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Laminar launches flow-1, a low-cost model that detects silent failures in AI agent traces

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Laminar, a Y Combinator S24 company building open-source observability tools for AI agents, has released flow-1, a model trained with reinforcement learning to find errors in agent traces. On the company's benchmark, flow-1 reportedly matches the detection quality of GPT-6-sol while costing about 23 times less, making it practical to analyze every production trace rather than sampling. The tool, called Signals, treats each trace as a repository and lets users define custom prompts and output schemas to flag issues such as silent failures or user frustration. Similar findings are grouped into clusters so teams can track recurring problems and build evaluation datasets for fixes. Laminar is released under the Apache 2.0 license, can be self-hosted, and generates revenue from its managed cloud service.

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