Freeplay Python LangGraph

Freeplay integration for LangGraph and LangChain, providing observability and prompt management for your AI applications.

Installation

pip install freeplay-python-langgraph

Features

• 🔍 Automatic Observability: OpenTelemetry instrumentation for LangChain and LangGraph applications
• 📝 Prompt Management: Call Freeplay-hosted prompts with version control and environment management
• 🤖 Auto-Model Instantiation: Automatically create LangChain models based on Freeplay's configuration
• 💬 Conversation History: Native support for multi-turn conversations with LangGraph MessagesState
• 🛠️ Tool Support: Seamless integration with LangChain tools
• 🧪 Test Execution Tracking: Track test runs and test cases for evaluation workflows
• 🎯 Multi-Provider Support: Works with OpenAI, Anthropic, Vertex AI, and more

Quick Start

Configuration

Set up your environment variables:

export FREEPLAY_API_URL="https://app.freeplay.ai/api"
export FREEPLAY_API_KEY="fp-..."
export FREEPLAY_PROJECT_ID="..."

Or pass them directly when initializing:

from freeplay_python_langgraph import FreeplayLangGraph

freeplay = FreeplayLangGraph(
    freeplay_api_url="https://api.freeplay.ai",
    freeplay_api_key="fp_...",
    project_id="proj_...",
)

Usage

Prompt Management with Auto-Model Instantiation

Call a Freeplay-hosted prompt and let the SDK automatically instantiate the correct model:

from freeplay_python_langgraph import FreeplayLangGraph

freeplay = FreeplayLangGraph()

# Invoke a prompt - model is automatically created based on Freeplay's config
response = freeplay.invoke(
    prompt_name="weather-assistant",
    variables={"city": "San Francisco"},
    environment="production"
)

Using Custom Models

You can also provide your own pre-configured model:

from langchain_openai import ChatOpenAI
from freeplay_python_langgraph import FreeplayLangGraph

freeplay = FreeplayLangGraph()
model = ChatOpenAI(model="gpt-4", temperature=0.7)

response = freeplay.invoke(
    prompt_name="weather-assistant",
    variables={"city": "New York"},
    model=model
)

Conversation History (Multi-turn Chat)

Maintain conversation context with history:

from langchain_core.messages import HumanMessage, AIMessage
from freeplay_python_langgraph import FreeplayLangGraph

freeplay = FreeplayLangGraph()

# Build conversation history
history = [
    HumanMessage(content="What's the weather in Paris?"),
    AIMessage(content="It's sunny and 22°C in Paris."),
    HumanMessage(content="What about in winter?")
]

response = freeplay.invoke(
    prompt_name="weather-assistant",
    variables={"city": "Paris"},
    history=history
)

Tool Calling

Bind LangChain tools to your prompts:

from langchain_core.tools import tool
from freeplay_python_langgraph import FreeplayLangGraph

@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    # Your weather API logic here
    return f"Weather in {city}: Sunny, 22°C"

freeplay = FreeplayLangGraph()

response = freeplay.invoke(
    prompt_name="weather-assistant",
    variables={"city": "London"},
    tools=[get_weather]
)

Test Execution Tracking

Track test runs for evaluation workflows:

from freeplay_python_langgraph import FreeplayLangGraph

freeplay = FreeplayLangGraph()

response = freeplay.invoke(
    prompt_name="my-prompt",
    variables={"input": "test input"},
    test_run_id="test_run_123",
    test_case_id="test_case_456"
)

Observability

The SDK automatically instruments your LangChain and LangGraph applications with OpenTelemetry. All traces are sent to Freeplay.

Provider Support

The SDK supports automatic model instantiation for the following providers:

• OpenAI: Requires langchain-openai package
• Anthropic: Requires langchain-anthropic package
• Vertex AI: Requires langchain-google-vertexai package

Install the required provider package:

pip install langchain-openai
# or
pip install langchain-anthropic
# or
pip install langchain-google-vertexai