Program language model interactions with constraints and modularity
LMQL is a programming language that lets you write code to interact with large language models like GPT in a more controlled way. It allows you to add constraints to AI responses, such as limiting length or ensuring specific formats, making outputs more reliable. This is great for developers and researchers who want to build AI applications without deep prompt engineering expertise.
Enables modular and reusable prompt components for efficiency
Enforces output constraints like length and stop conditions
Runs on multiple LLM backends with easy switching
Uses Python syntax for prompt construction and control flow
Guarantees outputs are in specified formats like integers
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Recent data shows that LMQL has 4.1K monthly visits (-19.3% decrease from the previous month), 43.0% bounce rate, and average 1.77 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 3 countries worldwide, led by United States contributing 61% of total traffic. SEO performance is shown by 5 tracked keywords, with "lmql" being the top-performing keyword with 160 monthly searches. See below for more info.
Monthly Visits
4.1K
Pages per Visit
1.77
Bounce Rate
43.0%
Average Time on Site
28s
| SEO Keyword | Volume | CPC |
|---|---|---|
| lmql | 160 | - |
| constrain llm output | 10 | - |
| llama.cpp | 28.6K | - |
| llama index | 11.3K | $2.88 |
| llama cpp | 5.6K | $2.37 |
| Source | Traffic Share |
|---|---|
| Direct | 45% |
| Search | 34% |
| Social | 8% |
| Referrals | 12% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 61% |
 | 24% |
 | 14% |
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