Opik vs LangSmith- Which Platform Wins for LLM Tracing & Evaluation?

As large language models (LLMs) increasingly become central to various applications, the need for robust tools to monitor, evaluate, and optimize these models is more important than ever. Two standout platforms that have emerged in this landscape are Opik and LangSmith. Both platforms offer powerful features for developing and managing LLM applications, yet they cater to distinct needs and workflows. 

In this blog, we’ll dive into a comprehensive comparison of Opik and LangSmith, examining their key features, strengths, and weaknesses. My recent experiments with both tools—focused on classifying emotions in Twitter data—provided valuable insights, particularly in terms of usability. I conducted two primary experiments: one centered on prompt refinement and the other on model comparison. Through these experiences, I aimed to highlight ease of use as a critical factor in choosing the right platform for your LLM projects. 

Overview of Opik

Opik is an advanced, open-source platform designed for logging, viewing, and evaluating large language model (LLM) traces throughout both development and production stages. Its primary objective is to empower developers with detailed insights to debug, evaluate, and optimize LLM applications effectively. Opik also has SDK support for direct use, you can just setup your account and use it.

Key Features of Opik: 

1. Self-Hosting Options:
   • Opik offers flexible deployment options for both local and production environments.
   • It supports local deployment via Docker Compose and scalable deployments using Kubernetes, making it adaptable for different scales of use.
2. Comprehensive Tracing:
   • Opik enables comprehensive logging and trace viewing, allowing developers to annotate traces and track LLM behavior in both local and distributed environments. This ensures greater visibility into model performance and helps identify issues quickly during both development and production phases.
3. Integrated Evaluation Tools:
   • Opik provides a set of built-in evaluation metrics, including heuristic performance measures and relevance assessments. It also supports metrics for detecting hallucinations and moderating content, and users can define custom metrics based on specific application needs.
4. Testing Frameworks:
   • Opik integrates with Pytest, providing developers with a framework to thoroughly test their LLM applications. This ensures that models are rigorously evaluated before deployment.
5. Integration: 
   Opik simplifies logging, viewing, and evaluating LLM traces with a robust set of integrations. Key features include:
   • OpenAI: Log all OpenAI LLM calls for easy tracking.
   • LangChain: Capture logs from LangChain interactions.
   • LlamaIndex: Monitor LlamaIndex LLM performance.
   • Ollama: Integrate logging for Ollama LLMs.
   • Predibase: Fine-tune and serve open-source LLMs while logging their usage.
   • Ragas: Evaluate Retrieval Augmented Generation (RAG) pipelines effectively. 

Overall, Opik’s rich set of tools and integrations make it a powerful asset for developers working with LLMs, offering end-to-end support for debugging, optimizing, and scaling LLM applications​.

* You can access a comprehensive exploration of Opik from this link. 

Overview of LangSmith

LangSmith is a comprehensive platform designed to streamline the development, debugging, testing, and monitoring of production-grade LLM (Large Language Model) applications. It bridges the gap between traditional software development processes and the unique challenges posed by LLMs, particularly around handling non-deterministic, complex workflows.

Key Features of LangSmith:

1. Advanced Tracing Capabilities:
   • LangSmith excels in tracing the performance of LLM applications by providing detailed insights into the sequence of calls and inputs/outputs at each step. It supports code annotations for automatic trace generation, with options to toggle traces on or off depending on needs. Developers can also control trace sampling rates, ensuring that they log only what’s necessary, particularly useful in high-volume applications.
   • The platform can trace multimodal interactions (e.g., text and image inputs) and distributed systems, ensuring a holistic view of an application’s performance.
2. Dataset Management:
   • LangSmith offers powerful dataset management, allowing developers to create and curate datasets for testing and evaluation.
   • This feature supports few-shot learning experiments, which is essential for optimizing LLM performance.
   • Developers can also organize experiments and results by dataset for better analysis and insights​.
3. Evaluation Metrics:
   • Built-in evaluators enable both automated and manual testing of LLM outputs, supporting various metrics like relevance, accuracy, harmfulness, hallucination, and more. LangSmith’s evaluation tools can assess how changes in prompts or model configurations impact overall performance​.
4. Playground and Prompts:
   • LangSmith includes an interactive playground that allows developers to tweak and experiment with prompts in real-time. This environment is user-friendly and removes friction from the iteration process, helping teams rapidly optimize their application’s behavior​.
5. Scalability:
   • Designed for scalability, LangSmith is built on a cloud architecture capable of handling LLM applications at large scales.
   • It supports robust data retention policies, and its monitoring tools ensure that applications run efficiently and cost-effectively, even under heavy use​.

