
Open-source AI observability & evals for agentic systems
By Tanmay Verma, Founder · Last verified 06 Jun 2026
In short
Opik (Comet) — Open-source AI observability & evals for agentic systems. Best for Developers building complex multi-step agents needing deep traceability, Enterprise teams requiring audit logs and compliance-ready AI deployment, Teams wanting to automatically fix agent failures with code-level remediation. Free to use.
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If you're building complex multi-step agents and need more than basic LLM monitoring, Opik is a standout choice. The auto-fix coding assistant and integrated test suites are genuinely innovative, though the platform is still maturing in documentation depth.
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Last verified: June 2026
Opik is one of the most comprehensive open-source observability platforms we've seen, especially for agentic workflows. Its ability to trace every step of an agent—from user input to context retrieval to tool calls—sets it apart from simpler LLM monitoring tools. The inclusion of Llama-as-a-judge metrics out of the box saves teams significant setup time. When to pick this: You’re developing complex agentic systems (e.g., multi-step reasoning, tool-using agents) and need deep traceability across dev, test, and prod. You value open-source flexibility and want to avoid lock-in. The Ollie coding assistant is a unique productivity booster for fixing failing traces. When to pass: If you only need basic prompt monitoring without agent tracing, simpler tools like LangFuse or Helicone might be easier to get started with. Opik’s feature set can feel overwhelming for simple chatbots. Also, if your team prefers a fully managed SaaS without self-hosting, the Comet cloud version adds costs beyond the free tier. Comparison to closest alternative: vs. LangSmith (LangChain): Opik is more focused on agentic tracing with built-in test suites and Ollie auto-fix—LangSmith excels in prompt management and LLM chain visibility but lacks Opik’s code-level remediation. vs. Weights & Biases Prompts: W&B is stronger for experiment tracking and model registry; Opik wins in production monitoring and guardrails. Real-world usage caveats: The platform is relatively new, so documentation and community support are still growing. Some advanced features (e.g., prompt optimizer) may require experimentation to tune. Self-hosting requires Kubernetes or Docker knowledge for scale.
Skip Opik (Comet) if Skip Opik if you're building a simple single-turn LLM app or lack team members with coding experience to handle setup and configuration.
Across the latest 7 updates: 4 feature updates, 2 launches and 1 news mention.
Opik introduces agent tracing and observability features for debugging complex AI systems.
Blog post comparing AI observability tools for agentic systems, likely referencing Opik.
Case study on debugging RAG systems using Opik, highlighting practical challenges.
Opik introduces LLM cost tracking capabilities for monitoring AI spend.
Launch of Opik Agent Playground for early-stage agent development and testing.
Introduction of Ollie, an auto-fix feature for agent codebases within Opik.
Launch of Opik Test Suites for unit and regression testing of AI agents.
How likely is Opik (Comet) to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
Opik by Comet is an open-source AI observability and evaluation platform purpose-built for the agentic era. It logs every step your agent takes—from user interactions and context retrieval to tool calls—and provides automated evaluation workflows to find and fix errors across development, testing, and production. With end-to-end tracing, LLM-as-a-judge metrics, and production monitoring, Opik helps developers and enterprise teams understand what their agents are doing, where they’re failing, and how to fix them. Key features include comprehensive trace and debug capabilities that capture, visualize, and annotate every action your agent takes. Opik offers 30+ evaluation metrics for answer relevance, context precision, task completion, hallucination detection, and more. It supports automated test suites with plain-text assertions that produce clear pass/fail results, eliminating the need for manual eval creation. The platform also includes Ollie, a coding assistant that analyzes traces, suggests fixes, and directly implements them in your codebase with version control and regression testing. Opik’s Agent Playground lets you run your entire agent end-to-end, experimenting with different configurations of models, prompts, and parameters. The Prompt Optimizer offers six advanced prompt optimization algorithms to improve agent performance. For production, Opik provides real-time monitoring, guardrails to block content violations and PII exposure, and token usage/cost tracking. It generates audit logs automatically for governance and compliance. Opik is truly open-source—its core observability and evaluation features are free in the source code available on GitHub (19k stars). You can run it locally or use Comet’s hosted version with a generous free tier (no credit card required). Compared to proprietary alternatives like LangSmith or Weights & Biases Prompts, Opik offers deeper agentic tracing, built-in code fixing via Ollie, and a stronger open-source community.
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Concrete scenarios for the personas Opik (Comet) actually fits — and what changes day-one when you adopt it.
Your chatbot agent sometimes produces irrelevant answers; set up Opik to log all traces, then run an evaluation with context precision metric to identify failing traces. Drill down to the specific step where context retrieval failed.
Outcome: Identified that the retrieval step returned irrelevant chunks due to a faulty embedding model configuration; fixed by adjusting parameters and redeploying.
You want to improve agent accuracy across 1000 test cases; use Opik's Prompt Optimizer to run seven optimization algorithms (e.g., DSPy, APE) on the prompt, and compare results in the UI.
Outcome: Optimized prompt improved F1 score by 12% across all test cases; the winning algorithm was automatically selected.
Deploy agent to production; configure Opik to evaluate each trace in real time with a guardrail that blocks any response containing PII (e.g., credit card numbers).
Outcome: Alerts triggered on 5 violations in first week; team reviewed flagged traces and updated the guardrail regex to reduce false positives.
As an open-source platform, initial setup and configuration require technical expertise. The platform's full enterprise features (scalability, compliance) are gated behind Comet's paid offering. Context window and rate limits depend on the underlying LLMs used, not Opik itself. The Ollie auto-fix feature may not work perfectly for all codebases. No native support for non-English languages in evaluation metrics.
The company stage and team size where Opik (Comet)'s pricing actually pencils out — and where peers do it cheaper.
Opik's open-source core is free, making it cost-effective for small teams and startups. The free tier offers generous usage with no credit card required. For enterprise scalability and compliance, Comet's custom pricing applies—similar to competitors like LangSmith and Weights & Biases. If you need a fully managed cloud experience, free tier suffices for development, but production-scale may require an enterprise plan.
How long it actually takes to get something useful out of Opik (Comet) — broken out by persona, not the marketing-page minute.
For a developer familiar with Python, setting up Opik takes about 10-15 minutes: install the SDK (`pip install opik`), log in with a free account, and add a few lines of instrumentation. Building custom evaluation metrics and test suites may take an additional hour. Enterprise self-hosting setup can take a few days depending on infrastructure.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
Full product docs from comet.com
Build, test, and optimize GenAI apps from prototype to production. Comprehensive tracing, evaluation, and prompt optimization for RAG, agents, and more.
Common stack mates teams adopt alongside Opik (Comet), with the specific reason each pairing earns its keep.
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Last calculated: June 2026
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