Is your feature request related to a problem? Please describe.
Tried to integrate JudgeVal's JudgevalCallbackHandler with complex LangGraph workflows because the current documentation only covers basic usage patterns. When building multi-node graphs with conditional edges, async workflows, or multi-agent systems, there are no comprehensive examples or error handling guidance available.
Describe the solution you'd like
A comprehensive LangGraph Integration Cookbook that provides:
-
Progressive Examples from simple to complex:
- Basic linear workflows (current documentation level)
- Multi-node workflows with conditional routing
- Loops and recursive patterns
- Multi-agent systems with separate handlers
- Async/await LangGraph integration
-
Error Handling Patterns:
- Graceful degradation when tracing fails
- Debugging techniques for callback issues
- Connection failure recovery
- Production monitoring best practices
-
Performance Guidelines:
- When to use
deep_tracing=True vs deep_tracing=False
- Memory management for long-running agents
- Batch evaluation strategies
- Resource cleanup patterns
-
Production Ready Examples:
Example of what's needed:
try:
handler = JudgevalCallbackHandler(judgment)
config = {"callbacks": [handler]}
except Exception as e:
logger.warning(f"Tracing disabled due to: {e}")
config = {} # Continue without tracing
final_state = graph.invoke(initial_state, config=config)
Describe alternatives you've considered
Trial-and-error development - Current approach, very time-consuming
Minimal integration - Only using basic patterns, missing advanced capabilities
Custom tracing implementation - Reinventing functionality that should be documented
Avoiding complex LangGraph patterns - Limiting agent capabilities due to integration uncertainty
Which component(s) does this affect?
Use case and impact
Describe your specific use case and how this feature would benefit you or other users. Include:
- How often would you use this feature?- Every Complex LangGraph integration Project
- How many users might benefit from this?- All developers building multi-step agents with LangGraph (likely 50%+ of advanced users)
- Is this blocking your current implementation?- Not completely blocking, but would accelerate adoption of JudgeVal for complex agent workflows
Proposed API/Interface (if applicable)
docs/integrations/langgraph/
Are you interested in contributing this feature?
The Judgment community is happy to provide guidance and review for contributions via Discord.
Is your feature request related to a problem? Please describe.
Tried to integrate JudgeVal's
JudgevalCallbackHandlerwith complex LangGraph workflows because the current documentation only covers basic usage patterns. When building multi-node graphs with conditional edges, async workflows, or multi-agent systems, there are no comprehensive examples or error handling guidance available.Describe the solution you'd like
A comprehensive LangGraph Integration Cookbook that provides:
Progressive Examples from simple to complex:
Error Handling Patterns:
Performance Guidelines:
deep_tracing=Truevsdeep_tracing=FalseProduction Ready Examples:
Example of what's needed:
try:
handler = JudgevalCallbackHandler(judgment)
config = {"callbacks": [handler]}
except Exception as e:
logger.warning(f"Tracing disabled due to: {e}")
config = {} # Continue without tracing
final_state = graph.invoke(initial_state, config=config)
Describe alternatives you've considered
Trial-and-error development - Current approach, very time-consuming
Minimal integration - Only using basic patterns, missing advanced capabilities
Custom tracing implementation - Reinventing functionality that should be documented
Avoiding complex LangGraph patterns - Limiting agent capabilities due to integration uncertainty
Which component(s) does this affect?
Use case and impact
Describe your specific use case and how this feature would benefit you or other users. Include:
Proposed API/Interface (if applicable)
docs/integrations/langgraph/
Are you interested in contributing this feature?
The Judgment community is happy to provide guidance and review for contributions via Discord.