Bedrock AgentCore

Call Bedrock AgentCore in the OpenAI Request/Response format.

Property	Details
Description	Amazon Bedrock AgentCore provides direct access to hosted agent runtimes for executing agentic workflows with foundation models.
Provider Route on LiteLLM	bedrock/agentcore/{AGENT_RUNTIME_ARN}
Provider Doc	AWS Bedrock AgentCore ↗

info

This documentation is for AgentCore Agents (agent runtimes). If you want to use AgentCore MCP servers, add them as you would any other MCP server. See the MCP documentation for details.

Quick Start

Model Format to LiteLLM

To call a bedrock agent runtime through LiteLLM, use the following model format.

Here the model=bedrock/agentcore/ tells LiteLLM to call the bedrock InvokeAgentRuntime API.

Model Format to LiteLLM

bedrock/agentcore/{AGENT_RUNTIME_ARN}

Example:

• bedrock/agentcore/arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent-runtime

You can find the Agent Runtime ARN in your AWS Bedrock console under AgentCore.

LiteLLM Python SDK

Basic AgentCore Completion

import litellm

# Make a completion request to your AgentCore runtime
response = litellm.completion(
    model="bedrock/agentcore/arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent-runtime",
    messages=[
        {
            "role": "user", 
            "content": "Explain machine learning in simple terms"
        }
    ],
)

print(response.choices[0].message.content)
print(f"Usage: {response.usage}")

Streaming AgentCore Responses

import litellm

# Stream responses from your AgentCore runtime
response = litellm.completion(
    model="bedrock/agentcore/arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent-runtime",
    messages=[
        {
            "role": "user",
            "content": "What are the key principles of software architecture?"
        }
    ],
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

LiteLLM Proxy

1. Configure your model in config.yaml

config.yaml

LiteLLM Proxy Configuration

model_list:
  - model_name: agentcore-runtime-1
    litellm_params:
      model: bedrock/agentcore/arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent-runtime
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: us-west-2

  - model_name: agentcore-runtime-2
    litellm_params:
      model: bedrock/agentcore/arn:aws:bedrock-agentcore:us-east-1:987654321098:runtime/production-runtime
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: us-east-1

2. Start the LiteLLM Proxy

Start LiteLLM Proxy

litellm --config config.yaml

3. Make requests to your AgentCore runtimes

Curl

Basic AgentCore Request

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -d '{
    "model": "agentcore-runtime-1",
    "messages": [
      {
        "role": "user", 
        "content": "Summarize the main benefits of cloud computing"
      }
    ]
  }'

Streaming AgentCore Request

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -d '{
    "model": "agentcore-runtime-2",
    "messages": [
      {
        "role": "user",
        "content": "Explain the differences between SQL and NoSQL databases"
      }
    ],
    "stream": true
  }'

OpenAI Python SDK

Using OpenAI SDK with LiteLLM Proxy

from openai import OpenAI

# Initialize client with your LiteLLM proxy URL
client = OpenAI(
    base_url="http://localhost:4000",
    api_key="your-litellm-api-key"
)

# Make a completion request to your AgentCore runtime
response = client.chat.completions.create(
    model="agentcore-runtime-1",
    messages=[
      {
        "role": "user",
        "content": "What are best practices for API design?"
      }
    ]
)

print(response.choices[0].message.content)

Streaming with OpenAI SDK

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:4000", 
    api_key="your-litellm-api-key"
)

# Stream AgentCore responses
stream = client.chat.completions.create(
    model="agentcore-runtime-2",
    messages=[
      {
        "role": "user",
        "content": "Describe the microservices architecture pattern"
      }
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Provider-specific Parameters

AgentCore supports additional parameters that can be passed to customize the runtime invocation.

SDK

Using AgentCore-specific parameters

from litellm import completion

response = litellm.completion(
    model="bedrock/agentcore/arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent-runtime",
    messages=[
        {
            "role": "user",
            "content": "Analyze this data and provide insights",
        }
    ],
    qualifier="production",  # PROVIDER-SPECIFIC: Runtime qualifier/version
    runtimeSessionId="session-abc-123",  # PROVIDER-SPECIFIC: Custom session ID
)

Proxy

LiteLLM Proxy Configuration with Parameters

model_list:
  - model_name: agentcore-runtime-prod
    litellm_params:
      model: bedrock/agentcore/arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent-runtime
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: us-west-2
      qualifier: production

Available Parameters

Parameter	Type	Description
qualifier	string	Optional runtime qualifier/version to invoke a specific version of the agent runtime
runtimeSessionId	string	Optional custom session ID (must be 33+ characters). If not provided, LiteLLM generates one automatically

Further Reading

• AWS Bedrock AgentCore Documentation
• LiteLLM Authentication to Bedrock