> ## Documentation Index > Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-eugene-1763577327-bfe51c2.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # ChatBedrock This doc will help you get started with AWS Bedrock [chat models](/oss/python/langchain/models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. AWS Bedrock maintains a [Converse API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). **We recommend the Converse API for users who do not need to use custom models. It can be accessed using [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).** For detailed documentation of all Bedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html). ## Overview ### Integration details | Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Downloads | Version | | :---------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------- | :---: | :----------: | :-------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: | | [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square\&label=%20) | | [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square\&label=%20) | ### Model features The below apply to both `ChatBedrock` and `ChatBedrockConverse`. | [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | JSON mode | [Image input](/oss/python/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) | | :-----------------------------------------: | :----------------------------------------------------------: | :-------: | :------------------------------------------------------: | :---------: | :---------: | :-------------------------------------------------------: | :----------: | :-----------------------------------------------------: | :--------------------------------------------------------: | | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ## Setup To access Bedrock models you'll need to create an AWS account, set up the Bedrock API service, get an access key ID and secret key, and install the `langchain-aws` integration package. ### Credentials Head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html) to sign up to AWS and setup your credentials. Alternatively, `ChatBedrockConverse` will read from the following environment variables by default: ```python theme={null} # os.environ["AWS_ACCESS_KEY_ID"] = "..." # os.environ["AWS_SECRET_ACCESS_KEY"] = "..." # Not required unless using temporary credentials. # os.environ["AWS_SESSION_TOKEN"] = "..." ``` You'll also need to turn on model access for your account, which you can do by following [these instructions](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html). To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key: ```python theme={null} os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ") os.environ["LANGSMITH_TRACING"] = "true" ``` ### Installation The LangChain Bedrock integration lives in the `langchain-aws` package: ```python theme={null} pip install -qU langchain-aws ``` ## Instantiation Now we can instantiate our model object and generate chat completions: ```python theme={null} from langchain_aws import ChatBedrockConverse llm = ChatBedrockConverse( model_id="anthropic.claude-3-5-sonnet-20240620-v1:0", # region_name=..., # aws_access_key_id=..., # aws_secret_access_key=..., # aws_session_token=..., # temperature=..., # max_tokens=..., # other params... ) ``` ## Invocation ```python theme={null} messages = [ ( "system", "You are a helpful assistant that translates English to French. Translate the user sentence.", ), ("human", "I love programming."), ] ai_msg = llm.invoke(messages) ai_msg ``` ```output theme={null} AIMessage(content="J'adore la programmation.", additional_kwargs={}, response_metadata={'ResponseMetadata': {'RequestId': 'b07d1630-06f2-44b1-82bf-e82538dd2215', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 16 Apr 2025 19:35:34 GMT', 'content-type': 'application/json', 'content-length': '206', 'connection': 'keep-alive', 'x-amzn-requestid': 'b07d1630-06f2-44b1-82bf-e82538dd2215'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': [488]}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'}, id='run-d09ed928-146a-4336-b1fd-b63c9e623494-0', usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}}) ``` ```python theme={null} print(ai_msg.content) ``` ```output theme={null} J'adore la programmation. ``` ### Streaming Note that `ChatBedrockConverse` emits content blocks while streaming: ```python theme={null} for chunk in llm.stream(messages): print(chunk) ``` ```output theme={null} content=[] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' content=[{'type': 'text', 'text': 'J', 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' content=[{'type': 'text', 'text': "'adore la", 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' content=[{'type': 'text', 'text': ' programmation.', 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' content=[{'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' content=[] additional_kwargs={} response_metadata={'stopReason': 'end_turn'} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' content=[] additional_kwargs={} response_metadata={'metrics': {'latencyMs': 600}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}} ``` You can filter to text using the [.text](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.text) property on the output: ```python theme={null} for chunk in llm.stream(messages): print(chunk.text, end="|") ``` ```output theme={null} |J|'adore la| programmation.