strands
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.
Why use this skill?
Master autonomous agent development with the OpenClaw AWS Strands SDK skill. Supports Bedrock, OpenAI, and Ollama for building custom AI tools and workflows.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/trippingkelsea/aws-strandsWhat This Skill Does
The AWS Strands SDK skill for OpenClaw provides a robust framework for building and orchestrating autonomous AI agents. Based on the open-source AWS Strands SDK, this skill allows users to leverage local models via Ollama or cloud-based providers like Amazon Bedrock, Anthropic, and OpenAI. It excels at multi-agent workflows, tool-calling, and custom logic implementation, making it an essential utility for developers looking to move beyond simple chat interfaces into programmatic agentic task execution.
Installation
To integrate this skill into your OpenClaw environment, execute the following command in your terminal:
clawhub install openclaw/skills/skills/trippingkelsea/aws-strands
Ensure you have Python installed and, ideally, pipx for environment isolation. After installation, verify your AWS environment variables or API keys are set for your chosen model provider.
Use Cases
- Autonomous File Management: Build agents capable of reading/writing system files, summarizing logs, or organizing directories using the
@tooldecorator. - Enterprise Workflow Automation: Connect agents to Bedrock to process sensitive corporate documents while maintaining strict data governance.
- Multi-Agent Research: Deploy a team of agents where one agent handles web retrieval, another processes the data, and a third synthesizes the findings.
- Local Tooling: Run private, data-secure agents using Ollama and local LLMs without needing constant internet connectivity.
Example Prompts
- "Initialize a new Strands agent using the Anthropic model that can automatically read files in my './data' directory and report back on their contents."
- "Create a custom tool for the Strands agent that allows it to execute shell commands securely and use it to summarize the last 10 lines of my project logs."
- "Set up a multi-agent system where one agent uses a Bedrock model for logic and a second agent handles file operations to process and clean my local CSV datasets."
Tips & Limitations
- Model Compatibility: Not every open-source model supports reliable tool-calling. Avoid using 'abliterated' models if you require strict function-call adherence; stock models like Llama 3 or Qwen 3 are recommended.
- Default Provider: Remember that
Agent()without arguments defaults to Amazon Bedrock. Ensure your~/.aws/credentialsfile is properly configured. - Tool Schema: Type hints are strictly used by the SDK to generate JSON schemas. Always provide clear, descriptive docstrings for your tools, as the LLM uses these to decide when and how to call your functions.
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-trippingkelsea-aws-strands": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: network-access, file-write, file-read, external-api, code-execution