LLMling-Agent (Yet another Agent framework)

Hacker News

Hello! I wanted to present my own new Agent framework! It is a "big one". Theres too much to explain in a few words. - The by a large distance most extensive possibilty to define Agents, Teams and their message connections via YAML. - Ability to consruct Agent workflows by two different approaches, either by creating a hierarchy of Agents, parallel teams or sequential teams, or by "connecting" Agents and creating observable and even routing-capable Connection objects - Support for both Pydantic-AI and LiteLLM as LLM backends - MCP server support for agents - Strong focus on pydantic-level Typing (Structured responses, generically typed communication objects) - Nested Agent teams which can be used like "regular" agents since both teams and Agents share a same base interface. - A CLI which allows to create Agent workflows using a slash-command-system and MUCH MUCH more. its really to much to cover. It doesnt have that many integrations yet, but apart from it, this kicks AutoGens and CrewAIs a*. ;) Check out https://phil65.github.io/llmling-agent https://github.com/phil65/llmling-agent Cheers and thank you!

Visit product View on Hacker NewsJan 28, 2025

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

91%91% 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.

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

49%49% 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

42%42% 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 HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

34%34% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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