OpenLM
Drop-in OpenAI-compatible library for calling any hosted inference API.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is OpenLM?
OpenLM is a TypeScript library that allows developers to call Large Language Models from various hosted APIs, providing compatibility with the OpenAI API. It simplifies integration and deployment of LLMs in applications.
Key differentiator
“The only TypeScript library providing a drop-in OpenAI-compatible interface to call any hosted inference API, offering flexibility in integrating multiple LLM providers.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The library is primarily designed for use with TypeScript and JavaScript, which can be a limitation for developers working in other languages.
Integration heavily relies on specific hosted API providers, making it difficult to switch or diversify LLM services without significant changes.
The official documentation lacks detailed examples and explanations for complex scenarios, leading to a steep learning curve for new users.
Users have reported increased latency and resource consumption when scaling the application beyond a certain number of concurrent requests.
Fit analysis
Who is it for?
✓ Best for
TypeScript teams building server-rendered apps who need to integrate multiple LLM services.
Projects requiring a flexible and compatible way to call various hosted inference APIs.
Developers looking for an easy-to-integrate library that supports different LLM providers.
✕ Not a fit for
Teams needing real-time streaming capabilities (OpenLM is designed for batch processing).
Projects with strict budget constraints as it requires hosting and managing the underlying LLM services.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with OpenLM
Step-by-step setup guide with code examples and common gotchas.