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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Agenta provides a complete open-source LLMOps solution that brings prompt engineering, evaluation, and observability together in one platform. Instead of storing prompts across scattered documents and communication channels, teams get a single source of truth for managing and versioning all prompt iterations. The platform includes a unified playground where users can compare prompts, models, and parameters side-by-side, making experimentation faster and more organized. Agenta supports automated evaluation pipelines that leverage LLM-as-a-judge, human reviewers, and custom evaluators to ensure changes actually improve performance. Its observability stack traces every request and highlights failure points, helping teams debug issues and convert problematic interactions into reusable test cases. Product managers, developers, and domain experts can collaborate through shared test sets, annotations, and interactive evaluations directly from the UI. Agenta integrates seamlessly with LangChain, LlamaIndex, OpenAI APIs, and any model provider, avoiding vendor lock-in. By consolidating collaboration, experimentation, testing, and monitoring, Agenta enables AI teams to move from chaotic workflows to streamlined, reliable LLM development.

Description

Kayba empowers AI agents to enhance their performance through experiential learning. By analyzing execution traces, it identifies and rectifies failures while assessing the effectiveness of these corrections. Rather than depending on generic evaluations that fail to clarify the reasons behind an agent's shortcomings, Kayba utilizes the agent's unique traces to identify failure modes and create tailored benchmarks relevant to the user's specific context, enabling teams to gauge improvements against authentic production failure patterns. With a simple one-line setup, Kayba integrates tracing into the agent, continuously monitors its performance, and promptly alerts users when any step ceases to be recorded. Since even effective tracing can degrade as teams implement changes, Kayba actively reviews existing tracing, highlights any broken elements, identifies the specific file requiring attention, and relays the issue to a coding agent via MCP. This coding agent then addresses the problem, after which Kayba confirms that the trace is fully functional again, ensuring ongoing reliability and performance enhancement. Ultimately, this process allows teams to maintain high standards of operational continuity while fostering continual improvement in their AI systems.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Cohere
Falcon AI
Hugging Face
LangChain
Llama
Llama 2
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Model Context Protocol (MCP)
OpenAI
Python

Integrations

Cohere
Falcon AI
Hugging Face
LangChain
Llama
Llama 2
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Model Context Protocol (MCP)
OpenAI
Python

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Agenta

Founded

2023

Country

Germany

Website

agenta.ai/

Vendor Details

Company Name

Kayba

Founded

2025

Country

United States

Website

kayba.ai/

Product Features

Alternatives

Alternatives