When you type a question into ChatGPT, the model only has what you typed to work with. But tools like Claude Code can plan, iterate, test, and recover from mistakes. They work more like we do. The difference is the agent harness: Planning tools, file system access, sub-agents, and carefully crafted system prompts that turn a raw LLM into something genuinely capable.

Sydney Runkle is back on Talk Python representing LangChain and their new open source library, Deep Agents: A framework for building your own deep agents with plain Python functions, middleware hooks, and MCP support. This is how the magic works under the hood.

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Links from the show

Guest
Sydney Runkle: github.com

Claude Code uses: x.com
Deep Research: openai.com
Manus: manus.im
Blog post announcement: blog.langchain.com
Claudes system prompt: github.com
sub agents: docs.anthropic.com
the quick start: docs.langchain.com
CLIs: github.com
Talk Python's CLI: talkpython.fm
custom tools: docs.langchain.com
DeepAgents Examples: github.com
Custom Middleware: docs.langchain.com
Built in middleware: docs.langchain.com
Improving Deep Agents with harness engineering: blog.langchain.com
Prebuilt middleware: docs.langchain.com

Watch this episode on YouTube: youtube.com
Episode #543 deep-dive: talkpython.fm/543
Episode transcripts: talkpython.fm

Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong

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#543: Deep Agents: LangChain's SDK for Agents That Plan and Delegate