Agenta

Agenta is a comprehensive, open-source LLMOps platform that provides the necessary infrastructure for teams to build, evaluate, and monitor large language model (LLM) applications effectively. It addresses common challenges in LLM development, such as scattered prompts, siloed team collaboration, and the lack of systematic evaluation, by offering a single source of truth. The platform is designed to bring developers, product managers, and domain experts together, enabling them to create reliable AI products through structured, data-driven workflows.

How to use Agenta

Getting started with Agenta involves a straightforward process: 1. Create a new prompt (either a completion prompt or a chat application) within the Agenta web UI. 2. Experiment with different configurations and commit your changes to a variant, which acts like a Git branch for version control. 3. Once satisfied, deploy the specific variant version to an environment such as development, staging, or production. 4. Finally, integrate the deployed prompt into your application's codebase using the Agenta Python SDK or by calling its API directly.

Core Features of Agenta

• Integrated Prompt Management: A unified playground for side-by-side comparison of prompts and models, complete version history, and a user-friendly UI for non-developers to contribute.
• Systematic Evaluation: Supports automated evaluations using LLM-as-a-judge or custom code, integrates human feedback and annotation, and provides online evaluation for monitoring production applications.
• Comprehensive Observability: Trace every request to debug failures, monitor costs and latency, gather user feedback, and convert production issues into test cases with a single click.
• Team Collaboration: A central hub designed to break down silos, allowing developers, product managers, and domain experts to experiment, version, and debug prompts collaboratively.
• Model & Framework Agnostic: Compatible with any LLM provider (OpenAI, Cohere, Vertex AI, etc.) and integrates seamlessly with popular frameworks like LangChain and LlamaIndex.
• Open-Source: Published under an MIT license, offering the flexibility to self-host, modify, and use in commercial projects without restrictions.

Use Cases for Agenta

Agenta is ideal for teams developing and maintaining complex LLM-powered applications, such as RAG systems, chatbots, or AI agents. It helps in systematically improving the reliability and performance of production AI products. The platform is also perfectly suited for organizations looking to establish a standardized LLMOps workflow that facilitates seamless collaboration between technical and non-technical team members on AI projects.

Advantages of Agenta

The primary advantage of Agenta is its ability to transform chaotic, 'vibe-based' testing into a structured, evidence-driven process. It provides full visibility into application performance, costs, and errors, empowering teams to make informed decisions. By being open-source and built on open standards like OpenTelemetry, it prevents vendor lock-in and ensures easy integration into existing tech stacks. Its collaborative features ensure that domain expertise is effectively incorporated into the development lifecycle.

Pricing and Plans

Agenta offers several plans to accommodate different needs:

• Hobby: A free plan that includes 2 users, 5,000 traces per month, 20 evaluations per month, and a 30-day data retention period.
• Pro: Priced at $49/month, this plan includes 3 users (with additional seats at $20/user), 10,000 traces per month, unlimited evaluations, and a 90-day retention period.
• Business: At $399/month, this plan offers unlimited seats, 1 million traces per month, Role-Based Access Control (RBAC), SOC2 reports, and a 365-day retention period.
• Enterprise: A custom plan with volume pricing, self-hosted deployment options, dedicated support, custom SLAs, and audit logs for large-scale requirements.