Agenta

About Agenta

Agenta is the open-source LLMOps platform designed to help AI teams build and ship reliable LLM applications with confidence. It solves the core challenge of unpredictability in large language models 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 work together seamlessly. Instead of having prompts scattered across emails and spreadsheets and debugging feeling like guesswork, Agenta creates a single source of truth. Its core value proposition is integrating the critical pillars of prompt management, systematic evaluation, and production observability into one intuitive workflow. This allows teams to experiment quickly, validate changes with evidence, and debug issues efficiently, dramatically reducing the time and risk involved in taking LLM applications from prototype to production. By being open-source and model-agnostic, Agenta offers flexibility and avoids vendor lock-in, empowering teams to use the best models and frameworks for their specific needs.

Features of Agenta

Unified Playground & Experimentation

Agenta provides a central playground where teams can experiment with different prompts, parameters, and models side-by-side. This allows for rapid iteration and comparison in a controlled environment. You can test changes using real production data, and the platform maintains a complete version history of every prompt, making it easy to track what was changed, by whom, and why. This turns chaotic prompt tweaking into a structured, auditable process.

Automated Evaluation Framework

Move beyond gut-feeling "vibe checks" with Agenta's robust evaluation system. It enables you to create a systematic process to validate every change. The platform supports multiple evaluator types, including LLM-as-a-judge, built-in metrics (like correctness), and custom code you write. Crucially, you can evaluate the full trace of an agent's reasoning, not just the final output, and even integrate human feedback from domain experts directly into the evaluation workflow.

Production Observability & Debugging

Gain full visibility into your live LLM applications. Agenta traces every request, allowing you to pinpoint the exact failure points in complex chains or agentic workflows. Any production trace can be instantly turned into a test case with a single click, closing the feedback loop between operations and development. You can also annotate traces with your team and set up live evaluations to monitor performance and detect regressions in real-time.

Collaborative Workflow for Teams

Agenta breaks down silos by bringing PMs, developers, and domain experts into one unified workflow. It provides a safe, UI-based environment for non-coders to edit prompts and run evaluations. This empowers subject matter experts to contribute directly to prompt engineering and product managers to compare experiment results, fostering collaboration and ensuring the final product aligns with business and domain requirements.

Use Cases of Agenta

Developing and Tuning Customer Support Agents

Teams building AI customer support agents can use Agenta to manage hundreds of prompt variations for different query types. Product managers and support leads can collaborate with developers in the playground to refine responses. Automated evaluations can check for correctness, tone, and safety, while observability traces help debug why a specific user interaction failed, turning that problematic trace into a new test case for improvement.

Building Reliable Data Extraction Pipelines

When creating LLM pipelines to extract structured data from documents, precision is key. Data scientists can experiment with different prompting strategies for various document formats in Agenta. They can run rigorous evaluations using ground-truth data to measure extraction accuracy. The observability features then monitor the pipeline in production, identifying drops in performance for specific document types and enabling quick retraining or prompt adjustment.

Managing Multi-Step Reasoning and Agentic Workflows

For complex applications involving chains or autonomous agents, debugging is a major challenge. Agenta's trace functionality allows developers to visualize and inspect every step of the agent's reasoning process. Teams can evaluate the performance at each intermediate step, not just the final answer. This is crucial for identifying where in a long chain a hallucination or logic error occurred, making development and troubleshooting far more efficient.

Governance and Compliance for Enterprise LLM Apps

Enterprises requiring audit trails and governance for their LLM applications can leverage Agenta as a central control plane. All prompt versions, experiment results, and evaluation scores are logged and trackable. Compliance teams can review the change history and validation evidence before deployment. The platform ensures that no untested prompts are pushed to production and provides a clear record of how and why the AI behaves as it does.

Frequently Asked Questions

Is Agenta really open-source?

Yes, Agenta is fully open-source. You can find the complete source code on GitHub, where you can review it, self-host it, and contribute to its development. The core platform is free to use, and the company operates on an open-core model, potentially offering additional enterprise features or hosted services.

How does Agenta integrate with my existing stack?

Agenta is designed to be framework and model-agnostic. It offers seamless integrations with popular frameworks like LangChain and LlamaIndex. You can use models from any provider, such as OpenAI, Anthropic, or open-source models from Hugging Face, without being locked into a single vendor's ecosystem.

Can non-technical team members use Agenta effectively?

Absolutely. A key design goal of Agenta is to enable collaboration. It provides a user-friendly web interface (UI) that allows product managers, domain experts, and other non-coders to safely experiment with prompts, configure evaluations, and review results without needing to write or understand code.

What is the difference between evaluation and observability in Agenta?

Evaluation in Agenta refers to the systematic, often automated, testing of your LLM application before or during deployment to validate its performance against set criteria. Observability happens after deployment, providing live monitoring, tracing, and debugging capabilities for your application running in production. Agenta connects these two by letting you turn any production trace (observability) into a test case for evaluation.