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Anime Akashic Records (AAR) is a semantic discovery and retrieval API covering anime, manga, light novels, visual novels, manhwa, and manhua. Explore 146,140 work metadata records, 7,535 anime series, 102,264 episodes, 762,318+ timestamped scene citations, and 10,565 verified story arcs. Features include hybrid semantic search, scene and episode retrieval, recommendations, and citation-grounded lore Q&A — built for developers, applications, and AI agents.
Ever thought about an anime or manga whose title you completely forgot, even though one specific scene is still vivid in your head?
Introducing Anime Akashic Records (AAR)—a semantic discovery engine and dense vector database built to find exact works and scenes from pure natural language descriptions.
The whole idea started when I forgot the name of a manga and spent three straight days digging through forums and tag databases trying to find it. Traditional databases like MAL, AniList, and VNDB only work when you already know the exact title or rigid tags, and general LLMs constantly hallucinate titles and episode numbers. Once I finally found that manga, I decided to build a real fix for the problem—and when I finished building AAR, it found that exact manga on the very first search in under 300ms.
What powers AAR under the hood:
- 146,140+ indexed works for general discovery (69.8k+ Manga, 37k+ Anime, 20k+ Visual Novels, 11.7k+ Light Novels, and 7.5k+ Manhwa) with cross-platform ID mapping (MAL, AniList, TMDB, VNDB, AniDB).
- 102,264+ indexed anime episodes across 7,535+ series (broken down into 762,318+ timestamped scenes) and 10,565+ story arcs for deep scene-level search.
- High-precision, sub-300ms retrieval powered by dense vector embeddings and a two-stage neural reranking pipeline.
- A full Developer REST API, recommendation engine, and MCP server for AI agents and third-party apps.
To celebrate our Product Hunt launch, we are offering 3 exclusive first-month discount codes (redeemable in the dashboard or directly at checkout):
- AAR1ST — 50% OFF the Starter Plan ($4.99 instead of $9.99)
- PHPRO20 — 20% OFF the Pro Plan ($23.99 instead of $29.99)
- PHBIZ40 — 40% OFF the Business Plan ($35.99 instead of $59.99)
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About Anime Akashic Records (AAR) on Product Hunt
“Semantic Discovery & Developer API”
Anime Akashic Records (AAR) was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #382 on the daily leaderboard. Anime Akashic Records (AAR) is a semantic discovery and retrieval API covering anime, manga, light novels, visual novels, manhwa, and manhua. Explore 146,140 work metadata records, 7,535 anime series, 102,264 episodes, 762,318+ timestamped scene citations, and 10,565 verified story arcs. Features include hybrid semantic search, scene and episode retrieval, recommendations, and citation-grounded lore Q&A — built for developers, applications, and AI agents.
Anime Akashic Records (AAR) was featured in API (98.8k followers), Developer Tools (522.6k followers) and Entertainment (2k followers) on Product Hunt. Together, these topics include over 108.7k products, making this a competitive space to launch in.
Who hunted Anime Akashic Records (AAR)?
Anime Akashic Records (AAR) was hunted by Mohamed Essam. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Anime Akashic Records (AAR) stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Mohamed Essam
Hi Product Hunt!
Ever thought about an anime or manga whose title you completely forgot, even though one specific scene is still vivid in your head?
Introducing Anime Akashic Records (AAR)—a semantic discovery engine and dense vector database built to find exact works and scenes from pure natural language descriptions.
The whole idea started when I forgot the name of a manga and spent three straight days digging through forums and tag databases trying to find it. Traditional databases like MAL, AniList, and VNDB only work when you already know the exact title or rigid tags, and general LLMs constantly hallucinate titles and episode numbers. Once I finally found that manga, I decided to build a real fix for the problem—and when I finished building AAR, it found that exact manga on the very first search in under 300ms.
What powers AAR under the hood:
- 146,140+ indexed works for general discovery (69.8k+ Manga, 37k+ Anime, 20k+ Visual Novels, 11.7k+ Light Novels, and 7.5k+ Manhwa) with cross-platform ID mapping (MAL, AniList, TMDB, VNDB, AniDB).
- 102,264+ indexed anime episodes across 7,535+ series (broken down into 762,318+ timestamped scenes) and 10,565+ story arcs for deep scene-level search.
- High-precision, sub-300ms retrieval powered by dense vector embeddings and a two-stage neural reranking pipeline.
- A full Developer REST API, recommendation engine, and MCP server for AI agents and third-party apps.
To celebrate our Product Hunt launch, we are offering 3 exclusive first-month discount codes (redeemable in the dashboard or directly at checkout):
- AAR1ST — 50% OFF the Starter Plan ($4.99 instead of $9.99)
- PHPRO20 — 20% OFF the Pro Plan ($23.99 instead of $29.99)
- PHBIZ40 — 40% OFF the Business Plan ($35.99 instead of $59.99)
You can test the search right away on the web with the free daily sandbox (no signup required) at https://aarecords.tech, or grab a free sandbox API key at https://aarecords.tech/dashboard.
Would love to hear your feedback—try stumping the engine with the most obscure anime or manga scene you remember!