opik

Opik

Overview

Opik is an open-source LLM observability, evaluation, and optimization platform built by Comet. With nearly 20,000 GitHub stars and over 1,500 forks, it has become one of the most adopted tools in the LLMOps space since its 2023 launch. The project is Apache 2.0 licensed and actively maintained — the last commit landed June 12, 2026, less than a day ago at the time of writing.

Comet is not a newcomer. Founded in 2017, the company spent years building experiment tracking tools for traditional machine learning. Their Comet ML platform is used by teams at Google, Uber, and various Fortune 500 companies. When the LLM explosion hit in 2023, they pivoted that observability expertise into Opik — a tool specifically designed for the unique challenges of generative AI applications. That background shows in the product’s maturity.

The core problem Opik solves is debugging AI applications in production. When your RAG chatbot gives a wrong answer or your agent takes a bizarre multi-step path, you need to see exactly what happened: which prompts were sent, what context was retrieved, how the model responded, and where things went sideways. Without this kind of tracing, you are essentially flying blind. Opik gives you full visibility into every LLM call, every agent decision, and every retrieval step — with automated evaluation to catch problems before your users do.

Why it matters

The LLM tooling landscape in 2026 is fragmented. You have LangSmith for LangChain users, Braintrust for a subset of teams, Weights & Biases trying to expand from ML tracking, and a dozen smaller players. Opik stands out because it is framework-agnostic with over 50 integrations — OpenAI, Anthropic, LangChain, LlamaIndex, CrewAI, Pydantic AI, Google ADK, DSPy, and many more. You do not need to commit to a specific AI framework to use it.

What makes Opik particularly relevant right now is the shift from “can we build this?” to “can we trust this?” in AI application development. Teams shipping AI features in production need evaluation pipelines, not just prompt playgrounds. The 2025 MLOps survey by Gradient Flow found that 67% of teams deploying LLMs cited “lack of observability” as their top production challenge. Opik addresses this head-on with automated LLM-as-a-judge evaluations, hallucination detection, and production monitoring dashboards that handle 40 million traces per day.

For fullstack developers specifically, Opik fills a gap that traditional APM tools like Datadog or New Relic do not cover. Those tools see HTTP requests and database queries, but they are blind to the semantic content of LLM interactions. Opik traces every prompt, completion, tool call, and agent step with full context, making it possible to debug AI behavior the same way you debug backend code.

Key Features

Comprehensive Tracing. Opik captures every LLM call, tool invocation, and agent step as a structured trace with full context. You see the exact prompts sent, tokens used, latency per call, and the complete conversation tree. Traces work identically during local development and in production, so what you debug locally is what you monitor in prod. The tracing is non-intrusive — decorators and context managers wrap your existing code without restructuring it.

LLM-as-a-Judge Evaluation. Instead of manually reviewing hundreds of outputs, Opik lets you define automated evaluation metrics using another LLM as the judge. Built-in metrics cover hallucination detection, answer relevance, context precision (for RAG), moderation, and custom criteria you define in natural language. This is not a toy feature — teams run these evaluations against thousands of test cases in CI pipelines using the PyTest integration.

Production Monitoring Dashboards. The Opik dashboard tracks trace volumes, token usage, feedback scores, latency percentiles, and error rates over time. Online Evaluation Rules automatically score incoming production traces using your LLM-as-a-judge metrics, flagging quality degradation in real time. The system is designed for scale — Comet reports handling 40M+ traces per day on their cloud infrastructure.

50+ Framework Integrations. Opik integrates natively with OpenAI, Anthropic, Google ADK, LangChain (Python and JS/TS), LlamaIndex, CrewAI, Pydantic AI, DSPy, Haystack, Autogen, AG2, Smolagents, Spring AI, and dozens more. Recent additions include Flowise AI, n8n, Cursor, and OpenWebUI. There is also an OpenTelemetry integration for any framework that supports OTLP. This breadth means you are rarely locked out regardless of your stack.

Prompt Playground and Optimization. The built-in Prompt Playground lets you experiment with prompts and models side by side, comparing outputs across different configurations. The Opik Agent Optimizer goes further — it provides an SDK with dedicated optimizers that automatically improve prompts and agent configurations based on evaluation results. This moves beyond observability into active optimization.

Agent-Specific Tracing. For agentic workflows (the kind you build with CrewAI, LangGraph, or custom agent loops), Opik traces the full decision tree — which tools the agent called, what reasoning it used, where it looped, and where it got stuck. This is critical for debugging multi-step agent behavior that would be impossible to reconstruct from logs alone.

Self-Hosting and Cloud Options. You can run Opik entirely on your infrastructure using Docker Compose (one command: ./opik.sh) or deploy to Kubernetes with Helm. The self-hosted version includes the full platform — tracing, evaluation, dashboards, and prompt playground. Comet also offers a free cloud tier for teams that do not want to manage infrastructure.

