Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Deploys LangGraph agents to AWS Bedrock AgentCore with support for multi-agent orchestration, persistent memory across sessions, and external tool integration via Gateway. Wraps LangGraph state graphs as HTTP services running on port 8080. Includes a CLI for configuration, deployment, and lifecycle management.
Combines LangGraph's stateful graph routing with AWS-managed memory, scaling, and tool connectivity in one deployment target, avoiding the need to wire these infrastructure pieces together manually.
When to use it
- Deploying a customer service agent ecosystem with specialist sub-agents
- Adding cross-session memory to a LangGraph chatbot so it recalls past decisions
- Exposing Lambda functions and REST APIs as MCP tools for agent use
- Running a local hot-reload dev server while iterating on agent logic
- Scripting unattended production deployments via agentcore CLI flags
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: deploy my LangGraph agent with persistent memory to AWS
- 1Installs bedrock-agentcore and langgraph packages
- 2Wraps existing StateGraph with BedrockAgentCoreApp and defines an entrypoint
- 3Runs agentcore configure -e agent.py --region us-east-1 to set up deployment config
- 4Runs agentcore launch --deployment-type container to build and deploy
- 5Runs agentcore invoke '{"prompt": "Hello"}' to verify the deployed endpoint responds
Agent running as a managed AWS service with /invocations and /ping endpoints, memory subsystem enabled, ready to handle production traffic
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.