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LangGraph 1.0 Alpha & Middleware

Presenter: Colin McNamara Date: October 1, 2025 Duration: 15 minutes (Thunderstorm Talk)

Overview​

Colin McNamara presented an exploration of LangGraph 1.0 alpha features, with a focus on the new middleware functionality that enables sophisticated agent control and optimization patterns.

Key Topics​

LangGraph 1.0 Transition​

  • Moving from beta to stable 1.0 releases
  • Version: LangGraph 1.0 alpha 4
  • LangChain 1.0 alpha 10 compatibility
  • Breaking changes and migration considerations

Middleware Architecture​

LangGraph introduces three middleware hooks for controlling agent execution:

  1. before_model - Pre-processing hook
  2. model - Model invocation hook
  3. after_model - Post-processing hook

Before Model Hook​

Capabilities:

  • Update agent state dynamically
  • Jump to different nodes in the graph
  • Manage context (token limits, message history)
  • Implement guardrails (Nemo Guardrails integration)
  • Control what enters the model

Example use cases:

  • Keeping only last 20 lines of context
  • Token limit management
  • Input validation and filtering

Model Hook​

Capabilities:

  • Swap models on-the-fly based on query complexity
  • Model routing (GPT-4o-mini vs o3 based on complexity)
  • Dynamic prompt adjustment per model
  • Tool scope management (security/safety)
  • Output format control (JSON, structured outputs)
  • Caching layer implementation

Cost optimization example:

  • Estimated $5,100/month savings for 1000-user agent
  • (Note: Numbers require production validation)

After Model Hook​

Capabilities:

  • Output inspection and validation
  • Circuit breakers (halt on unsafe/unexpected output)
  • Human-in-the-loop integration
  • Metrics collection (OpenTelemetry integration)
  • Anomaly detection

Human-in-the-Loop Pattern​

# Interrupt on specific tool calls
if tool == "delete_database":
    interrupt()  # Require human approval

Use cases:

  • Email sending approval
  • Database modifications
  • High-value transactions
  • Capacity-exceeding orders

Real-World Use Case: FinOps Workflow​

Colin shared his production use case for food manufacturing:

Scenario: EDI order processing with QuickBooks integration

Requirements:

  • Automatic processing for normal-sized orders
  • Human review for capacity-exceeding orders
  • State persistence in PostgreSQL
  • Rollback capabilities
  • OpenTelemetry logging

Why LangGraph:

  • State management with checkpointing
  • Graph execution framework
  • OpenTelemetry native support
  • Human-in-the-loop hooks
  • Rollback capabilities

Technical Implementation​

Installation​

pip install langgraph==1.0.0a4
pip install langchain==1.0.0a10

Key Features​

  1. Context Management

    • Automatic message history truncation
    • Token-based limiting
    • Smart context windowing

  2. Caching Middleware

    • Agent-level caching
    • Cost reduction
    • Performance improvement

  3. Dynamic Behaviors

    • Runtime model selection
    • Adaptive prompt engineering
    • Tool availability control

  4. Composition

    • Stack middleware functions
    • Chain multiple hooks
    • Modular architecture

Gotchas & Limitations​

⚠️ Alpha Code Warnings:

  • Explicit tool definitions required
  • Must define interrupts explicitly
  • Cannot use generic configurations
  • Specific summarization model calls needed
  • Checkpointer must be explicitly defined

Production Considerations​

When to Use LangGraph Middleware​

  • ✅ Complex agent workflows
  • ✅ Human-in-the-loop requirements
  • ✅ Cost optimization needs
  • ✅ State management critical
  • ✅ Observability requirements

Alternative Solutions​

  • Native OpenAI for simple use cases
  • Other orchestration frameworks
  • Direct API calls for basic scenarios

Comparison: JPMorgan Chase & NuvoBank​

Major financial institutions using LangGraph:

  • JPMorgan Chase
  • NuvoBank
  • LinkedIn
  • Production-proven at scale

Testing & Development​

Getting Started​

  1. Install alpha versions
  2. Start with summarization middleware
  3. Log everything
  4. Test thoroughly
  5. Share results with community

Resources​

Deep Agents Integration​

LangGraph Middleware is now integrated into Deep Agents (Quad Code clone):

  • Agent-based coding assistance
  • Middleware-powered optimizations
  • Production-ready patterns

Community Use Cases​

From the discussion:

  1. Manufacturing/CPG:

    • EDI order processing
    • QuickBooks integration
    • Inventory management
    • Capacity planning

  2. Software Development:

    • Code generation workflows
    • Testing automation
    • Deployment pipelines

  3. Data Processing:

    • PDF processing
    • RAG implementations
    • Batch operations

Key Takeaways​

  1. Middleware is Game-Changing: Control agent behavior at three critical points
  2. Production-Ready Path: 1.0 stable coming soon, plan migrations now
  3. Cost Optimization: Caching and model routing can significantly reduce costs
  4. Human-in-the-Loop: Essential for production agents handling critical operations
  5. Observability: Native OpenTelemetry support for production monitoring

Q&A Highlights​

Q: Why LangGraph vs other tools? A: State management, human-in-the-loop, observability, and production support from major companies

Q: What about alternative frameworks? A: Use what works for you - Colin uses both LangGraph and native OpenAI depending on use case

Q: Production readiness? A: Waiting for 1.0 stable, but alpha is usable for testing

Code Examples​

Examples and implementations available in:

  • Austin LangChain repo
  • AIMUG.org presentations
  • Colin's blog

Next Steps​

  1. Try the alpha versions in development
  2. Test middleware patterns
  3. Share findings in Discord
  4. Contribute to community knowledge
  5. Plan for 1.0 stable migration

Related Sessions:

Video: Watch the full presentation in the October 2025 showcase recording