Praval Agentic AI Framework

Hacker News

Praval started as a small side project to build a light weight AI framework, and is evolving into a multi-agent AI framework with lots of features. Praval can be used to build a multi-agent system today with native agent-to-agent communication. Agents can be imbued with memory (native Chroma DB integration) and the Reef communication sub-system is akin to a message queue. Reef has native support for RabbitMQ as well. Observability features are being built with Open Telemetry compatibility. It supports OpenAI LLMs as of now, and I'm hoping to build support for Anthropic, Cohere, and other models via OpenRouter and other providers soon. I'm building a couple of apps on top of Praval - one of them is Praval Deep Research: https://github.com/aiexplorations/praval_deep_research - this is a local-first, agentic research tool for people using Arxiv to do research and ask questions to understand papers. Can help you search, index and do Q&A on top of research papers, and also get prompted on research topics. Praval is on PyPI: https://pypi.org/project/praval/ There's a site with documentation here: https://pravalagents.com/ Still an early project and I'm sure there's lots of scope for new features. Give it a spin and let me know how you like it.

Visit product View on Hacker NewsNov 25, 2025

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

94%94% 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: supports, started · Missing: reddit linkedin, podcasting, created

51%51% 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 · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

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