Agenta is an open-source LLMOps platform designed to help engineering and product teams build, evaluate, and monitor production-grade LLM applications. It unifies prompt management, evaluation frameworks, and observability tools into a single interface, addressing the fragmentation common in LLM development workflows. By providing version-controlled prompts, automated and human-in-the-loop evaluation, and OpenTelemetry-compatible tracing, Agenta reduces the complexity of deploying LLMs at scale. It’s built for teams that need to collaborate across roles—engineers, data scientists, and subject matter experts—while maintaining visibility into model performance and costs in production.
What You Get
- Interactive LLM Playground - Compare multiple prompts side-by-side with real test cases and support for 50+ LLM models, including custom model integration via API endpoints.
- Prompt Version Control & Environments - Track changes to prompts and configurations with branching, environment isolation (dev/staging/prod), and rollbacks to prevent production regressions.
- Flexible Testsets & Evaluators - Create test cases from production logs, CSV uploads, or playground experiments; use 20+ pre-built evaluators (LLM-as-judge, exact match, regex) or define custom Python-based evaluators.
- LLM Observability with Tracing - Monitor latency, cost, and token usage in real time; trace complex LLM workflows with OpenTelemetry and OpenInference compatibility for seamless integration with existing monitoring stacks.
- Human Feedback Integration - Collect and annotate evaluations from SMEs directly in the UI to improve prompt quality iteratively without code changes.
- Self-Hostable Architecture - Deploy Agenta on-premises using Docker Compose with full control over data and infrastructure, including Traefik-based reverse proxy support.
Common Use Cases
- Building a multi-tenant SaaS dashboard with dynamic prompts - Product teams use Agenta to manage and version different prompt templates per customer segment, test them against real user queries, and deploy changes with confidence using evaluation metrics.
- Creating a customer support chatbot with evolving knowledge - Engineering teams evaluate response quality using LLM-as-judge and human feedback loops, track token costs per session, and trace errors back to specific prompt versions.
- Problem: Unreliable LLM outputs in production → Solution: Agenta’s evaluation pipelines - Teams experience inconsistent responses after prompt updates; Agenta automates regression testing using historical test cases and flags degradation before deployment.
- Team: DevOps managing LLM microservices across cloud providers - Agenta’s OpenTelemetry tracing and API-first design allow teams to instrument LLM calls in Kubernetes clusters, monitor performance across AWS, GCP, and Azure, and correlate failures with specific model versions.
Under The Hood
Agenta is a comprehensive LLMOps platform designed to streamline the development, deployment, and management of AI agents. It supports both open-source and enterprise features, offering a modular architecture that enables scalable and extensible AI workflows.
Architecture
Agenta follows a layered and component-based architecture that promotes clear separation of concerns and extensibility.
- The system is structured into distinct modules for API handling, database operations, authentication, and enterprise features, ensuring well-defined boundaries.
- Services and routers are organized to support loose coupling and modular integration across different system components.
- The architecture incorporates service-oriented design principles with middleware-based authentication and enterprise-grade features like billing and organization management.
- It supports both monolithic and microservice-like deployment patterns, enabling flexible scaling and feature isolation.
Tech Stack
The project leverages a multi-language tech stack centered around Python for backend services and TypeScript/JavaScript for frontend.
- The backend is built with FastAPI in Python, while the frontend uses React and Next.js with TypeScript for web interfaces.
- Key dependencies include SQLAlchemy, Alembic, Pydantic, and Taskiq for database operations, migrations, data validation, and async task handling.
- Development tools such as Poetry, Docusaurus, Tailwind CSS, and Vite are used for dependency management, documentation, styling, and build processes.
- Testing is handled through pytest, httpx, and a combination of integration and end-to-end test configurations.
Code Quality
The codebase reflects a mixed quality profile with strengths in testing and error handling, but some technical debt remains.
- A broad range of tests is present, particularly in utility and migration modules, ensuring robust functionality validation.
- Comprehensive exception handling is evident through widespread use of try/except blocks in core modules.
- Code organization and naming conventions show moderate consistency, with some variation in module structure and documentation practices.
- Signs of legacy code and temporary files indicate ongoing efforts to refactor older components, pointing to areas of technical debt.
What Makes It Unique
Agenta stands out through its innovative approach to AI agent lifecycle management and enterprise integration.
- It introduces extensible architecture tailored for multi-tenant environments, supporting both development and production-grade AI workflows.
- The platform emphasizes developer-first design, bridging the gap between experimentation and deployment in AI systems.
- Unique data migration strategies and seamless integration with external services like OpenAI and Stripe enhance its operational flexibility.