Claude Agent SDK

The Claude Agent SDK adapter allows you to run AgentMark prompts as agentic tasks using Anthropic’s Claude Agent SDK. This adapter is suited for agentic execution with tool use, budget controls, and built-in tracing. Available for both TypeScript and Python.

Installation

TypeScript
Python

npm install @agentmark-ai/claude-agent-sdk-adapter @anthropic-ai/claude-agent-sdk

pip install agentmark-claude-agent-sdk agentmark-prompt-core claude-agent-sdk

Setup

TypeScript
Python

Create your AgentMark client with a ClaudeAgentModelRegistry. The registry supports maxThinkingTokens for extended thinking:

agentmark.client.ts

import { createAgentMarkClient, ClaudeAgentModelRegistry } from "@agentmark-ai/claude-agent-sdk-adapter";

const modelRegistry = new ClaudeAgentModelRegistry()
  .registerModels(["claude-sonnet-4-20250514"])
  .registerModels(["claude-opus-4-20250514"], {
    maxThinkingTokens: 10000,
  });

export const client = createAgentMarkClient({
  loader: fileLoader,
  modelRegistry,
});

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_claude_agent_sdk import (
    create_claude_agent_client,
    ClaudeAgentModelRegistry,
    ClaudeAgentAdapterOptions,
    ModelConfig,
)

load_dotenv()

# Default registry passes model names through directly
model_registry = ClaudeAgentModelRegistry.create_default()

# Or configure with extended thinking
model_registry = ClaudeAgentModelRegistry()
model_registry.register_models(
    ["claude-sonnet-4-20250514"],
    lambda name, _: ModelConfig(model=name)
)
model_registry.register_models(
    ["claude-opus-4-20250514"],
    lambda name, _: ModelConfig(model=name, max_thinking_tokens=10000)
)

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

client = create_claude_agent_client(
    model_registry=model_registry,
    loader=loader,
)

Running Prompts

The Claude Agent SDK adapter runs prompts as agentic tasks. The agent executes autonomously, using tools and multi-turn reasoning:

TypeScript
Python

import { client } from "./agentmark.client";

const prompt = await client.loadTextPrompt("task.prompt.mdx");
const input = await prompt.format({
  props: { task: "Analyze the auth module and suggest improvements" },
});

const result = await runAgent(input);
console.log(result.text);

import asyncio
from agentmark_claude_agent_sdk import run_text_prompt
from agentmark_client import client

async def main():
    prompt = await client.load_text_prompt("code-reviewer.prompt.mdx")
    params = await prompt.format(props={
        "task": "Analyze the auth module and suggest improvements"
    })

    result = await run_text_prompt(params)
    print(result.output)

asyncio.run(main())

Adapter Options

Configure agent behavior through adapter options:

TypeScript
Python

const input = await prompt.format({
  props: { task: "Refactor the database layer" },
  adapterOptions: {
    permissionMode: "auto",
    maxTurns: 10,
    cwd: "/path/to/project",
    maxBudgetUsd: 5.00,
    allowedTools: ["read", "write", "bash"],
    disallowedTools: ["browser"],
    systemPromptPreset: "default",
    onWarning: (warning) => {
      console.warn("Agent warning:", warning);
    },
  },
});

from agentmark_claude_agent_sdk import ClaudeAgentAdapterOptions

client = create_claude_agent_client(
    model_registry=model_registry,
    loader=loader,
    adapter_options=ClaudeAgentAdapterOptions(
        permission_mode="bypassPermissions",
        max_turns=10,
        cwd="/path/to/project",
        max_budget_usd=5.00,
        allowed_tools=["read", "write", "bash"],
        disallowed_tools=["browser"],
        system_prompt_preset=False,
        on_warning=lambda w: print(f"Warning: {w}"),
    ),
)

Option	TypeScript	Python	Description
Permission mode	`permissionMode`	`permission_mode`	How the agent requests permission for actions
Max turns	`maxTurns`	`max_turns`	Maximum number of agentic reasoning turns
Working directory	`cwd`	`cwd`	Working directory for file system operations
Budget limit	`maxBudgetUsd`	`max_budget_usd`	Maximum USD budget for the task
Allowed tools	`allowedTools`	`allowed_tools`	Whitelist of tools the agent can use
Disallowed tools	`disallowedTools`	`disallowed_tools`	Blacklist of tools the agent cannot use
System prompt	`systemPromptPreset`	`system_prompt_preset`	System prompt preset to use
Warning handler	`onWarning`	`on_warning`	Callback for agent warnings

