Opik

Open-source LLM observability and evaluation platform for tracing, debugging, monitoring, and optimizing generative AI applications including RAG systems and agentic workflows. Provides comprehensive tracing infrastructure, LLM-as-a-judge evaluation metrics, experiment management, and production dashboards.

Evaluated Apr 04, 2026 (0d ago)
Repo ↗ LLM Observability & Evaluation llm-observability evaluation tracing rag agents prompt-engineering open-source monitoring production testing
⚙ Agent Friendliness
70
/ 100
Can an agent use this?
🔒 Security
71
/ 100
Is it safe for agents?
⚡ Reliability
70
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
0
Documentation
75
Error Messages
--
Auth Simplicity
65
Rate Limits
60

🔒 Security

TLS Enforcement
85
Auth Strength
75
Scope Granularity
50
Dep. Hygiene
70
Secret Handling
75

Cloud API uses API keys with workspace isolation. Self-hosted has no built-in authentication but runs in customer environment. Python SDK recommends API key management via environment variables. Open-source project with Apache 2.0 license enables security auditing.

⚡ Reliability

Uptime/SLA
75
Version Stability
70
Breaking Changes
65
Error Recovery
72
AF Security Reliability

Best When

Building production-grade LLM applications that require comprehensive observability, rigorous evaluation, and continuous optimization. Particularly valuable for teams managing complex agentic systems or RAG applications where understanding LLM behavior is critical.

Avoid When

You only need basic logging without structured evaluation. You require edge deployment without internet connectivity (though self-hosted option mitigates this). You have minimal tracing volume or simple LLM applications.

Use Cases

  • Tracing and debugging LLM calls during development and in production
  • Evaluating RAG chatbots and retrieval systems
  • Testing and optimizing prompts in the Playground
  • Monitoring agentic workflows and multi-step LLM applications
  • Automated evaluation with datasets and experiments
  • Hallucination detection and content moderation for LLM outputs
  • CI/CD integration for LLM application testing
  • Production monitoring with online evaluation rules
  • Multi-framework observability (LangChain, LlamaIndex, Anthropic, OpenAI, etc.)

Not For

  • Real-time inference serving (it's an observability tool, not an LLM provider)
  • Model training or fine-tuning
  • Non-LLM application monitoring
  • Simple logging needs without structured evaluation requirements

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
No

Authentication

Methods: API Key (for Comet.com cloud) Local server (no auth required for self-hosted development)
OAuth: No Scopes: No

Comet.com cloud requires API key and workspace configuration. Self-hosted instances can run without authentication. SDK configuration via 'opik configure' command.

Pricing

Model: Freemium (cloud) + Open Source Self-Host
Free tier: Yes
Requires CC: No

Open-source self-hosting has zero software costs. Comet.com cloud pricing based on trace volume and features. No credit card required for free tier.

Agent Metadata

Pagination
cursor-based
Idempotent
True
Retry Guidance
Documented

Known Gotchas

  • No MCP server - agents must use REST API or Python SDK directly
  • Cloud deployment requires Comet.com signup and API key management
  • Self-hosted setup requires Docker/Kubernetes knowledge
  • Large-scale trace ingestion (40M+ traces/day) may require dedicated infrastructure planning
  • Python SDK requires 'opik configure' step before use - not zero-config

Alternatives

Full Evaluation Report

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Score Monitoring

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Scores are editorial opinions as of 2026-04-04.

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Packages Evaluated
17761
Need Evaluation
586
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