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langgraph cli

The LangGraph CLI project is a command-line interface tool designed to assist developers in working with LangGraph, a framework built on top of LangChain that enables the creation of complex, multi-step workflows and stateful agents.

Purpose of LangGraph CLI

The primary goals of the LangGraph CLI project are:

• Facilitating development, debugging, and execution of LangGraph-based workflows.
• Providing graph visualization tools for better understanding the execution flow of agents.
• Offering testing and simulation capabilities for LangChain-based applications.
• Enabling rapid prototyping of multi-step AI systems with a structured graph approach.

Modules Within LangGraph CLI

1. Graph Definition Module

• Defines nodes, edges, and transitions for AI workflows.
• Allows developers to structure multi-step interactions efficiently.

2. Execution Engine

• Runs the defined LangGraph workflows.
• Supports step-by-step execution, batch processing, and real-time inference.

3. Debugging & Logging Tools

• Provides insights into execution paths, intermediate outputs, and errors.
• Helps developers optimize and refine their agents.

4. Visualization & Graph Tools

• Generates flowcharts or directed graphs representing agent workflows.
• Assists in understanding decision points and branching logic.

5. Configuration & Deployment Module

• Allows developers to set up and customize LangGraph workflows via CLI.
• Provides options for local execution or cloud deployment.

6. Testing & Simulation Framework

• Enables running test scenarios to evaluate agent behavior.
• Supports input/output validation for different graph states.

LangGraph CLI streamlines the process of creating, managing, and debugging LangChain-powered agents by providing an intuitive interface for defining structured workflows.

Understand the Modules

From the output, the key modules that stand out are:

cli: Likely contains functions or a main() method to drive CLI operations.

analytics, exec, progress: May include tools for processing, execution, or tracking progress.

config, constants, templates, util: Likely provide configuration, helper functions, and reusable components.

docker: May interact with Docker, possibly for containerized graph operations.

version: Likely provides version information.

Start by examining the contents of specific modules:

    import langgraph_cli
      help(langgraph_cli.cli)

Write a Script Using langgraph_cli

Here’s a step-by-step guide to create a script based on your goals:

Example: Load Data and Query

Suppose your goal is to load a graph, query it, and display results.

Explore cli or config for Initialization Check if there’s a function to initialize or configure the environment.

Use Relevant Modules for Tasks Look for functions in modules like analytics, exec, or util for specific operations.

Sample Script Here’s an example script:

    import langgraph_cli
    from langgraph_cli.cli import main
    from langgraph_cli.config import Config
    from langgraph_cli.analytics import analyze_data
    def main_script():
      # Initialize configuration
      config = Config.load("path/to/config.yaml")
      # Perform some operation, like analyzing data
      result = analyze_data("path/to/graph_data.json")
      print("Analysis Result:", result)
      # Use CLI's main function if it exists
      # (e.g., simulate running the CLI programmatically)
      main()
      if __name__ == "__main__":
        main_script()

Explore Submodules for Specific Needs

If you have a clear goal (e.g., querying the graph or exporting data), focus on the submodules:

Use help(langgraph_cli.<module>) to inspect the available functions.

For example:

from langgraph_cli.exec import run_query
      query_result = run_query("MATCH (n) RETURN n LIMIT 10")
      print(query_result)

Debugging and Iteration

If you encounter errors or unclear functionality, use:

dir(langgraph_cli.<module>)

This lists all attributes of the module.

Use Python’s inspect module to understand function signatures:

import inspect
      from langgraph_cli.cli import main
      print(inspect.signature(main))

Common Commands

Here are 10 common examples of commands for the LangGraph CLI that can be used in various stages of a LangGraph project:

1. Create a New LangGraph Project

Initializes a new LangGraph project with default configurations.

langgraph init my_project

2. Define a Graph Workflow

Define the graph structure and nodes for a LangGraph project.

langgraph define --file workflow.yaml

3. Execute a LangGraph Workflow

Execute a pre-defined LangGraph workflow.

langgraph execute --workflow my_workflow.graph

4. Visualize the Graph Workflow

Generates a visual representation of the LangGraph workflow for debugging or documentation purposes.

langgraph visualize --workflow my_workflow.graph --output workflow.png

5. Test a LangGraph Workflow

Run a test simulation of a LangGraph workflow with specific input parameters.

langgraph test --workflow my_workflow.graph --input '{"param": "value"}'

6. Run LangGraph in Debug Mode

Execute a workflow with detailed logging and debugging output.

langgraph debug --workflow my_workflow.graph

7. Deploy LangGraph Workflow to Cloud

Deploy the workflow to a cloud environment (e.g., AWS, GCP).

langgraph deploy --workflow my_workflow.graph --cloud aws

8. Export LangGraph Workflow to JSON

Convert a graph workflow to a JSON format for integration with other tools.

langgraph export --workflow my_workflow.graph --format json --output workflow.json

9. Update LangGraph Project Dependencies

Update any external dependencies or packages required for the LangGraph project.

langgraph update --dependencies

10. Clean Up Workflow Artifacts

Clean up temporary files or generated artifacts from previous runs.

langgraph clean --workflow my_workflow.graph

These commands represent the most common tasks you would perform when working with LangGraph CLI, including project initialization, workflow definition, execution, debugging, visualization, testing, deployment, and maintenance.