University of Kwazulu-Natal

Braintrust Vs Langsmith Vs Langfuse Comparison Sourceforge

Bonisiwe Shabane
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braintrust vs langsmith vs langfuse comparison sourceforge

As GenAI moves into mainstream enterprise and production, evaluation and monitoring tools for Large Language Models (LLMs) are no longer optional — they’re mission-critical. Whether you’re building agentic systems, RAG pipelines, or domain-specific chat applications, evaluating and monitoring LLM performance is essential to ensure accuracy, cost-efficiency, and trustworthiness. This guide breaks down the best LLM evaluation platforms in 2025 — with practical advice on choosing what fits your team. LLMs can be unpredictable. Hallucinations, regressions across versions, and inconsistent outputs in production are all common pain points. Run side-by-side tests for prompt or model changes.

Benchmark outputs using automated or human-in-the-loop evaluation. This article compares Langfuse and Braintrust, two platforms designed to empower developers to build and improve AI applications. Braintrust is an LLM logging and experimentation platform. It provides tools for model evaluation, performance insights, real-time monitoring, and human review. They offer an LLM proxy to log application data and an in-UI playground for rapid prototyping. Read our view on using LLM proxies for LLM application development here.

Langfuse is an open-source LLM observability platform that offers comprehensive tracing, prompt management, evaluations, and human annotation queues. It empowers teams to understand and debug complex LLM applications, evaluate and iterate them in production, and maintain full control over their data. Both platforms offer functionalities to support developers working with LLMs, but they differ in their features and underlying philosophy. Langfuse and Braintrust both provide LLM observability and tracing. However, they differ significantly in scope and approach. Langfuse is an open-source observability platform focused on tracing, monitoring, and analytics.

It provides building blocks for LLM development that teams assemble into custom workflows. Braintrust is an end-to-end AI development platform that connects observability directly to systematic improvement. Production traces become evaluation cases with one click. Eval results appear on every pull request through CI/CD. PMs and engineers iterate together in a unified workspace without handoffs. The core difference: Langfuse shows you what happened in production.

Braintrust shows you what happened and helps you fix it to prevent regressions before they ship. Langfuse is an open-source LLM observability platform that provides comprehensive tracing and monitoring for LLM applications. It helps teams understand what their AI systems are doing in production through detailed traces and analytics dashboards. Shipping an AI feature without structured events means guessing why things fail. One bad response turns into a Slack storm and a guessing game about prompts, context length, or some hidden chain step. That’s avoidable.

Log the right details at every step and the fix usually becomes obvious. This piece shows how to make traces useful, not just pretty. It walks through the tradeoffs between LangSmith and Langfuse and ends with a practical checklist. The goal: faster loops, fewer surprises, lower bills. Structured events turn vague failures into precise faults. Capture inputs, outputs, token counts, latencies, and tool calls for each step; suddenly root causes pop.

Google’s cross-stack tags model is a solid bar for context depth and consistency. Uniform keys reduce noise and drift so you avoid schema sprawl and missing fields Google’s approach to observability. Observability 2.0 pushes unified storage and tighter feedback loops. Keep traces, metrics, and evaluations together; triage gets much faster, and guesswork drops Pragmatic Engineer. That unified view matters even more with multi-step chains and tools. Here’s what to capture on every LLM step:

Building a great GenAI app requires generating high-quality AI responses for a large volume of custom user inputs. This means developers need a good system for running evaluations during both development and production. Here are my learnings from looking at dozens of implementations. Clarify Evaluation Goals: Set the key metrics that align with your application's objectives. Having clear goals will guide tool selection and evaluation design. You may also want to find a “Principal Domain Expert” whose judgment is crucial for the success of your AI product.

Choose the Right Tool for Your Team: Align the tool's capabilities with your team's expertise and workflow. For developer-centric teams, code-first tools like LangSmith or Langfuse may be preferable; if you’re collaborating with non-technical subject matter experts, you may find e.g. Braintrust serves your needs better. Leverage AI for Efficiency: Use an LLM-as-a-Judge approach to scale qualitative evaluations effectively. Some tools even offer features that let you use AI to generate datasets and evaluation prompts, saving time and resources. There’s a HUGE number of LLM eval tools - I will focus on the ones with the highest adoption and most convincing offering.

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Netzero, Digitisation for the Energy Transition If you're working with LLMs, you've probably heard of Langfuse and LangSmith, two powerful tools designed to bring structure, observability, and reliability to your AI workflows. But how do they really compare? What are their strengths, and which one fits best in your stack? In this two-part series, we dive into prompt versioning and tracing, showing how each tool handles interaction tracking and offering hands-on examples with Python and LangChain and we tackle the topic of datasets and... We compare how each tool approaches dataset creation, experiment tracking, and evaluation flows.

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