LangSmith, by LangChain is a framework-agnostic Agent Engineering Platform designed for the observation, evaluation, and deployment of Large Language Model (LLM) agents. The solution provides a unified environment to transform Trace Data into actionable insights for iterative agent improvement and enterprise-scale production management.
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meshIQ
Score8.9 out of 10
Enterprise companies (1,001+ employees)
meshIQ provides observability and management capabilities for middleware platforms, from modern Kafka-based streaming to legacy messaging technologies such as IBM MQ, across cloud and on-premises environments.
I feel like LangSmith Agent Engineering Platform is great for complex AI agents built with LangChain that take many steps. It is very useful in projects which requires multiple subagents. We built an AI Answer engine for our product where we use many subagents in LangSmith Agent Engineering Platform for query generation, optimization, summarization, etc.
If you're involved in any business that is related to banking, insurance and lending industry, take the short way and save yourself from thousands of wasted dollars on fraud detection and application management issues, Nastel is my savior and it's the most powerful killer solution to beat the fraud and have a winning application performance management, It's like hiring a mathematics magician with super powers to help businesses get over the delayed amount of time spent on a problem, and the average time to process a claim, reduce errors and most importantly to decrease the number of tickets at the help center for a better and higher customer satisfaction score.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The UI needs a lot of improvements to meet today's standards while keeping the functional part highly performing as it used to be
Being unable to run reports through the workgroup security manager is a serious con, I think they need to consider giving it more attention
the auto-monitoring feature performance goes low when it's enabled during a heavy workloads, it leads to a lower/heavier performance, but during the latest update I feel it got lighter somehow.
They also need to give more attention to the encryption and enhance it with the latest encryption technologies
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
In my opinion the technologies and concepts which I use with LangSmith Agent Engineering Platform like API tracing, prompt management, Prompt Hub and Agent Builder were like pioneer in the market with LangSmith Agent Engineering Platform. There are some scopes of improvements in terms of compatibilities and open-source tooling gaps but overall, it has to be on the list if you are doing AI-engineering at Production level.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
