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julep-ai
julep
Python2024–2025
julep-ai / julep

julep

Julep — durable, composable AI agents. Flows that crash and resume, retry safely, and explain every step.

5Distinct people
6Mentions
0Explicit recommenders
6.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Julep — durable, composable AI agents. Flows that crash and resume, retry safely, and explain every step.

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 month6 dated mentions

Observed mentions: 2024-05-09 18:48:45 UTC to 2025-08-08 17:15:43 UTC.

012342024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 commentsMay 20242024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 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): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 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 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsAug 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
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC413
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC000
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC000
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC000
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 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
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
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC110

What Hacker News says

6 sources
ishita159·Aug 8, 2025Submission

Show HN: BaaS to build agents as data, not code

Agents and workflows as data instead of code. Julep is a backend-as-a-service to build agents and workflows. Main features: 1. Experimentation Velocity: With most agent frameworks, agents are code that need to be deployed. To iterate on the prompts or update the agent, you need to make changes to the code and deploy it to your server. This makes agents hard to iterate on quickly. But with agents and tasks declared as data, all that happens in the background is make an api call to update or make a new entry in the database. This increases the experimentation velocity. 2. Scalability and long-running background tasks: Scaling is a hard software engineering problem and the solution is to define large scale systems to be distributed, stateful and scalable. Agents as data is a very helpful paradigm because you have to solve for this problem o
Read the rest
nly once at the orchestrator level. Julep can handle 100s of thousands of executions paralely. 3. Updating an agent / User Personalization: Tools such as create agent, update agent etc. can update the agent recursively. Let’s assume an agent is chatting with a user and mentions that they only like to communicate in a professional tone. The agent can call a tool to 'update the agent' and modify/update the instructions to include that the user only likes to communicate in a professional tone. This was the reason to create a backend when the market was flooded with agent frameworks. In Julep, you can declare a workflow in YAML, and it can automatically scale Millions of users.
5 points · Open discussion
Yeah, you can’t do sh*t in an hour. I spend a good 6-8 hours every day using Claude Code, and I actually spend an hour every day trying new AI tools, it’s a constant process. Here’s what my today’s task looks like: 1. Test TRAE/Refact.ai/Zencoder: 70% on SWE verified 2. https://github.com/kbwo/ccmanager: use git tree to manage multiple Claude Code sessions 3. https://github.com/julep-ai/julep/blob/dev/AGENTS.md: Read and implement 4. https://github.com/snagasuri/deebo-prototype: Autonomous debugging agent (MCP) 5. https://github.com/claude-did-this/claude-hub: connects Claude Code to GitHub repositories.
In Generative AI coding tools and agents do not work for me

Show HN: Julep: A platform to manage memories, knowledge and tools for LLM apps

Hi all! We've built our fair share of LLM apps, everything from Shopify agents to real-time clones of "Samantha" from "Her". We started facing a lot of annoyances & issues repetitively with building functional apps. So we sifted through 97K posts from the OpenAI Community to confirm and find people with similar problems (& we did). It turns out that there are a handful of low-level problems that everybody who is building an AI app that works well needs to solve: statefulness to manage conversations interaction interface between multiple agents and multiple users. robust document search dynamic tool calling from different apps swapping out different LLMs/models while preserving state, And that's exactly what we ended up making Julep to be. We built this as a prototype to solve our problems, and now we want to lower the barrier for someone
Read the rest
to create functional and production-ready LLM apps. For more advanced stuff; We're also working on a task specification that defines a task execution framework for LLMs using a YAML format. It includes task triggers, available tools, debugging options, input and output schemas, and workflows. The main workflow is the entry point, with conditional transitions, error handling, and mapping operations for parallel processing. We want to make a Supabase-like backend for AI apps with a great DX. So if you're interested in this stuff or want a platform that you can make design decisions for, specifically for yourself, please submit PRs, become a maintainer, or join the Discord to check out cool examples we're building!
23 points · Open discussion
We are using ellipsis and sweep both for our open source project and they are quite helpful in their own ways. I think selling them as an automated engineer is a little over the top at the moment but once you get the hang of it they can spot common problems in PRs or do small documentation related stuff quite accurately. Take a look at this PR for example: https://github.com/julep-ai/julep/pull/311 Ellipsis caught a bunch of things that would have come up only in code review later. It also got a few things wrong but they are easy to ignore. I like it overall, helpful once you get the hang of it although far from a “junior dev”.
In Show HN: Ellipsis – Automated PR reviews and bug fixes

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