llama-index-llms-bedrock-converse
llama-index llms bedrock converse integration
Decision gist · record as of 2026-08-14
Yes. This is a straightforward, actively maintained integration with low install friction and no known vulnerabilities. Install it if you're already using LlamaIndex and want to run Bedrock models; skip it if you're calling Bedrock directly or using a different LLM framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires AWS credentials (profile, access keys, or session token) and Bedrock model access configured in your AWS account.
- Installs with low friction—only two runtime dependencies (boto3 and llama-index-core).
- Actively maintained with a recent release.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions.
last release 2026-08-04 (10 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 247,061 downloads/mo, #8,700 on PyPI
Alternatives
Verify before relying
from llama_index.llms.bedrock_converse import BedrockConverse
llm = BedrockConverse(
model="anthropic.claude-3-haiku-20240307-v1:0",
profile_name="your_aws_profile"
)
resp = llm.complete("Paul Graham is ")
print(resp)- Whether all Bedrock foundation models are supported or only specific ones.
- Performance characteristics and latency when streaming large responses.
- Cost implications of using Bedrock vs. direct API calls.
What it is and what it does
This package bridges LlamaIndex and AWS Bedrock's Converse API, allowing you to build LLM applications that call foundation models (Claude, Command, Mistral) through Bedrock instead of directly. It wraps boto3 calls and integrates with LlamaIndex's LLM interface, so you can use Bedrock models anywhere LlamaIndex expects an LLM—in RAG pipelines, agents, or standalone completions.
The package supports the full Bedrock Converse feature set: streaming completions and chat, function calling with tool integration, prompt caching to reduce costs on repeated context, and async operations. You authenticate via AWS profiles, access keys, or session tokens, and can optionally route requests through Application Inference Profiles for provisioned capacity.
Use it for
- Build RAG pipelines using Claude or other Bedrock models within LlamaIndex without vendor lock-in to direct API calls.
- Stream LLM responses in real-time chat applications by calling Bedrock's streaming endpoints through LlamaIndex.
- Enable agents to call external functions via Bedrock's native function-calling support integrated with LlamaIndex tools.
- Reduce inference costs on repeated queries by leveraging Bedrock's prompt caching with strategic cache points.
- Use provisioned Bedrock capacity via Application Inference Profiles for predictable, scaled workloads.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is a straightforward, actively maintained integration with low install friction and no known vulnerabilities. Install it if you're already using LlamaIndex and want to run Bedrock models; skip it if you're calling Bedrock directly or using a different LLM framework.
Install
llama-index-llms-bedrock-converse on PyPI
Before you install
Installs with low friction—only two runtime dependencies (boto3 and llama-index-core). Actively maintained with a recent release.
Requires AWS credentials (profile, access keys, or session token) and Bedrock model access configured in your AWS account.
License in practice
MIT license permits commercial and private use with minimal restrictions.
Quickstart
from llama_index.llms.bedrock_converse import BedrockConverse
llm = BedrockConverse(
model="anthropic.claude-3-haiku-20240307-v1:0",
profile_name="your_aws_profile"
)
resp = llm.complete("Paul Graham is ")
print(resp)Verify before relying
- Whether all Bedrock foundation models are supported or only specific ones.
- Performance characteristics and latency when streaming large responses.
- Cost implications of using Bedrock vs. direct API calls.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesboto3llama-index-core |
| Maintenance | Actively maintained 10 days since the last release |
| First released | |
| Downloads | 247,061 / month, #8,700 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_converse-0.14.18-py3-none-any.whl
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See also llama-index-embeddings-bedrock · llama-index-llms-bedrock · llama-index-llms-openai · llama-index-llms-litellm · llama-index-llms-anthropic · llama-index-llms-azure-openai · llama-index-llms-ollama · openinference-instrumentation-bedrock · llama-index-llms-google-genai · llama-index-llms-langchain