duragraph-python 0.1.0

Python SDK for DuraGraph - Reliable AI Workflow Orchestration

DuraGraph Python SDK

Python SDK for DuraGraph - Reliable AI Workflow Orchestration.

Build AI agents with decorators, deploy to a control plane, and get full observability out of the box.

Installation

pip install duragraph-python

# With OpenAI support
pip install duragraph-python[openai]

# With Anthropic support
pip install duragraph-python[anthropic]

# All features
pip install duragraph-python[all]

Quick Start

from duragraph import Graph, llm_node, entrypoint

@Graph(id="customer_support")
class CustomerSupportAgent:
    """A customer support agent that classifies and responds to queries."""

    @entrypoint
    @llm_node(model="gpt-4o-mini")
    def classify(self, state):
        """Classify the customer intent."""
        return {"intent": "billing"}

    @llm_node(model="gpt-4o-mini")
    def respond(self, state):
        """Generate a response based on intent."""
        return {"response": f"I'll help you with {state['intent']}."}

    # Define flow
    classify >> respond


# Run locally
agent = CustomerSupportAgent()
result = agent.run({"message": "I have a billing question"})
print(result)

# Or deploy to control plane
agent.serve("http://localhost:8081")

Features

Decorator-Based Graph Definition

from duragraph import Graph, llm_node, tool_node, router_node, human_node

@Graph(id="my_agent")
class MyAgent:
    @llm_node(model="gpt-4o-mini", temperature=0.7)
    def process(self, state):
        return state

    @tool_node
    def search(self, state):
        results = my_search_function(state["query"])
        return {"results": results}

    @router_node
    def route(self, state):
        return "path_a" if state["condition"] else "path_b"

    @human_node(prompt="Please review")
    def review(self, state):
        return state

Prompt Management

from duragraph.prompts import prompt

@llm_node(model="gpt-4o-mini")
@prompt("support/classify_intent", version="2.1.0")
def classify(self, state):
    # Prompt is fetched from prompt store automatically
    return state

Streaming

async for event in agent.stream({"message": "Hello"}):
    if event.type == "token":
        print(event.data, end="")
    elif event.type == "node_complete":
        print(f"\nNode {event.node_id} completed")

Subgraphs

@Graph(id="research")
class ResearchAgent:
    @llm_node
    def research(self, state):
        return {"findings": "..."}

@Graph(id="main")
class MainAgent:
    research = ResearchAgent.as_subgraph()

    @entrypoint
    def plan(self, state):
        return state

    plan >> research

CLI

# Initialize new project
duragraph init my-agent

# Run locally in development mode
duragraph dev

# Deploy to control plane
duragraph deploy --control-plane http://localhost:8081

# Visualize graph
duragraph visualize my_agent.py

Configuration

# pyproject.toml
[tool.duragraph]
control_plane = "http://localhost:8081"

[tool.duragraph.llm]
default_model = "gpt-4o-mini"

[tool.duragraph.llm.providers.openai]
api_key = "${OPENAI_API_KEY}"

Documentation

• Full Documentation
• API Reference
• Examples

Requirements

• Python 3.10+
• DuraGraph Control Plane (for deployment)

Contributing

Contributions are welcome! Please read our Contributing Guide.

License

Apache 2.0 - see LICENSE for details.