The Short Answer
LangFuse is an open-source observability platform for LLM applications — it helps you trace, evaluate, and monitor your agent's behavior. Kayba is a learning layer — it analyzes traces and automatically improves how your agent behaves next time.
LangFuse shows you what happened. Kayba teaches your agent to do better.
Both are open-source, both work with any LLM provider, and they complement each other well: use LangFuse to observe, use Kayba to learn and improve.
What Each Tool Does
LangFuse
LangFuse provides:
- Tracing: Detailed execution traces for LLM calls, tool use, and retrieval steps
- Evaluation: Score-based evals, human annotation, and model-based evaluation pipelines
- Monitoring: Dashboards for cost, latency, and quality metrics
- Prompt management: Version, deploy, and A/B test prompts
- Datasets: Create evaluation datasets from production traces
LangFuse is fully open-source (MIT licensed) and can be self-hosted. It's framework-agnostic with SDKs for Python, JavaScript, and integrations with LangChain, LlamaIndex, and others.
Kayba
Kayba provides:
- Trace analysis: The Recursive Reflector programmatically analyzes agent execution traces via REPL-based code execution — extracting actionable insights, not just visualizing them
- Skill extraction: Failures and successes are distilled into atomic, reusable skills with helpful/harmful counters
- Skillbook: A persistent, transparent collection of everything the agent has learned — organized, auditable, with provenance tracking
- Prompt generation: Approved skills are compiled into optimized system prompts
- Continuous learning: Delta updates refine the Skillbook incrementally over time
Kayba is also open-source (MIT licensed, 2k+ GitHub stars) and framework-agnostic.
The Key Difference
Both tools start with traces. The difference is what happens next.
| Capability | LangFuse | Kayba |
|---|---|---|
| Collect traces | Detailed tracing with nested spans | Accepts traces from any source |
| Understand failures | Manual inspection + eval scores | Automated failure analysis via Recursive Reflector |
| Fix behavior | You manually update prompts based on observations | Skills extracted automatically, prompts generated from Skillbook |
| Remember fixes | Prompt versioning in LangFuse | Skillbook with provenance — every skill links to its source trace |
| Prevent recurrence | Eval datasets catch regressions | Continuous learning — the Skillbook grows with each cycle |
With LangFuse alone:
- Agent fails → 2. Check traces in LangFuse → 3. Identify the pattern → 4. Manually edit prompts → 5. Deploy new version → 6. Hope it doesn't break something else
With Kayba added:
- Agent fails → 2. Kayba analyzes traces → 3. Skills extracted automatically → 4. Review and approve → 5. New prompt generated → 6. Agent improves
Comparison
| Dimension | LangFuse | Kayba |
|---|---|---|
| Primary function | Observability, evaluation, monitoring | Learning & prompt improvement |
| Trace handling | Visualize, search, and score | Analyze and extract skills |
| Open source | Yes (MIT) | Yes (MIT, 2k+ stars) |
| Self-hostable | Yes | Yes (pip install) |
| Framework support | LangChain, LlamaIndex, OpenAI SDK, etc. | Framework-agnostic (any trace format) |
| Eval approach | Score-based evals, annotation queues | Trace-based skill extraction with helpful/harmful tracking |
| Output | Dashboards, metrics, prompt versions | Skillbook + generated system prompts |
| Learning mechanism | None (observability only) | Recursive Reflector + Skillbook + delta updates |
| Pricing | Free (self-hosted) / Cloud from $0 | Free (OSS) / $29/month (hosted dashboard) |
Using Them Together
LangFuse and Kayba are natural partners — especially since both are open-source and can be self-hosted side by side.
A practical workflow:
- LangFuse traces every agent execution in production — monitoring cost, latency, and quality
- Export traces from LangFuse (or collect them directly from your agent)
- Kayba analyzes those traces, extracts skills, and generates improved prompts
- LangFuse evals verify that the new prompts actually improve agent performance
- Deploy and repeat — the Skillbook grows, the agent improves
LangFuse gives you visibility. Kayba gives you improvement. Together: observe → learn → improve → verify.
When to Use LangFuse Alone
LangFuse is sufficient if:
- You need production monitoring for cost, latency, and quality metrics
- Your team has capacity to review traces and manually improve prompts
- You want prompt versioning and A/B testing capabilities
- You need human annotation workflows for evaluation
When to Add Kayba
Add Kayba when:
- Manual trace review doesn't scale with your agent's volume
- You want automated failure pattern detection instead of manual inspection
- You need a systematic record of what the agent has learned (the Skillbook)
- You want the improvement step automated, not just the observation step
- You're looking for in-context learning without fine-tuning or GPU costs
Getting Started
Kayba is open-source and analyzes traces from any source — including LangFuse exports.
pip install ace-framework
- Documentation — Setup guides and API reference
- GitHub — Source code and examples
- Dashboard — Hosted version with visual Skillbook management