Open-source LLM development platform for prompt engineering, evaluation, and deployment. Teams compare prompts side-by-side, run automated evaluations, and deploy with A/B testing. Free self-hosted or $20/month for cloud.
An open-source platform for testing and improving AI prompts — experiment with different approaches and deploy the best one.
Agenta exists because most LLM applications ship with vibes-based testing. A developer writes a prompt, tries a few examples in a chat window, and pushes to production. Agenta replaces that workflow with systematic evaluation: side-by-side prompt comparison, automated test suites, version tracking, and A/B deployment. It works with any LLM, any framework, and any model provider.
The visual playground is Agenta's centerpiece. You load two or more prompt variants, feed them the same inputs, and see outputs side by side. PMs, developers, and domain experts all see the same screen. No more screenshots in Slack or "I think prompt B sounds better" without data.
This matters most for teams where non-technical people influence prompt quality. A legal team reviewing contract analysis prompts can compare outputs directly. A marketing team can evaluate tone and accuracy without asking a developer to rerun tests manually.
Agenta supports three evaluation modes: automated metrics (BLEU, exact match, custom Python functions), LLM-as-judge (where a model scores another model's output), and human evaluation (team members rate outputs through the UI). You can mix all three in a single evaluation run.
Custom evaluators are where Agenta pulls ahead of simpler tools. Write a Python function that checks your specific criteria. "Does the response mention our product name?" "Is the output under 200 words?" "Does it contain a valid JSON object?" These run across your full test set in seconds.
The self-hosted free tier covers most small team needs: 2 users, unlimited projects, 5,000 traces per month. To replicate this with alternatives: LangSmith Developer tier (free but limited), plus a separate deployment tool for A/B testing ($50-200/month), plus version control tooling. Agenta bundles evaluation, deployment, and version management for $0-20/month. The value is in the bundle, not any single feature.
GitHub discussions highlight the framework-agnostic design as the top draw. Users switching from LangSmith-only setups appreciate not being locked into one framework. The main criticism: Agenta's community is smaller, documentation has gaps for advanced use cases, and performance slows with very large evaluation datasets. For teams running thousands of evaluations, the cloud plan handles this better than self-hosting.
| Plan | Price | Details |
|------|-------|---------|
| Open Source | Free | 2 users, unlimited projects, 5k traces/month, 30-day retention |
| Team | $20/month | 10 users, 10k traces/month, 90-day retention, priority support |
| Enterprise | Custom | Unlimited users, 1M+ traces/month, 365-day retention, custom security |
Source: Agenta pricing
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Agenta fills the gap between basic prompt testing and enterprise LLMOps platforms. Framework-agnostic design and MIT license make it the best free option for teams that need structured prompt evaluation without LangChain lock-in. Smaller community and documentation gaps hold it back from competing with LangSmith at scale.
Side-by-side prompt comparison interface for testing different models, parameters, and configurations with real-time output comparison.
Use Case:
Comparing GPT-4 and Claude responses to the same customer support prompt to determine which produces better outcomes.
Automated and human evaluation workflows with pre-built evaluators, custom Python evaluators, and LLM-as-judge patterns for systematic quality assessment.
Use Case:
Running automated evaluations on 500 test cases after each prompt change to measure impact on accuracy.
Track prompt versions, configurations, and evaluation results over time with comparison views and rollback capabilities.
Use Case:
Maintaining a history of prompt iterations with performance metrics to understand what changes improved or degraded quality.
Deploy LLM application variants as API endpoints with traffic splitting for production A/B testing of different configurations.
Use Case:
Testing a new prompt version on 20% of production traffic while monitoring quality metrics before full rollout.
Works with RAG pipelines, chains, agents, and custom code — not limited to simple prompt-response patterns.
Use Case:
Evaluating and deploying a RAG application that retrieves from a knowledge base and generates responses with citations.
Multi-user workspace with shared experiments, evaluations, and deployments for collaborative LLM application development.
Use Case:
Product managers reviewing prompt experiment results alongside engineers to make data-driven decisions about production configurations.
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View Pricing Options →Systematic prompt engineering with version tracking and evaluation
A/B testing different LLM configurations in production
Collaborative LLM application development across technical and non-technical teams
Building evaluation pipelines for quality assurance in AI applications
We believe in transparent reviews. Here's what Agenta doesn't handle well:
Both provide evaluation and deployment for LLM apps, but Agenta is open-source and framework-agnostic while LangSmith is tied to the LangChain ecosystem. Agenta's visual playground and A/B testing features are strong, while LangSmith offers deeper tracing for LangChain applications.
Yes, Agenta is framework-agnostic. It works with direct API calls, LlamaIndex, custom Python code, or any other approach. You define your LLM application logic and Agenta handles versioning, evaluation, and deployment.
Yes, Agenta is MIT-licensed and provides Docker Compose files for self-hosting. The full platform including the UI, API, and evaluation engine can run on your own infrastructure.
Yes, Agenta supports human evaluation workflows where evaluators review and score outputs through the web interface. Results are tracked alongside automated evaluations for comprehensive quality assessment.
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Continued active development on GitHub with focus on prompt management, evaluation, and observability features. Growing community adoption as framework-agnostic alternative to LangSmith.
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