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The Pydantic AI adapter allows you to use AgentMark prompts with Pydantic AI in Python applications. This is the recommended adapter for Python projects.

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Installation

pip install agentmark-pydantic-ai-v0 agentmark-prompt-core agentmark-loader-api
Package names vs import names:
  • agentmark-prompt-core → from agentmark.prompt_core import ...
  • agentmark-loader-api → from agentmark.loader_api import ...
  • agentmark-pydantic-ai-v0 → from agentmark_pydantic_ai_v0 import ...
For specific providers, install the optional extras: 
pip install "pydantic-ai[openai]"    # OpenAI
pip install "pydantic-ai[anthropic]" # Anthropic
pip install "pydantic-ai[gemini]"    # Google Gemini

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Setup

Create your AgentMark client in agentmark_client.py:
agentmark_client.py
from pathlib import Path
from dotenv import load_dotenv
from agentmark.prompt_core import FileLoader
from agentmark_pydantic_ai_v0 import (
    create_pydantic_ai_client,
    create_default_model_registry,
    PydanticAIToolRegistry,
)

load_dotenv()

# Default registry supports gpt-*, claude-*, gemini-*, mistral-*
model_registry = create_default_model_registry()

tool_registry = PydanticAIToolRegistry()

loader = FileLoader(base_dir=str(Path(__file__).parent.resolve()))

client = create_pydantic_ai_client(
    model_registry=model_registry,
    tool_registry=tool_registry,
    loader=loader,
)

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Custom Model Registry

For more control over model resolution, create a custom registry with exact names, regex patterns, or a default fallback: 
from agentmark_pydantic_ai_v0 import PydanticAIModelRegistry
import re

model_registry = PydanticAIModelRegistry()

# Exact match
model_registry.register_models(
    ["gpt-4o", "gpt-4o-mini"],
    lambda name, _: f"openai:{name}"
)

# Regex pattern
model_registry.register_models(
    re.compile(r"^claude-"),
    lambda name, _: f"anthropic:{name}"
)

# Default fallback for unmatched names
model_registry.set_default(lambda name, _: name)

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Running Prompts

Load and run prompts using run_text_prompt: 
import asyncio
from agentmark_pydantic_ai_v0 import run_text_prompt
from agentmark_client import client

async def main():
    prompt = await client.load_text_prompt("greeting.prompt.mdx")
    params = await prompt.format(props={"name": "Alice"})

    result = await run_text_prompt(params)
    print(result.output)
    print(f"Tokens: {result.usage.total_tokens}")

asyncio.run(main())

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Object Generation

For structured output, the adapter automatically converts JSON Schema to Pydantic models: 
from agentmark_pydantic_ai_v0 import run_object_prompt
from agentmark_client import client

prompt = await client.load_object_prompt("sentiment.prompt.mdx")
params = await prompt.format(props={"text": "This product is amazing!"})

result = await run_object_prompt(params)
print(result.output)  # Typed Pydantic model instance
print(result.output.sentiment)  # 'positive'

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Streaming

Stream text responses for real-time output: 
from agentmark_pydantic_ai_v0 import stream_text_prompt

params = await prompt.format(props={"query": "Explain quantum computing"})

async for chunk in stream_text_prompt(params):
    print(chunk, end="", flush=True)

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Tools

Register sync or async tool functions:
agentmark_client.py
from agentmark_pydantic_ai_v0 import PydanticAIToolRegistry

tool_registry = PydanticAIToolRegistry()

# Sync tool
tool_registry.register(
    "search",
    lambda args, ctx: f"Results for: {args['query']}"
)

# Async tool
async def fetch_data(args, ctx):
    return await api.get(args["url"])

tool_registry.register("fetch", fetch_data)

# Tool that receives Pydantic AI's RunContext
tool_registry.register("db_query", db_tool_fn, takes_ctx=True)
Then reference tools in your prompts:
search.prompt.mdx
---
name: search
text_config:
  model_name: gpt-4o
  tools:
    - search
---

<System>You are a helpful search assistant.</System>
<User>Search for {props.query}</User>

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MCP Servers

Configure MCP servers for extended tool capabilities:
agentmark_client.py
from agentmark_pydantic_ai_v0 import McpServerRegistry

mcp_registry = McpServerRegistry()

# URL-based server
mcp_registry.register("search", {
    "url": "http://localhost:8000/mcp",
})

# Stdio-based server
mcp_registry.register("python-runner", {
    "command": "python",
    "args": ["-m", "mcp_server"],
    "cwd": "/app",
})

client = create_pydantic_ai_client(
    model_registry=model_registry,
    mcp_registry=mcp_registry,
    loader=loader,
)
Reference MCP tools in prompts with the mcp:// prefix: 
---
name: task
text_config:
  model_name: gpt-4o
  tools:
    - mcp://search/web_search
    - mcp://python-runner/*
---

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Getting Started

The fastest way to scaffold a Python project: 
npm create agentmark@latest my-app
# Select "Python" when prompted for language
# Select "Pydantic AI" as the adapter
Run the dev server: 
agentmark dev
The CLI automatically detects Python projects via pyproject.toml or agentmark_client.py.

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Limitations

  • No image generation — Use the AI SDK adapter (TypeScript) for experimental_generateImage
  • No speech generation — Use the AI SDK adapter (TypeScript) for experimental_generateSpeech

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Next Steps

AI SDK

TypeScript adapter for Node.js

Claude Agent SDK

Agentic tasks with Claude

Prompts

Learn about prompt syntax

Observability

Monitor prompts in production

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Have Questions?

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