# a16z-infra/llm-app-stack

Repository: https://github.com/a16z-infra/llm-app-stack
Canonical: https://ross.abutalabs.com/products/llm-app-stack
License Family: other
Last push: 2024-07-26T13:43:18+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 81
- inputs: {"age_days": 1134, "days_push": 768, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1319, forks 152 (observed 2026-08-28T04:04:21.238776+00:00)

## What it is
A curated reference list from a16z cataloging tools, projects, and vendors at each layer of the LLM application stack, from data pipelines and vector databases to LLM APIs and hosting platforms. It accompanies an article on emerging architectures for LLM applications and includes prompt templates for searching and formatting the tables.

## Use cases
- find vector database options for my LLM app
- compare LLM API providers open source vs proprietary
- discover tools for each layer of the LLM app stack
- research the emerging architectures for LLM applications
- find data pipeline tools for preparing documents for LLMs
- look up logging and evaluation tools for LLM apps
- choose an app hosting platform for an AI application

## When to choose
- you want a broad survey of the LLM tooling ecosystem before picking components
- you're researching or writing about LLM application architecture
- you need a starting point to discover vendors and open-source projects per stack layer

## When to avoid
- you need runnable software rather than a list of links
- you need up-to-date, maintained recommendations since the list may lag the fast-moving ecosystem
- you need unbiased benchmarks rather than a catalog of options

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools, rag, llm-inference, vector-database, etl, monitoring
- domain: large-language-models, artificial-intelligence, awesome-lists, developer-tools, databases
- platform: cross-platform
- tags: awesome-list, llm-stack, curated-list, reference, emerging-architectures, data-engineering

## Member repositories
- a16z-infra/llm-app-stack (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:21.238776+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:47:53.075019+00:00, confidence not recorded.
  - readme: https://github.com/a16z-infra/llm-app-stack (fetched 2026-08-28T04:04:21.238776+00:00, sha d2ae88783718)
- Data as of 2026-08-30T08:39:29.467469+00:00.