Ag

Agentflow – Run Complex LLM Workflows from Simple JSON

Hacker News

Agentflow – Run Complex LLM Workflows from Simple JSON

So, it feels like this should exist. But I couldn't find it. So I tried to build it. Agentflow lets you run complex LLM workflows from a simple JSON file. This can be as little as a list of tasks. Tasks can include variables, so you can reuse workflows for different outputs by providing different variable values. They can also include custom functions, so you can go beyond text generation to do anything you want to write a function for. Someone might say: "Why not just use ChatGPT?" Among other reasons, I'd say that you can't template a workflow with ChatGPT, trigger it with different variable values, easily add in custom functions, or force the use of custom functions for steps in the workflow. Someone might also say: "Then why not use Auto-GPT or BabyAGI?" Among other reasons, I'd say you can't if you want consistency because these tools operate autonomously, creating and executing their own tasks. Agentflow, on the other and, lets you define a step-by-step workflow to give you more control. I'd like to do more with this, including adding more custom functions, and more examples, and more ways to trigger workflows (such as in response to events). But first, I want to make sure I'm not wasting my time! For starters, if something like this already exists, please tell me.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, chatgpt, tasks · Missing: mac, agents, macos
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.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, io, including · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% 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
33%33% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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