisor and research team interactions
22:00 Troubleshooting rate limits and errors
25:30 Analyzing traces in LangSmith
30:00 Real-world applications and customization ideas

🔗 Resources:
LangChain Open Deep Research:
https://github.com/langchain-ai/open-...

Demos from the Podcast:
https://github.com/The-Build-Podcast

💡 Uncovered:
Why Bing optimization matters for AI search in 2025
Build for evaluations on day 1
Repurposing research agents for domain-specific tasks

🏢 Real-World Use Cases Discussed:
Commercial real estate property appraisals
Cybersecurity
Compliance audits
Vendor assessment documentation

🎙️ Hosts:
Tom Spencer: https://tomspencer.co
Cameron Rohn: https://cameronrohn.com

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