kantan-llm 0.1.1

Minimal LLM client getter for OpenAI Responses + OpenAI-compatible Chat Completions.

kantan-llm 😺✨

A tiny Python library that removes the boring boilerplate (keys/URLs/provider selection) so you can call LLMs with a single get_llm() 💨

Big idea: set env vars for the providers/models you use, then just do get_llm("model-name") and it “just connects” 😺✨

Supported providers (roughly) 🌍

• OpenAI (Responses)
• Anthropic (Claude via OpenAI-compatible SDK)
• OpenRouter (OpenAI-compatible Chat)
• Google (Gemini via OpenAI-compatible Chat)
• LMStudio / Ollama / any OpenAI-compatible Chat

Install 📦

pip install kantan-llm

Quickstart 🚀

OpenAI (Responses API is the source of truth)

export OPENAI_API_KEY="sk-..."

from kantan_llm import get_llm

llm = get_llm("gpt-4.1-mini")
res = llm.responses.create(input="Say hi in one short line.")
print(res.output_text)

OpenAI-compatible (Chat Completions is the source of truth)

LMStudio (example: openai/gpt-oss-20b)

export LMSTUDIO_BASE_URL="http://192.168.11.16:1234"  # `/v1` is optional

from kantan_llm import get_llm

llm = get_llm("openai/gpt-oss-20b", provider="lmstudio")
cc = llm.chat.completions.create(messages=[{"role": "user", "content": "Return exactly: OK"}], max_tokens=16)
print(cc.choices[0].message.content)

Ollama (example)

export OLLAMA_BASE_URL="http://localhost:11434"  # `/v1` is optional

from kantan_llm import get_llm

llm = get_llm("llama3.2", provider="ollama")
cc = llm.chat.completions.create(messages=[{"role": "user", "content": "Return exactly: OK"}], max_tokens=16)
print(cc.choices[0].message.content)

Anthropic (Claude via OpenAI-compatible SDK)

export CLAUDE_API_KEY="sk-ant-..."

from kantan_llm import get_llm

llm = get_llm("claude-3-5-sonnet-latest")  # if `CLAUDE_API_KEY` exists -> provider=anthropic (inferred)
cc = llm.chat.completions.create(messages=[{"role": "user", "content": "Return exactly: OK"}], max_tokens=16)
print(cc.choices[0].message.content)

OpenRouter (includes Claude, etc.)

export OPENROUTER_API_KEY="..."

from kantan_llm import get_llm

llm = get_llm("anthropic/claude-3.5-sonnet", provider="openrouter")  # explicit is recommended (Anthropic takes precedence)
cc = llm.chat.completions.create(messages=[{"role": "user", "content": "Return exactly: OK"}], max_tokens=16)
print(cc.choices[0].message.content)

Google (Gemini via an OpenAI-compatible endpoint)

export GOOGLE_API_KEY="..."

from kantan_llm import get_llm

llm = get_llm("gemini-2.0-flash")
cc = llm.chat.completions.create(messages=[{"role": "user", "content": "Return exactly: OK"}], max_tokens=16)
print(cc.choices[0].message.content)

Provider rules 🧭

• gpt-* → openai
• gemini-* → google
• claude-* → anthropic (if CLAUDE_API_KEY is set) → openrouter (if OPENROUTER_API_KEY is set) → otherwise compat
• If the model name is not recognizable, it picks the first available provider by env vars: lmstudio → ollama → openrouter → anthropic → google

Explicit provider 🎯

from kantan_llm import get_llm

llm = get_llm("gpt-4.1-mini", provider="openai")

Fallback (order = priority) 🧯

from kantan_llm import get_llm

llm = get_llm("gpt-4.1-mini", providers=["openai", "lmstudio", "openrouter"])

Environment variables 🔐

• OpenAI
  • OPENAI_API_KEY (required)
  • OPENAI_BASE_URL (optional)
• Generic compatible (compat)
  • KANTAN_LLM_BASE_URL (required)
  • KANTAN_LLM_API_KEY (optional; falls back to a dummy value)
• LMStudio
  • LMSTUDIO_BASE_URL (required)
• Ollama
  • OLLAMA_BASE_URL (required)
• OpenRouter
  • OPENROUTER_API_KEY (required)
• Anthropic
  • CLAUDE_API_KEY (required)
  • CLAUDE_BASE_URL (optional)
• Google
  • GOOGLE_API_KEY (required)
  • GOOGLE_BASE_URL (optional)

Error example 💥

• Missing OpenAI key: python -c 'from kantan_llm import get_llm; get_llm(\"gpt-4.1-mini\")' → [kantan-llm][E2] Missing OPENAI_API_KEY for provider: openai

Tests 🧪

Live integration tests (real APIs) are opt-in:

KANTAN_LLM_RUN_LIVE_TESTS=1 pytest -q -m integration