Primary Recommendations
6
Total Mentions
19
Win Rate
32%
Implementation Rate
67%
How well your documentation and SDK help AI assistants recommend and implement your tool
out of 100
How often AI writes code after recommending
How often selected as primary choice
% of prompt constraints addressed
Fewer gotchas = more AI-friendly
Consistency across assistants
Win Rate Trend
32% → 32%
Mention Volume
Weekly Activity
1 week of data
| Category | Recommended | Compared | Rejected | Total | Win Rate |
|---|---|---|---|---|---|
| 🔭 LLM Observability | 4 | - | - | 10 | 40% |
| 🤖 Agentic Tooling | 2 | - | - | 6 | 33% |
| unknown | - | - | - | 3 | 0% |
Constraints in prompts where this vendor was mentioned but a competitor was chosen
| Competitor | Wins Over You | Scenarios |
|---|---|---|
| LangSmith | 2 | Automated Agent Evaluation with CI Gate |
| Langfuse | 1 | LLM Observability for Customer Support Bot |
Braintrust wins for your use case:**
Braintrust wins for your use case:**
Prioritized by estimated impact on AI recommendation ranking • Based on 19 benchmark responses
langsmith beats you in 2 head-to-head scenarios. Their advantage: addressing regression detection.
langfuse beats you in 1 head-to-head scenario.