Kayba vs Modaic

The Short Answer

Modaic is infrastructure for teams building with DSPy. Kayba is a framework-agnostic learning layer that works with any agent framework.

If you're all-in on DSPy, Modaic is built for you. If you use any other framework (or multiple), Kayba is the choice.

What Each Tool Does

Modaic

Modaic positions itself as "infrastructure for the DSPy era," providing a collaborative platform for DSPy-based optimization:

• DSPy program management: Tools for building, versioning, and collaborating on DSPy programs
• Collaborative optimization: Team-oriented workflows around DSPy's compilation and optimization cycles
• DSPy ecosystem tooling: Purpose-built for DSPy's signatures, modules, and optimizers

Modaic is solving a real coordination problem for DSPy teams. If your entire stack runs on DSPy, having dedicated infrastructure around it makes sense.

Kayba

Kayba (2k+ stars, MIT license) is an open-source learning layer for AI agents, built at ETH Zurich AI Center:

• Trace analysis: The Recursive Reflector programmatically analyzes agent execution traces via REPL-based code execution, extracting patterns from real-world behavior
• Skill extraction: Successes and failures are distilled into atomic, reusable skills with helpful/harmful tracking
• Skillbook: A persistent, transparent collection of learned behaviors -- auditable, with provenance linking each skill to the trace that produced it
• Prompt generation: Approved skills compile into optimized system prompts
• Continuous learning: Delta updates refine the Skillbook incrementally as new traces arrive

Kayba synthesizes three research contributions: ACE (arXiv:2510.04618), RLM (arXiv:2512.24601), and Dynamic Cheatsheet (arXiv:2504.07952).

The Key Difference: DSPy-Only vs. Framework-Agnostic

This is the defining distinction.

Modaic is tied to the DSPy ecosystem. If you're using LangChain, CrewAI, OpenAI Agents, browser-use, or a custom framework, Modaic doesn't apply. It's infrastructure specifically for teams that have committed to DSPy as their framework.

Kayba works with any agent framework. It operates on execution traces, not framework internals. Feed it traces from LangChain, CrewAI, OpenAI Agents, browser-use, DSPy, or anything else -- the learning pipeline is the same. You can even use Kayba across multiple frameworks simultaneously, building a single Skillbook from agents running on different stacks.

Comparison

When to Choose Modaic

Modaic may be a fit if:

• Your entire agent stack is built on DSPy and you have no plans to use other frameworks
• You need team collaboration tooling specifically around DSPy programs
• DSPy's compilation-based optimization model is your preferred approach to prompt improvement

When to Choose Kayba

Kayba is the stronger choice if:

• You use any framework other than DSPy (LangChain, CrewAI, OpenAI Agents, browser-use, custom code)
• You run agents on multiple frameworks and want a single learning layer across all of them
• You want continuous learning from production traces, not just compile-time optimization
• Transparency matters -- the Skillbook shows every learned behavior, its source trace, and whether it helps or hurts
• You want open-source software you can inspect, self-host, and contribute to
• You value research-backed methods with published, reproducible results

Benchmarks

Kayba's framework-agnostic approach has shown significant improvements across different agent types:

• t2-bench: pass@1 improvement of +27.4%, scaling to +100% at pass@4
• Browser agents: Success rate from 30% to 100%, with 82% fewer steps and 65% lower costs

These results come from the published research papers and are reproducible with the open-source framework.

Getting Started

Kayba is open-source and ready to use today:

pip install ace-framework

• Documentation -- Setup guides and API reference
• GitHub -- Source code and examples
• Dashboard -- Hosted version with visual Skillbook management