Agenta is an open-source LLMOps platform designed to help developers and product teams efficiently build, manage, and deploy reliable large language model (LLM) applications. It supports the full LLM development lifecycle, including prompt engineering, evaluation, and observability.

What features does Agenta offer?

Agenta offers Prompt Engineering & Management, which organizes prompts so subject matter experts can collaborate with developers without modifying code and supports versioning, deployment, side-by-side prompt testing, and a playground for experimentation. Observability features include production monitoring, user feedback collection, debugging via traces, cost tracking, edge case identification, and annotation support. For collaboration, it enables non-developers to iterate on, evaluate, and deploy custom LLM applications entirely via the user interface. Compatibility wise, it works with any LLM app architecture (Chain of Prompts, RAG, LLM agents), integrates with popular frameworks like Langchain or LlamaIndex, and supports model providers such as OpenAI, Cohere, Anthropic, Gemini, OpenRouter, and local/self-hosted models like Ollama.

Is Agenta open source?

Yes, Agenta is MIT licensed and open source, allowing self-hosting, modification, and commercial use without restrictions.

How do I get started with Agenta?

You can quickly get started by using code templates provided by Agenta. Integration with your existing codebase typically takes under an hour. Agenta creates API endpoints to connect your workflows, and an SDK makes setup simple.

Do I need to host my production application in Agenta?

No. You can keep your production application hosted on your premises or your own environment and use Agenta solely for prompt engineering, evaluation, and configuration management. Agenta fetches the latest configuration to integrate with your app.

What support channels are available for Agenta?

Detailed documentation and self-help resources are available online. Community support is provided via Slack (#05_support channel) during CEST hours. Feature suggestions and discussions are on GitHub Discussions.

What about self-hosting Agenta?

Agenta offers options for self-hosting with Docker images. You can lock Agenta to specific versions for stability. The documentation contains troubleshooting tips for common deployment issues.

Which models can Agenta work with?

Agenta supports any OpenAI-compatible endpoint, including self-hosted and custom models (like Ollama). It also dynamically supports new providers such as OpenRouter, Anthropic, Gemini, and Cohere among others.

Agenta

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Agenta is an open-source platform designed to help teams build reliable AI applications using large language models. It centralizes prompt management, testing, and monitoring, making it easier for developers, product managers, and experts to collaborate, experiment, and quickly fix any issues.

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About Agenta

Who It's For

Agenta is for teams building reliable AI applications with large language models. This includes developers, product managers, and experts. It helps them all work together, even if they don't code. The tool ensures AI apps are stable and trustworthy for everyone.

What You Get

You get a central platform to manage your AI app development. Easily test different prompts and models side-by-side. Track changes and measure AI performance. The platform includes tools for automated and human testing, plus ways to monitor your AI in production and gather user feedback.

How It Works

Agenta brings all your AI tasks into one system. You can compare ideas quickly and track every step your AI takes to find problems. Non-developers can safely experiment with prompts using a simple interface. This allows teams to iterate faster and ship dependable AI products.

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Features & Capabilities

⚙️ Core LLMOps Platform

Centralized LLMOps Platform

Provides a unified platform for prompt management, evaluation, and observability for LLM development.

Model Agnostic Support

Supports integration with various LLM providers and models, preventing vendor lock-in.

API & UI Parity

Offers consistent functionality and workflows whether using the graphical user interface or programmatic API.

🧪 Prompt Experimentation & Management

Unified Prompt Playground

Allows side-by-side comparison and iteration of prompts and models in a single interface.

Prompt Versioning & History

Tracks all changes to prompts, providing a complete version history for reproducibility.

No-code Prompt Experimentation

Empowers domain experts to edit and experiment with prompts safely without requiring coding knowledge.

📊 Evaluation & Validation

Automated Evaluation System

Systematically runs experiments, tracks results, and validates changes to LLM applications.

Flexible Evaluator Integration

Supports various evaluation methods including LLM-as-a-judge, built-in tools, or custom code evaluators.

Human-in-the-loop Evaluation

Integrates feedback from domain experts directly into the evaluation workflow for comprehensive assessment.

Accessible Evaluation UI

Enables product managers and experts to easily run and compare experiments through a user-friendly interface.

🔍 Observability & Debugging

End-to-End Request Tracing

Traces every LLM request to pinpoint exact failure points within the application flow.

Trace Annotation & User Feedback

Allows teams and users to annotate traces and provide feedback for debugging and improvement.

Convert Traces to Tests

Transforms production traces into new test cases with a single click, facilitating a continuous feedback loop.

Live Performance Monitoring

Continuously monitors system performance and detects regressions using live, online evaluations.

🤝 Collaboration & Integration

Cross-functional Team Collaboration

Unites product managers, domain experts, and developers within a single workflow for LLM development.

Seamless Ecosystem Integration

Integrates with popular LLM frameworks and providers like LangChain, LlamaIndex, and OpenAI.

Centralized Workflow Hub

Provides a single source of truth for prompts, evaluations, and traces, streamlining team efforts.

Use Cases

Automating LLM Prompt Experimentation and Version Control

LLM development often involves manual, scattered prompt adjustments and a lack of version control, leading to slow iteration and unpredictable results. Agenta provides a unified platform with prompt management, complete version history, and side-by-side comparison, enabling development teams to rapidly experiment, track changes, and refine LLM prompts efficiently.

AI/ML DevelopmentFor: AI Developers, Machine Learning Engineers, Prompt Engineers

Ensuring Production Reliability and Performance for LLM Apps

Building reliable LLM applications is challenged by the difficulty in systematically evaluating performance, debugging issues in production, and ensuring changes improve outcomes. Agenta offers automated evaluation pipelines, comprehensive observability to trace requests, monitor performance, and detect regressions, ensuring only validated and performant changes go live.

Technology, Enterprise AIFor: ML Engineers, AI Product Managers, Quality Assurance Teams

Fostering Cross-Functional Collaboration in LLM Development

Effective LLM development often suffers from fragmented workflows where product managers, developers, and domain experts struggle to collaborate directly on prompt engineering and evaluation. Agenta bridges this gap with a user-friendly UI that enables non-technical team members to safely experiment with prompts and run evaluations, fostering seamless collaboration and faster iteration across the entire team.

B2B SaaS, TechnologyFor: Product Managers, Domain Experts, AI Developers

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