Athina IDE is a collaborative editor for AI teams to prototype, experiment, and evaluate LLM-powered applications.
View a Demo Video to learn more about Athina IDE.
A quickstart guide for running evals in the UI or programmatically.
Experiment with different prompts and models
Organize prompts into folders, add versioning, and manage collaboratively
Execute your saved prompts programmatically via API or SDK
Chain prompts and API calls to build complex pipelines
Choose from 50+ preset evals or create custom evaluations
Automatically evaluate production traffic for quality and safety
Prevent regressions by running evals in your CI/CD pipeline
Build custom evaluation logic for your specific use case
Start logging LLM interactions in 2 lines of code
Track costs and usage across models, prompts, and customers
Visualize and debug complex LLM chains
Access your data via GraphQL API or export as CSV/JSON
Import data from logs, files, or generate synthetic datasets
Add computed columns using LLM calls, code, or API requests
Compare performance across different models or prompts
Test different approaches and measure improvements
Athina IDE is a collaborative editor for AI teams to prototype, experiment, and evaluate LLM-powered applications.
View a Demo Video to learn more about Athina IDE.
A quickstart guide for running evals in the UI or programmatically.
Experiment with different prompts and models
Organize prompts into folders, add versioning, and manage collaboratively
Execute your saved prompts programmatically via API or SDK
Chain prompts and API calls to build complex pipelines
Choose from 50+ preset evals or create custom evaluations
Automatically evaluate production traffic for quality and safety
Prevent regressions by running evals in your CI/CD pipeline
Build custom evaluation logic for your specific use case
Start logging LLM interactions in 2 lines of code
Track costs and usage across models, prompts, and customers
Visualize and debug complex LLM chains
Access your data via GraphQL API or export as CSV/JSON
Import data from logs, files, or generate synthetic datasets
Add computed columns using LLM calls, code, or API requests
Compare performance across different models or prompts
Test different approaches and measure improvements