Agenta
Agenta is an open-source LLMOps platform designed to help development teams build reliable LLM applications through integrated prompt management, evaluation, and observability tools.
The Problem It Solves
Most AI teams struggle with building reliable LLM products due to:
- Scattered prompts across Slack, Google Sheets, and emails
- Siloed workflows between Product Managers, Developers, and Domain Experts
- Lack of systematic testing and evaluation processes
- Zero visibility into whether experiments actually improve performance
- Debugging that feels like guesswork
Core Capabilities
Experiment & Iterate
Agenta provides a unified playground to compare prompts and models side-by-side, complete version history for tracking changes, and model-agnostic support so teams can use the best model from any provider without vendor lock-in.
Evaluate
Replace guesswork with evidence through automated evaluation workflows. Teams can integrate any evaluator (LLM-as-a-judge, built-in, or custom code), evaluate full agent traces, and incorporate human evaluation from domain experts.
Observe
Debug AI systems with full request tracing to find exact failure points. Annotate traces with team feedback or user input, and turn any trace into a test with a single click to close the feedback loop.
Collaborate
Bring PMs, experts, and developers into one workflow with a UI that enables domain experts to safely edit prompts without touching code, run evaluations directly from the UI, and maintain full API and UI parity.
Target Audience
LLM development teams, AI engineers, product managers, and domain experts building production LLM applications
Key Features
- Unified prompt playground for side-by-side comparison
- Complete version history for prompt management
- Model-agnostic support (no vendor lock-in)
- Automated evaluation workflows
- LLM-as-a-judge and custom evaluator integration
- Full trace evaluation for agent reasoning
- Human evaluation integration
- Request tracing and failure point identification
- Trace annotation and feedback collection
- Live online evaluations for regression detection
- Role-based collaboration UI for PMs, experts, and devs
- Full API and UI parity
Use Cases
- Iterating and optimizing prompts with cross-functional teams
- Systematic evaluation of LLM outputs before deployment
- Debugging production AI systems with full trace visibility
- Monitoring LLM application performance and detecting regressions
- Enabling non-technical domain experts to participate in prompt engineering
- Creating test sets from production errors for continuous improvement
Pros
- Open-source with full transparency
- Model-agnostic - no vendor lock-in
- Unified platform for prompt management, evaluation, and observability
- Enables collaboration between technical and non-technical team members
- Full API and UI parity for flexible workflows
- Closes the feedback loop from production to testing
Cons
- May require initial setup and integration effort
- Learning curve for teams new to LLMOps practices
Pricing
Open-source platform with enterprise options available
