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no/llm

Standard Interface for Large Language Models
PyPI versions license

no/llm is a Python library that provides a unified interface for working with LLMs, with built-in support for model configuration, parameter validation, and provider management.

Quick Install

uv pip install "no_llm[pydantic-ai]"

Quick Example with Pydantic AI

import os

from no_llm.integrations.pydantic_ai import NoLLMModel
from no_llm.registry import ModelRegistry
from no_llm.settings import ValidationMode, settings
from pydantic_ai import Agent
from pydantic_ai.settings import ModelSettings

settings.validation_mode = ValidationMode.ERROR
# or ValidationMode.WARN, ValidationMode.CLAMP

os.environ["OPENROUTER_API_KEY"] = "..."

# Get model from registry
registry = ModelRegistry()
openrouter_models = list(registry.list_models(provider="openrouter"))
print([m.identity.id for m in openrouter_models])
# > ['claude-3.5-haiku', 'claude-3.5-sonnet-v2', 'claude-3.7-sonnet', 'deepseek-chat', 'deepseek-r1-llama-70b-distilled', 'deepseek-reasoner', ...]
no_llm_model = NoLLMModel(*openrouter_models)

# Use with Pydantic AI
agent = Agent(no_llm_model, model_settings=ModelSettings(temperature=1.2))
result = agent.run_sync("What is the capital of France?")
print(result.data)
# > 2025-04-09 09:50:51.375 | WARNING  | no_llm.integrations.pydantic_ai:request:220 - Model deepseek-chat failed, trying next fallback. Error: Invalid value for parameter 'temperature'
# > Current value: 1.2
# > Valid range: (0.0, 1.0)
# > Error: Value 1.2 outside range [0.0, 1.0]
# > 2025-04-09 09:50:51.375 | WARNING  | no_llm.integrations.pydantic_ai:request:220 - Model deepseek-r1-llama-70b-distilled failed, trying next fallback. Error: Invalid value for parameter 'temperature'
# > Current value: 1.2
# > Valid range: (0.0, 1.0)
# > Error: Value 1.2 outside range [0.0, 1.0]
# ✅ gemini-2.0-flash
# > The capital of France is **Paris**.

Why no/llm?

  • Provider Agnostic: Support for OpenAI, Anthropic, Google, Mistral, Groq, and more through a single interface

  • Built-in Validation: Type-safe parameter validation and capability checking

  • Provider Fallbacks: Automatic fallback between providers and data centers

  • Configuration System: YAML-based model configurations with inheritance support

  • Model Registry: Central management of models with capability-based filtering

  • Integration Ready: Works with Pydantic AI, and more frameworks coming soon

Next Steps

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