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dbos-inc
dbos-transact-py
Python2024–2026
dbos-inc / dbos-transact-py

dbos-transact-py

Database-Backed Durable Python Workflows

Data & DatabasesAI & Machine LearningDevOps & CloudAutomation & Workflows
Explore on GitHubRead Hacker News discussions
7Distinct people
17Mentions
0Explicit recommenders
1.7KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Database-Backed Durable Python Workflows

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

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:01:16 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 month17 dated mentions

Observed mentions: 2024-09-10 15:53:40 UTC to 2026-06-15 07:12:01 UTC.

0122024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsSep 20242024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 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): 1 mentions; 0 submissions, 1 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 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): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 20252025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJun 2026

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
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC211
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC220
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC101
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC101
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC202
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 UTC101
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC101
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 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC101
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC202
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC101
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC000
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC101
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC101
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC000
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC101

What Hacker News says

2 sources
KraftyOne·Oct 29, 2024Submission

Show HN: Durable Swarm – A Framework for Building Reliable AI Agents

Hi HN! Today me and qianli_cs want to share a new open-source project we've been working on called Durable Swarm. It's a drop-in replacement for OpenAI’s Swarm that augments it with durable execution to make your agentic workflows resilient to failures, so that if they are interrupted or restarted, they automatically resume from their last completed steps. https://github.com/dbos-inc/durable-swarm We believe that as multi-agent workflows become more common, longer-running, and more interactive, it's important to make them reliable. If an agent spends hours waiting for user inputs or processing complex workflows, it needs to be resilient to transient failures, such as a server restart. However, reliable multi-agent orchestration isn't easy—it requires complex rearchitecting like routing agent communication through SQS or Kafka. Durable exe
Read the rest
cution helps you write reliable agents while preserving the ease of use of a framework like Swarm. The idea is to automatically persist the execution state of your Swarm workflow in a Postgres database. That way, if your program is interrupted, it can automatically resume your agentic workflows from the last completed step. Here’s an example application–a durable refund agent that automatically recovers from interruptions when processing refunds: https://github.com/dbos-inc/durable-swarm/tree/main/examples... We also converted all of OpenAI’s example applications to Durable Swarm: https://github.com/dbos-inc/durable-swarm/tree/main/examples Under the hood, we implemented Durable Swarm using DBOS (https://github.com/dbos-inc/dbos-transact-py), an open-source lightweight durable execution library that we developed. The entire implementation of Durable Swarm is 24 lines of code, declaring the main loop of Swarm to be a durable workflow and each chat completion or tool call to be a step in that workflow. Check it out here: https://github.com/dbos-inc/durable-swarm/blob/main/durable_...
12 points · Open discussion
KraftyOne·Oct 17, 2024Submission

Show HN: Durable Swarm – Reliable Multi-Agent Orchestration with OpenAI's Swarm

Hi HN! Today we want to share Durable Swarm, a drop-in replacement for OpenAI’s Swarm that augments it with durable execution to help you build reliable and scalable multi-agent systems. Durable Swarm makes your agentic workflows resilient to failures, so that if they are interrupted or restarted, they automatically resume from their last completed steps. https://github.com/dbos-inc/durable-swarm We believe that as multi-agent workflows become more common, longer-running, and more interactive, it's important to make them reliable. If an agent spends hours waiting for user inputs or processing complex workflows, it needs to be resilient to transient failures, such as a server restart. However, reliable multi-agent orchestration isn't easy—it requires complex rearchitecting like routing agent communication through SQS or Kafka. Durable exec
Read the rest
ution helps you write reliable agents while preserving the ease of use of a framework like Swarm. The idea is to automatically persist the execution state of your Swarm workflow in a Postgres database. That way, if your program is interrupted, it can automatically resume your agentic workflows from the last completed step. Here’s an example application–a durable refund agent that automatically recovers from interruptions when processing refunds: https://github.com/dbos-inc/durable-swarm/tree/main/examples... We also converted all of OpenAI’s example applications to Durable Swarm: https://github.com/dbos-inc/durable-swarm/tree/main/examples Under the hood, we implemented Durable Swarm using DBOS (https://github.com/dbos-inc/dbos-transact-py), an open-source lightweight durable execution library that (full disclosure) we developed. The entire implementation of Durable Swarm is 24 lines of code, declaring the main loop of swarm to be a durable workflow and each chat completion or tool call to be a step in that workflow. Check it out here: https://github.com/dbos-inc/durable-swarm/blob/main/durable_... Me and qianli_cs are here to answer any questions!
7 points · Open discussion

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