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Introducing flow-1, our new model trained with RL to find errors in agent traces. It matches GPT-6-sol in trace intelligence while being 23x cheaper. It also costs 25% less to run than GPT-6-luna. flow-1 finally makes it possible to monitor and understand every agent run, without sampling. 1/6

Robert

3,535 subscribers

244,417 次观看 • 9 天前 •via X (Twitter)

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Robert 的头像

Robert9 天前

flow-1 runs inside Signals agent - a harness deeply optimized for agent trace analysis. It operates like a coding agent, treating each trace like repo and each span like a file. As an input it takes a user prompt, which could as general as "find logical errors in this trace", an output schema, and a trace. It greps span content, reads tool results and returns structured findings.

Robert 的头像

Robert9 天前

On our Signals benchmark of 523 difficult agent traces across coding, legal, customer support, healthcare and more, flow-1 matches GPT-6-sol in analysis quality. Sol flags more traces. flow-1 is more precise about which ones contain real failures.

Robert 的头像

Robert9 天前

We compared costs by running the models inside the Signals agent on traces of various difficulty that have under 100k total tokens. flow-1 delivers GPT-6-sol-level analysis while processing 23x more traces per $1!

Robert 的头像

Robert9 天前

We started with supervised fine-tuning on synthetic investigations of realistic workflows across software, legal, customer support, CRM and more. We then trained with RL inside the Signals agent to improve tool use, schema adherence and investigation of complex traces. Here’s how the training data breaks down by industry.

Robert 的头像

Robert9 天前

With flow-1, production traces become a source of better evals, better agents and insight into what users need. Read the full post for results, training details and more examples of what flow-1 finds:

Hercules Giaretta Gimenes 的头像

Hercules Giaretta Gimenes9 天前

Amazing launch, Robert and team!! Love what you're building and the direction Laminar is heading!

John Enevoldsen 的头像

John Enevoldsen9 天前

Hell yeah, congrats Robert! This looks really cool.

Yechan Do 的头像

Yechan Do9 天前

How to get AGI: 1. Train a model that can generate response 2. Train a model that can detect errors at the previous model. 3. Repeat 1~2

Gaurav Atavale 的头像

Gaurav Atavale8 天前

Very interesting! won't it lose generalization ability if you do this?

Robert 的头像

Robert8 天前

It’s not a general model, it’s purpose built for trace analysis and works primarily in our Signals harness

Gaurav Atavale 的头像

Gaurav Atavale8 天前

excellent idea if the boundary is well defined!

yersultan 的头像

yersultan9 天前

congrats guys!

Rakhman Asmatullayev 的头像

Rakhman Asmatullayev9 天前

analyzing 100K agent traces of 50k tokens each costs under $250 with flow-1. crazy

Maxi 的头像

Maxi8 天前

Congrats on the launch!

kenneth 的头像

kenneth8 天前

super cool!

Roko ʕ •ᴥ•ʔっ🪄✨🐍 的头像

Roko ʕ •ᴥ•ʔっ🪄✨🐍9 天前

yummy @ClevrPwn

Shabah 的头像

Shabah8 天前

Is error detection a classification problem?

AI Psychosis 的头像

AI Psychosis8 天前

Why would I use this over OAI or Anthropic? Lmao

Ben Newman 的头像

Ben Newman8 天前

Beautiful video btw

Eris Margeta:: ubi:// 的头像

Eris Margeta:: ubi://8 天前

Incredible tracing solution

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