llmio - Lightweight Python Library for Building LLM Agents

Hacker News

Hey HN! llmio is a lightweight Python library for building LLM-based agents with ease without adding unneccessary bloat to your projects. ### Key Features: - Lightweight: A minimalistic library that integrates seamlessly into your projects without adding unnecessary bulk. - Type Annotation-Based Tooling: Define tools effortlessly using Python’s type annotations. - Broad API Compatibility: Works out of the box with OpenAI, Azure, Google Gemini, AWS, and Huggingface APIs. - Easy to Use: Designed to be user-friendly, with a simple and intuitive API. Thanks to those who check it out, I'd love to hear your feedback

Visit product View on Hacker NewsAug 25, 2024

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

84%84% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, friendly · Missing: plus, platform, reviews

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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