Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Strands Agents SDK
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.
skill openclawclawhub Free
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.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/cheatsheet.md, scripts/create-agent.py, scripts/run-agent.py, tests/test_imports.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
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- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.0.3
Provenance
- Publisher
- TrippingKelsea
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Documentation
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 โ 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 โ 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 โ 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 โ Bedrock (Default Provider)
from strands import Agent # No model specified โ 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 โ Anthropic (Direct API)
from strands import Agent from strands.models.anthropic import AnthropicModel # max_tokens is Required[int] โ 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 โ 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 โ 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 โ 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"))
Agents as Tools
Nest agents โ 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 โ 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") Supports cycles (feedback loops) with builder.reset_on_revisit(True), execution timeouts, and nested graphs (Graph as a node in another Graph).
A2A Protocol (Agent-to-Agent)
Expose a Strands agent as an A2A-compatible server for inter-agent communication: from strands.multiagent.a2a import A2AServer server = A2AServer( agent=my_agent, host="127.0.0.1", port=9000, version="0.0.1" ) server.start() # runs uvicorn Connect to A2A agents with the a2a_client tool from strands-agents-tools. A2A implements Google's Agent-to-Agent protocol for standardized cross-process/cross-network agent communication.
Session Persistence
Persist conversations across agent runs: from strands.session.file_session_manager import FileSessionManager session = FileSessionManager(session_file_path="./sessions/my_session.json") agent = Agent(model=model, session_manager=session) # Also available: from strands.session.s3_session_manager import S3SessionManager session = S3SessionManager(bucket_name="my-bucket", session_id="session-1") Both Swarm and Graph support session managers for persisting multi-agent state.
Bidirectional Streaming (Experimental)
Real-time voice/text conversations with persistent audio streams: from strands.experimental.bidi.agent import BidiAgent from strands.experimental.bidi.models.nova_sonic import NovaSonicModel # Supports: NovaSonicModel, GeminiLiveModel, OpenAIRealtimeModel model = NovaSonicModel(region="us-east-1") agent = BidiAgent(model=model, tools=[my_tool]) Supports interruption detection, concurrent tool execution, and continuous back-and-forth audio. Experimental โ API subject to change.
System Prompts
agent = Agent( model=model, system_prompt="You are Hex, a sharp and witty AI assistant.", tools=[read_file, write_file] ) Strands also supports list[SystemContentBlock] for structured system prompts with cache control.
Observability
Native OpenTelemetry tracing: agent = Agent( model=model, trace_attributes={"project": "my-agent", "environment": "dev"} ) Every tool call, model invocation, handoff, and lifecycle event is instrumentable.
Bedrock-Specific Features
Guardrails: guardrail_id + guardrail_version in BedrockModel config โ content filtering, PII detection, input/output redaction Cache points: System prompt and tool definition caching for cost optimization Streaming: On by default, disable with streaming=False Region: Defaults to us-west-2, override via region_name param or AWS_REGION env Cross-region inference: Model IDs prefixed with us. use cross-region inference profiles
Scaffolding a New Agent
python3 {baseDir}/scripts/create-agent.py my-agent --provider ollama --model qwen3:latest python3 {baseDir}/scripts/create-agent.py my-agent --provider anthropic python3 {baseDir}/scripts/create-agent.py my-agent --provider bedrock python3 {baseDir}/scripts/create-agent.py my-agent --provider openai --model gpt-4.1 Creates a ready-to-run agent directory with tools, config, and entry point.
Running an Agent
python3 {baseDir}/scripts/run-agent.py path/to/agent.py "Your prompt here" python3 {baseDir}/scripts/run-agent.py path/to/agent.py --interactive
Model Providers Reference (11 total)
ProviderClassInitNotesBedrockBedrockModelBedrockModel(model_id=...)Default, eagerly importedOllamaOllamaModelOllamaModel("http://host:11434", model_id=...)host is positionalAnthropicAnthropicModelAnthropicModel(model_id=..., max_tokens=4096)max_tokens requiredOpenAIOpenAIModelOpenAIModel(model_id=...)OPENAI_API_KEYGeminiGeminiModelGeminiModel(model_id=...)api_key in client_argsMistralMistralModelMistralModel(model_id=...)Mistral API keyLiteLLMLiteLLMModelLiteLLMModel(model_id=...)Meta-provider (Cohere, Groq, etc.)LlamaAPILlamaAPIModelLlamaAPIModel(model_id=...)Meta Llama APIllama.cppLlamaCppModelLlamaCppModel(...)Local server, OpenAI-compatibleSageMakerSageMakerAIModelSageMakerAIModel(...)Custom AWS endpointsWriterWriterModelWriterModel(model_id=...)Writer platform All non-Bedrock providers are lazy-loaded โ dependencies imported only when referenced. Import pattern: from strands.models.<provider> import <Class> (or from strands.models import <Class> for lazy-load).
Tips
Agent() without model= requires AWS credentials (Bedrock default) AnthropicModel requires max_tokens โ omitting it causes a runtime error OllamaModel host is positional: OllamaModel("http://...", model_id="...") Abliterated Ollama models often lose tool-calling support โ use stock models for tool-using agents Swarm agents need name= and description= for handoff routing Agent(load_tools_from_directory=True) watches ./tools/ for hot-reloaded tool files Use agent.tool.my_tool() to call tools directly without LLM routing MCPClient is a ToolProvider โ pass it directly in tools=[mcp], don't call list_tools_sync() manually when using with Agent Session managers work with Agent, Swarm, and Graph Pin your strands-agents version โ the SDK is young and APIs evolve between releases
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
3 Scripts2 Docs
- SKILL.md
- references/cheatsheet.md
- scripts/create-agent.py
- scripts/run-agent.py
- tests/test_imports.py