Usability: Comparative Experiments

I conducted an experiment with Opik and LangSmith and explored their usability while classifying emotions in Twitter data. I conducted two main experiments: one focused on prompt refinement and the other on model comparison. Here’s a breakdown of my findings, emphasizing ease of use rather than performance.

For the prompt refinement experiment, I used the Emotion dataset from Twitter to classify tweets into happiness, sadness, or neutral categories. Both platforms required only an API key and client initialization for setup, which was straightforward. For the model comparison experiment, I applied the best-performing prompt from the first experiment to compare two models: gpt-4o-mini and claude-3-sonnet.

Open-Source Flexibility vs. Closed-Source Stability

• Opik: 
  • Open-Source: Opik is an open-source platform, giving developers the freedom to access, modify, and customize the platform’s source code. This flexibility fosters a collaborative environment where developers can contribute to the platform, improve it, and tailor it to their specific project needs.
  • Customization: The open-source nature allows Opik users to implement unique, project-specific features or adjustments, which is valuable for teams with highly specialized requirements. This community-driven development model also allows the platform to evolve continuously based on user contributions.
  • Ideal for Developers Seeking Flexibility: For teams or individuals who prefer to have control over their tools and the ability to customize according to their workflow, Opik is well-suited. It enables full transparency and adaptability, empowering developers to iterate on the platform as they wish.
• LangSmith: 
  • Closed-Source: LangSmith, on the other hand, is a proprietary, closed-source platform. While this restricts customization compared to Opik, it offers the advantage of being a more stable and streamlined platform. LangSmith’s closed-source nature ensures that updates are consistent and cohesive, with dedicated support to maintain the platform’s performance and reliability.
  • Stability and Support: Being closed-source allows LangSmith to provide a more stable user experience, particularly important for enterprise users. It ensures regular updates, dedicated customer support, and a fully integrated suite of tools that work seamlessly together.
  • Ideal for Enterprises Seeking Stability: Enterprises or teams that prioritize stability and dedicated support may prefer LangSmith. The closed-source model can provide peace of mind, knowing that the platform will continue to function reliably with cohesive updates and minimal disruption.

Self-hosting

• Opik: 
  • Local Installation: Opik offers a local installation option, which is quick to set up and allows developers to get started immediately. However, this local setup is not intended for production environments, as it lacks the robustness required for large-scale operations. The local installation is suitable for quick testing and experimentation. It operates through a local URL and requires basic configuration of the SDK to interact with the self-hosted instance. This setup makes it very user-friendly for small-scale or short-term tasks.
  • Kubernetes Installation: For production-ready deployment, Opik supports installation via Kubernetes. This option allows for scalability and ensures that all of Opik’s core functionalities—such as tracing and evaluation—are accessible in a more stable environment. Despite the production readiness of the Kubernetes setup, Opik lacks certain user management features in its self-hosted mode, which might be a drawback for larger teams needing detailed access control. There is no mention of built-in storage options in Opik’s self-hosted mode, implying that developers may need to set up external storage solutions for data management.
  • Managed Options: For organizations seeking reduced maintenance, Opik provides managed deployment options through Comet. This allows teams to focus more on development and analysis without worrying about infrastructure maintenance.
     
• LangSmith:
  • Docker and Kubernetes Support: LangSmith can be self-hosted via Docker or Kubernetes, making it suitable for both controlled cloud environments and large-scale production deployments. This flexibility allows LangSmith to cater to different organizational needs, from small startups to large enterprises.
  • Componentized Architecture: LangSmith’s architecture is more complex than Opik’s, as it comprises multiple components including the Frontend, Backend, Platform Backend, Playground, and Queue. This setup ensures that LangSmith is highly modular and scalable but also requires more infrastructure management. The need to expose the Frontend for UI and API access adds to the operational complexity.
  • Storage Bundling: Unlike Opik, LangSmith includes bundled storage services by default, making it easier for teams to get started without needing to configure external storage systems. However, users still have the option to configure external storage systems if their project demands it.
  • Enterprise Focus: LangSmith is designed with large, security-conscious enterprises in mind. Its multi-component infrastructure is intended to support complex, secure environments. However, this also means that LangSmith may have a higher maintenance overhead compared to simpler platforms like Opik. The increased complexity requires careful configuration and management to ensure all components operate smoothly.

