REresiros·Sep 29, 2026
In case you are looking for an open-source alternative without vendor lock-in (https://github.com/agenta-ai/agenta) [although less personal assistant and more targeted towards teams and work]
In Dots: Always-on agentsAgenta-AI
agenta
TypeScript2023–2026
Agenta-AI / agenta
agenta
Agenta is a workspace where you and your team build agents and automations.
2Distinct people
13Mentions
0Explicit recommenders
4.9KGitHub stars
What to know before exploring
What it is
Agenta is a workspace where you and your team build agents and automations.
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 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 year13 dated mentions
Observed mentions: 2023-07-19 11:55:38 UTC to 2026-09-29 18:30:11 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 7 | 1 | 6 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
13 sourcesFor other open-source alternatives, check out https://github.com/agenta-ai/agenta (I am a maintainer), we're more focused on evals and collaboration between subject matter experts and engineers (friendlier UI, powerful prompt engineering flow (playground, eval from UI, etc..)).
In Open SWE by LangChainOne missing in the list below is Agenta (https://github.com/agenta-ai/agenta).
We're oss, otel compliant with stronger focus on evals and the enabling collaboration between subject matter experts and devs.
In Launch HN: Langfuse (YC W23) – OSS Tracing and Workflows to Improve LLM AppsCheck out Agenta (https://github.com/agenta-ai/agenta or agenta.ai), it allows you to debug as in you have a playground where you can see the traces for each call you make and run evals on your workflow
In Are there any debugging tools similar to Jupyter available now?Show HN: PromptDoggy – Prompt Management for Product and Engineering Teams
Hi HN,
We’re Leon and Jingrui, and we’re excited to share our project: PromptDoggy. It’s a prompt management tool designed to streamline collaboration between product and engineering teams when building LLM apps.
Key features:
- Prompt management dashboard for product managers: Product managers can write prompts and deploy them to different environments, such as development or production.
- APIs for engineers to fetch prompts: Engineers can retrieve prompts by name through the API.
- Copilot textarea: Inspired by GitHub Copilot, we implemented a similar experience to see if it could benefit product managers.
- Static prompts: For prompts that don’t change often, such as system prompts, you can mark them as static. This allows filtering in the API and caching during the CI process.
- Hosting prompts on S3 and accessing them through CloudFr
Read the rest
ont: This setup provides faster access and enables you to host PromptDoggy on a low-cost server without worrying about concurrency issues.
- Encryption: All prompts are encrypted in the database and are client-encrypted when uploaded to S3. We believe prompts are a vital secret for any LLM app, with PMs and engineers spending countless hours tweaking and testing them. We want to ensure they are secure and inaccessible to others.
Why we built it:
We’re both product managers (I was an engineer before). We had an idea about a product manager copilot and worked on it for a while before moving on.
1. Prompt collaboration challenges: Jingrui designed the prompts, and I had to manually copy and paste them into my code before testing and deploying. This back-and-forth process was inefficient and frustrating.
2. Difficulty managing prompts: As we developed rapidly, prompts were scattered throughout the code, leading to duplication and hardcoding. This made management and debugging a nightmare. Eventually, I wrote a PromptManager class to centralize and organize prompts using a hash map.
Why I chose Ruby despite the AI trend toward Python:
Ruby and Rails make me happy. I first encountered Ruby while working as an SDE at Udacity, responsible for our payment system. Before that, I was a Golang engineer. I was fascinated by the magic of Rails and felt energized when writing apps with it. While Python might be more convenient in the short term, I believe this happiness will sustain me for much longer and help me contribute more to the community.
Inspirations:
There are some fantastic products in this field that I really respect:
- https://github.com/Agenta-AI/agenta
- https://github.com/pezzolabs/pezzo
- https://humanloop.com/
I also want to thank CopilotKit (https://github.com/CopilotKit/CopilotKit) for teaching me the philosophy of implementing a copilot textarea. I wish I could have used the project directly, but integrating just one React component into Rails while keeping importmap and StimulusJS was quite challenging. Given the limited time, I decided to move on with StimulusJS.
