Primary Recommendations
2
Total Mentions
14
Win Rate
14%
Implementation Rate
0%
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
14% → 14%
Mention Volume
Weekly Activity
1 week of data
| Category | Recommended | Compared | Rejected | Total | Win Rate |
|---|---|---|---|---|---|
| 🤖 Agentic Tooling | 2 | - | - | 8 | 25% |
| 🔭 LLM Observability | - | - | - | 6 | 0% |
Constraints in prompts where this vendor was mentioned but a competitor was chosen
| Competitor | Wins Over You | Scenarios |
|---|---|---|
| Braintrust | 4 | Automated Agent Evaluation with CI Gate, RAG Pipeline Debugging and Evaluation, LLM Observability for Customer Support Bot |
| Langfuse | 1 | LLM Observability for Customer Support Bot |
Prioritized by estimated impact on AI recommendation ranking • Based on 14 benchmark responses
AI assistants recommend you but often don't write the setup code. This suggests SDK complexity or missing AI-friendly documentation. Implementation gaps concentrated on codex_cli.
You're mentioned in 8 agent dev scenarios but only win 2. Analyze the constraints in losing scenarios for targeted improvements.
You're mentioned in 6 llm observability scenarios but only win 0. Analyze the constraints in losing scenarios for targeted improvements.
braintrust beats you in 4 head-to-head scenarios. Their advantage: addressing prompt versioning, ci eval suite, pii redaction.
Your win rate drops from 14% to 0% when "no langchain" is required. This constraint appears in 2 benchmark prompts. langfuse addresses it 1× in winning scenarios.
Your win rate drops from 14% to 0% when "pii redaction" is required. This constraint appears in 2 benchmark prompts. langfuse addresses it 1× in winning scenarios.
Your win rate drops from 14% to 0% when "quality evaluation" is required. This constraint appears in 2 benchmark prompts. langfuse addresses it 1× in winning scenarios.
Your win rate drops from 14% to 0% when "conversation threading" is required. This constraint appears in 2 benchmark prompts. langfuse addresses it 1× in winning scenarios.
Your win rate drops from 14% to 0% when "cost tracking" is required. This constraint appears in 2 benchmark prompts. langfuse addresses it 1× in winning scenarios.
Only recommended on codex_cli (2×). claude_code and cursor are not recommending you — improve discoverability through documentation, npm package naming, and example code.
langfuse beats you in 1 head-to-head scenario. Their advantage: addressing no langchain, pii redaction, quality evaluation.