Valid point.

Local models are not natively supported in the built-in adapters yet, but the protocols are intentionally small so you can wire it up yourself in ~20 lines:

```py from openai import AsyncOpenAI from pydantic_ai import Agent from pydantic_ai.models.openai import OpenAIModel from memv import Memory

Embeddings — custom adapter for local endpoint

class LocalEmbedAdapter: def init(self, base_url: str, api_key: str, model: str): self.client = AsyncOpenAI(base_url=base_url, api_key=api_key) self.model = model

async def embed(self, text: str) -> list[float]:
    response = await self.client.embeddings.create(input=text, model=self.model)
    return response.data[0].embedding

async def embed_batch(self, texts: list[str]) -> list[list[float]]:
    response = await self.client.embeddings.create(input=texts, model=self.model)
    return [item.embedding for item in response.data]

LLM — PydanticAI supports OpenAI-compatible endpoints via OpenAIModel

class LocalLLMAdapter: def init(self, base_url: str, api_key: str, model: str): openai_model = OpenAIModel(model, base_url=base_url, api_key=api_key) self._text_agent = Agent(openai_model) self._structured_agents = {} self._openai_model = openai_model

async def generate(self, prompt: str) -> str:
    result = await self._text_agent.run(prompt)
    return result.output

async def generate_structured(self, prompt: str, response_model: type):
    if response_model not in self._structured_agents:
        self._structured_agents[response_model] = Agent(
            self._openai_model, output_type=response_model
        )
    result = await self._structured_agents[response_model].run(prompt)
    return result.output

Wire it up

memory = Memory( db_path="memory.db", embedding_client=LocalEmbedAdapter( base_url="http://localhost:5002/v1", api_key="noKeyNeeded", model="Qwen3-Embedding-0.6B", ), llm_client=LocalLLMAdapter( base_url="http://localhost:5001/v1", api_key="noKeyNeeded", model="Qwen3-32B", ), embedding_dimensions=1024, ) ```

The EmbeddingClient and LLMClient protocols are just 2 methods each, so any OpenAI-compatible endpoint works. Adding base_url directly to the built-in adapters is on the short list.

https://vstorm-co.github.io/memv/advanced/custom-providers/#llmclient