opik
Debug, evaluate, and monitor your LLM applications with comprehensive tracing and production-ready dashboards
GraphCanon updated 1w · GitHub synced 1w
Decision brief
Opik is designed for evaluating and monitoring LLM applications, offering comprehensive tracing and dashboards.
Good fit when
- - Use Opik when you need to deploy a self-hosted solution that offers granular control over your LLM monitoring setup.
- - Deploy Opik if you are working with RAG systems or other complex agentic workflows requiring detailed evaluation and monitoring tools.
Avoid when
- - Avoid using Opik if you prefer cloud-based deployment options where extensive self-hosting management may be less preferable or feasible.
- - Do not use Opik if your project does not involve LLM applications or RAG systems, as its specific features will offer little benefit in a narrower context.
- Pricing:
- freemium - Opik is available under the Apache-2.0 license and can be deployed freely using Docker or Kubernetes, with no explicit mention of premium cloud-based services.
- Requirements:
- Compatible with Python environments.; Can be deployed via Docker for local use or Kubernetes for scalable installations.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
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- Provenance
- Not a fork · Organization account
- As of 1w
- Security (OSV)
- No lockfile
- As of 1mo
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Install
pip install opikHow it fits your stack(16)
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Evidence and technical details
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Overview
Opik Server provides tools for the evaluation, observability, and monitoring of LLM applications and workflows. It supports deployment through Docker or Kubernetes.
Capability facts
- Languages
- python
Source: github.language · Aug 7, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 7, 2026)
Install the Python SDK and configure it:Source link
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README
⚡ Quick Start
Install the Python SDK and configure it:
pip install opik
opik configure
Wrap any function with the @track decorator to start logging traces:
from opik import track
@track
def my_function(input: str) -> str:
return input
Every call to my_function is now logged to Opik, including nested calls, so this works for full agent and pipeline traces, not just single LLM calls. See the Quickstart guide for the TypeScript SDK and other setup options.
🛠️ Opik Server Installation
Get your Opik server running in minutes. Choose the option that best suits your needs:
Option 2: Self-Host Opik for Full Control
Deploy Opik in your own environment. Choose between Docker for local setups or Kubernetes for scalability.
Self-Hosting with Docker Compose (for Local Development & Testing)
This is the simplest way to get a local Opik instance running. Note the new ./opik.sh installation script:
On Linux or Mac Environment:
---
# Start only infrastructure services (databases, caches etc.)
./opik.sh --infra
---
# Start infrastructure + backend services
./opik.sh --backend
---
### Python SDK Quick Start
To get started with the Python SDK:
Install the package:
```bash
---
# or install with uv
uv pip install opik
Configure the python SDK by running the opik configure command, which will prompt you for your Opik server address (for self-hosted instances) or your API key and workspace (for Comet.com):
opik configure
[!TIP] You can also call
opik.configure(use_local=True)from your Python code to configure the SDK to run on a local self-hosted installation, or provide API key and workspace details directly for Comet.com. Refer to the Python SDK documentation for more configuration options.
You are now ready to start logging traces using the Python SDK.
For agents
This page has a .md twin and JSON over the API.