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What is Opik?

Utilizing a comprehensive set of observability tools enables you to thoroughly assess, test, and deploy LLM applications throughout both development and production phases. You can efficiently log traces and spans, while also defining and computing evaluation metrics to gauge performance. Scoring LLM outputs and comparing the efficiencies of different app versions becomes a seamless process. Furthermore, you have the capability to document, categorize, locate, and understand each action your LLM application undertakes to produce a result. For deeper analysis, you can manually annotate and juxtapose LLM results within a table. Both development and production logging are essential, and you can conduct experiments using various prompts, measuring them against a curated test collection. The flexibility to select and implement preconfigured evaluation metrics, or even develop custom ones through our SDK library, is another significant advantage. In addition, the built-in LLM judges are invaluable for addressing intricate challenges like hallucination detection, factual accuracy, and content moderation. The Opik LLM unit tests, designed with PyTest, ensure that you maintain robust performance baselines. In essence, building extensive test suites for each deployment allows for a thorough evaluation of your entire LLM pipeline, fostering continuous improvement and reliability. This level of scrutiny ultimately enhances the overall quality and trustworthiness of your LLM applications.

What is LLM Council?

The LLM Council functions as an efficient coordination platform that enables users to interact with multiple large language models at once and amalgamate their responses into a single, more trustworthy answer. Instead of relying on a solitary AI, it dispatches a query to a consortium of models, each producing its own independent output, which are then anonymously assessed and ranked by the other models. After this evaluation, a selected "Chairman" model consolidates the most persuasive insights into a unified final response, similar to how experts reach a consensus in collaborative discussions. Generally, this system is accessed through a user-friendly local web interface that utilizes a Python backend and a React frontend, while seamlessly connecting to models from various providers such as OpenAI, Google, and Anthropic through aggregation services. This structured peer-review methodology seeks to identify possible blind spots, reduce instances of hallucinations, and improve the reliability of answers by integrating a range of perspectives and enabling cross-model assessments. By fostering collaboration, the LLM Council not only enhances the output's quality but also cultivates a deeper understanding of the inquiries made, ultimately providing users with richer and more informed answers. This approach encourages ongoing dialogue among the models, promoting continuous refinement and evolution of the responses generated.

Media

Media

Integrations Supported

Azure OpenAI Service
Claude
Claude Opus 3
DeepEval
DeepSeek V3.1
GPT-5.2
GPT-5.3 Instant
Gemini 3 Pro
Google Slides
Grok 4
Kong AI Gateway
Mistral AI
OpenAI
OpenAI o1
Pinecone
Predibase
Python
Qwen
React
pytest

Integrations Supported

Azure OpenAI Service
Claude
Claude Opus 3
DeepEval
DeepSeek V3.1
GPT-5.2
GPT-5.3 Instant
Gemini 3 Pro
Google Slides
Grok 4
Kong AI Gateway
Mistral AI
OpenAI
OpenAI o1
Pinecone
Predibase
Python
Qwen
React
pytest

API Availability

Has API

API Availability

Has API

Pricing Information

$39 per month
Free Trial Offered?
Free Version

Pricing Information

$25 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Comet

Date Founded

2017

Company Location

United States

Company Website

www.comet.com/site/products/opik/

Company Facts

Organization Name

LLM Council

Company Location

United States

Company Website

llmcouncil.ai/

Categories and Features

Categories and Features

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