LLM Providers Overview
Memori works with all major LLM providers and frameworks. Register any supported client and Memori handles memory capture, augmentation, and recall automatically.
Want a zero-setup option? The Memori Cloud at app.memorilabs.ai.
Supported Providers
| Provider | Integration | Install |
|---|---|---|
| OpenAI | Direct SDK wrapper | pip install memori openai |
| Anthropic | Direct SDK wrapper | pip install memori anthropic |
| Google Gemini | Direct SDK wrapper | pip install memori google-genai |
| xAI Grok | OpenAI-compatible | pip install memori openai |
| AWS Bedrock | LangChain adapter | pip install memori langchain-aws |
| LangChain | Framework support | pip install memori langchain-openai |
| Agno | Framework support | pip install memori agno |
| Pydantic AI | Framework support | pip install memori pydantic-ai |
| Nebius AI Studio | OpenAI-compatible | pip install memori openai |
| DeepSeek | OpenAI-compatible | pip install memori openai |
All providers support sync, async, streamed, and unstreamed modes.
Any provider with an OpenAI-compatible API works by setting a custom base_url — including Nebius AI Studio, DeepSeek, NVIDIA NIM, Azure OpenAI, and more. See OpenAI-Compatible Providers below.
Pydantic AI
Register the Agent instance directly — Memori wraps run_sync and run automatically.
from memori import Memori
from pydantic_ai import Agent
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)
agent = Agent("openai:gpt-4o-mini")
mem = Memori(conn=SessionLocal).llm.register(agent)
mem.config.storage.build()
mem.attribution(entity_id="user_123", process_id="pydantic_agent")
result = agent.run_sync("Hello!")
print(result.output)
OpenAI-Compatible Providers
Any provider with an OpenAI-compatible API works by setting a custom base_url. Examples: xAI, Nebius AI Studio, DeepSeek, Azure OpenAI.
import os
from memori import Memori
from openai import OpenAI
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)
client = OpenAI(
base_url="https://api.studio.nebius.com/v1/",
api_key=os.getenv("NEBIUS_API_KEY"),
)
mem = Memori(conn=SessionLocal).llm.register(client)
mem.attribution(entity_id="user_123", process_id="my_agent")
response = client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
OpenAI Responses API
from memori import Memori
from openai import OpenAI
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)
client = OpenAI()
mem = Memori(conn=SessionLocal).llm.register(client)
mem.attribution(entity_id="user_123", process_id="my_agent")
response = client.responses.create(
model="gpt-4o-mini",
input="Hello!",
instructions="You are a helpful assistant."
)
print(response.output_text)