agent-sdk

name	agent-sdk
description	Build AI agents using the Claude Agent SDK with tool use, memory, and multi-step orchestration

Claude Agent SDK

When to activate

Building a Python or TypeScript application that uses Claude Code capabilities programmatically; deploying Claude as an autonomous agent inside a product; writing code that drives the claude CLI in non-interactive mode; scripting agentic workflows that need tool calls, retries, and context management handled automatically.

When NOT to use

Using Claude Code interactively in the terminal — that is the default experience, not an SDK use case; building a simple chatbot or single-turn Q&A interface (use the Messages API directly); when Anthropic Managed Agents is a better fit (hosted infrastructure, automatic scaling, built-in memory persistence).

Instructions

What the Agent SDK is: Same tool loop, context management, and agent capabilities as interactive Claude Code — packaged as a library you embed in your own application. You control the infrastructure; Anthropic provides the model and agent loop.

SDK vs alternatives — choose the right layer:

Need	Use
Embed agentic Claude in your app, own the infra	Agent SDK
Agentic Claude hosted by Anthropic, hands-off ops	Managed Agents
Single-turn responses, no tool loop needed	Messages API
Interactive terminal workflow	Claude Code CLI

Installation:

Python:

pip install claude-code-sdk

TypeScript:

npm install @anthropic-ai/claude-code

--bare flag via options: Skips CLAUDE.md loading and MCP server discovery. Use this in CI and scripting contexts where startup speed matters — approximately 10× faster initialization.

Billing (June 15, 2026+): Agent SDK sessions draw from a dedicated Agent SDK credit pool, separate from interactive session limits.

In-process tools: Tools run in-process rather than spawning subprocesses. Use this for high-frequency calls where subprocess overhead adds up.

Cloud provider support: AWS Bedrock, Google Vertex AI, and Microsoft Azure AI Foundry are all supported. Configure via environment variables — no SDK code changes required.

Python example:

import asyncio
from claude_code_sdk import query, ClaudeCodeOptions

async def run_agent(task: str):
    options = ClaudeCodeOptions(system_prompt="You are a code reviewer.")
    async for message in query(prompt=task, options=options):
        if message.type == "result":
            print(message.result)

asyncio.run(run_agent("Review this PR diff and list security issues"))

TypeScript example:

import { query, ClaudeCodeOptions } from "@anthropic-ai/claude-code";

const options: ClaudeCodeOptions = {
  systemPrompt: "You are a code reviewer.",
};

for await (const message of query({ prompt: "Review this PR diff", options })) {
  if (message.type === "result") {
    console.log(message.result);
  }
}

Agent SDK vs Managed Agents — decision guide:

• Agent SDK: full infrastructure control, runs in your CI/CD, latency-sensitive workloads, custom logging and observability
• Managed Agents: Anthropic handles crashes, scaling, and memory persistence; no infrastructure to manage; better for non-technical teams deploying agents as a product feature

Example

A code review pipeline in CI: on every PR open event, a GitHub Actions job calls the Agent SDK with the PR diff as the prompt. The agent reviews the diff, calls internal tools to check the test coverage database, and posts a structured review comment back to the PR via the GitHub API. The --bare flag keeps cold-start time under 2 seconds.

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