Alternatives
Products that do what kassette – Durable agent workflows backed by object storage does
Agent runs often fail after expensive model calls and executing tools that have real-world side effects. This problem is made even worse by how common it is to deploy agents to serverless environments. When your agent dies, it needs to be restarted, but doing so safely isn't easy and everyone building agents has to solve this same problem of durability. The stack you're running probably already has half of what you need for durable execution already though, ie, a queue or job runner that can invoke work at least once. kassette gives you the other half by journaling completed steps to object…
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Trace, evaluate, and improve AI agents in production
30d ago · telerik.com
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Hey HN, I built Superserve, a compute layer that lets AI agents live inside isolated Firecracker microVMs with no session time limits. The problem I kept running into: most sandbox providers kill your agent after 24 hours. If you're running something autonomous that needs to work for days — refactoring a codebase, running tests in a loop — you're constantly fighting timeouts and rebuilding state. Superserve lets you snapshot a running VM at any point, fork it into parallel branches, and resume exactly where you left off. Each agent gets its own VM, no shared kernels. There's also a…
Jul 2026 · superserve.ai
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Hey HN, I'm Anurag, founder and CEO of Render, a cloud for application developers. We've just launched Render Workflows, a way to define durable tasks by decorating plain TypeScript or Python functions and running them on Render without operating queues, worker pools, retry logic, and state management. Code example below [1]. The use cases we have in mind include agent loops, ETL/data pipelines, billing flows, and other long-running jobs. The model is simple: mark an existing function as a Task, deploy the repo as a Workflow, then trigger runs from your application code or via our API.…
Apr 2026 · render.com
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Hi HN - I’m Peter, here with Max (hmaxdml), and we’re building DBOS Go, an open-source Go library for durable workflows, backed by Postgres. https://github.com/dbos-inc/dbos-transact-golang DBOS workflows make your programs durable by automatically checkpointing their state to Postgres. If your program crashes or fails, all workflows seamlessly resume from their last completed step when your program restarts. This durability makes workflows useful for solving many different problems, including: - Operating an AI agent, or anything that connects to an unreliable or…
Sep 2025 · github.com
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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…
2025 · github.com
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I built this because I was tired of creating pull requests in 20 repositories just to change a single line of workflow job version. With Infra as AI, just mention the change. Agents work on all repos in parallel, read the docs, make a bunch of PRs and fill in the description. You can see the demo of the actual dashboard in the landing. Let me know your thoughts :) It means a lot to me!
Sep 2025 · infrastructureas.ai
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Coding agent management is all the rage right now, and many tools are being created to fill the gap. As a power user for all tools I've used since I've started my software engineering career, I've always taken the time to test multiple tools thoroughly before deciding on one, and an agentic manager was no different. I've tested many tools, but ultimately landed on Agent of Empires (AoE for short). Why ? Because it's fast, the development is active and it's feature complete, and easy to contribute to. So I did (contribute). In my day to day workflow for my job, I need the ability to start…
May 2026 · github.com
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Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
Mar 2026 · github.com
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Hey HN! I am super excited (and slightly nervous) to introduce AgentServe! AgentServe is a framework to make hosting scalable AI agents as easy as possible. With 4 lines of code AS wraps your agent (any framework) in a FastAPI and connects it to a Task Queue (celery or redis). Why Should You Care? Standardized Communication Pattern: AgentServe proposes that all agents should communicate with each other and the outside world with “Tasks” that can be submitted in a sync or async way. This simple API wil enable Framework Agnostic: No favorites. OpenAI, LangChain, LlamaIndex, CrewAI are all…
2024 · github.com
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Hi HN, we’re Jessie and Eric. We’ve been baking away at Cakework (https://www.cakework.com/), which is a way to build async backends without needing to manage cloud infrastructure. Cakework is for operations that take time or more compute, like file processing, report generation, or machine learning. Devs write backends as Python functions and deploy them with our CLI. They use our client SDKs to make requests, get status, and get processing results. Each request runs with its own CPU and memory parameters in its own microVM, with no timeouts. Devs can query for failures and…
2023 · cakework.com
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Hi folks, I built this guide after watching AI agent prototypes repeatedly fail in production. It demonstrates transforming a monolithic marketplace assistant into a resilient multi-agent system using orra, an open-source platform I also built for production-ready multi-agent applications. The patterns shown are valuable *even if you're building your own orchestration layer*. Each stage builds on the previous one, showing the evolution from fragile prototype to resilient system. What makes this guide valuable: * Architectural transformation with working code examples - split monolithic…
2025 · github.com
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Systematic testing of browser agents today is not easy: testing on real websites is flaky, rate-limited and potentially expensive (e.g. using proxies or bypassing Captcha), while static-HTML benchmarks lack state and dynamic behavior. Resurf gives your browser agent a realistic, stateful, instrumented framework — built on synthetic websites with failure-mode injection: - Realistic, dynamic, interactive environment - Deterministic & reproducible - Failure-mode injection (latency, payment errors, 5xx) - Auditable success eval (DB state, not LLM judge) - No dependency on live websites - Browser…
May 2026 · github.com
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AI agents increasingly execute real system actions: issuing refunds, modifying databases, deploying infrastructure, calling external APIs. Because agents retry steps, re-plan tasks, and run asynchronously, the same action can sometimes execute more than once. In production systems this can cause duplicate payouts, repeated mutations, or inconsistent state. Kybernis is a reliability layer that sits at the execution boundary of agent systems. When an agent calls a tool: 1. execution intent is captured 2. the action is recorded in an execution ledger 3. idempotency guarantees are attached 4.…
Mar 2026 · kybernis.io
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