agentmark-pydantic-ai-v0 0.1.3

Pydantic AI adapter for AgentMark - integrate AgentMark prompts with Pydantic AI

AgentMark Pydantic AI Adapter

Pydantic AI adapter for AgentMark - integrate AgentMark prompts with Pydantic AI for type-safe LLM interactions in Python.

Installation

pip install agentmark-pydantic-ai-v0

For specific providers, install with extras:

pip install agentmark-pydantic-ai-v0[openai]
pip install agentmark-pydantic-ai-v0[anthropic]
pip install agentmark-pydantic-ai-v0[gemini]

Quick Start

from agentmark_pydantic_ai_v0 import create_pydantic_ai_client, run_text_prompt

# Create client
client = create_pydantic_ai_client()

# Load and format a prompt
prompt = await client.load_text_prompt(ast)  # AST from AgentMark compiler
params = await prompt.format(props={"name": "Alice"})

# Execute with runner utility
result = await run_text_prompt(params)
print(result.output)

# Or use Pydantic AI directly
from pydantic_ai import Agent

agent = Agent(params.model, system_prompt=params.system_prompt)
result = await agent.run(params.user_prompt)
print(result.output)

Features

• Type-safe integration: Full type safety from AgentMark prompts to Pydantic AI
• Model registry: Flexible model name resolution with pattern matching
• Native tools: Pass pydantic-ai Tool objects or callables directly
• MCP support: Model Context Protocol integration for external tool servers
• Structured output: Automatic JSON Schema to Pydantic model conversion
• Runner utilities: Convenience functions for common execution patterns

API Reference

Factory Function

from agentmark_pydantic_ai_v0 import create_pydantic_ai_client

client = create_pydantic_ai_client(
    model_registry=None,      # Optional custom model registry
    tools=None,               # Optional list of native Tool objects or callables
    mcp_registry=None,        # Optional MCP server registry
    eval_registry=None,       # Optional eval registry
    loader=None,              # Optional prompt loader
)

Model Registry

from agentmark_pydantic_ai_v0 import PydanticAIModelRegistry
import re

# Default registry (passthrough — model names forwarded as-is to Pydantic AI)
registry = PydanticAIModelRegistry.create_default()

# Register providers you need (user-driven, not pre-registered)
registry = (
    PydanticAIModelRegistry.create_default()
    .register_providers({"openai": "openai", "anthropic": "anthropic"})
)

# Or register specific models
registry = PydanticAIModelRegistry()
registry.register_models("gpt-4o", lambda name, opts: f"openai:{name}")
registry.register_models(
    re.compile(r"^claude-"),
    lambda name, opts: f"anthropic:{name}"
)

Tools

Tools are passed as native pydantic-ai Tool objects or plain callables:

from pydantic_ai import Tool

# Using callables (name is inferred from function name)
def search(query: str) -> str:
    return search_web(query)

client = create_pydantic_ai_client(tools=[search])

# Using Tool objects for more control
tool = Tool(function=search, name="search", description="Search the web")
client = create_pydantic_ai_client(tools=[tool])

The MDX config references tools by name. Only tools whose names match entries in the MDX tools list will be included at adapt time.

MCP Server Registry

from agentmark_pydantic_ai_v0 import McpServerRegistry

registry = McpServerRegistry()

# Register HTTP-based MCP server
registry.register("search-server", {
    "url": "http://localhost:8000/mcp",
    "headers": {"Authorization": "Bearer token"},  # Optional
})

# Register stdio-based MCP server
registry.register("python-runner", {
    "command": "python",
    "args": ["-m", "mcp_server"],
    "cwd": "/app",
    "env": {"API_KEY": "secret"},
})

# Use with client
client = create_pydantic_ai_client(mcp_registry=registry)

Then in your AgentMark prompt, reference MCP tools:

tools:
  - mcp://search-server/web-search    # Single MCP tool
  - mcp://search-server/*             # All tools from MCP server
  - search                            # Native tool by name

Runner Utilities

from agentmark_pydantic_ai_v0 import run_text_prompt, run_object_prompt, stream_text_prompt

# Run text prompt
result = await run_text_prompt(params)
print(result.output)       # str
print(result.usage)        # Usage stats

# Run object prompt (structured output)
result = await run_object_prompt(params)
print(result.output)       # Typed Pydantic model

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

Webhook Handler

For building HTTP servers that execute AgentMark prompts (used by the CLI dev server):

from agentmark_pydantic_ai_v0 import create_pydantic_ai_client, PydanticAIWebhookHandler

# Create client and handler
client = create_pydantic_ai_client()
handler = PydanticAIWebhookHandler(client)

# Execute a prompt (non-streaming)
result = await handler.run_prompt(prompt_ast, {"shouldStream": False})
print(result["result"])  # "Hello, world!"

# Execute a prompt (streaming)
result = await handler.run_prompt(prompt_ast, {"shouldStream": True})
async for chunk in result["stream"]:
    print(chunk)  # NDJSON chunks

The webhook handler implements the AgentMark webhook protocol, producing NDJSON responses compatible with the CLI.

License

MIT