langgraph/Docs/Cloud/How To/Prompt Engineer in LangGraph Studio

• Prompt Engineering in LangGraph Studio

  • My Notes

    • Great way to let you customize the model and the LLM for each node!
      • • Note the configuration icon in the top right corner of the call_model node:
          • 
      • • Note that these values reflect the currently active assistant, and saving will update the assistant with the new values.
          • 

  • Reference ¶

    • When defining your configuration, you can use special metadata keys to instruct LangGraph Studio how to handle different fields.
      

    • langgraph_nodes ¶

      • Description: Specifies which graph nodes a configuration field is associated with.
      • Value Type: Array of strings, where each string is the name of a node in your graph.
      • Usage Context: Include in the json_schema_extra dictionary for Pydantic models or the metadata["json_schema_extra"] dictionary for dataclasses.
      • Required: No, but necessary if you want a field to be editable for specific nodes in the UI.

      • #Example

        • python

          system_prompt: str = Field(
              default="You are a helpful AI assistant.",
              json_schema_extra={"langgraph_nodes": ["call_model", "other_node"]},
          )

    • langgraph_type ¶

      • Description: Specifies the type of configuration field, which determines how it's handled in the UI.
      • Value Type: String
      • Supported Values:
      • "prompt": Indicates the field contains prompt text that should be treated specially in the UI.
      • Usage Context: Include in the json_schema_extra dictionary for Pydantic models or the metadata["json_schema_extra"] dictionary for dataclasses.
      • Required: No, but helpful for prompt fields to enable special handling.

      • #Example

        • python

          system_prompt: str = Field(
              default="You are a helpful AI assistant.",
              json_schema_extra={
                  "langgraph_nodes": ["call_model"],
                  "langgraph_type": "prompt",
              },
          )

  • Iterating on prompts ¶

    • Node Configuration ¶

      • With this set up, running your graph and viewing in LangGraph Studio will result in the graph rendering like such.
      • Note the configuration icon in the top right corner of the call_model node:
        • 
      • Clicking this icon will open a modal where you can edit the configuration for all of the fields associated with the call_model node. From here, you can save your changes and apply them to the graph.
      • Note that these values reflect the currently active assistant, and saving will update the assistant with the new values.
        •