llama-index-llms-bedrock
llama-index llms bedrock integration
Decision gist · record as of 2026-08-14
Yes, if you are already using LlamaIndex and need to route requests through AWS Bedrock. The package has low install friction, active maintenance, MIT licensing, no known vulnerabilities, and clear integration points. Install it only if you have AWS Bedrock access and want to use it as your LlamaIndex LLM backend; otherwise it adds no value.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires AWS credentials (via profile_name, access keys, or environment) and access to Bedrock models in your AWS account; Python 3.10 or later.
- Low friction: pure Python wheel with three runtime dependencies (boto3, llama-index-core, llama-index-llms-anthropic).
- Package is actively maintained with recent releases.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute this package freely provided you retain the license notice.
last release 2026-03-12 (155 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 105,529 downloads/mo, #12,693 on PyPI
Alternatives
Verify before relying
pip install llama-index-llms-bedrock
from llama_index.llms.bedrock import Bedrock
llm = Bedrock(model="amazon.titan-text-express-v1", profile_name="your-aws-profile")
response = llm.complete("Paul Graham is ")
print(response)- Which Bedrock model endpoints are supported beyond amazon.titan-text-express-v1
- Whether streaming operations have latency or throughput guarantees
- Support for model-specific parameters or inference configuration options
What it is and what it does
This package bridges AWS Bedrock LLMs into the LlamaIndex ecosystem, letting you use Bedrock-hosted models (like Amazon Titan) as the language model backend for LlamaIndex applications. It wraps boto3 calls and exposes Bedrock models through LlamaIndex's standard LLM interface, supporting text completion, chat with message history, and streaming variants of both.
You instantiate a Bedrock object with a model name and AWS credentials (profile, access keys, or session token), then call complete(), chat(), stream_complete(), or stream_chat() just as you would with any other LlamaIndex LLM. The package handles authentication and request marshaling to Bedrock, making it straightforward to swap Bedrock into existing LlamaIndex pipelines or build new ones around Bedrock models.
Use it for
- Build LlamaIndex RAG pipelines using Bedrock models as the generation backend instead of OpenAI or other providers.
- Stream long-form completions from Bedrock models in real-time within LlamaIndex chat or agentic workflows.
- Use Bedrock's managed LLM infrastructure with LlamaIndex's retrieval and indexing tools for cost-optimized deployments.
- Integrate multi-turn conversations with Bedrock models while leveraging LlamaIndex's message history and context management.
- Deploy LlamaIndex applications on AWS without external LLM API dependencies by using Bedrock as the sole LLM provider.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using LlamaIndex and need to route requests through AWS Bedrock.
The package has low install friction, active maintenance, MIT licensing, no known vulnerabilities, and clear integration points. Install it only if you have AWS Bedrock access and want to use it as your LlamaIndex LLM backend; otherwise it adds no value.
Install
llama-index-llms-bedrock on PyPI
Before you install
Low friction: pure Python wheel with three runtime dependencies (boto3, llama-index-core, llama-index-llms-anthropic). Package is actively maintained with recent releases.
Requires AWS credentials (via profile_name, access keys, or environment) and access to Bedrock models in your AWS account; Python 3.10 or later.
License in practice
MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute this package freely provided you retain the license notice.
Quickstart
pip install llama-index-llms-bedrock
from llama_index.llms.bedrock import Bedrock
llm = Bedrock(model="amazon.titan-text-express-v1", profile_name="your-aws-profile")
response = llm.complete("Paul Graham is ")
print(response)Verify before relying
- Which Bedrock model endpoints are supported beyond amazon.titan-text-express-v1
- Whether streaming operations have latency or throughput guarantees
- Support for model-specific parameters or inference configuration options
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesboto3llama-index-corellama-index-llms-anthropic |
| Maintenance | Actively maintained 155 days since the last release |
| First released | |
| Downloads | 105,529 / month, #12,693 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_llms_bedrock-0.5.0-py3-none-any.whl
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See also llama-index-llms-bedrock-converse · llama-index-embeddings-bedrock · llama-index-llms-openai · llama-index-llms-ollama · llama-index-llms-anthropic · llama-index-llms-azure-openai · llama-index-llms-litellm · llama-index-llms-langchain · llama-index-llms-google-genai · pyjokes