Providers

NeuroCore injects an LLMProvider into any skill whose SkillMeta sets requires_llm=True. You configure the provider once; skills stay provider-agnostic.

Configuration

# neurocore.yaml
llm:
  provider: anthropic        # see table below
  model: claude-sonnet-4-6
  api_key_env: ANTHROPIC_API_KEY   # name of the env var holding the key
  # api_key: "sk-..."        # or set the key literally (api_key wins over api_key_env)
  # base_url: ...            # for openai-compatible / ollama / vllm / litellm
  max_tokens: 8192
  temperature: 1.0

Per-skill overrides go under skills.<name> in neurocore.yaml or in a blueprint component’s config.

Supported providers

provider	Backend	Install	Notes
anthropic	Claude	(core dep)	default claude-sonnet-4-6
openai	OpenAI	pip install "neurocore-ai[openai]"	set base_url to reach a gateway
gemini	Google Gemini	pip install "neurocore-ai[gemini]"
openai-compatible	any OpenAI-wire API	pip install "neurocore-ai[local]"	requires base_url
ollama	local Ollama	pip install "neurocore-ai[local]"	default base_url http://localhost:11434/v1
vllm	local vLLM	pip install "neurocore-ai[local]"	default base_url http://localhost:8000/v1
litellm	100+ APIs via LiteLLM	pip install "neurocore-ai[litellm]"	routes by model
mock	deterministic test double	(core dep)	for tests

ollama, vllm, and openai-compatible all speak the OpenAI chat-completions wire format, so they share one implementation and need only the openai SDK.

Local models, zero cloud keys

llm:
  provider: ollama
  model: llama3.2
  base_url: http://localhost:11434/v1
  api_key_env: OLLAMA_API_KEY   # Ollama ignores it; any non-empty value works

pip install "neurocore-ai[local]"
ollama serve & ; ollama pull llama3.2
neurocore run blueprints/chat.flow.yaml --data query="Hello"

Using the provider in a skill

from neurocore import AsyncSkill, SkillMeta
from neurocore.llm.provider import LLMMessage

class ChatSkill(AsyncSkill):
    skill_meta = SkillMeta(name="chat", version="0.1.0",
                           requires_llm=True, consumes=["query"], provides=["answer"])

    async def process(self, context):
        resp = await self.llm.complete(
            [LLMMessage(role="user", content=context.get("query", ""))]
        )
        context.set("answer", resp.content)
        return context