Agenta

About Agenta

Agenta is an open-source LLMOps platform designed to help AI teams build and ship reliable LLM applications. It addresses the core challenge of LLM unpredictability by providing a centralized, collaborative environment for the entire development lifecycle. The platform is built for cross-functional teams, including developers, product managers, and subject matter experts, who need to move from chaotic, siloed workflows to structured, evidence-based processes. Agenta's main value proposition is unifying the critical pillars of LLM development—experimentation, evaluation, and observability—into a single source of truth. This eliminates guesswork by enabling teams to systematically compare prompts and models, run automated and human evaluations, and debug production issues using real trace data. By integrating seamlessly with popular frameworks like LangChain and LlamaIndex and being model-agnostic, Agenta prevents vendor lock-in and accelerates the path to deploying robust, high-performance AI products.

Features of Agenta

Unified Experimentation Playground

Agenta provides a central playground where teams can rapidly experiment with different prompts, parameters, and models from various providers side-by-side. It maintains a complete version history of all changes, allowing for easy rollback and comparison. This model-agnostic approach ensures teams can use the best model for the task without being locked into a single vendor, streamlining the iterative development process.

Automated Evaluation Framework

The platform replaces guesswork with evidence through a robust evaluation system. Teams can integrate LLM-as-a-judge setups, use built-in evaluators, or write custom code to assess performance. Crucially, evaluations can analyze full execution traces, testing each intermediate step in an agent's reasoning, not just the final output. This enables systematic validation of every change before deployment.

Production Observability & Debugging

Agenta offers comprehensive observability by tracing every LLM request in production. Teams can pinpoint exact failure points, annotate traces collaboratively, and monitor system performance with live evaluations. A key feature is the ability to turn any problematic production trace into a test case with a single click, closing the feedback loop between debugging and development.

Cross-Functional Collaboration Tools

Agenta breaks down silos by providing tools for the whole team. It offers a safe UI for domain experts to edit and test prompts without coding. Product managers and experts can run evaluations and compare experiments directly from the interface. Full parity between the API and UI ensures both programmatic and manual workflows integrate into one central hub.

Use Cases of Agenta

Streamlining Prompt Engineering Workflows

Teams struggling with prompts scattered across emails, Slack, and sheets use Agenta to centralize all prompt experimentation. Developers and domain experts collaborate in the unified playground to iterate quickly, compare versions, and maintain a searchable history, dramatically reducing time-to-market for new features.

Validating LLM Application Performance

Before shipping updates, teams use Agenta's evaluation framework to run automated tests. They can create test sets from production errors, use multiple evaluators (LLM, code-based, human), and get quantitative evidence that a new prompt or model configuration actually improves performance, preventing regressions.

Debugging Complex Production Issues

When an LLM application behaves unexpectedly in production, engineers use Agenta's observability to trace the exact request. They can inspect the full chain of reasoning, identify the faulty step, and immediately save the trace as a test case to reproduce and fix the issue, transforming debugging from guesswork to a precise science.

Enabling Non-Technical Stakeholder Input

Product managers and subject matter experts use Agenta's UI to participate directly in the LLM development process. They can safely tweak prompts in a controlled environment, run their own evaluations on experiment variants, and provide annotated feedback on traces, ensuring the final product aligns with business and domain expertise.

Frequently Asked Questions

Is Agenta really open-source?

Yes, Agenta is a fully open-source platform. You can view the source code on GitHub, contribute to the project, and self-host the platform. This provides transparency, allows for customization, and avoids vendor lock-in, while a community of AI builders collaborates on Slack.

How does Agenta integrate with existing AI stacks?

Agenta is designed for seamless integration. It works with any LLM provider (OpenAI, Anthropic, etc.) and is compatible with popular frameworks like LangChain and LlamaIndex. You can connect your existing applications via API, allowing you to add evaluation, observability, and prompt management without a full rewrite.

Can non-developers use Agenta effectively?

Absolutely. A core goal of Agenta is to bridge the gap between technical and non-technical team members. The platform provides an intuitive web UI that allows product managers and domain experts to experiment with prompts, run evaluations, and annotate traces without writing any code.

What is the difference between offline and live evaluations?

Agenta supports both. Offline evaluations are run on static test datasets to validate changes before deployment. Live (online) evaluations run continuously on real production traffic to monitor performance, detect regressions, and gather user feedback in real-time, ensuring ongoing reliability.