Sensei, documentation agent for coding agents

Hacker News

For the last 4 months, I've been programming exclusively with AI. One of the most painful parts has been getting correct and up-to-date documentation for third-party libraries. So I made an agent to fix that. You can add it as an MCP tool to anything. It works because it's just focused on doing research for code. You can check out the prompts https://github.com/803/sensei/blob/main/sensei/prompts.py . I combined this with a couple specialized tools for this use-case: - Kura - Query cache. Works even better because the prompt instructs the agent to decompose queries into sub-queries, and usually the sub-queries are more generic. Each gets cached as a reusable building block. - Scout - Source code explorer. Let's the agent glob, grep, and tree any public repo at any tag/branch/SHA. When docs are unclear or unspecific, there's nothing better than reading what the code actually does. - Tome - llms.txt ingestion. Super simple. Does it on-demand for any domain and then stores the results in postgres for search. I'm not charging for this right now and it's completely open-source and you can run it yourselves with your own API keys. If things go nuts I'll have to change course. I made it to scratch my itch, maybe it'll scratch yours as well.

Visit product View on Hacker NewsDec 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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