型号和说明书

默认为create-harness：

aws bedrock-agentcore-control create-harness \
  --harness-name "research-agent" \
  --execution-role-arn "arn:aws:iam::123456789012:role/MyHarnessRole" \
  --system-prompt '[{"text": "You are a research assistant."}]' \
  --tools '[{"type": "agentcore_browser", "name": "browser"}]'

每次调用的重写次数：

response = client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    # These apply only to this call; the harness defaults stay intact
    model={"bedrockModelConfig": {"modelId": "us.anthropic.claude-opus-4-5-20251101-v1:0"}},
    systemPrompt=[{"text": "You are a terse research assistant. One paragraph answers only."}],
    tools=[
        {"type": "agentcore_browser", "name": "browser"},
        {"type": "agentcore_code_interpreter", "name": "code_interpreter"},
    ],
    messages=[{"role": "user", "content": [{"text": "Summarize this paper as a bullet list."}]}],
)

要永久更改默认值，请使用update-harness。

创建或更新线束时设置默认值：

# Create a project if one does not already exist.
agentcore create

# Add a harness to the project
agentcore add harness \
  --name research-agent \
  --model-id us.anthropic.claude-sonnet-4-6-20250514-v1:0 \
  --system-prompt "You are a research assistant." \
  --tools agentcore-browser

agentcore deploy

在调用时覆盖：

# Switch the model for one call
agentcore invoke --harness research-agent \
  --model-id us.anthropic.claude-opus-4-5-20251101-v1:0 \
  "Summarize this research paper"

# Swap tools for one call
agentcore invoke --harness research-agent \
  --tools agentcore-browser,code-interpreter \
  "Plot the citation counts as a bar chart"

在调用时可重写：--model-id、、--tools、--system-prompt、--max-iterations、--max-tokens、、--harness-timeout--skills、--allowed-tools。--actor-id添加--verbose以打印原始流式传输 JSON 事件以进行调试。

要永久更改默认值，请编辑app/<name>/harness.json并运行agentcore deploy。

在项目目录agentcore中运行以打开 TUI，选择添加，然后选择 H arness 。该向导将逐步引导您完成模型和指令配置。

继续完成其余步骤（环境、内存、高级设置）并确认。然后跑agentcore deploy去申请。

使用AgentCore 身份注册 API 密钥：

aws bedrock-agentcore-control create-api-key-credential-provider \
  --name my-openai-key \
  --api-key "$OPENAI_API_KEY"

在同一会话中轮流切换提供商：

# Turn 1: Bedrock (native Converse API)
response = client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    model={"bedrockModelConfig": {"modelId": "us.anthropic.claude-sonnet-4-5-20250514-v1:0"}},
    messages=[{"role": "user", "content": [{"text": "Analyze this codebase."}]}],
)

# Turn 2: Bedrock Mantle (OpenAI Responses format, no API key needed)
response = client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    model={
        "bedrockModelConfig": {
            "modelId": "openai.gpt-4o",
            "apiFormat": "responses",
            "additionalParams": {"reasoning": {"effort": "high"}},
        }
    },
    messages=[{"role": "user", "content": [{"text": "Now suggest fixes for the top three issues."}]}],
)

# Turn 3: OpenAI model via Bedrock Mantle (no API key needed)
response = client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    model={
        "openAiModelConfig": {
            "modelId": "gpt-5.4",
            "apiFormat": "responses",
            "endpoint": {"bedrockMantle": {}},
        }
    },
    messages=[{"role": "user", "content": [{"text": "Summarize the fixes as a bullet list."}]}],
)

在 “AgentCore 身份” 中添加 API 密钥：

agentcore add credential --type api-key --name my-openai-key --api-key $OPENAI_API_KEY
agentcore deploy

使用 Bedrock Mantle 调用（响应格式，不需要 API 密钥）：

SESSION_ID="$(uuidgen)"

# Turn 1: Bedrock Mantle (Responses format)
agentcore invoke --harness my-agent \
  --model-id us.anthropic.claude-sonnet-4-5-20250514-v1:0 \
  --api-format responses \
  --session-id "$SESSION_ID" \
  "Analyze this codebase and identify performance bottlenecks."

在同一个会话中直接切换到 OpenAI：

# Turn 2: OpenAI direct on the same session
agentcore invoke --harness my-agent \
  --model-provider open_ai \
  --model-id gpt-5.4 \
  --api-key-arn arn:aws:bedrock-agentcore:us-west-2:123456789012:token-vault/default/apikeycredentialprovider/my-openai-key \
  --session-id "$SESSION_ID" \
  "Now suggest fixes for the top three issues."

使用以下--api-format标志创建或更新安全带时设置 API 格式：

# Bedrock model through the OpenAI-compatible Responses API (bedrock-mantle endpoint)
agentcore add harness --name research-agent \
  --model-provider bedrock \
  --model-id us.anthropic.claude-sonnet-4-5-20250514-v1:0 \
  --api-format responses
agentcore deploy

# OpenAI model using the Chat Completions API
agentcore add harness --name openai-agent \
  --model-provider open_ai \
  --model-id gpt-5.4 \
  --api-key-arn arn:aws:bedrock-agentcore:us-west-2:123456789012:token-vault/default/apikeycredentialprovider/my-openai-key \
  --api-format chat_completions
agentcore deploy

配置 litelLM 模型：

response = client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    model={
        "liteLlmModelConfig": {
            "modelId": "gemini/gemini-2.5-pro",
            "apiKeyArn": "arn:aws:bedrock-agentcore:us-west-2:123456789012:token-vault/default/apikeycredentialprovider/my-gemini-key",
        }
    },
    messages=[{"role": "user", "content": [{"text": "Summarize this paper."}]}],
)

使用以下命令配置 LitelLM 模型：--model-provider lite_llm

agentcore add harness --name research-agent \
  --model-provider lite_llm \
  --model-id gemini/gemini-2.5-pro \
  --api-key-arn arn:aws:bedrock-agentcore:us-west-2:123456789012:token-vault/default/apikeycredentialprovider/my-gemini-key
agentcore deploy

使用以下方式访问 OpenAI-compatible 网关--api-base，并通过以下方式传递提供商特定的参数：--additional-params

agentcore add harness --name proxy-agent \
  --model-provider lite_llm \
  --model-id openai/gpt-5.4 \
  --api-base https://my-llm-gateway.example.com/v1 \
  --api-key-arn arn:aws:bedrock-agentcore:us-west-2:123456789012:token-vault/default/apikeycredentialprovider/my-gateway-key \
  --additional-params '{"timeout": 30}'
agentcore deploy