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aws-agentcore-langgraph

Deploy production LangGraph agents on AWS Bedrock AgentCore. Use for (1) multi-agent systems with orchestrator and specialist agent patterns, (2) building stateful agents with persistent cross-session memory, (3) connecting external tools via AgentCore Gateway (MCP, Lambda, APIs), (4) managing shared context across distributed agents, or (5) deploying complex agent ecosystems via CLI with production observability and scaling.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, references/agentcore-cli.md, references/agentcore-gateway.md, references/agentcore-memory.md, references/agentcore-runtime.md, references/langgraph-patterns.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.2

Provenance

Publisher: killerapp
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 12 sections Open source page

AWS AgentCore + LangGraph

Multi-agent systems on AWS Bedrock AgentCore with LangGraph orchestration. Source: https://github.com/aws/bedrock-agentcore-starter-toolkit

Install

pip install bedrock-agentcore bedrock-agentcore-starter-toolkit langgraph uv tool install bedrock-agentcore-starter-toolkit # installs agentcore CLI

Quick Start

from langgraph.graph import StateGraph, START from langgraph.graph.message import add_messages from langgraph.prebuilt import ToolNode, tools_condition # routing + tool execution from bedrock_agentcore.runtime import BedrockAgentCoreApp from typing import Annotated from typing_extensions import TypedDict class State(TypedDict): messages: Annotated[list, add_messages] builder = StateGraph(State) builder.add_node("agent", agent_node) builder.add_node("tools", ToolNode(tools)) # prebuilt tool executor builder.add_conditional_edges("agent", tools_condition) # routes to tools or END builder.add_edge(START, "agent") graph = builder.compile() app = BedrockAgentCoreApp() # Wraps as HTTP service on port 8080 (/invocations, /ping) @app.entrypoint def invoke(payload, context): result = graph.invoke({"messages": [("user", payload.get("prompt", ""))]}) return {"result": result["messages"][-1].content} app.run()

CLI Commands

CommandPurposeagentcore configure -e agent.py --region us-east-1Setupagentcore configure -e agent.py --region us-east-1 --name my_agent --non-interactiveScripted setupagentcore launch --deployment-type containerDeploy (container mode)agentcore launch --disable-memoryDeploy without memory subsystemagentcore devHot-reload local dev serveragentcore invoke '{"prompt": "Hello"}'Testagentcore destroyCleanup

Multi-Agent Orchestration

Orchestrator delegates to specialists (customer service, e-commerce, healthcare, financial, etc.) Specialists: inline functions or separate deployed agents; all share session_id for context

Memory (STM/LTM)

from bedrock_agentcore.memory import MemoryClient memory = MemoryClient() memory.create_event(session_id, actor_id, event_type, payload) # Store events = memory.list_events(session_id) # Retrieve (returns list) STM: Turn-by-turn within session | LTM: Facts/decisions across sessions/agents ~10s eventual consistency after writes

Gateway Tools

python -m bedrock_agentcore.gateway.deploy --stack-name my-agents --region us-east-1 from bedrock_agentcore.gateway import GatewayToolClient gateway = GatewayToolClient() result = gateway.call("tool_name", param1=value1, param2=value2) Transport: Fallback Mock (local), Local MCP servers, Production Gateway (Lambda/REST/MCP) Auto-configures BEDROCK_AGENTCORE_GATEWAY_URL after deploy

Decision Tree

Multiple agents coordinating? → Orchestrator + specialists pattern Persistent cross-session memory? → AgentCore Memory (not LangGraph checkpoints) External APIs/Lambda? → AgentCore Gateway Single agent, simple? → Quick Start above Complex multi-step logic? → StateGraph + tools_condition + ToolNode

Key Concepts

AgentCore Runtime: HTTP service on port 8080 (handles /invocations, /ping) AgentCore Memory: Managed cross-session/cross-agent memory LangGraph Routing: tools_condition for agent→tool routing, ToolNode for execution AgentCore Gateway: Transforms APIs/Lambda into MCP tools with auth

Naming Rules

Start with letter, only letters/numbers/underscores, 1-48 chars: my_agent not my-agent

Troubleshooting

IssueFixon-demand throughput isn't supportedUse us.anthropic.claude-* inference profilesModel use case details not submittedFill Anthropic form in Bedrock ConsoleInvalid agent nameUse underscores not hyphensMemory empty after writeWait ~10s (eventual consistency)Container not reading .envSet ENV in Dockerfile, not .envMemory not working after deployCheck logs for "Memory enabled/disabled"list_events returns emptyCheck actor_id/session_id match; event['payload'] is a listGateway "Unknown tool"Lambda must strip ___ prefix from bedrockAgentCoreToolNamePlatform mismatch warningNormal - CodeBuild handles ARM64 cross-platform builds

References

agentcore-cli.md - CLI commands, deployment, lifecycle agentcore-runtime.md - Streaming, async, observability agentcore-memory.md - STM/LTM patterns, API reference agentcore-gateway.md - Tool integration, MCP, Lambda langgraph-patterns.md - StateGraph design, routing reference-architecture-advertising-agents-use-case.pdf - Example multi-agent architecture

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

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Package contents

Included in package

6 Docs

SKILL.md Primary doc
references/agentcore-cli.md Docs
references/agentcore-gateway.md Docs
references/agentcore-memory.md Docs
references/agentcore-runtime.md Docs
references/langgraph-patterns.md Docs