bedrock-chat and foundational-llm-chat

These are ecosystem siblings—one is a lightweight AWS-native reference implementation using Bedrock's native chat API, while the other is a more feature-complete production template that adds enterprise patterns (CDK infrastructure-as-code, Cognito authentication) and integrates Claude through the same Bedrock service.

bedrock-chat
67
Established
foundational-llm-chat
50
Established
Maintenance 16/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 10/25
Adoption 7/25
Maturity 16/25
Community 17/25
Stars: 1,274
Forks: 516
Downloads:
Commits (30d): 1
Language: TypeScript
License: MIT-0
Stars: 36
Forks: 11
Downloads:
Commits (30d): 0
Language: Python
License: MIT-0
No Package No Dependents
No Package No Dependents

About bedrock-chat

aws-samples/bedrock-chat

AWS-native chatbot using Bedrock

Supports RAG-powered custom bots with knowledge base integration, bot marketplace sharing, and agentic task automation alongside standard chat. Built on AWS CDK for infrastructure-as-code deployment across 18+ regions, leveraging OpenSearch Serverless for vector embeddings and Bedrock's foundation models with multi-tenant knowledge base optimization. Includes administrative features for API governance, usage analytics, and fine-grained permission controls via Cognito user groups.

About foundational-llm-chat

aws-samples/foundational-llm-chat

Chainlit application built using AWS CDK, secured with Amazon Cognito, that allows you to interact with Anthropic's Claude language models from Amazon Bedrock.

Supports multimodal interactions including image and document upload with vision-enabled models, leverages Bedrock's Converse API for enhanced inference across 10+ model families, and uses AWS ECS/Fargate with CloudFront CDN for globally distributed, scalable deployment. Includes advanced features like cross-region inference profiles, centralized prompt versioning via Bedrock Prompt Manager, MCP tool integration, and dual reasoning modes (Anthropic-style thinking and OpenAI-style effort levels).

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