Tracing

• Opik: Opik offers versatile tracing options, allowing you to log traces to the Comet LLM Evaluation platform via either the REST API or the Opik Python SDK. It supports integrations with a variety of tools, including LangChain, LlamaIndex, Ragas, Ollama, and Predibase, making it a flexible choice for developers looking to track their LLM performance across multiple frameworks.
• LangSmith:  LangSmith provides tracing support primarily with LangChain, Vercel AI, and LangGraph. While it may have fewer integrations compared to Opik, LangSmith compensates with more advanced and low-level features for tracing. This can be beneficial for users who require in-depth analysis and customization in their LLM evaluations. 

As shown, LangSmith allows you to view more detailed information, including input, total tokens used, latency, feedback (i.e., evaluation score), metadata, and more. In contrast, Opik provides limited information, showing only input, output, scores, metadata, and so on. 

Here's a detailed comparison of Opik’s tracing and LangSmith’s tracing based on their dashboard visuals: 

Similarities:

1. Tracing and Logging of Inputs/Outputs:
   • Both Opik and LangSmith provide a clear breakdown of the input and output logs for evaluation tasks. Each platform displays detailed information regarding the input prompts and the model-generated outputs, which is essential for understanding the context and accuracy of the LLM response.
   • The platforms also show additional details like feedback scores (Opik) or evaluation metrics (LangSmith), enabling users to assess performance in an organized format.
2. Structured Presentation:
   • Both dashboards offer a structured format where evaluation tasks are broken down into sections like "Input/Output," "Feedback Scores," and "Metadata." This ensures that users can navigate easily through the various components of the model evaluation.
3. Status Indicators:
   • Both platforms highlight the success/failure status of each evaluation task. This feature is useful for quickly identifying which tasks were successful and which may need further investigation.

Differences:

1. Visualization of Trace Details:
   • Opik provides a more simplified view of the trace spans, with a focus on essential data such as input and output in a straightforward format. The left panel of the Opik dashboard groups spans hierarchically but is relatively simple.
   • LangSmith, however, offers a more detailed tracing breakdown. It displays additional technical details like token usage, latency, and trace spans with granular timing (e.g., 0.2s). The dashboard offers richer metadata and breakdowns on a more technical level, making it more suitable for in-depth performance analysis.
2. Feedback and Evaluation:
   • Opik allows for quick feedback scores and custom metrics within the same pane, which are summarized easily in the CLI or notebook interface. The evaluation task is shown with simple input/output YAML formatting.
   • LangSmith focuses more on detailed feedback evaluations. It provides more elaborate evaluation results, including a link to the platform dashboard for viewing advanced statistics and data visualizations.
3. Visual Complexity:
   • LangSmith has a more sophisticated interface with more detailed trace spans and multiple evaluation layers. This visual complexity can provide more powerful insights but may require more effort to navigate.
   • Opik is more minimalist, prioritizing simplicity in its presentation. This could be more user-friendly for developers who prefer a lightweight and efficient interface.

Evaluation

• Opik: Opik simplifies the process of defining metrics, allowing users to easily initialize and pass them as parameters during evaluation. It supports both heuristic and LLM-based judge metrics, with the added flexibility to create custom metrics tailored to specific needs. This user-friendly approach makes it accessible for developers looking to assess their LLM applications efficiently. Opik also summarizes results directly in the CLI or notebook, allowing for easy access to insights on-the-fly.
• LangSmith: LangSmith requires a more hands-on approach to metric definition. In LangSmith, evaluators are functions that score application performance based on specific examples from your dataset and the outputs generated during execution. Each evaluator returns an EvaluationResult, which includes: key, score and comment. LangSmith provides a link to its dashboard for viewing results, which, while informative, required navigating away from the immediate workflow. 

Both LangSmith and Opik provide overall metric scores as well as scores for each individual dataset item. In summary, both platforms give evaluation results in a similar way; the main difference lies in the setup of the metrics. In Opik, the setup is straightforward, while in LangSmith, it requires more effort to configure.

