Here’s a one-minute walkthrough: https://youtu.be/KJa3h6ZJNLQ
We also put together a post with example traces and analyses from flow-1 and other models: https://laminar.sh/blog/flow-1-in-action
Agent observability is crowded, so instead of a bullet list of how Laminar differs from X, we’ll explain how we’re solving agent observability from first principles.
Agents now run for many steps and fail implicitly. An agent can click the wrong button or cite the wrong document while every call completes successfully. These errors are the most useful ones to catch and understand. To find them, you need to understand what the agent was trying to do and inspect its LLM calls and tool results. Simply looking at the final agent output is not enough.
Collecting traces alone doesn’t solve this. A lot of agent observability platforms are still data collectors at their core. You can end up paying to store traces that nobody has time to read or extract value from.
Using another LLM to inspect traces is THE solution, but capable models are expensive at scale and cheaper ones often miss errors or flag unrelated things.
So we built our model to solve exactly this problem. We finetuned a small OSS model and then post trained it with RL on thousands of synthetic investigations of realistic agent workflows in software, legal, customer support, etc. We got a model that is both good at agent trace analysis and very cheap to run.
flow-1 runs inside a harness we call Signals. It acts as a coding agent, treating an agent trace like a repo, with each span (an LLM call, tool call, etc.) as a file. It starts from a compressed string representation of a full agent trace and a user prompt, then greps across spans and reads the relevant ones in full.
The beauty of Signals is that it can respond to a general prompt. You can ask it to identify user frustration, check whether your browser agent completed a checkout, and so on. It’s not limited to raw error detection. It returns findings as JSON in a schema users can freely define. We call these signal events.
Once you’re analyzing every trace, you also have too many individual events to read. That’s why we automatically group similar signal events into clusters. You can see recurring problems and how often they happen, then drill into individual events and the traces behind them.
You can use traces from a cluster to build an eval dataset for that type of failure. You can then use your favourite coding agent to hillclimb on those evals and check that the fix works across these cases before shipping it to prod.
Trace → analyze → catch errors and form clusters → fix → release. Making this loop easy is why we built Laminar.
We’ve put the same thought into the rest of Laminar. Our blog (https://laminar.sh/blog) explains how looking closely at how agents actually work led us to build 20x trace compression, a custom SQL query engine and automatic task extraction.
Laminar is Apache 2.0 and self-hostable (https://github.com/lmnr-ai/lmnr). We make money from the managed cloud.
We’d love to hear how you track your agents’ performance today. How much do you learn from user feedback versus your own monitoring? And when you fix something, how do you check that it hasn’t introduced a different failure?
I'd say in general, with respect to errors/low quality responses etc you have to be extremely clear about what you are doing because once you get into the weeds, what is and is not an error is very grey in many cases.
Also, some of what you describe sounds expensive. Do you find these techniques + cheaper models better than single-shot with larger models?
Yes, it can get expensive. We do something in financial markets where the cost of a single end to end task could be $100. That doesn't mean cost doesn't matter though, it matters a lot - if we screw up and it propagates, it's money down the toilet. And if we could get the task down to $20, it would make a difference.
I wonder if you have thought of using this for the voice AI space for creating traces from many test calls to speed up testing of voice bots? It's true there are a lot of challenges _before_ one gets to the stage of running actual calls like creating good test data etc, but I wonder if you see a definite role for this framework in that space?
I assume you mean tracing voice agents in general. If so, it depends on the where do you want to trace things. Right now, Laminar, unfortunately, doesn't trace voice SDKs like OpenAI's realtime, but we're very actively looking into it.
However, if somewhere in your pipeline you have transcripts of your voice agents, then it shouldn't be that different from tracing any other agent. In this case, Laminar and Signals work perfectly.
We also trained and benchmarked flow-1 on a lot of multi-turn voice agent like conversations.
Workflows are constantly changing but our design process happens across Figma, old-fashioned paper, and code.