Agenta

About Agenta

Agenta is the collaborative, open-source LLMOps platform designed to unify AI teams around the shared mission of building and shipping reliable LLM applications. It directly addresses the core challenges that slow down AI development: unpredictable model behavior, scattered workflows, and siloed teams. By bringing developers, product managers, and subject matter experts into a single, integrated environment, Agenta transforms chaotic, ad-hoc processes into a structured, evidence-based workflow. The platform serves as your team's single source of truth, centralizing the entire LLM development lifecycle—from initial prompt experimentation and rigorous evaluation to production observability and debugging. Its core value proposition is enabling seamless cooperation, allowing every team member to contribute their expertise safely, compare iterations systematically, and validate every change before it impacts users, ultimately fostering synergy to ship robust AI products faster and with greater confidence.

Features of Agenta

Unified Experimentation Playground

Agenta's unified playground is the central hub for team-based iteration. It allows developers and domain experts to collaboratively experiment with different prompts, parameters, and models from various providers side-by-side in a single interface. Every change is automatically versioned, creating a complete history of experiments that the entire team can reference, understand, and build upon, eliminating the chaos of prompts scattered across emails and documents.

Automated and Flexible Evaluation Framework

Replace guesswork and "vibe checks" with a systematic, evidence-based evaluation process. Agenta enables teams to create automated test suites using LLM-as-a-judge, built-in metrics, or custom code evaluators. Crucially, you can evaluate the full reasoning trace of complex agents, not just the final output, and seamlessly integrate human feedback from domain experts into the evaluation workflow for comprehensive validation.

Production Observability & Debugging

Gain deep visibility into your live LLM applications. Agenta traces every production request, making it possible to pinpoint exact failure points when issues arise. Teams can annotate these traces collaboratively and, with a single click, turn any problematic trace into a test case for the playground, closing the feedback loop between production incidents and development fixes.

Model-Agnostic Collaboration Hub

Agenta is built for diverse teams and tech stacks. It provides full parity between its UI and API, enabling both programmatic and visual workflows. This allows domain experts to safely edit and experiment with prompts without writing code, while developers integrate Agenta seamlessly with their existing frameworks like LangChain or LlamaIndex and any model provider, preventing vendor lock-in.

Use Cases of Agenta

Cross-Functional AI Product Development

Ideal for teams where product managers, subject matter experts, and engineers need to collaborate closely. The platform allows PMs to define evaluation criteria, experts to refine prompts and provide feedback via the UI, and developers to implement and deploy changes, all within a shared, version-controlled environment that aligns everyone on objectives and results.

Rigorous Testing of Complex AI Agents

For teams building multi-step agents with retrieval or tool use, Agenta's ability to evaluate full reasoning traces is critical. Developers can systematically test each intermediate step of an agent's logic, identify where hallucinations or errors originate, and iteratively improve reliability before deployment, moving beyond just testing final answers.

Production Monitoring and Rapid Issue Resolution

When an LLM application behaves unexpectedly in production, engineering teams can use Agenta's observability to instantly trace the error. By examining annotated traces and converting them into tests, teams can collaboratively debug, replicate the issue in the playground, and validate a fix, dramatically reducing mean time to resolution (MTTR).

Centralized Prompt Management and Governance

Organizations struggling with prompt sprawl across Slack, Google Docs, and code repositories use Agenta as a centralized system of record. It provides a secure, searchable, and versioned library for all prompts, ensuring consistency, enabling audit trails, and allowing safe experimentation without risking production stability.

Frequently Asked Questions

Is Agenta really open-source?

Yes, Agenta is a fully open-source platform. You can view the source code on GitHub, self-host the platform on your own infrastructure, and contribute to its development. This ensures transparency, avoids vendor lock-in, and allows for deep customization to fit your team's specific workflow and security requirements.

How does Agenta handle collaboration between technical and non-technical team members?

Agenta is designed with a collaborative UI that bridges this gap. Non-technical domain experts and product managers can use the visual playground to edit prompts, run evaluations, and review traces without touching code. Meanwhile, developers work with the same features via a powerful API, ensuring both groups are synchronized and contributing to a unified development process.

Can I use Agenta with my existing LLM framework and model providers?

Absolutely. Agenta is model-agnostic and framework-agnostic. It seamlessly integrates with popular frameworks like LangChain and LlamaIndex and supports any model provider (OpenAI, Anthropic, Cohere, open-source models, etc.). You can bring your existing stack and use Agenta to manage the experimentation, evaluation, and observability layer on top of it.

What is the difference between offline and live (online) evaluations?

Offline evaluations are run on static test datasets to validate changes before deployment. Agenta also supports live, online evaluations that monitor your application in production. This means you can set up evaluators to continuously assess real user requests, enabling you to detect performance regressions or drift as soon as they occur, not just during pre-release testing.