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Deep Agents - Batteries-Included Deep Research Framework

Speaker: Collier King
Duration: 15 minutes
Time: 7:00 PM - 7:15 PM

Overview​

Collier King presented LangChain's new Deep Agents framework - a Python package that brings Claude Code's best patterns to LangGraph. The talk demonstrated how Deep Agents solves the "shallow agent" problem through sustained context, long-term planning, complex workflows, and parallelization.

📺 Watch the Talk​

The Problem: Shallow Agents​

Traditional Agent Limitations​

  • Simple LLM calling tools in a loop until condition met
  • Doesn't work for complicated use cases
  • Key pain points:
    • Sustained context is difficult
    • Long-term planning is tough
    • Complex workflows not feasible
    • Parallelization and delegation not easy

The Solution: Deep Agents​

What is Deep Agents?​

  • Python package inspired by Claude Code
  • Harrison (LangChain founder) reverse-engineered Claude Code
  • All LangGraph under the hood
  • Simple function call bootstraps entire agent: create_deep_agent()

Growth Trajectory​

  • Brand new framework but growing rapidly
  • Log-scaled star history shows strong adoption
  • Growth trajectory comparable to LangChain/LangGraph

Four Core Components​

1. Planning Tool​

  • Reverse-engineered from Claude Code
  • Creates and tracks plans through to-do lists
  • Maintains focus over long executions
  • Enables agents to stay on track

2. Sub-Agents System​

  • Subordinate agents with own prompts and tools
  • Context quarantine prevents pollution
  • Main agent not flooded with irrelevant details
  • General purpose sub-agent handles background tasks

Example sub-agent creation:

# Simple sub-agent definition
- Name
- Description  
- Prompt
- Model (can differ from main agent)

3. System Prompts​

  • Built-in system prompt from Claude Code
  • Currently not editable (part of the package)
  • LangChain says: "This is what makes this package good"
  • User can only customize via instructions parameter
  • On roadmap to allow customization

4. Virtual File System​

  • Built on LangGraph state object
  • In-memory file system (not actual files)
  • Enables parallel execution without conflicts
  • Sub-agents can create/edit virtual files
  • Scales well for concurrent operations

Built-in Tools​

Five essential tools come standard:

  1. To-do list: write_todos() function
  2. Write file: Create virtual files
  3. Read file: Access virtual files
  4. LS: List virtual files
  5. Edit file: Modify virtual files

These tools enable:

  • Sub-agents tracking their work
  • Main agent delegating effectively
  • Rapid file editing in succession
  • State management across agents

Live Demo: Product Manager Research Agent​

Use Case​

Collier demonstrated a product manager research tool for Cloudflare's Workers AI:

Questions addressed:

  • Who are our customers?
  • What are their use cases?
  • Are we solving their problems?
  • What are we telling them (marketing)?
  • What are they telling us (social media)?

Implementation​

  • Marketing sub-agent: Analyzes outbound materials
  • Social media sub-agent: Analyzes customer feedback
  • Both extract use cases and personas
  • Main agent compares outputs (Venn diagram analysis)
  • Identifies gaps and overlaps

Results (1 minute 48 seconds with Sonnet)​

Overlapping use cases:

  • AI application development
  • Serverless AI computing
  • Edge AI processing
  • Image processing

Marketing overemphasis:

  • Threat intelligence
  • Automated social media
  • Focus on CTOs/CISOs vs individual developers
  • Large enterprise focus

Performance notes:

  • Analyzed 500 random social media posts
  • Processed extensive marketing materials
  • Sonnet: ~1:48 execution time
  • Opus: Higher quality but slower

Configuration & Setup​

Simple initialization:​

create_deep_agent(
    tools=[...],        # Your custom tools
    prompt=instructions, # Your instructions
    sub_agents=[...],   # Optional sub-agents
    model=...          # LLM selection
)

Returns:​

  • Planning tool
  • File system
  • System prompts for sub-agents
  • General purpose sub-agent
  • Complete LangGraph agent

Advanced Features​

Observability​

  • Post-model hook parameter for logging
  • Verbose logging recommended
  • Without logging: "What is going on?"
  • Essential for debugging complex workflows

Human-in-the-Loop​

  • Supported via LangGraph features
  • Interrupt capabilities
  • MCP adapter integration

Execution Pattern​

  1. Agent writes to-dos
  2. Launches sub-agents
  3. Tracks completion status
  4. Compares results
  5. Provides recommendations

Q&A Insights​

Q: Does it support human-in-the-loop?

  • Yes, built on LangGraph features
  • Documented in README
  • Full interrupt capabilities

Q: How do you add observability?

  • Use post_model_hook parameter
  • Pass custom logger
  • Essential for understanding execution

Key Takeaways​

  1. Deep Agents = Claude Code patterns + LangGraph power
  2. Solves shallow agent problems through better architecture
  3. Virtual file system enables safe parallelization
  4. Context quarantine prevents pollution
  5. Production-ready for deep research use cases

Recommendations​

  • Check out Colin's blog for detailed tutorials
  • Start with simple use cases
  • Use verbose logging initially
  • Test with different models (Sonnet vs Opus)
  • Consider for research-heavy applications

Resources​

About the Speaker​

Collier King works at Cloudflare and specializes in distributed AI systems and multi-agent coordination. He actively uses LangGraph in production systems and contributes to the Austin AI community.