GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Rapid-MLX is a high-performance local AI engine optimized for Apple Silicon, providing faster inference times and support for tool calling, prompt caching, and reasoning separation. It serves as a drop-in replacement for
Good fit when
- When you need a fast local AI engine specifically optimized for Apple Silicon devices, offering up to 4.2x faster performance compared to Ollama.
- If you require a tool that supports prompt caching with a cached time-to-first-token (TTFT) of 0.08 seconds, ensuring quick responses.
Avoid when
- If you are using Cursor, as it routes BYOK requests through its own servers, which cannot reach a Rapid-MLX endpoint on localhost, unless you intentionally expose the server through a public HTTPS隧道并为
Observed Sep 18, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 18, 2026
- Security (OSV)
- No lockfile
- As of Sep 18, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install Rapid-MLXSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Rapid-MLX is a high-performance local AI engine optimized for Apple Silicon, offering faster inference times and support for tool calling, prompt caching, and reasoning separation. It serves as a drop-in replacement for OpenAI and supports various tools and frameworks.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Sep 18, 2026
- Languages
- python
Source: github.language+pyproject.toml · Sep 18, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 18, 2026)
localhost:8000`. Point any client that supports a local custom endpoint (Aider, LangChain, OpenCode, PydanticAI, your own scripts) at **`http://localhost:8000/v1`**; ClaSource link
Source: README excerpt (regex_v1, Sep 18, 2026)
```python from openai import OpenAISource link
Source: README excerpt (regex_v1, Sep 18, 2026)
> **Cursor:** Cursor currently routes BYOK requests through its own servers, so its serverSource link
Tags
README
Quick Start (60 seconds) 1. Chat with a model right now: Defaults to . First run downloads the weights ( 3 GB) with a progress bar and drops you into a REPL. Type for slash commands, to quit. 2. Or serve it for use from other apps: Starts an OpenAI compatible HTTP server bound to...
For agents
This page has a .md twin and JSON over the API.