Here's a detailed comparison of Opik’s dataset and LangSmith’s dataset based on their dashboard visuals:

Similarities:

1. Experiment Tracking:
   • Both Opik and LangSmith provide a clear overview of experiments conducted on datasets. Each experiment is tracked with a unique identifier or name, and the results are logged in a structured manner.
   • They both display the correctness of the evaluation (precision, recall, or label correctness) in a way that allows users to immediately grasp the performance of the model for each dataset item.
2. Metric Display:
   • Both systems display evaluation metrics for each experiment, such as precision, recall, and other relevant scores. This enables developers to gauge how well a specific model or experiment performed based on specific performance indicators.
3. Dataset Connection:
   • In both systems, experiments are linked to datasets, which allows for context-driven evaluation. This connection between the experiment and dataset ensures that users can quickly refer back to the dataset and see how the model performed against each data point.

Differences:

1. Visualization of Metrics:
   • Opik: In the Opik evaluation dashboard, you can see metrics such as context precision and recall displayed prominently at the top of the interface. Each dataset entry is evaluated based on these metrics, and results are presented for each item. The emphasis is on immediate metric visibility for each input/output pair within the dataset.
   • LangSmith: LangSmith provides an aggregate view of the experiment performance. Instead of breaking down individual metrics per dataset entry, LangSmith focuses on displaying experiment-level metrics such as Correct Label scores across multiple runs. This is useful for a more general performance comparison between different models or experiment configurations over time. Apart from that, you can also view metrics for each dataset entry by clicking on any specific experiment.
2. Detailed Experiment Comparison:
   • LangSmith: The LangSmith evaluation dashboard provides an overview of multiple experiments at once, listing them with splits, repetitions, and correctness scores. This allows users to quickly compare how different versions of models or setups have performed relative to one another, ideal for tracking improvements or regressions over time.
   • Opik: The Opik evaluation dashboard focuses on individual metrics for each input. It presents a more fine-grained evaluation, especially when comparing precision and recall for specific inputs. However, it lacks a broad overview of multiple experiments in one glance.

Dataset

• Opik: Opik presents a more straightforward view of dataset information, displaying inputs and expected outputs clearly. However, it lacks the advanced visualization capabilities found in LangSmith, which may limit users’ ability to quickly identify trends and insights.
• LangSmith: LangSmith excels in offering advanced visualization features that clearly showcase trends and evaluation metrics within the dataset tab. It provides rich support for datasets, allowing users to view experiments conducted on the dataset, perform pairwise experiments, and explore various formats, including key-value pairs, LLM, and chat data. This comprehensive approach makes it easier to analyze and understand the dataset’s performance and evaluation. 

As shown, LangSmith allows you to see how many experiments were run on a dataset, along with their metric scores and other details. In contrast, Opik only provides information about the dataset and its items.

Here's a detailed comparison of Opik’s dataset and LangSmith’s dataset based on their dashboard visuals:

Similarities:

1. Sentiment Dataset:
   • Both dashboards displays a dataset, with inputs and expected outputs. Each dataset item includes both the original input and the expected label.
2. Dataset Structure:
   • Both platforms show the dataset in a structured table format, where inputs and expected outputs are clearly listed. This ensures transparency and consistency in dataset management for both platforms.
3. Support for Experimentation:
   • Both platforms support running experiments on the datasets. They allow users to test different models or versions of a model and compare the performance based on these input/output pairs.

Differences:

1. Visualization:
   • Opik Dataset: The Opik dataset interface is minimalistic, showing only the input/output pairs. It lacks advanced visualization capabilities, focusing instead on providing clear data entries for developers to reference.
   • LangSmith Dataset: In contrast, the LangSmith dataset interface provides rich visualizations. For example, it shows a chart of experiments, enabling users to see the results of evaluations over time or across multiple experiments. This provides better analytical tools for users who want to track model performance trends.
2. Experiment Features:
   • Opik Dataset: The Opik interface offers simplicity, focusing on basic dataset information and expected outcomes. While it supports dataset-based evaluations, it lacks advanced tools for conducting complex experiments directly from the interface.
   • LangSmith Dataset: LangSmith offers more advanced options for conducting experiments, such as pairwise experiments and the ability to add evaluators and generate new examples. It also supports few-shot learning, giving users more flexibility to perform sophisticated analyses on their datasets. 
3. Customization and Flexibility:
   • LangSmith offers more features for interacting with datasets, such as tagging dataset versions, adding new examples, and generating examples. These features make it easier for users to experiment with their datasets and modify them on the go, offering more flexibility and control over data.
   • Opik, on the other hand, is streamlined for straightforward dataset management and lacks these interactive features, focusing on simplicity and clarity for the user.

* You can access a code and other exploration details of this comparison from this link.

The table below highlights the functionality supported in Opik vs. LangSmith: