LangSmith excels in providing comprehensive observability for AI agents with cloud-based infrastructure, though it faces critiques for its cost and lack of open-source options. Langfuse offers deep visibility into LLM operations and boasts significant community engagement with 24,100 GitHub stars and 870,710 npm downloads/week, though it may lack in agent topology understanding.
Best for
Langfuse is the better choice when seeking robust community support and extensive metrics visibility for LLM applications, suited for smaller teams looking for flexible subscription options.
Best for
LangSmith is the better choice when managing deployment and monitoring of AI agents in production is crucial, especially for teams deeply integrated within CI/CD workflows.
Key Differences
Verdict
LangSmith is ideal for organizations focused on extensive AI agent evaluation and deployment workflows, despite the higher cost. In contrast, Langfuse offers an excellent choice for smaller teams prioritizing LLM application metrics, with budget-friendly pricing and a strong user community. Choose based on specific operational needs and growth stage.
Langfuse
Traces, evals, prompt management and metrics to debug and improve your LLM application.
Langfuse is recognized for its capability to effectively track LLM calls, providing visibility into AI operations which is crucial for production environments. However, some users have raised concerns about its lack of understanding of agent topology and potential interoperability limitations with other tracing formats. There isn't much specific sentiment mentioned about pricing, but there seems to be an implication that it's a paid solution compared to some open-source alternatives. Overall, Langfuse is appreciated as a valuable tool for observability in AI, though it faces some competition from both paid and open-source tools offering varied features.
LangSmith
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LangSmith is recognized for its capabilities in providing observability for AI agents, a necessary feature due to the risk associated with running these agents in production environments. A key complaint highlighted is that LangSmith is a cloud-only service with paid access, which may not be ideal for all users, especially those preferring open-source alternatives. The general sentiment around its pricing is somewhat negative, as users express a preference for non-commercial options. Overall, LangSmith appears to have a solid reputation for its functional strengths but faces criticism regarding its availability and cost structure.
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Pricing found: $29 / month, $8/100k, $199 / month, $8/100k, $300/mo
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Langfuse Context: All things MCP with Adam Jones (Tech Lead at Anthropic)
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Collect User Feedback of your LLM Agent in Langfuse
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Langfuse Launch Week Day 6: Dataset Schema Enforcement & Folders
Nov 8, 2025
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Anyone actually built a real feedback loop for Claude agents in production? Because "run evals and pray" isn't cutting it
So I've been running a multi-agent setup with Claude for a few months now mostly customer-facing stuff, some internal tooling. And i keep hitting this problem that I think a lot of people here are probably dealing with too but nobody really talks about. You ship a prompt change. Or you swap from So
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After 6 months of running AI agents in production I think the framework you pick barely matters. The thing that kills them is something else.
Going to get downvoted for this but here we go. I've been running about 30 agents in production for paying customers for the last 6 months and I'm convinced the framework debate is mostly a distraction. LangChain, CrewAI, AutoGen, OpenAI Agents SDK. Pick whichever one your team already knows. It do
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LangSmith provides better tools for monitoring AI agent performance due to its features like real-time observability metrics and error tracking.
LangSmith is perceived as more expensive due to its cloud-only service model, in contrast to Langfuse's affordable subscription tiers starting at $29/month.
Langfuse, with 24,100 GitHub stars and 870,710 weekly npm downloads, offers better community support than LangSmith.
Yes, both tools can potentially be integrated together as they share common integrations like OpenAI, AWS Lambda, and GitHub.
Langfuse may be easier to start with due to its tiered pricing model and large community resources.