Use Cases

• RAG application debugging — Trace the full retrieval-to-generation pipeline, see which documents were retrieved, evaluate whether the answer is grounded in the context, and identify retrieval failures before they reach users.

• Agent workflow monitoring — Track multi-step agent executions across tool calls, memory lookups, and reasoning chains. Debug why an agent took 12 steps to accomplish what should have taken 3.

• CI/CD evaluation pipelines — Run automated LLM evaluations in your test suite using the PyTest integration. Gate deployments on quality metrics instead of gut feeling.

• Production quality monitoring — Set up online evaluation rules that automatically score every production request against hallucination, moderation, and relevance metrics. Get alerts when quality drops.

• Prompt engineering iteration — Use the Prompt Playground to compare prompt variants side by side, then track which versions perform best across your evaluation dataset over time.

• Multi-model comparison — Run the same test cases against GPT-5, Claude, Gemini, and open-source models to make data-driven model selection decisions based on quality, latency, and cost.

Pros and Cons

Pros:

• Framework-agnostic with 50+ integrations means you can adopt it regardless of your AI stack. Unlike LangSmith, you are not tied to LangChain.
• The self-hosted option is genuinely full-featured. Run ./opik.sh and you get the complete platform on your machine, no feature gating behind a cloud plan.
• LLM-as-a-judge evaluations are production-ready, not experimental. The PyTest integration means you can enforce quality gates in CI like any other test.
• Scale-tested at 40M+ traces per day, which is far beyond what most teams need. You will not outgrow it quickly.
• Active development with a responsive team — the changelog shows frequent releases and the GitHub issues are actively triaged.

Cons:

• Python SDK is the primary client. TypeScript and Ruby support exists via OpenTelemetry, but the Python SDK gets features first and has the richest API. If your stack is entirely Node.js, the experience is thinner.
• The self-hosted Docker setup pulls a significant number of containers (ClickHouse, Redis, MySQL, frontend, backend, and more). Resource requirements are non-trivial for a local dev machine — plan for at least 8GB of RAM.
• The free cloud tier has usage limits that active teams will hit quickly. Production-scale monitoring on Comet’s cloud requires a paid plan, though pricing is not publicly listed.
• Learning curve for the evaluation framework is real. Defining good LLM-as-a-judge metrics requires understanding prompt engineering for evaluation, which is its own skill.

Getting Started

# Install the Python SDK
pip install opik

# Configure with Comet cloud (free tier) or local instance
opik configure

# Quick trace example
import opik

opik.configure(use_local=True)  # For self-hosted

client = opik.Opik()

# Log a trace
trace = client.trace(
    name="my-llm-call",
    input={"user_message": "What is the capital of France?"},
)

# Log an LLM call within the trace
span = trace.span(
    name="openai-call",
    provider="openai",
    model="gpt-4o",
    input={"prompt": "What is the capital of France?"},
    output={"response": "Paris is the capital of France."},
)

span.end()
trace.end()

# Self-host with Docker (full platform)
git clone https://github.com/comet-ml/opik.git
cd opik
./opik.sh
# Open http://localhost:5173

For evaluation in CI/CD:

pip install opik pytest

# Run evaluations in your test suite
pytest tests/test_llm_quality.py --opik

Alternatives

LangSmith — Built by the LangChain team, LangSmith offers similar tracing and evaluation capabilities but is tightly coupled to the LangChain ecosystem. If you are already deep in LangChain, LangSmith has better first-party integration. But if you use multiple frameworks or want to avoid vendor lock-in to LangChain, Opik’s framework-agnostic approach is the safer bet.

Braintrust — A newer entrant focused on evaluation and prompt versioning with a slick UI. Braintrust’s evaluation workflow is arguably more polished for prompt iteration, but it has fewer integrations than Opik and the self-hosting story is less mature. Better choice if prompt optimization is your primary concern and you do not need broad framework support.

Weights & Biases Weave — W&B expanded their ML experiment tracking platform to cover LLM applications with Weave. It is a natural choice if your team already uses W&B for traditional ML. However, Weave’s LLM-specific features lag behind Opik’s depth, and the pricing model is more aggressive for production workloads.

Verdict

Opik is the best open-source option for LLM observability in 2026. That is a strong claim, but the numbers back it up — 19.6K stars, 50+ integrations, a self-hosted option that actually works, and evaluation tooling that goes beyond simple logging into automated quality assurance. The Comet team’s years of ML observability experience show in the product’s maturity. If you are shipping AI features in production and you do not have a tracing and evaluation platform yet, Opik should be your first stop. The self-hosted Docker setup takes five minutes, the Python SDK wraps your existing code with minimal changes, and the evaluation framework can be integrated into your CI pipeline in an afternoon. For fullstack developers working with React frontends and NestJS or Django backends calling LLMs, Opik provides the missing observability layer that traditional APM tools cannot offer.