Token导航 LogoToken导航TokenDH.com
开发external-serviceclawhub未标认证来源可访问clear审计提醒

aws-agentcore-langgraphAWS agentcore langgraph 部署

Agent Skill

用于辅助云资源、部署、容器、基础设施和运维自动化任务。它适合让 Agent 检查配置、整理部署步骤、分析资源状态、生成排障思路或辅助云服务接入。使用时需要明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作;涉及删除资源、重启服务、修改网络或权限配置时,应先确认影响范围。

总安装

51,171

周安装

2,111

GitHub Stars

3

下载量

16,719
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:aws-agentcore-langgraph(AWS agentcore langgraph 部署)
来源仓库:https://github.com/killerapp/aws-agentcore-langgraph
安装命令:
openclaw skills install aws-agentcore-langgraph
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install aws-agentcore-langgraph

简介

在 AWS Bedrock AgentCore 上部署生产 LangGraph 代理。用于 (1) 具有协调器和专业代理模式的多代理系统,(2) 构建具有持久跨会话内存的有状态代理,(3) 通过 AgentCore Gateway(MCP、Lambda、API)连接外部工具,(4) 管理跨分布式代理的共享上下文,或 (5) 通过具有生产可观察性和扩展能力的 CLI 部署复杂的代理生态系统。

SKILL.md

name
aws-agentcore-langgraph
description
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.

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

CommandPurpose
agentcore configure -e agent.py --region us-east-1Setup
agentcore configure -e agent.py --region us-east-1 --name my_agent --non-interactiveScripted setup
agentcore launch --deployment-type containerDeploy (container mode)
agentcore launch --disable-memoryDeploy without memory subsystem
agentcore devHot-reload local dev server
agentcore invoke '{"prompt": "Hello"}'Test
agentcore destroyCleanup

Core Patterns

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

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

References

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

98.39%
按下载量换算16,450

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills