Idea : Generating LangGraph Agent Code with Jinja2 Templates

[hangingman]

Context

LangGraph GUI provides a visual interface for designing workflows using React Flow.

By the way,

• According to API doc , an agent in LangGraph is equivalent to a graph. ( A graph represents a workflow, The relationship between a workflow (graph) and an agent is one-to-many. )
• There is no built-in method to dynamically deploy graphs as executable agents. (If this assumption is wrong, sorry!)
• Users must manually convert workflow designs into deployable scripts.

Jinja2 templates can address this by automating the generation of Python agent code from exported workflows, simplifying the deployment process.

---

Solution

Use Jinja2 Templates for Code Generation

Jinja2, a templating engine, can dynamically transform exported JSON workflows into executable Python code for LangGraph agents. This approach streamlines the process of deploying agents, ensuring consistency and reducing manual coding errors.

---

Example Workflow to Code Conversion

Input: Exported JSON Workflow

{
  "nodes": [
    { "id": "start", "type": "start", "label": "Start Node" },
    { "id": "step1", "type": "step", "label": "Step 1" },
    { "id": "end", "type": "end", "label": "End Node" }
  ],
  "edges": [
    { "source": "start", "target": "step1" },
    { "source": "step1", "target": "end" }
  ]
}

Jinja2 Template

from langgraph.graph import StateGraph

def build_graph():
    graph = StateGraph(initial_state={})

    # Nodes
    {% for node in nodes %}
    graph.add_node(
        "{{ node.id }}",
        lambda state: print("Executing {{ node.label }}")
    )
    {% endfor %}

    # Edges
    {% for edge in edges %}
    graph.add_edge("{{ edge.source }}", "{{ edge.target }}")
    {% endfor %}

    graph.set_entry_point("start")
    return graph

Generated Python Code

from langgraph.graph import StateGraph

def build_graph():
    graph = StateGraph(initial_state={})

    # Nodes
    graph.add_node(
        "start",
        lambda state: print("Executing Start Node")
    )
    graph.add_node(
        "step1",
        lambda state: print("Executing Step 1")
    )
    graph.add_node(
        "end",
        lambda state: print("Executing End Node")
    )

    # Edges
    graph.add_edge("start", "step1")
    graph.add_edge("step1", "end")

    graph.set_entry_point("start")
    return graph

---

Implementation Steps

• Export Workflow: Extract nodes and edges from the LangGraph GUI as a JSON file.
• Apply Jinja2 Template: Use the JSON data to populate a Jinja2 template for Python agent code.
• Generate Python Script: Output the rendered template as a deployable Python script.
• Deploy Agent: Use the generated script in a LangGraph-compatible environment to execute the workflow.

---

Benefits

• Efficiency: Automates the transition from workflow design to agent deployment.
• Consistency: Ensures parity between visual workflows and backend execution.
• Flexibility: Allows users to customize templates for specific use cases.

---

Next Steps

• Integrate Jinja2-based code generation into the LangGraph GUI export process.
• Provide default templates for common workflows, allowing users to customize as needed.
• Document the process to help users adapt the feature for their projects.

[hangingman]

I used ChatGPT to generate this statements.
If this is done, we could deploy agent code in our-own langgraph-api project.

my-app/
├── my_agent # all project code lies within here
│   ├── utils # utilities for your graph
│   │   ├── __init__.py
│   │   ├── tools.py # tools for your graph
│   │   ├── nodes.py # node functions for you graph
│   │   └── state.py # state definition of your graph
│   ├── requirements.txt # package dependencies
│   ├── __init__.py
│   └── agent.py # code for constructing your graph
├── .env # environment variables
└── langgraph.json # configuration file for LangGraph

https://langchain-ai.github.io/langgraph/concepts/application_structure/#file-structure

[HomunMage]

open issue: #8

Thank you, I love this idea.
Interesting concepts, I will thinking how to merge this into current design. But i need some time to deep thinking better architecture.

And I'm thinking langgraph subgraph can also merge into this concept.

I hope we spent more time discuss on this design. it's looks like very useful and helpful

[hangingman]

@HomunMage
Thank you.

[HomunMage]

@HomunMage Thank you.

Hi, new update info:
new feature "subgraph" can do like this design. you can package a subgraph like agent and reuse it in other graph