> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AWS Bedrock Integration

> Use AWS Bedrock models with Cognee's native Bedrock provider.

AWS Bedrock is a **first-class LLM provider** in Cognee. You configure it directly with `LLM_PROVIDER="bedrock"` and a few AWS environment variables — Cognee talks to Bedrock natively through its built-in Bedrock adapter.

## Prerequisites

* AWS account with Bedrock model access enabled for the models you want to use
* Python 3.10+
* Cognee installed with the AWS extra (see below)

## Setup (Native Provider)

### 1. Install Cognee with the AWS extra

```bash theme={null}
pip install cognee[aws]
```

### 2. Configure your `.env`

Set `LLM_PROVIDER="bedrock"` and provide your AWS details:

```dotenv theme={null}
LLM_PROVIDER="bedrock"
LLM_MODEL="eu.amazon.nova-lite-v1:0"
LLM_API_KEY="<your_bedrock_api_key>"
LLM_MAX_COMPLETION_TOKENS="16384"
AWS_REGION="<your_aws_region>"
AWS_ACCESS_KEY_ID="<your_aws_access_key_id>"
AWS_SECRET_ACCESS_KEY="<your_aws_secret_access_key>"
AWS_SESSION_TOKEN="<your_aws_session_token>"

# Optional parameters
# AWS_BEDROCK_RUNTIME_ENDPOINT="bedrock-runtime.eu-west-1.amazonaws.com"
# AWS_PROFILE_NAME="<your_aws_profile_name>"
```

### 3. Choose an authentication method

The Bedrock adapter supports four ways to authenticate (it uses the first one it finds, in this order):

1. **API key** — generate a Bedrock API key on AWS and set it in `LLM_API_KEY`.
2. **AWS credentials** — set `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` (you can leave `LLM_API_KEY` unset). If you use temporary credentials (an access key ID starting with `ASIA...`), you must also set `AWS_SESSION_TOKEN`.
3. **AWS profile** — set `AWS_PROFILE_NAME` to use a profile from your AWS credentials file (the standard boto3 credential chain).
4. **Ambient IAM role** — set none of the above. Cognee then sends no credentials at all and boto3 resolves them from the default AWS credential chain, so an EC2 instance profile, ECS task role, or EKS IRSA service-account role with Bedrock permissions is enough.

`AWS_REGION` is applied with any of these methods, and `AWS_BEDROCK_RUNTIME_ENDPOINT` optionally overrides the Bedrock runtime endpoint.

### Running without explicit credentials

Explicit credentials are optional: `bedrock` is one of the providers for which Cognee does not require `LLM_API_KEY`, so leaving it unset does not raise a missing-key error. On AWS-hosted infrastructure, `LLM_PROVIDER`, `LLM_MODEL`, and `AWS_REGION` are enough to authenticate to Bedrock:

```dotenv theme={null}
LLM_PROVIDER="bedrock"
LLM_MODEL="us.amazon.nova-lite-v1:0"
AWS_REGION="us-east-1"
```

Because the order above is a first-match, make sure no stale `LLM_API_KEY`, `AWS_ACCESS_KEY_ID`/`AWS_SECRET_ACCESS_KEY`, or `AWS_PROFILE_NAME` values are left in your `.env` or shell — any of them will be used instead of the instance role.

Embeddings are configured separately: the default embedding provider is OpenAI and falls back to `LLM_API_KEY` for its key, so with no keys set you still need to configure an [embedding provider](/setup-configuration/embedding-providers) before `remember()` works.

### Model naming

Use the Bedrock model ID directly in `LLM_MODEL` — no `bedrock/` prefix is needed because the provider is already set to `bedrock`. Model IDs are region-scoped, so the prefix depends on your region (`eu.` for Europe, `us.` for the US, etc.):

* Amazon Nova: `eu.amazon.nova-lite-v1:0`, `us.amazon.nova-pro-v1:0`
* Anthropic Claude: `anthropic.claude-3-5-sonnet-20240620-v1:0`
* OpenAI GPT-OSS: `openai.gpt-oss-120b-1:0`

<Info>
  The exact model ID (and whether it needs a region prefix) varies by AWS region. Check the [AWS Bedrock model catalog](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) for the ID that applies to your region.
</Info>

## Usage Example

```python theme={null}
import cognee
import asyncio


async def main():
    # Remember text with Cognee
    await cognee.remember(
        "Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval."
    )

    # Query the knowledge graph
    results = await cognee.recall("Tell me about NLP")

    # Display the results
    for result in results:
        print(result)


if __name__ == '__main__':
    asyncio.run(main())
```

## Optional: Using a LiteLLM Proxy

A LiteLLM proxy is **not required** for Bedrock. Use this approach only if you already run a LiteLLM proxy to centralize model routing, credentials, or logging across multiple services.

<Warning>
  Use **LiteLLM Proxy** (not the SDK) for this approach. The proxy runs as a server that Cognee connects to over HTTP.
</Warning>

Install and configure the proxy:

```bash theme={null}
pip install litellm[proxy]
```

Create a `config.yaml`:

```yaml theme={null}
model_list:
  - model_name: bedrock-claude-3-5-sonnet
    litellm_params:
      model: bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_access_key_id: your_aws_id
      aws_secret_access_key: your_aws_key
      aws_region_name: your_aws_region_name
      drop_params: true
```

Start the proxy (it runs on `http://localhost:4000` by default):

```bash theme={null}
litellm --config config.yaml
```

Then point Cognee at the proxy by treating it as an OpenAI-compatible endpoint:

```dotenv theme={null}
LLM_PROVIDER="openai"
LLM_MODEL="litellm_proxy/bedrock-claude-3-5-sonnet"
LLM_ENDPOINT="http://localhost:4000"
LLM_API_KEY="doesn't matter"
```

<Note>
  For detailed proxy setup, see the [official LiteLLM Bedrock tutorial](https://docs.litellm.ai/docs/providers/bedrock).
</Note>

## Troubleshooting

1. **Authentication Errors**: Verify your AWS credentials, region, and that Bedrock model access is enabled in the AWS console. For temporary (`ASIA...`) credentials, ensure `AWS_SESSION_TOKEN` is set **alongside** `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` — a session token is not used on its own.
2. **Model Not Found**: Confirm the model ID matches your region exactly (including any `eu.`/`us.` prefix).
3. **Rate Limit / Throttling Errors (`BedrockException`)**: When Bedrock returns throttling errors (e.g. `Too many requests`, `ThrottlingException`), Cognee automatically retries them with exponential backoff (up to 5 retries). If errors persist, request a quota increase in the AWS console, or enable client-side rate limiting by setting `LLM_RATE_LIMIT_ENABLED="true"` and tuning `LLM_RATE_LIMIT_REQUESTS` to stay within your account's requests-per-minute limit. See [Rate Limiting](/setup-configuration/llm-providers) for details.
4. **Connection Issues (proxy only)**: Check that the LiteLLM proxy is running on the expected port.

To enable verbose LLM logging, set `LITELLM_LOG="DEBUG"` in your `.env`.

## Resources

<CardGroup cols={2}>
  <Card title="LLM Providers" href="/setup-configuration/llm-providers" icon="brain">
    **Cognee LLM Configuration**

    Full reference for configuring AWS Bedrock and other LLM providers.
  </Card>

  <Card title="AWS Bedrock Models" href="https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html" icon="cloud">
    **Available Models**

    Browse all Bedrock models and find the model ID for your region.
  </Card>
</CardGroup>