|||| ``` ## Extended Thinking This guide focuses on implementing Extended Thinking using AWS Bedrock with LangChain's `ChatBedrockConverse` integration. ### Supported Models Extended Thinking is available for the following Claude models on AWS Bedrock: | Model | Model ID | | --------------------- | ---------------------------------------------- | | **Claude Opus 4** | `anthropic.claude-opus-4-20250514-v1:0` | | **Claude Sonnet 4** | `anthropic.claude-sonnet-4-20250514-v1:0` | | **Claude 3.7 Sonnet** | `us.anthropic.claude-3-7-sonnet-20250219-v1:0` | ```python theme={null} from langchain_aws import ChatBedrockConverse llm = ChatBedrockConverse( model_id="us.anthropic.claude-sonnet-4-20250514-v1:0", region_name="us-west-2", max_tokens=4096, additional_model_request_fields={ "thinking": {"type": "enabled", "budget_tokens": 1024}, }, ) ai_msg = llm.invoke(messages) ai_msg.content_blocks ``` ```output theme={null} [{'type': 'reasoning', 'reasoning': 'The user wants me to translate "I love programming" from English to French.\n\n"I love" translates to "J\'aime" in French.\n"Programming" translates to "la programmation" in French.\n\nSo the full translation would be "J\'aime la programmation."', 'extras': {'signature': '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'}}, {'type': 'text', 'text': "J'aime la programmation."}] ``` ### How extended thinking works When extended thinking is turned on, Claude creates thinking content blocks where it outputs its internal reasoning. Claude incorporates insights from this reasoning before crafting a final response. The API response will include thinking content blocks, followed by text content blocks. ```python theme={null} next_messages = messages + [("ai", ai_msg.content), ("human", "I love AI")] ai_msg = llm.invoke(next_messages) ai_msg.content_blocks ``` ```output theme={null} [{'type': 'reasoning', 'reasoning': 'The user wants me to translate "I love AI" from English to French. \n\n"I love" translates to "J\'aime" in French.\n"AI" stands for "Artificial Intelligence" which in French is "Intelligence Artificielle" or "IA" (the French abbreviation).\n\nSo the translation would be "J\'aime l\'IA" or "J\'aime l\'intelligence artificielle".\n\nI think using the abbreviation "IA" would be more natural and concise, similar to how the user used "AI" in English.', 'extras': {'signature': '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'}}, {'type': 'text', 'text': "J'aime l'IA."}] ``` ## Prompt caching Bedrock supports [caching](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html) of elements of your prompts, including messages and tools. This allows you to re-use large documents, instructions, [few-shot documents](/langsmith/create-few-shot-evaluators), and other data to reduce latency and costs. **Not all models support prompt caching. See supported models [here](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html#prompt-caching-models).** To enable caching on an element of a prompt, mark its associated content block using the `cachePoint` key. See example below: ```python theme={null} import requests from langchain_aws import ChatBedrockConverse llm = ChatBedrockConverse(model="us.anthropic.claude-sonnet-4-5-20250929-v1:0") # Pull LangChain readme get_response = requests.get( "https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md" ) readme = get_response.text messages = [ { "role": "user", "content": [ { "type": "text", "text": "What's LangChain, according to its README?", }, { "type": "text", "text": f"{readme}", }, { "cachePoint": {"type": "default"}, }, ], }, ] response_1 = llm.invoke(messages) response_2 = llm.invoke(messages) usage_1 = response_1.usage_metadata["input_token_details"] usage_2 = response_2.usage_metadata["input_token_details"] print(f"First invocation:\n{usage_1}") print(f"\nSecond:\n{usage_2}") ``` ```output theme={null} First invocation: {'cache_creation': 1528, 'cache_read': 0} Second: {'cache_creation': 0, 'cache_read': 1528} ``` ## Citations Citations can be generated if they are enabled on input documents. Documents can be specified in Bedrock's [native format](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_DocumentBlock.html) or LangChain's [standard types](/oss/python/langchain/messages#multimodal): ```python Bedrock format theme={null} from langchain_aws import ChatBedrockConverse llm = ChatBedrockConverse(model="us.anthropic.claude-sonnet-4-20250514-v1:0") pdf_path = "path/to/your/file.pdf" with open(pdf_path, "rb") as f: pdf_bytes = f.read() document = { "document": { "format": "pdf", "source": {"bytes": pdf_bytes}, "name": "my-pdf", "citations": {"enabled": True}, # [!code highlight] }, } response = llm.invoke( [ { "role": "user", "content": [ {"type": "text", "text": "Describe this document."}, document, ] }, ] ) response.content_blocks ``` ```python LangChain standard format theme={null} import base64 from langchain_aws import ChatBedrockConverse llm = ChatBedrockConverse(model="us.anthropic.claude-sonnet-4-20250514-v1:0") pdf_path = "path/to/your/file.pdf" with open(pdf_path, "rb") as f: pdf_base64 = base64.b64encode(f.read()).decode("utf-8") document = { "type": "file", "mime_type": "application/pdf", "base64": pdf_base64, "name": "my-pdf", # Converse requires a filename "citations": {"enabled": True}, # [!code highlight] } response = llm.invoke( [ { "role": "user", "content": [ {"type": "text", "text": "Describe this document."}, document, ] }, ] ) response.content_blocks ``` *** ## API reference For detailed documentation of all ChatBedrock features and configurations head to the API reference: [python.langchain.com/api\_reference/aws/chat\_models/langchain\_aws.chat\_models.bedrock.ChatBedrock.html](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: [python.langchain.com/api\_reference/aws/chat\_models/langchain\_aws.chat\_models.bedrock\_converse.ChatBedrockConverse.html](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) *** [Edit the source of this page on GitHub.](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/chat/bedrock.mdx) [Connect these docs programmatically](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.