Built on 10 years of UC Berkeley research, RunLLM reads logs, code and docs to resolve complex support issues. Saves 30%+ eng time, cuts MTTR by 50%, deflects up to 99% of tickets. Trusted by Databricks, Sourcegraph and Corelight—try for free on your product.
RunLLM is a solid AI support tool, it speeds up debugging and fits right into your current workflow without making things complicated. The clean interface and smart automation really help cut down on repetitive tasks. If you're in tech support and drowning in tickets, this is definitely something worth trying. I'd recommend it to any team looking to boost efficiency without reinventing their process.
The future of AI is already here. The automation of complex processes is already common in the technological world. The progress made in all of science is incredible.
This is a great product. I was shocked by my first experience. I hope you keep working hard and you will definitely succeed!
Super impressive work. Love how RunLLM moves beyond generic GPT wrappers and actually tackles the hard parts of support automation — log analysis, multi-agent orchestration, and custom workflows. The focus on real-world efficiency (30%+ eng time saved, 99% ticket deflection!) makes it clear this was built with and for technical teams. Big fan of the direction here!
RunLLM unlocks private, offline LLM access on your own machine. It delivers a smooth ChatGPT-like experience for open-source models, ideal for developers and researchers prioritizing data control, avoiding vendor lock-in, and fast local prototyping—no cloud costs required. An essential tool for independent, secure AI work.
Tinkered with runLLM lately. Nice to mess with local LLMs without the hassle. Works okay for small stuff, though bigger models slow down a bit. Decent for casual use.
Super impressed with how far RunLLM has come — especially love the focus on agentic reasoning and custom workflows. It's clear you're not just chasing trends but actually solving real pain points for complex product teams. Seeing success stories like vLLM and Arize AI is super compelling. Can’t wait to try it out on our docs and see how it handles edge cases. Congrats on the v2 launch!
Looks like it’s built to deeply understand product docs, logs, code and deliver answers that feel reliable. I’m curious to take a closer look and see if it really lives up to the promise of trust‑worthy automation.
Congrats on the launch! 🔥
RunLLM looks super impressive, love the focus on actually resolving, not just replying.
Hi! I'm Saurav - one of the engineers at RunLLM and wanted to share a bit about why I'm so excited about the product that we've built.
It's been incredible going from the foundations we laid out in RunLLM v1 to a complete agent-centric operating mode that has more flexibility in the actions it can take in order to solve customer requests. The tricky part at the core of all LLM-powered applications is making sure that your agent stays within guardrails even as you increase the scope of the actions that it can take. We've iterated a lot on this and I think we've built out something really special that gives users insight into each step the agent is taking. I'm also especially excited about the tool use integrations where agents can now analyze logs and telemetry data. The combination of the two makes RunLLM v2 feel like a big step in the direction towards making a RunLLM agent a core part of your team!
There's a lot more to come and I can't wait to keep building!
Congrats! Agentic reasoning + log analysis is a huge leap from simple retrieval.
How do you handle real-time telemetry ingestion at scale, and what’s your approach to chaining agent actions without creating feedback loops?
Okay, this is brilliant—auto-resolving support tickets would save my team so much headache (and sleep). Does it handle really gnarly logs or just the easy stuff?
An very useful tool which we have been using recently in our Discord server and soon to be GitHub package. At first we expected the answers to be inadequate, however after just asking one hard question about our GitHub package, we immediately knew that the answer was very high quality and seemed to have been written by one of our team members.
Now many of our users just ask (including myself) about questions related to our package or any bugs/issues or suggestions they encounter and it gets it right 95% of the time, and even when it gets it wrong it directly links to the sources of where they have derived the information so the user can investigate.
Hi ProductHunt! My name is Vikram — I’m co-founder & CEO of RunLLM. RunLLM’s an AI Support Engineer that works how you work.
Background
The promise of AI is that customer support will become dramatically more scalable — so that your team can focus on high-value customer relationships. But anyone who’s building a complex product knows that a good support agent requires a lot more than a vector DB and GPT-4.1
The first version of RunLLM started off building an engine that generated the highest-quality answers we could get, and that helped us earn the trust of customers like Databricks, Monte Carlo Data, and Sourcegraph. But what we’ve found over the last 6 months is that there’s so much more we can do to help support teams operate efficiently.
RunLLM v2
In response to that feedback, we’ve built RunLLM v2, and we’re excited to share support for:
🤖 Agentic reasoning: Agents are all the rage, we know, but we promise this is for real. RunLLM’s reasoning engine focuses on deeply understanding user questions and can take actions like asking for clarification, searching your knowledge base, refining its search, and even analyzing logs & telemetry.
🖼️ Multi-agent support: You can now create agents tailored to the expectations that specific teams have — across support, success, and sales. Each agent can be given its own specific data and instructions, so you have full control over how it behaves.
⚙️ Custom workflows: Every support team is different, and your agent should behave accordingly. RunLLM’s new Python SDK enables you to control how your agent handles each situation, what types of responses it gives, and when it escalates a conversation.
Early Returns
Some of our early customers have been generous enough to share their feedback with us, and the results have been impressive:
- DataHub: $1MM of cost savings in engineering time
- vLLM: RunLLM handles 99% of all questions across the community
- Arize AI: 50% reduction in support workload
Try it & tell us what breaks
Spin up an agent on your own docs—for free—ask your hardest question, and see how far it gets. If it stumbles, let us know. We learn fast.
👉 Get started with a free account, then paste the URL to your documentation site. That’s it. In just a few minutes, we’ll process your data and you’ll be able to start asking questions about your own product.
