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Mellea accesses AWS Bedrock via the Bedrock Mantle endpoint, which exposes an OpenAI-compatible API authenticated with an AWS Bearer Token. Prerequisites: pip install mellea (no extra needed — uses the OpenAI client already included), a valid AWS_BEARER_TOKEN_BEDROCK value.

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Getting a Bedrock API key

Generate a long-term API key from the AWS console: us-east-1 Bedrock API keys Export it before running Mellea: 
export AWS_BEARER_TOKEN_BEDROCK=your-bedrock-key

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Connecting with create_bedrock_mantle_backend

from mellea import MelleaSession
from mellea.backends import model_ids
from mellea.backends.bedrock import create_bedrock_mantle_backend
from mellea.stdlib.context import ChatContext

m = MelleaSession(
    backend=create_bedrock_mantle_backend(model_id=model_ids.OPENAI_GPT_OSS_120B),
    ctx=ChatContext(),
)

result = m.chat("Give me three facts about the Amazon rainforest.")
print(str(result))
# Output will vary — LLM responses depend on model and temperature.
create_bedrock_mantle_backend returns an OpenAIBackend pointed at the Bedrock Mantle endpoint. Pass it to MelleaSession as shown above. It reads AWS_BEARER_TOKEN_BEDROCK from the environment and checks that the requested model is available in the target region before returning.

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Specifying a region

The default region is us-east-1. Pass region to target a different region: 
from mellea import MelleaSession
from mellea.backends.bedrock import create_bedrock_mantle_backend

m = MelleaSession(
    backend=create_bedrock_mantle_backend(
        model_id="amazon.nova-pro-v1:0",
        region="eu-west-1",
    )
)

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Using a model string directly

If the ModelIdentifier for a Bedrock model is not in model_ids, pass the Bedrock model ID string directly: 
from mellea import MelleaSession
from mellea.backends.bedrock import create_bedrock_mantle_backend

m = MelleaSession(
    backend=create_bedrock_mantle_backend(
        model_id="anthropic.claude-3-haiku-20240307-v1:0"
    )
)
Listing available models in your region: 
from mellea.backends.bedrock import stringify_mantle_model_ids

print(stringify_mantle_model_ids())

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Bedrock via LiteLLM

An alternative path to Bedrock is the LiteLLMBackend, which uses the standard AWS credentials chain (IAM roles, ~/.aws/credentials, environment variables): 
pip install 'mellea[litellm]'
export AWS_BEARER_TOKEN_BEDROCK=your-bedrock-key

import mellea

m = mellea.start_session(
    backend_name="litellm",
    model_id="bedrock/converse/us.amazon.nova-pro-v1:0",
)
result = m.chat("Give me three facts about the Amazon rainforest.")
print(str(result))
# Output will vary — LLM responses depend on model and temperature.
The LiteLLM model ID format for Bedrock is bedrock/converse/<bedrock-model-id>. See the LiteLLM documentation for available model IDs and credential setup.
Full example: docs/examples/bedrock/bedrock_openai_example.py

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Troubleshooting

AWS_BEARER_TOKEN_BEDROCK not set: 
AssertionError: Using AWS Bedrock requires setting a AWS_BEARER_TOKEN_BEDROCK environment variable.
Export the environment variable before running your script: 
export AWS_BEARER_TOKEN_BEDROCK=your-key
Model not available in region: 
Model X is not supported in region us-east-1.
Either enable model access for the requested model in your AWS account at Bedrock Model Access, or pass a different region to create_bedrock_mantle_backend.

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Vision support

Bedrock models accessed via the Mantle endpoint use the OpenAIBackend under the hood, so vision-capable models (e.g., amazon.nova-pro-v1:0) support image input via images=[...]. Pass a PIL image or an ImageBlock to instruct() or chat(). See Use Images and Vision Models.
See also: Backends and Configuration