aws-samples/aws-genai-llm-chatbot
A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS observed · 2026-08-28
Health v2 · maintenance only
60/100
- Activity 90
- Release rhythm 8
- Longevity 83
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1174
- days_rel: 587
- days_push: 64
- n_releases_24m: 1
Adoption not part of the score
1400 stars · 436 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A modular, production-ready AWS solution for deploying a multi-LLM, multi-RAG powered chatbot using AWS CDK. It supports Amazon Bedrock, SageMaker, and external providers (Anthropic, OpenAI, HuggingFace, Meta, AI21, Cohere, Mistral) with a React web UI, workspaces for document indexing, and multiple vector database backends.
Use cases
- deploy a chatbot with RAG on AWS
- compare responses from multiple LLMs side by side
- index company documents for semantic search and retrieval
- experiment with Amazon Bedrock models in my own account
- build domain-specific chatbots with role-based access control
- run multimodal LLMs that answer questions about images
- set up a self-hosted ChatGPT alternative on AWS
When to choose
- you want a full-stack, production-ready RAG chatbot deployed on AWS with IaC
- you need to evaluate or serve multiple LLM providers (Bedrock, SageMaker, OpenAI, HuggingFace) behind one interface
- you require enterprise features like user roles, audit logging, and multiple vector store options (OpenSearch, Aurora pgvector, Kendra)
- you want a ready-made React UI plus API for chatbot interactions
When to avoid
- you need a simple local chatbot without cloud infrastructure or AWS costs
- your stack is not AWS-based (GCP, Azure, on-prem)
- you want a lightweight library to embed in an existing app rather than a deployable solution
- you cannot grant the AWS permissions required for CDK deployment of Bedrock, SageMaker, and vector stores
Facets
application · maturity active
chatbot rag llm-inference search-engine vector-database web-framework infrastructure-as-code auth self-hosted artificial-intelligence large-language-models chatbots cloud-computing web-development self-hosted cloud browser python aws-cdk amazon-bedrock sagemaker opensearch pgvector aurora kendra langchain multi-llm react-ui enterprise semantic-search huggingface claude retrieval-augmented-generation web-server typescript docker
2 sources
- readme: https://github.com/aws-samples/aws-genai-llm-chatbot · fetched 2026-08-28 · 79d57075b66e
- homepage: https://aws-samples.github.io/aws-genai-llm-chatbot/ · fetched 2026-08-29 · 600419c0f079
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| aws-samples/aws-genai-llm-chatbot | main | 60 |
For agents
markdown · JSON · MCP: product_card(name="aws-samples/aws-genai-llm-chatbot")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem