How to integrate PromptLayer MCP with Pydantic AI

This guide walks you through connecting PromptLayer to Pydantic AI using the Composio tool router. By the end, you'll have a working PromptLayer agent that can browse your prompt templates, inspect request logs, list registered tools, and review saved workflows through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a PromptLayer account through Composio's PromptLayer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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PromptLayer is a prompt management and observability platform for LLM apps. Use it to version prompts, run evaluations, trace requests, and analyze model behavior.

16 Tools

Introduction

This guide walks you through connecting PromptLayer to Pydantic AI using the Composio tool router. By the end, you'll have a working PromptLayer agent that can browse your prompt templates, inspect request logs, list registered tools, and review saved workflows through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a PromptLayer account through Composio's PromptLayer MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate PromptLayer with

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for PromptLayer
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your PromptLayer workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the PromptLayer MCP server, and what's possible with it?

The PromptLayer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your PromptLayer account. It provides structured and secure access to your prompts, request logs, workflows, tools, skill collections, and tables, so your agent can find prompt templates, inspect LLM activity, browse workflows, review registered resources, and analyze table data on your behalf.

  • Prompt discovery and inspection: Have your agent search prompt templates and retrieve specific versions, messages, model settings, snippets, or release details for review.
  • Request monitoring and analysis: Instruct your agent to search LLM request logs and inspect recorded inputs, outputs, timing, usage, metadata, tags, and scores.
  • Workflow and tool exploration: Let the agent find workflows and registered tools, then retrieve their definitions without running them.
  • Skill and workspace browsing: Direct your agent to browse folders, registry items, and skill collections, including the files stored in a selected collection.
  • Table data review: Have your agent find tables, inspect their sheets and columns, and read rows with optional schema and execution summaries.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like PromptLayer
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to PromptLayer
  • MCPServerStreamableHTTP connects to the PromptLayer MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for PromptLayer
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["promptlayer"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to PromptLayer tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
promptlayer_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[promptlayer_mcp],
    instructions=(
        "You are a PromptLayer assistant. Use PromptLayer tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the PromptLayer endpoint
  • The agent uses GPT-5 to interpret user commands and perform PromptLayer operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with PromptLayer.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • PromptLayer API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with PromptLayer and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for PromptLayer
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["promptlayer"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    promptlayer_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[promptlayer_mcp],
        instructions=(
            "You are a PromptLayer assistant. Use PromptLayer tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with PromptLayer.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with PromptLayer through Composio's Tool Router. With this setup, your agent can perform real PromptLayer actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + PromptLayer for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every PromptLayer action and event your agent gets out of the box.

Get Prompt Template

Retrieve a prompt template version, release label, messages, model metadata, snippets, and optionally provider-ready kwargs without executing it.

Get Registered Tool

Retrieve a PromptLayer registered tool definition by ID or name at a selected version or release label without test execution.

Get Request Log

Retrieve one PromptLayer request log with its recorded input, output, timing, usage, metadata, tags, and scores.

Get Skill Collection

Retrieve a PromptLayer skill collection and its files as JSON at a selected version or release label.

Get Table

Retrieve one PromptLayer Table's metadata and per-sheet row counts by UUID.

Get Workflow

Retrieve a PromptLayer workflow definition by ID or name at a specific version or release label without running it.

List Folder Entities

Browse or search PromptLayer folders and registry entities in the API-key workspace, optionally recursively and with metadata.

List Prompt Templates

Search and page through PromptLayer prompt templates and snippets without executing a model.

List Registered Tools

List and filter tools defined in PromptLayer's tool registry without executing them.

List Skill Collections

List and filter PromptLayer skill collections available in the API-key workspace.

List Table Columns

List one page of user and optionally system columns for a PromptLayer Table sheet.

List Table Rows

Read one page of rows and cells from a PromptLayer Table sheet, with optional schema and execution aggregates.

List Tables

Search and page through PromptLayer Tables by folder, name, or referenced prompt.

List Table Sheets

List one page of sheets in a PromptLayer Table, optionally filtering by referenced prompt.

List Workflows

Search and page through PromptLayer workflows without running them.

Search Request Logs

Search PromptLayer request logs by text or structured filters and return one rate-limited page sorted by operational fields.

FRAMEWORKS

How to build PromptLayer MCP Agent with another framework

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OpenAI Agents SDK

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Claude Agent SDK

Use PromptLayer MCP with Claude Agent SDK

Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

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Codex logo

Codex

Use PromptLayer MCP with Codex

OpenClaw logo

OpenClaw

Use PromptLayer MCP with OpenClaw

Hermes logo

Hermes

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CLI logo

CLI

Use PromptLayer MCP with CLI

Google ADK logo

Google ADK

Use PromptLayer MCP with Google ADK

LangChain logo

LangChain

Use PromptLayer MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

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LlamaIndex logo

LlamaIndex

Use PromptLayer MCP with LlamaIndex

CrewAI logo

CrewAI

Use PromptLayer MCP with CrewAI

FAQ

Frequently asked questions

With a standalone PromptLayer MCP server, the agents and LLMs can only access a fixed set of PromptLayer tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from PromptLayer and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right PromptLayer tools.

Yes, absolutely. You can configure which PromptLayer scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your PromptLayer data and credentials are handled as safely as possible.

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