Agenta

About Agenta

Agenta is an open-source LLMOps platform designed to help AI teams build, evaluate, and ship reliable LLM applications. It addresses the core challenge of unpredictability in large language models by providing a structured, 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 beyond scattered prompts and siloed workflows. Agenta's main value proposition is serving as a single source of truth, centralizing the critical processes of experimentation, evaluation, and observability into one integrated system. By replacing ad-hoc testing and guesswork with systematic processes, Agenta enables teams to version prompts, run automated and human evaluations, debug production issues with full traceability, and validate every change before deployment. This systematic approach ensures that AI applications are not only built faster but are also more robust, measurable, and maintainable in production.

Features of Agenta

Unified Playground for Experimentation

Agenta provides a centralized playground where teams can rapidly experiment with different prompts, parameters, and foundation models from various providers side-by-side. This model-agnostic approach prevents vendor lock-in and allows developers and domain experts to collaboratively iterate. The environment supports using real production data and errors for testing, enabling teams to debug issues and immediately turn them into reproducible test cases, closing the feedback loop between development and production.

Automated and Integrated Evaluation Framework

The platform replaces guesswork with evidence-based validation through a comprehensive evaluation system. Teams can create automated evaluation workflows using LLM-as-a-judge, built-in metrics, or custom code evaluators. Crucially, Agenta allows for the evaluation of full agentic traces, assessing each intermediate reasoning step, not just the final output. This granular insight is essential for debugging complex chains and agents, ensuring improvements are accurately measured.

Production Observability and Tracing

Agenta offers full observability by tracing every LLM request in production systems. This allows teams to pinpoint exact failure points in complex chains or agent workflows. Any trace can be annotated by the team or end-users and converted into a test case with a single click. Furthermore, live online evaluations monitor performance continuously, helping detect regressions and ensuring application reliability post-deployment.

Collaborative Workflow for Cross-Functional Teams

Agenta breaks down silos by providing tools for seamless collaboration between technical and non-technical roles. It features a safe UI for domain experts to edit and experiment with prompts without writing code. Product managers and experts can run evaluations and compare experiment results directly from the interface. This is complemented by full parity between the API and UI, ensuring both programmatic and manual workflows integrate into one central hub.

Use Cases of Agenta

Developing and Refining Customer Support Agents

Teams building LLM-powered customer support chatbots use Agenta to manage hundreds of prompt variations for handling different query types. Subject matter experts can directly refine responses in the playground, while automated evaluations on accuracy and tone ensure each prompt version improves performance before being deployed to live channels, reducing escalations and improving customer satisfaction.

Building Reliable Data Analysis and Reporting Tools

For applications that use LLMs to analyze datasets and generate reports, consistency is critical. Data scientists and product managers use Agenta to evaluate the factual correctness and formatting of outputs across different models. By tracing failures in production and adding them to a test set, they systematically eliminate recurring errors, ensuring the tool produces reliable, actionable insights.

Managing Complex Multi-Step AI Agents

When developing agents that perform sequential tasks (e.g., research, summarization, and drafting), debugging is challenging. Engineering teams use Agenta's trace observability to visualize each step in the agent's reasoning. They evaluate the success of individual steps and the overall chain, allowing them to isolate and fix specific failures in logic or external tool calls, leading to more robust agentic systems.

Streamlining LLM Application Lifecycle for Enterprise Teams

Large organizations with multiple AI products use Agenta to standardize their LLMOps practices. It centralizes prompt versioning from all teams, establishes a unified framework for running pre-deployment evaluations, and provides a shared observability layer. This eliminates scattered workflows, enables knowledge sharing, and institutes governance, ensuring all LLM applications meet a common standard of reliability.

Frequently Asked Questions

Is Agenta an open-source platform?

Yes, Agenta is fully open-source. You can dive into the code, contribute to the project, and self-host the platform. This open approach provides transparency, avoids vendor lock-in, and allows hundreds of developers in the community to influence its direction through feedback and contributions.

How does Agenta handle collaboration between developers and non-coders?

Agenta is designed for cross-functional collaboration. It provides a user-friendly web interface where domain experts and product managers can safely experiment with prompts, run evaluations, and review traces without needing to write or understand code. This bridges the gap between technical implementation and subject matter expertise.

Can I use Agenta with my existing AI stack?

Absolutely. Agenta is built to be framework and model-agnostic. It seamlessly integrates with popular development frameworks like LangChain and LlamaIndex, and can work with models from any provider, including OpenAI, Anthropic, and open-source models. It fits into your existing workflow rather than forcing you to rebuild.

What makes Agenta different from just using a spreadsheet and separate monitoring tools?

While spreadsheets and disparate tools are common, they lead to siloed information, lost version history, and inefficient debugging. Agenta consolidates experimentation, evaluation, and observability into a single, interconnected platform. This creates a systematic feedback loop where production issues directly inform new tests, and all changes are validated, providing a true LLMOps workflow that scattered tools cannot replicate.