Agenta

Overview

Agenta is an open-source LLMOps platform that helps engineering and product teams build production-grade LLM applications faster. It combines prompt management, systematic evaluation, and observability in one place. You can experiment with 50+ LLM models, version control your prompts, run automated and human evaluations, and trace production behavior with OpenTelemetry-compliant monitoring.

Key Features

Interactive Prompt Playground: Compare prompts side by side against test cases and experiment with 50+ LLM models or bring your own models to find the best configuration for your use case.
Version Control for Prompts: Track and manage prompt variations with branching, environments, and a dedicated registry to maintain consistency across development and production.
Systematic Evaluation Tools: Test LLM outputs with both automated evaluators (similarity match, regex, AI critique) and human annotation to ensure quality before deployment.
OpenTelemetry-Compliant Observability: Trace and debug LLM calls, retrieval operations, tool executions, and agent reasoning steps with full OTel compatibility for seamless integration.
Production Monitoring: Track cost, performance, latency, and usage patterns in real-time, turning production traces into test cases for continuous improvement.
Non-Developer Collaboration: Enable product teams and subject matter experts to iterate on configurations, evaluate outputs, and deploy changes through the UI without code.
RAG and Chain Support: Build complex workflows including Retrieval Augmented Generation and chain-of-prompts with compatibility for Langchain and LlamaIndex frameworks.

Pros

Open Source Flexibility: MIT licensed and self-hostable, giving you full control over your infrastructure and data without vendor lock-in.
All-in-One Platform: Consolidates prompt engineering, evaluation, and monitoring in a single tool, reducing tool sprawl and context switching.
Strong Observability: OpenTelemetry-native tracing provides detailed debugging capabilities that help catch edge cases and production issues quickly.
Generous Free Tier: Free plan includes 2 users and 5,000 traces per month with no credit card required, making it accessible for small teams and startups.
Feedback Loop Integration: Capture user feedback through APIs and convert production traces into test cases for continuous quality improvement.

Cons

Learning Curve for Non-Technical Users: UI and terminology can feel technical for product managers and subject matter experts without developer backgrounds.
Integration Philosophy: Platform positions itself as the core application rather than just a testing layer, which may not fit teams wanting lightweight integration.
Automated Evaluation Limitations: AI-based evaluators can miss qualitative aspects like tone, context nuances, and coherence without human review.
Limited Documentation on Specific Limitations: As a growing open-source project, some edge cases and advanced use case documentation may still be developing.

Use Cases

Customer Support Chatbot Development: Build and refine chatbot responses by comparing prompt variations in the playground, evaluating quality with automated metrics and human annotation, then monitoring production conversations to identify failure patterns and create regression tests.
RAG Application Optimization: Develop retrieval-augmented generation systems by testing different prompt strategies, tracking retrieval operation performance, and monitoring which document chunks lead to better answers in production.
Multi-Model Experimentation: Compare outputs from different LLM providers (OpenAI, Anthropic, open-source models) against your test cases to find the optimal balance of cost, speed, and quality for your specific use case.
Prompt Version Management: Maintain multiple prompt versions for different environments (development, staging, production) with branching, allowing product teams to test changes safely before rolling out to users.
Production Debugging and Monitoring: Trace complex agent workflows in production to identify where failures occur, monitor token usage and costs across different application variants, and build golden test sets from real user interactions.
Collaborative LLM Development: Enable cross-functional collaboration where engineers build the application structure while domain experts iterate on prompts, evaluation criteria, and quality standards through the UI.

Frequently Asked Questions

What is Agenta?

Agenta is an open-source LLMOps platform that combines prompt management, evaluation, and observability for building production-grade LLM applications. It helps teams experiment with prompts, test outputs systematically, and monitor production behavior in one integrated platform.

Is Agenta free to use?

Yes, Agenta offers a free tier with 2 users, 5,000 traces per month, basic prompt management, and up to 20 evaluations per month. Since it's open-source (MIT license), you can also self-host it completely free with unlimited usage.

What LLM models does Agenta support?

Agenta supports 50+ LLM models out of the box, including models from OpenAI, Anthropic, and other providers. You can also bring your own models and integrate them into the platform.

Can non-developers use Agenta?

Yes, Agenta is designed for collaboration between engineers and non-technical team members. Product managers and subject matter experts can iterate on prompts, run evaluations, annotate results, and deploy changes through the UI without writing code, though some users report the interface can feel technical initially.

How does Agenta handle observability?

Agenta uses OpenTelemetry-compliant tracing to monitor LLM applications in production. You can trace LLM calls, retrieval operations, tool executions, and agent reasoning steps, while tracking cost, latency, and usage patterns. The platform integrates with existing OTel-compatible services.

What's the difference between self-hosted and cloud versions?

The self-hosted version is completely free and open-source, giving you full control over your infrastructure and data. The cloud version offers managed hosting with a free tier (5,000 traces/month) and paid plans for larger teams with additional features like longer retention, audit logs, and SOC2 compliance.

Does Agenta work with existing LLM frameworks?

Yes, Agenta is compatible with popular frameworks like Langchain and LlamaIndex, supporting various workflows including chain-of-prompts and Retrieval Augmented Generation (RAG).

How do I evaluate LLM outputs in Agenta?

Agenta offers multiple evaluation methods: automated evaluators (similarity match, regex, AI critique), human annotation through the UI, custom evaluators you can build, and LLM-as-judge approaches. You can create test sets from production data, playground experiments, or CSV uploads.