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free-llm-apis

咨询式指南:按速率限制、模型数量、速度或地域偏好推荐免费 LLM 提供商,读取 references 中的设置步骤,指导获取 API key、设置环境变量与 OpenAI 兼容端点,并用 Python/curl 示例验证密钥可用。

mnfst
热度 0.00
最近更新 2026-10-19 04:13:46
分类:开发依赖:第三方 API应用场景:API 集成 / 教学辅导 / 概念解释

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add mnfst/awesome-free-llm-apis/free-llm-apis
Metadata
name
free-llm-apis
description
Guide users through obtaining and configuring free API keys for LLM providers. Use when the user wants to set up a free LLM API, get a free API key, connect to a free model provider, configure an OpenAI-compatible endpoint at no cost, or asks about free tiers for AI models. Triggers on "free API key", "free LLM", "set up Gemini/Groq/Mistral/etc.", "which free provider", "how to get an API key", "free model access", "configure LLM for free".

Free LLM API Setup

Help users pick a free LLM provider and configure their API key. Every provider here has a permanent free tier, no credit card needed.

Provider Selection

Ask the user what matters most, then recommend accordingly:

Priority Best picks
Highest rate limits Groq (30 RPM, 14.4K RPD), Cerebras (30 RPM, 14.4K RPD)
Largest model selection Cloudflare Workers AI (49+ models), OpenRouter (32+ models)
Strongest proprietary models Google Gemini (Gemini 2.5 Pro), GitHub Models (GPT-4o)
Fastest inference Groq, Cerebras (both optimized for speed)
Highest token budget Mistral AI (1B tokens/month)
European provider Mistral AI (EU), LLM7.io (UK)
No signup required LLM7.io (basic tier works without token)

Provider categories

Provider APIs -- run by the companies that train the models:

Inference providers -- third-party platforms hosting open-weight models:

  • GitHub Models, NVIDIA NIM, Groq, Cerebras, Cloudflare Workers AI, LLM7.io, Kluster AI, OpenRouter, Hugging Face
  • See references/inference-providers.md for setup instructions.

Workflow

  1. Ask what models, rate limits, or features the user cares about. If they already know which provider they want, skip to step 3.
  2. Match their priorities against the table above. Suggest 1-2 options with a short reason.
  3. Load the right reference file and walk through the setup steps for that provider (API key, code example, env var).
  4. Offer a quick test script or curl command so they can confirm the key works.

Quick Test Template

After setup, use this to verify any provider:

from openai import OpenAI

client = OpenAI(api_key="KEY", base_url="BASE_URL")

response = client.chat.completions.create(
    model="MODEL_NAME",
    messages=[{"role": "user", "content": "Say hello in one sentence."}],
    max_tokens=50
)
print(response.choices[0].message.content)

Replace KEY, BASE_URL, and MODEL_NAME with values from the provider's setup guide.

Key Notes

  • All endpoints work with the OpenAI SDK unless noted.
  • RPM = requests per minute. RPD = requests per day.
  • Google Gemini's free tier is blocked in the EEA, UK, and Switzerland.
  • "Limits undocumented" = the provider doesn't publish rate limits.
  • Don't hardcode API keys. Use environment variables or a secrets manager.