Anthropic Agent SDK — Build Production AI Agents

What it is

The Anthropic Agent SDK is the official framework for building AI agents powered by Claude. It provides structured tool use, memory management, multi-step orchestration, and guardrails for production deployments. The SDK handles the agent loop (observe, think, act, repeat) with built-in error recovery, token management, and conversation persistence.

The SDK targets developers building autonomous AI applications: customer support agents, research assistants, code generation pipelines, and workflow automation. It provides the production scaffolding that turns a single LLM call into a reliable agent system.

How it saves time or tokens

The SDK manages the agent loop internally, handling retries, tool call parsing, and conversation state. Without it, you write custom code for each of these concerns. Built-in token management prevents context window overflow by summarizing or truncating conversation history automatically. Error recovery means failed tool calls are retried with context rather than crashing the entire agent session.

How to use

1. Install the SDK:

pip install claude-agent-sdk

1. Define tools and create an agent:

from claude_agent_sdk import Agent, tool

@tool
def search_web(query: str) -> str:
    'Search the web for information.'
    return do_search(query)

agent = Agent(
    model='claude-sonnet-4-20250514',
    tools=[search_web],
    system_prompt='You are a research assistant. Answer questions using web search.'
)

1. Run the agent on a task:

result = agent.run('What are the latest developments in quantum computing?')
print(result.output)

Example

Multi-tool agent with memory:

from claude_agent_sdk import Agent, tool, MemoryStore

@tool
def query_database(sql: str) -> str:
    'Execute a SQL query against the analytics database.'
    return db.execute(sql)

@tool
def send_email(to: str, subject: str, body: str) -> str:
    'Send an email notification.'
    return email_service.send(to, subject, body)

memory = MemoryStore(backend='sqlite')

agent = Agent(
    model='claude-sonnet-4-20250514',
    tools=[query_database, send_email],
    memory=memory,
)

# Agent remembers context across sessions
agent.run('Send a weekly revenue report to the finance team')

Related on TokRepo

• AI Tools for Agents — Compare agent frameworks for building autonomous AI systems
• Multi-Agent Frameworks — Multi-agent orchestration for complex task decomposition

Common pitfalls

• Tool functions must have clear docstrings. The SDK sends docstrings to Claude as tool descriptions. Vague docstrings lead to incorrect tool selection.
• Memory persistence requires explicit configuration. Without a memory backend, agent context is lost between sessions.
• Long-running agent tasks can accumulate significant token costs. Set max_turns or token budgets to prevent runaway execution.
• Always check the official documentation for the latest version-specific changes and migration guides before upgrading in production environments.
• For team deployments, establish clear guidelines on configuration and usage patterns to ensure consistency across developers.