LangGraph's create_supervisor function is a powerful way to manage communication between multiple agents. However, its performance can be improved. A key optimization lies in how agents hand off tasks to one another. I've found that by modifying the create_handoff_tool, you can significantly reduce data transfer and improve overall performance.
The Problem
The default create_handoff_tool passes the entire conversation history to the new agent when a handoff occurs. While this ensures context, it is inefficient. For long conversations, the message list can become huge, leading to:
- Slower Execution: Large data payloads take time to serialize and send.
- Wasted Resources: The new agent has to process a lot of unnecessary information.
- Increased Errors: A large, messy context can sometimes confuse the new agent, causing it to fail on simple tasks. The Solution: A Smarter Handoff The fix is to make the handoff tool smarter. Instead of passing the entire state, we can modify the tool to explicitly require and pass only a specific query. This forces the LLM to summarize the handoff request into a concise query string. Here's the code for the modified create_handoff_tool:
By adding query: Annotated[str, ...], we are guiding the LLM to extract the most critical piece of information. The handoff_message sent to the new agent will now be short and precise.
The Impact
This small change has a big impact on the overall performance of your multi-agent supervisor system:
- Improved Efficiency: The system no longer wastes time and resources on irrelevant data.
- Better Performance: Faster handoffs lead to a more responsive application.
- Clearer Communication: The receiving agent gets a focused, clean message and can start its task immediately without being confused by previous conversation history. This simple optimization shows how being deliberate about data flow can drastically improve the performance of your LangGraph-based applications.
Top comments (0)