Our take
Activeregular commits- Activity
- 36
- Community
- 38
- Issues
- 21
- Pull requests
- 20
+1 stars in the last 30 days
- Fewer contributors than 86% of the projects we track
This is a broad, frequently updated discovery index, but its small and concentrated contributor base and lack of recent issue or pull-request resolution make it better suited to exploration than as a vetted adoption guide.
Good fit if
- You want a categorized starting point for discovering AI APIs, tools, frameworks, or learning resources.
- You value breadth across hosted services, open-source software, and local or self-hosted options.
- You can verify a candidate’s current status and suitability at its linked project or provider.
Look elsewhere if
- You need a maintained software package, installation instructions, or tests. This repository is a list, not an implementation.
- You require every entry to be independently tested, ranked, or verified for current capabilities.
- Your adoption process depends on demonstrated issue and pull-request turnaround; recent activity data shows no closures or merges in the measured period.
All health signals
| Last commit | 2026-10-26 (2 days ago) |
|---|---|
| Commits, last 90 days | 20 |
| Releases, last 12 months | 0 |
| Contributors | 2 (top contributor: 56% of commits) |
| Issues closed, last 90 days | 0 |
| Pull requests merged, last 90 days | 0 |
| Project age | 8 months |
Checked on 2026-10-29 with the GitHub API.
Project history
Daily GitHub snapshots since Oct 7, 2026. History covers stars and forks; health signals are not tracked historically.
Stars
7Forks
26Change since Nov 14, 2026: 0 stars·-1 forks
Overview
awesome-ai is a curated list of AI APIs, tools, platforms, and learning resources. It helps developers and teams navigate a broad, fast-changing AI ecosystem by grouping options into categories and providing a task-based guide for finding a starting point.
The repository is a reference directory, not an executable framework or service. Its coverage includes LLM providers and models, agent frameworks, search, media generation, vector databases, RAG, evaluation, deployment, MCP, and learning resources. Entries link to their own projects and services, so readers can compare options and continue to the source for details.
Key Features
- Organizes AI resources into 40+ categories, from LLM APIs and agent frameworks to MLOps and learning materials.
- Provides a “How to Choose” table that maps common tasks to relevant sections.
- Includes brief descriptions alongside links to listed tools and platforms.
- Covers both commercial services and open-source projects, including local inference tools and self-hosting options.
- Marks some entries as archived or inactive for more than 12 months, with a reminder to verify their status.
- Includes GitHub Actions status badges for link checks and star updates.
Use Cases
- Developers starting an AI feature can use the task guide to find candidates for model APIs, speech, search, or image generation.
- Teams planning an agent or RAG system can scan the framework, memory, vector database, and evaluation categories for relevant components.
- Engineers comparing deployment approaches can explore managed cloud platforms, model hosting services, and local inference options.
- Learners looking for material on generative AI, machine learning, or agent development can browse the guides, courses, and books sections.
What you need
Detected in the repository
- Automated checks on GitHub Actions
License in plain words
CC0-1.0public-domain-like
- Commercial use: yes
- Modify and redistribute: yes
- You must keep: nothing
- Share your changes: no
A summary, not legal advice: the LICENSE file is what applies.
Getting Started
Open the README and use the “How to Choose” table or browse the category headings. Follow links to the original projects and services for current documentation and adoption details.
Considerations
- The list is actively updated, but it has only two contributors and most contributions come from one person. That concentration may affect how reliably it stays current.
- There are no published releases, and the measured period shows no issues closed or pull requests merged. Recent commits indicate ongoing edits, but do not establish that listed entries are fully reviewed.
- The README itself advises readers to verify the status of entries marked archived or inactive. Tool descriptions and capabilities may also change after listing.
- The repository does not provide software to install or run, and its supplied requirements data reports no tests. Use linked upstream documentation to assess each item.
- The repository is licensed under CC0-1.0. Check the licenses and terms of the individual listed projects and services separately before using them.
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