AWS Agentcore Langgraph

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