Agenta
Open-source LLMOps platform for prompt management, evaluation, and observability
About Agenta
Agenta is an open-source LLMOps platform that provides the complete lifecycle for building reliable LLM applications. From prompt engineering with Git-like versioning, to systematic evaluation with LLM-as-judge and human annotation, to production observability — it gives teams a unified workflow to ship AI apps with confidence.
Pros & Cons
Pros
- Complete LLMOps lifecycle in one platform — prompt management, evaluation, and observability
- Git-like prompt versioning makes it easy to experiment without affecting production
- Non-technical team members can contribute through the no-code UI
- Open-source MIT license with self-hosting option for full data control
- Systematic evaluation framework reduces the risk of deploying broken prompts
Cons
- Relatively small team and community compared to commercial LLMOps platforms
- Cloud pricing not publicly listed — requires contacting sales
- Setup and configuration can be complex for self-hosted deployments
- Documentation still evolving as the platform matures
Key Features
Prompt Management
Git-like versioning for prompts with branches, commits, and environment deployments (dev/staging/prod)
Evaluation Framework
Run automated evaluations with LLM-as-judge, built-in evaluators, and A/B comparisons on test sets
Human Annotation
Subject matter experts can review, annotate, and validate LLM outputs through an intuitive UI
Observability
Trace LLM calls in production, link prompts to traces, and run online evaluations on live data
No-Code Prompt Editor
Non-technical team members can edit prompts, run evaluations, and deploy changes without code
Multi-Provider Support
Works with OpenAI, Anthropic, and other LLM providers — no vendor lock-in
Pricing
- Full platform features
- Self-hosted deployment
- Unlimited prompts
- Community support
- MIT license
- Managed hosting
- Team collaboration
- Priority support
- SSO integration
- SLA guarantees
Best For
LLM App Development
Build and iterate on LLM-powered applications with structured prompt versioning and testing
Prompt Quality Assurance
Run systematic evaluations before deploying prompt changes to catch regressions early
Production Monitoring
Monitor LLM application performance in production with tracing and online evaluation
Team Collaboration on AI
Enable subject matter experts and developers to collaborate on prompt engineering
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