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Agent with DeepSeek

  • Add your DEEPSEEK_API_KEY in the .env file

  • Select your model_name like deepseek/deepseek-chat follows LiteLLM conventions

  • Execute your agent!

from swarms import Agent
import os
from dotenv import load_dotenv

load_dotenv()

# Initialize the agent with ChromaDB memory
agent = Agent(
    agent_name="Financial-Analysis-Agent",
    model_name="deepseek/deepseek-chat",
    system_prompt="Agent system prompt here",
    agent_description="Agent performs financial analysis.",
)

# Run a query
agent.run("What are the components of a startup's stock incentive equity plan?")

R1

This is a simple example of how to use the DeepSeek Reasoner model otherwise known as R1.

import os
from swarms import Agent
from dotenv import load_dotenv

load_dotenv()

# Initialize the agent with ChromaDB memory
agent = Agent(
    agent_name="Financial-Analysis-Agent",
    model_name="deepseek/deepseek-reasoner",
    system_prompt="Agent system prompt here",
    agent_description="Agent performs financial analysis.",
)

# Run a query
agent.run("What are the components of a startup's stock incentive equity plan?")