--- name: strands version: 2.0.0 description: Build and run Python-based AI agents using the AWS Strands SDK. Use when you need to create autonomous agents, multi-agent workflows, custom tools, or integrate with MCP servers. Supports Ollama (local), Anthropic, OpenAI, Bedrock, and other model providers. Use for agent scaffolding, tool creation, and running agent tasks programmatically. homepage: https://github.com/strands-agents/sdk-python metadata: openclaw: emoji: 🧬 requires: bins: [python3] packages: [strands-agents] ---

Strands Agents SDK

Build AI agents in Python using the Strands SDK (Apache-2.0, from AWS).

Validated against: strands-agents==1.23.0, strands-agents-tools==0.2.19

Prerequisites

# Install SDK + tools (via pipx for isolation — recommended)
pipx install strands-agents-builder  # includes strands-agents + strands-agents-tools + CLI

# Or install directly
pip install strands-agents strands-agents-tools

Core Concept: Bedrock Is the Default

Agent() with no model= argument defaults to Amazon Bedrock — specifically us.anthropic.claude-sonnet-4-20250514-v1:0 in us-west-2. This requires AWS credentials. To use a different provider, pass model= explicitly.

Default model constant: strands.models.bedrock.DEFAULT_BEDROCK_MODEL_ID

Quick Start — Local Agent (Ollama)

from strands import Agent
from strands.models.ollama import OllamaModel

# host is a required positional argument
model = OllamaModel("http://localhost:11434", model_id="qwen3:latest")
agent = Agent(model=model)
result = agent("What is the capital of France?")
print(result)

Note: Not all open-source models support tool-calling. Abliterated models often lose function-calling during the abliteration process. Test with a stock model (qwen3, llama3.x, mistral) first.

Quick Start — Bedrock (Default Provider)

from strands import Agent

# No model specified → BedrockModel (Claude Sonnet 4, us-west-2)
# Requires AWS credentials (~/.aws/credentials or env vars)
agent = Agent()
result = agent("Explain quantum computing")

# Explicit Bedrock model:
from strands.models import BedrockModel
model = BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0")
agent = Agent(model=model)

Quick Start — Anthropic (Direct API)

from strands import Agent
from strands.models.anthropic import AnthropicModel

# max_tokens is Required[int] — must be provided
model = AnthropicModel(model_id="claude-sonnet-4-20250514", max_tokens=4096)
agent = Agent(model=model)
result = agent("Explain quantum computing")

Requires ANTHROPIC_API_KEY environment variable.

Quick Start — OpenAI

from strands import Agent
from strands.models.openai import OpenAIModel

model = OpenAIModel(model_id="gpt-4.1")
agent = Agent(model=model)

Requires OPENAI_API_KEY environment variable.

Creating Custom Tools

Use the @tool decorator. Type hints become the schema; the docstring becomes the description:

from strands import Agent, tool

@tool
def read_file(path: str) -> str:
    """Read contents of a file at the given path.

    Args:
        path: Filesystem path to read.
    """
    with open(path) as f:
        return f.read()

@tool
def write_file(path: str, content: str) -> str:
    """Write content to a file.

    Args:
        path: Filesystem path to write.
        content: Text content to write.
    """
    with open(path, 'w') as f:
        f.write(content)
    return f"Wrote {len(content)} bytes to {path}"

agent = Agent(model=model, tools=[read_file, write_file])
agent("Read /tmp/test.txt and summarize it")

ToolContext

Tools can access agent state via ToolContext:

from strands import tool
from strands.types.tools import ToolContext

@tool
def stateful_tool(query: str, tool_context: ToolContext) -> str:
    """A tool that accesses agent state.

    Args:
        query: Input query.
    """
    # Access shared agent state
    count = tool_context.state.get("call_count", 0) + 1
    tool_context.state["call_count"] = count
    return f"Call #{count}: {query}"

Built-in Tools (46 available)

strands-agents-tools provides pre-built tools:

from strands_tools import calculator, file_read, file_write, shell, http_request
agent = Agent(model=model, tools=[calculator, file_read, shell])

Full list: calculator, file_read, file_write, shell, http_request, editor, image_reader, python_repl, current_time, think, stop, sleep, environment, retrieve, search_video, chat_video, speak, generate_image, generate_image_stability, diagram, journal, memory, agent_core_memory, elasticsearch_memory, mongodb_memory, mem0_memory, rss, cron, batch, workflow, use_agent, use_llm, use_aws, use_computer, load_tool, handoff_to_user, slack, swarm, graph, a2a_client, mcp_client, exa, tavily, bright_data, nova_reels.

Hot reload: Agent(load_tools_from_directory=True) watches ./tools/ for changes.

MCP Integration

Connect to any Model Context Protocol server. MCPClient implements ToolProvider — pass it directly in the tools list:

from strands import Agent
from strands.tools.mcp import MCPClient
from mcp import stdio_client, StdioServerParameters

# MCPClient takes a callable that returns the transport
mcp = MCPClient(lambda: stdio_client(StdioServerParameters(
    command="uvx",
    args=["some-mcp-server@latest"]
)))

# Use as context manager — MCPClient is a ToolProvider
with mcp:
    agent = Agent(model=model, tools=[mcp])
    agent("Use the MCP tools to do something")

SSE transport:

from mcp.client.sse import sse_client
mcp = MCPClient(lambda: sse_client("http://localhost:8080/sse"))

Multi-Agent Patterns

Agents as Tools

Nest agents — inner agents become tools for the outer agent:

researcher = Agent(model=model, system_prompt="You are a research assistant.")
writer = Agent(model=model, system_prompt="You are a writer.")

orchestrator = Agent(
    model=model,
    tools=[researcher, writer],
    system_prompt="You coordinate research and writing tasks."
)
orchestrator("Research quantum computing and write a blog post")

Swarm Pattern

Self-organizing agent teams with shared context and autonomous handoff coordination:

from strands.multiagent.swarm import Swarm

# Agents need name + description for handoff identification
researcher = Agent(
    model=model,
    name="researcher",
    description="Finds and summarizes information"
)
writer = Agent(
    model=model,
    name="writer",
    description="Creates polished content"
)

swarm = Swarm(
    nodes=[researcher, writer],
    entry_point=researcher,    # optional — defaults to first agent
    max_handoffs=20,           # default
    max_iterations=20,         # default
    execution_timeout=900.0,   # 15 min default
    node_timeout=300.0         # 5 min per node default
)
result = swarm("Research AI agents, then hand off to writer for a blog post")

Swarm auto-injects a handoff_to_agent tool. Agents hand off by calling it with the target agent's name. Supports interrupt/resume, session persistence, and repetitive-handoff detection.

Graph Pattern (DAG)

Deterministic dependency-based execution via GraphBuilder:

from strands.multiagent.graph import GraphBuilder

builder = GraphBuilder()
research_node = builder.add_node(researcher, node_id="research")
writing_node = builder.add_node(writer, node_id="writing")
builder.add_edge("research", "writing")
builder.set_entry_point("research")

# Optional: conditional edges
# builder.add_edge("research", "writing",
#     condition=lambda state: "complete" in str(state.completed_nodes))

graph = builder.build()
result = graph("Write a blog post about AI agents")

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