Object Generation

For structured output, use object prompts:

TypeScript
Python

import { client } from "./agentmark.client";
import { z } from "zod";

const prompt = await client.loadObjectPrompt("extract.prompt.mdx", {
  schema: z.object({
    sentiment: z.enum(["positive", "negative", "neutral"]),
    confidence: z.number(),
  }),
});

const input = await prompt.format({
  props: { text: "This product is amazing!" },
});

const result = await runAgent(input);
console.log(result.object);

from agentmark_claude_agent_sdk import run_object_prompt

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

result = await run_object_prompt(params)
print(result.output)

Tools

The Claude Agent SDK adapter handles tools differently from the AI SDK adapter. Instead of registering custom tool executors, you list tool names in your prompt frontmatter. The adapter passes these names as allowedTools to the Claude Agent SDK. Tools can be any of the SDK’s built-in tools (Read, Write, Bash, etc.) or tools provided by MCP servers. To use custom tools, configure MCP servers when creating the client:

TypeScript
Python

agentmark.client.ts

import { createAgentMarkClient, ClaudeAgentModelRegistry } from "@agentmark-ai/claude-agent-sdk-adapter";

export const client = createAgentMarkClient({
  loader: fileLoader,
  modelRegistry,
  mcp_servers: {
    weather: { url: "https://weather-mcp.example.com/sse" },
  },
});

agentmark_client.py

client = create_claude_agent_client(
    model_registry=model_registry,
    mcp_servers={"weather": {"url": "https://weather-mcp.example.com/sse"}},
    loader=loader,
)

Then reference tools by name in your prompts:

task.prompt.mdx

---
name: task
text_config:
  model_name: claude-sonnet-4-20250514
  tools:
    - weather
---

<System>You are a helpful assistant with access to weather data.</System>
<User>{props.task}</User>

Tracing

TypeScript
Python

Enable tracing using the /traced export:

import { createAgentMarkClient, ClaudeAgentModelRegistry } from "@agentmark-ai/claude-agent-sdk-adapter/traced";

const client = createAgentMarkClient({
  loader: fileLoader,
  modelRegistry,
});

// All prompt runs are now automatically traced
const prompt = await client.loadTextPrompt("task.prompt.mdx");
const input = await prompt.format({ props: { task: "..." } });
const result = await runAgent(input);

The Python adapter integrates with OpenTelemetry for tracing via hooks:

from agentmark_claude_agent_sdk import (
    create_telemetry_hooks,
    TelemetryConfig,
)

telemetry_config = TelemetryConfig(
    is_enabled=True,
    prompt_name="my-task",
    props={"task": "..."},
    function_id="task-handler",
)

hooks = create_telemetry_hooks(telemetry_config)

Learn more in the Observability documentation.

Getting Started (Python)

Scaffold a Python project with the Claude Agent SDK adapter:

npm create agentmark@latest my-app
# Select "Python" when prompted for language
# Select "Claude Agent SDK" as the adapter

Run the dev server:

agentmark dev

Limitations

No image generation — Use the AI SDK adapter for experimental_generateImage
No speech generation — Use the AI SDK adapter for experimental_generateSpeech
No streaming — Results are returned after the agent completes all turns

Next Steps

Prompts

Learn about prompt syntax

Tools & Agents

Configure tools for your agents

Observability

Monitor your agents in production

Other Integrations

Explore other AI frameworks

Have Questions?

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Getting Started

Prompts and Agents

Testing

Observability

Integrations

Python

Further Reference

Installation

Setup

Running Prompts

Adapter Options

Object Generation

Tools

Tracing

Getting Started (Python)

Limitations

Next Steps

Prompts

Tools & Agents

Observability

Other Integrations

Have Questions?

Getting Started

Prompts and Agents

Testing

Observability

Integrations

Python

Further Reference

​Installation

​Setup

​Running Prompts

​Adapter Options

​Object Generation

​Tools

​Tracing

​Getting Started (Python)

​Limitations

​Next Steps

Prompts

Tools & Agents

Observability

Other Integrations

​Have Questions?

Installation

Setup

Running Prompts

Adapter Options

Object Generation

Tools

Tracing

Getting Started (Python)

Limitations

Next Steps

Have Questions?