---

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

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

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