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Deploy Python agents to AWS Bedrock AgentCore. Use when deploying agents to AWS, setting up serverless agent hosting, configuring AgentCore components (Runtime, Gateway, Memory, Identity, Policy), or troubleshooting deployment errors.

Install Skill

Shared

Installs to .agents/skills, used by Codex, Amp, Warp, Cursor, OpenCode, and more.

CodexAmp
Warp
CursorOpenCode
Cline
Gemini CLI
GitHub Copilot
Personal

Available across projects.

$npx skills-installer add @majiayu000/claude-skill-registry/deploy-agentcore --client shared
Project

Writes to .agents/skills.

$npx skills-installer add @majiayu000/claude-skill-registry/deploy-agentcore -p --client shared
Note: Review the skill instructions before using it.

SKILL.md

name deploy-agentcore
description Deploy Python agents to AWS Bedrock AgentCore. Use when deploying agents to AWS, setting up serverless agent hosting, configuring AgentCore components (Runtime, Gateway, Memory, Identity, Policy), or troubleshooting deployment errors.
AWS Bedrock AgentCore is a serverless platform for AI agents at scale.

Architecture

AgentCore has 6 modular components:

  • Runtime - Serverless hosting (direct_code_deploy or container)
  • Gateway - Tool access via MCP (Lambda, OpenAPI, Smithy targets)
  • Memory - STM (session) and LTM (persistent) storage
  • Identity - Auth via IAM, Cognito, AWS JWT, external OAuth
  • Observability - CloudWatch + OpenTelemetry tracing
  • Policy - Cedar-based governance and authorization

Entry Point Pattern

All agents use BedrockAgentCoreApp with @app.entrypoint decorator:

from bedrock_agentcore import BedrockAgentCoreApp

app = BedrockAgentCoreApp()

@app.entrypoint
def invoke(payload: dict) -> dict:
    prompt = payload.get("prompt", "")
    result = your_agent_logic(prompt)
    return {"result": result}

if __name__ == "__main__":
    app.run()

Key CLI Commands

All commands: uv run agentcore [command]

Runtime: configure, deploy, invoke, status, destroy, stop-session Gateway: gateway create-mcp-gateway, gateway create-mcp-gateway-target Memory: memory create, memory list, memory status Identity: identity setup-cognito, identity setup-aws-jwt Policy: policy create-policy-engine, policy create-policy

See references/cli-reference.md for full command list.

Rules

  • Agent names: underscores only (my_agent not my-agent)
  • Never hardcode API keys - use Secrets Manager
  • Windows: prefix with PYTHONIOENCODING=utf-8
  • Memory mode order: STM_AND_LTM (not LTM_AND_STM)
What would you like to do?
  1. Deploy a new agent
  2. Update existing deployment
  3. Add Google OAuth
  4. Create chat UI
  5. Set up Gateway (MCP tools)
  6. Configure Memory
  7. Set up Identity/Auth
  8. View logs/observability
  9. Troubleshoot errors
  10. Something else

Wait for response before proceeding.

| Response | Workflow | |----------|----------| | 1, "deploy", "new" | workflows/deploy-agent.md | | 2, "update", "redeploy" | workflows/update-deployment.md | | 3, "oauth", "google" | workflows/add-oauth.md | | 4, "ui", "chat", "streamlit" | workflows/create-chat-ui.md | | 5, "gateway", "mcp", "tools" | workflows/setup-gateway.md | | 6, "memory", "stm", "ltm" | workflows/setup-memory.md | | 7, "identity", "auth", "cognito", "jwt" | workflows/setup-identity.md | | 8, "logs", "observability", "cloudwatch" | workflows/view-logs.md | | 9, "error", "troubleshoot", "fix" | workflows/troubleshoot.md | | 10, other | Clarify, then select |

After reading the workflow, follow it exactly.

All domain knowledge in `references/`:
  • architecture.md - All AgentCore components explained
  • cli-reference.md - Complete CLI command reference
  • prerequisites.md - AWS setup, Python, uv requirements
  • memory-modes.md - Memory configuration details
  • common-errors.md - Error messages and fixes
  • iam-policies.md - IAM role configuration
| Workflow | Purpose | |----------|---------| | deploy-agent.md | Deploy Python agent to AgentCore | | update-deployment.md | Redeploy with code changes | | add-oauth.md | Add Google OAuth for cloud environment | | create-chat-ui.md | Create Streamlit chat interface | | setup-gateway.md | Create MCP gateway with targets | | setup-memory.md | Configure memory modes | | setup-identity.md | Set up auth (Cognito, JWT, OAuth) | | view-logs.md | Access CloudWatch logs and metrics | | troubleshoot.md | Fix common deployment errors | | Template | Purpose | |----------|---------| | entry_claude_sdk.py | Entry point for Claude SDK agents | | entry_langchain.py | Entry point for LangChain agents | | entry_custom.py | Entry point for custom Python agents | | entry_minimal.py | Bare minimum entry point | | policy_minimal.json | IAM policy for Secrets Manager only | | policy_oauth.json | IAM policy for OAuth (Secrets + S3) | | policy_full.json | IAM policy with all common permissions | | chat_ui.py | Streamlit chat interface | Deployment successful when: - `uv run agentcore deploy` completes without errors - `uv run agentcore invoke` returns expected response - Agent handles sessions correctly - External API keys work via Secrets Manager