This is our first time building an open-source project to share with the world, and we’re a bit nervous about it.
We’d love to hear your thoughts. If you’re facing any problems related to this field, feel free to schedule a quick call or email us.
Agenta | Hybrid (Berlin) | Full Time
Agenta is an open-source LLM developer platform that provides all the necessary tools for rapidly building reliable LLM applications. Features include prompt management, evaluation, human feedback, and observability. Find out more about us at https://github.com/agenta-ai/agenta
Our tech stack includes Python, FastAPI, MongoDB, Typescript, ReactJS, and NextJS.
We are hiring:
Founding Product Engineer - Frontend: Ideally, you have previously worked in early-stage startups, can manage features from design to production, and have some background in LLMs.
You can find more details about this role here: https://agentaai.notion.site/Founding-Product-Engineer-Front...
If you're interested, please contact us directly at: careers@agenta.ai
In Ask HN: Who is hiring? (July 2024)Agenta | Hybrid (Berlin) or Remote (CET +/- 5) | Multiple Roles | Full Time
Agenta is an open-source LLM developer platform that provides all the necessary tools for rapidly building reliable LLM applications. Features include prompt management, evaluation, human feedback, and observability. Find out more about us at https://github.com/agenta-ai/agenta
Our tech stack includes Python, FastAPI, MongoDB, Typescript, ReactJS, and NextJS.
We have several positions available:
Founding Product Engineer - Frontend: Ideally, you have previously worked in early-stage startups, can manage features from design to production, and have some background in LLMs.
Founding Product Designer: This role is perfect for someone who has experience designing complex B2B products, ideally developer tools.
You can find more details about these roles here: https://a
Read the rest
gentaai.notion.site/Careers-at-agenta-fb8a76d4d9044...
If you're interested, please contact us directly at: careers@agenta.ai
I am biased, but I would use a platform and not roll your own solution. You will tend to underestimate the depth of capabilities needed for an eval framework.
Now for solutions, shameless plug here, we are building an open-source platform for experimenting and evaluating complex LLM apps (https://github.com/agenta-ai/agenta). We offer automatic evaluators as well as human annotation capabilities. Currently, we only provide testing before deployment, but we have plans to include post-production evaluations as well.
Other tools I would look at in the space are promptfoo (also open-source, more dev oriented), humanloop (one of the most feature complete tools in the space, enterprise oriented), however more enterprise oriented / costly) and vellum (YC company, more focused towards product teams)
In Ask HN: How are you testing your LLM applications?Shameless promotion, I might have the tool for that :) https://github.com/agenta-ai/agenta
We're building a platform for evaluating prompts (and more complex LLM workflows).
From what we've seen from users, the results for prompts are highly stochastic. It's hard to make generalizations. For example, a user building a sales assistant discovered that by simply changing the order of the sentences in the prompt, the accuracy improved significantly.
In Show HN: A simple ChatGPT prompt builderI'd add ours too, although we're trying to be an end-to-end one-stop platform.
https://github.com/agenta-ai/agenta
In DeepEval – Unit Testing for LLMsCan you add https://github.com/Agenta-AI/agenta (Agenta.ai)
We're an open-source platform that includes prompt engineering, end-to-end evaluation (even for larger workflows with multiple-prompts and retrieval augmented generation), and deployment.
In Show HN: Knit – A Better LLM PlaygroundGreat project! We're building an open-source platform for building robust LLM apps (https://github.com/Agenta-AI/agenta), we'd love to integrate your library into our evaluation!
In Patterns for building LLM-based systems and productsShameless plug: we're are building in the same space. We built an open-source platform for experimenting and evaluating LLM applications. Compared to LangSmith we are framework and model agnostic and are self-hosted. Here is our repo: http://github.com/agenta-ai/agenta
In LangSmith – debugging, testing, evaluating, and monitoring for LLM applications