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SylphAI-Inc
AdalFlow
Python2024–2025
SylphAI-Inc / AdalFlow

AdalFlow

AdalFlow: The library to build & auto-optimize LLM applications.

3Distinct people
8Mentions
0Explicit recommenders
4.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

AdalFlow: The library to build & auto-optimize LLM applications.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month8 dated mentions

Observed mentions: 2024-07-05 17:23:57 UTC to 2025-06-05 11:43:53 UTC.

01232024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 commentsJul 20242024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments2024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsDec 20242025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJun 2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC220
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC321
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC000
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC000
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC000
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC101
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC101
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC000
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC101

What Hacker News says

8 sources
Here's my recommendation: What you're attempting to do, integrating an agent in your business, is difficult. It is however relatively easy to fake. Just setup a quick RAG tool, plug it into your LLM, and you're done. From the outside, the only difference between a quick-n-dirty integration and a much more robust approach will be in numbers. One will be more accurate than the other, but you need to actually measure and count performance to establish it as a fact and not just a vibe. First advice: build up a dataset and measure performance as you develop your agent. Or just don't, and deliver what hype demands. As for advices ... and looking at those other commenters left ... If you want to do this seriously, I'd recommend that you hire someone who already did that kind of integration, at least as a consultant. Someone whose first reflex wo
Read the rest
n't be to just tell you LLMs are fixed and can't learn but will also add this isn't a limitation since RAG pipelines are better suited for this task than fine-tuning [1]. Also RAG isn't a monolithic solutions, there are many, many variations. For your use-case, I'd consider more elaborate solutions than just baseline RAG, such as GraphRAG [2]. For the box problem above, you might want to consider integrating symbolic reasoning tools such as prolog, or consider using reasoning models and developing your own reinforcement learning environments. Needless to say, all of these aspects need to be carefully balanced and optimized to work together, and you need to follow a benchmark/dataset centric-approach to developing your solution. For this task consider frameworks that were designed to optimize llm/agentic workflows as a whole [3][4]. Shit is complex really. [1] https://arxiv.org/abs/2505.24832 tells us generalization happens in LLM once their capacity for remembering things is saturated, and this might explain why fine-tuning has been less efficient than RAG so far. [2] https://microsoft.github.io/graphrag/ [3] https://github.com/microsoft/Trace [4] https://github.com/SylphAI-Inc/AdalFlow
In A practical guide to building agents [pdf]

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