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1-Click Deploy for Langchain LLM Agents from Google Colab to Web-App

Hacker News

1-Click Deploy for Langchain LLM Agents from Google Colab to Web-App

We’ve just released Berri AI - a Python package https://github.com/ClerkieAI/berri_ai that makes it easy for developers to quickly deploy their LLM Agent from Google Colab to production (Web App and API Endpoint). Building LLM Apps can require working in online coding environments, like Colab, due to local environment limitations (e.g. running pytorch on older Macs). This can cause long dev cycles when deploying the app to production, as ported changes can only be tested once it is deployed after lengthy (>20min+) Docker builds. Berri lets you deploy directly from your coding environment, eliminating the need to download, transfer your code to a wrapper, deploy and debug. Taking an LLM app from your notebook and sharing it with people is made easy. How it works? Just install the package, import the function, and run deploy. At the end of the deploy (~10-15mins), you will get: A web app to interact with your agent https://agent-repo-35aa2cf3-a0a1-4cf8-834f-302e5b7fe07e-4524... An endpoint you can query https://agent-repo-35aa2cf3-a0a1-4cf8-834f-302e5b7fe07e-4524... is obama?" Want a more detailed walkthrough? Check out our loom - https://www.loom.com/share/fd4375b4a77f4ea7802369cb06a16d43 We’re still early so would love your feedback and opinions. Feel free to try us out for free – and if you need help building an agent / want a specific integration, just let us know!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
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.
Hacker NewsStrong engagement from HN community · Strong signals: just released, io · Missing: https docs, excited, exist
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, google · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
20%20% 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
18%18% 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.

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