Use Cases
Explore how Kayba helps AI agents self-improve across different verticals, frameworks, and constraints.
Alternatives to Manual Agent Tuning
A complete guide to agent improvement approaches: manual prompt engineering, fine-tuning, DSPy, evolutionary optimization, and trace-based learning. Compare all options.
Agent Learning for Browser Agents
How Kayba took browser agents from 30% to 100% success rate with 82% fewer steps. Up to 2x consistency improvement on τ2-bench. Learn from navigation failures, form-filling errors, and task completion gaps.
Agent Learning for Coding Agents
How Kayba helps coding agents self-improve by learning from code review failures, wrong file edits, and test regressions. Reduce repeated mistakes automatically.
Agent Learning for Customer Support Agents
How Kayba helps customer support agents learn from policy violations, escalation mistakes, and resolution failures. Reduce repeated errors and improve consistency automatically.
Agent Learning for LangChain Agents
Add a self-improving learning layer to your LangChain agents. Kayba analyzes LangChain traces, extracts skills, and generates better prompts — no code changes required.
Memory vs Learning for AI Agents
Memory stores what happened. Learning teaches how to succeed. Your AI agent needs both — understand the difference and why agent learning is the missing piece.
Open-Source Agent Learning Framework
Kayba is the leading open-source framework for self-improving AI agents. MIT licensed, 2k+ GitHub stars, built on published research from ACE, RLM, and Dynamic Cheatsheets.
Best Agent Improvement Tool for Startups
Why startups building AI agents choose Kayba. Open-source, no GPU costs, $29/month Pro tier — make your agents self-improve without enterprise pricing or ML infrastructure.
What is Context Engineering for AI Agents?
Context engineering is the discipline of building the right context for every AI agent step. Learn how it works, why it matters, and how Kayba automates it.
Agent Learning Without Fine-Tuning
Improve your AI agents without fine-tuning, GPU costs, or training data. Kayba uses in-context learning to make agents self-improve from their own execution traces.