aws-harness

npm install -g @aws/agentcore
agentcore --help

# interactive wizard:
agentcore create

# or non-interactive:
agentcore create --name MyAgent --framework Strands --model-provider Bedrock --memory none

# or accept all defaults (Python, Strands, Bedrock, no memory):
agentcore create --name MyAgent --defaults

MyAgent/
  agentcore/
    agentcore.json      # project + agent config
    aws-targets.json    # AWS account / region
    .env.local          # local secrets (gitignored)
  app/
    MyAgent/
      main.py           # your starter agent, in the chosen framework
      pyproject.toml
  README.md

from bedrock_agentcore import BedrockAgentCoreApp   # the harness wrapper
from strands import Agent                           # the framework (brain)

app = BedrockAgentCoreApp()
agent = Agent()

@app.entrypoint                       # every request lands here
def invoke(payload):
    user_message = payload.get("prompt", "Hello! How can I help you today?")
    result = agent(user_message)      # this one call IS the entire agent loop
    return {"result": result.message}

if __name__ == "__main__":
    app.run()                         # `agentcore dev` runs exactly this

from bedrock_agentcore import BedrockAgentCoreApp
from strands import Agent, tool

app = BedrockAgentCoreApp()

@tool
def word_count(text: str) -> str:
    """Count the number of words in a piece of text.

    Args:
        text: The text to count words in.
    """
    return f"The text has {len(text.split())} words."

agent = Agent(tools=[word_count])     # add/remove tools here = change abilities

@app.entrypoint
def invoke(payload):
    user_message = payload.get("prompt", "Hello!")
    result = agent(user_message)
    return {"result": result.message}

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

cd MyAgent
agentcore dev                         # hot-reload server on localhost:8080 + browser inspector
agentcore dev "count the words here"  # invoke the local agent

agentcore deploy --plan   # dry run: preview the infra
agentcore deploy          # provision for real

agentcore invoke "Tell me a joke" --stream
agentcore invoke --session-id my-session "and another"   # continuity across calls

import json, uuid, boto3

client = boto3.client("bedrock-agentcore")
response = client.invoke_agent_runtime(
    agentRuntimeArn="<AGENT_ARN>",         # from `agentcore status`
    runtimeSessionId=str(uuid.uuid4()),    # must be 33+ chars; uuid4 is 36
    payload=json.dumps({"prompt": "Tell me a joke"}).encode(),
    qualifier="DEFAULT",
)
content = []
for chunk in response.get("response", []):
    content.append(chunk.decode("utf-8"))
print(json.loads("".join(content)))

agentcore add memory --name MyMemory --strategies SEMANTIC       # long-term recall
agentcore add agent  --name SecondAgent --framework Strands       # multi-agent
# also available: identity, target (gateway), evaluator
agentcore deploy                                                  # provision the additions

agentcore logs           # stream runtime logs
agentcore traces list    # inspect steps, tokens, latency

agentcore remove all   # mark resources for deletion
agentcore deploy       # apply teardown via CloudFormation

# main.py - the only new file you write
from bedrock_agentcore import BedrockAgentCoreApp
from my_agent import agent          # <-- your already-built agent (any framework)

app = BedrockAgentCoreApp()

@app.entrypoint
def invoke(payload):
    result = agent(payload.get("prompt", ""))
    return {"result": result.message if hasattr(result, "message") else str(result)}

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

pip install bedrock-agentcore

pip install bedrock-agentcore-starter-toolkit
agentcore configure --entrypoint main.py    # generates IAM role, ECR repo, Dockerfile
agentcore launch                             # build + deploy
agentcore invoke '{"prompt": "hello"}'       # note: JSON string argument

mkdir my-custom-agent && cd my-custom-agent
uv init --python 3.11
uv add fastapi 'uvicorn[standard]' pydantic httpx strands-agents

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Dict, Any
from datetime import datetime
from strands import Agent

app = FastAPI(title="Strands Agent Server", version="1.0.0")
strands_agent = Agent()   # replace with your prepared agent

class InvocationRequest(BaseModel):
    input: Dict[str, Any]

class InvocationResponse(BaseModel):
    output: Dict[str, Any]

@app.post("/invocations", response_model=InvocationResponse)
async def invoke_agent(request: InvocationRequest):
    user_message = request.input.get("prompt", "")
    if not user_message:
        raise HTTPException(status_code=400, detail="No 'prompt' in input.")
    result = strands_agent(user_message)
    return InvocationResponse(output={
        "message": result.message,
        "timestamp": datetime.utcnow().isoformat(),
    })

@app.get("/ping")
async def ping():
    return {"status": "healthy"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8080)

FROM --platform=linux/arm64 ghcr.io/astral-sh/uv:python3.11-bookworm-slim
WORKDIR /app
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-cache
COPY agent.py ./
EXPOSE 8080
CMD ["uv", "run", "uvicorn", "agent:app", "--host", "0.0.0.0", "--port", "8080"]

docker buildx create --use
aws ecr create-repository --repository-name my-strands-agent --region us-west-2
aws ecr get-login-password --region us-west-2 \
  | docker login --username AWS --password-stdin <account-id>.dkr.ecr.us-west-2.amazonaws.com
docker buildx build --platform linux/arm64 \
  -t <account-id>.dkr.ecr.us-west-2.amazonaws.com/my-strands-agent:latest --push .

import boto3

client = boto3.client("bedrock-agentcore-control", region_name="us-west-2")
response = client.create_agent_runtime(
    agentRuntimeName="strands_agent",
    agentRuntimeArtifact={"containerConfiguration": {
        "containerUri": "<account-id>.dkr.ecr.us-west-2.amazonaws.com/my-strands-agent:latest"
    }},
    networkConfiguration={"networkMode": "PUBLIC"},
    roleArn="arn:aws:iam::<account-id>:role/AgentRuntimeRole",
)
print(response["agentRuntimeArn"], response["status"])

import boto3, json

client = boto3.client("bedrock-agentcore", region_name="us-west-2")
response = client.invoke_agent_runtime(
    agentRuntimeArn="<AGENT_RUNTIME_ARN>",
    runtimeSessionId="a" * 40,   # must be 33+ chars
    payload=json.dumps({"input": {"prompt": "Explain machine learning simply"}}),
    qualifier="DEFAULT",
)
print(json.loads(response["response"].read()))

import boto3
boto3.client("bedrock-agentcore", region_name="us-west-2").stop_runtime_session(
    agentRuntimeArn="<AGENT_RUNTIME_ARN>",
    runtimeSessionId="a" * 40,
    qualifier="DEFAULT",
)