The Short Answer
Braintrust is an evaluation and logging platform — it helps you measure how well your AI products perform, manage datasets, and iterate on prompts. Kayba is a learning layer — it analyzes agent execution traces and automatically improves how the agent behaves next time.
Braintrust tells you what's wrong. Kayba teaches your agent to fix it.
They solve different problems and work well together: use Braintrust to evaluate, use Kayba to learn and improve.
What Each Tool Does
Braintrust
Braintrust, backed by $36M in Series A funding and trusted by teams at Notion, Stripe, and Vercel, provides:
- Evaluations: Run scoring functions against datasets, compare model and prompt versions side-by-side
- Logging: Capture LLM calls, latency, token usage, and custom metadata in production
- Datasets: Create, version, and manage golden datasets for regression testing
- Playground: Experiment with prompts, models, and parameters interactively
- Proxy: Unified API gateway for multiple LLM providers with caching and rate limiting
It's a strong platform for teams shipping AI products that need rigorous evaluation workflows. The eval framework is well-designed, the dataset management is solid, and the developer experience is polished.
Kayba
Kayba provides:
- Trace analysis: The Recursive Reflector programmatically analyzes agent execution traces via REPL-based code execution — not just scoring them, but extracting actionable insights
- 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
The Key Difference
The distinction is evaluate vs. learn.
| Capability | Braintrust | Kayba |
|---|---|---|
| Measure performance | Scoring functions, dataset-based evals | Trace analysis with pattern extraction |
| Understand why it failed | Compare eval results across versions | Automated failure analysis via Recursive Reflector |
| Fix the behavior | You manually revise prompts based on eval results | Skills extracted automatically, prompts generated from Skillbook |
| Remember the fix | Dataset versioning and eval history | Skillbook with provenance — every skill links to its source trace |
| Prevent recurrence | Run evals against golden datasets | Continuous learning — the Skillbook grows with each analysis cycle |
With Braintrust alone, the workflow is:
- Agent fails → 2. Check Braintrust evals → 3. Identify low-scoring cases → 4. Manually edit system prompt → 5. Re-run evals to verify → 6. Repeat
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 | Braintrust | Kayba |
|---|---|---|
| Primary function | Evaluation & logging | Learning & prompt improvement |
| Trace handling | Log and score | Analyze and extract skills |
| Eval approach | Scoring functions against datasets | Trace-based skill extraction with helpful/harmful tracking |
| Output | Eval scores, experiment comparisons, logs | Skillbook + generated system prompts |
| Learning mechanism | None (evaluation only) | Recursive Reflector + Skillbook + delta updates |
| Framework dependency | Framework-agnostic (TypeScript & Python SDKs) | Framework-agnostic (any trace format) |
| LLM proxy | Built-in multi-provider proxy with caching | Not included (focused on learning, not routing) |
| Open source | Partially (eval framework is OSS, platform is proprietary) | Fully open-source (MIT, 2k+ stars) |
| Pricing | Free tier + usage-based (custom enterprise pricing) | Free (OSS) / $29/month (hosted dashboard) |
Using Them Together
Braintrust and Kayba are complementary. A practical setup:
- Braintrust logs every agent execution in production and scores responses against your criteria
- Export traces from Braintrust (or collect them directly from your agent)
- Kayba analyzes those traces, extracts skills, and generates improved prompts
- Braintrust evals verify that the new prompts actually improve agent performance before deployment
Braintrust gives you measurement. Kayba gives you improvement. Together, you have a closed loop: evaluate → learn → improve → verify.
When to Use Braintrust Alone
Braintrust is sufficient if:
- You mainly need structured evaluation workflows with scoring functions and dataset management
- Your team has bandwidth to manually review eval results and iterate on prompts
- You need an LLM proxy with caching and multi-provider support
- You're building AI features (not autonomous agents) where eval-driven iteration is the primary workflow
When to Add Kayba
Add Kayba when:
- Your evals surface failures but you're still manually figuring out what to change in the prompt
- Your agent is in production with enough volume that manual prompt iteration doesn't scale
- You want a systematic record of what the agent has learned (not scattered prompt edits across eval cycles)
- You need the improvement step automated, not just the measurement step
- You want continuous learning — each batch of traces makes the agent better without manual intervention
Getting Started
Kayba is open-source and analyzes traces from any source — including traces logged by Braintrust.
pip install ace-framework
- Documentation — Setup guides and API reference
- GitHub — Source code and examples
- Dashboard — Hosted version with visual Skillbook management