Langfuse and LangSmith both excel in observability for AI applications, but cater to different needs. Langfuse offers substantial community support with 24,100 GitHub stars and 870,710 npm downloads per week, focusing on LLM application visibility. In contrast, LangSmith provides comprehensive agent debugging and deployment management but lacks open-source flexibility and has faced criticism for cost constraints.
Best for
Langfuse is the better choice when a small to medium-sized team requires extensive monitoring and debugging tools for LLM applications, particularly in environments using OpenAI, AWS, or other integrated platforms.
Best for
LangSmith is the better choice when larger teams need a robust cloud-based solution for AI agent performance evaluation and deployment management, benefiting from its integration capabilities with CI/CD pipelines and Docker infrastructure.
Key Differences
Verdict
Engineers should choose Langfuse if their focus is on comprehensive LLM observability with strong community backing and a flexible price model. Alternatively, those who prioritize cloud-based deployment management and require detailed agent evaluation in larger-scale projects may find LangSmith to be a more suitable option, despite its cost implications.
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)
Jan 6, 2026
Continuous Evaluation, Monitoring, and Operations of AI Agents with AWS Bedrock AgentCore & Langfuse
Nov 25, 2025
Collect User Feedback of your LLM Agent in Langfuse
Nov 14, 2025
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
LangSmith
Ask HN: How are you monitoring AI agents in production?
With the recent incidents (DataTalks database wipe by Claude Code, Replit agent deleting data during code freeze), it's clear that running AI agents in production without observability is risky.<p>Common failure modes I've seen: no visibility into what the agent did step-by-step, surprise
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LangSmith is better suited for detailed AI agent performance monitoring with its real-time observability metrics and customizable alerting systems.
Langfuse offers a tiered subscription model starting at $29/month, making it more flexible than LangSmith’s potentially higher cloud-based pricing.
Langfuse demonstrates stronger community support with 24,100 GitHub stars and substantial npm downloads, indicating a vibrant and engaged user base.
While not natively integrated, teams can use both tools together to leverage Langfuse's LLM monitoring and LangSmith's agent debugging features, provided they manage data separately.
Langfuse may be easier to get started with due to its community-driven support and modular pricing, catering to varying user levels and use cases.
