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DeepSeek’s models — particularly the R1 reasoner — are state-of-the-art on math, code, and multi-step reasoning at a fraction of the cost of comparable frontier models.

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Installation

pip install -U swarms

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Environment Setup

export DEEPSEEK_API_KEY="..."
Get an API key at platform.deepseek.com.

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Quick Start

Every DeepSeek model uses the deepseek/ prefix: 
from swarms import Agent

agent = Agent(
    agent_name="DeepSeek-Agent",
    model_name="deepseek/deepseek-chat",
    max_loops=1,
)

print(agent.run("Summarize the architectural ideas behind mixture-of-experts in three paragraphs."))

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Model Names

Modelmodel_nameBest for
DeepSeek Chat"deepseek/deepseek-chat"General-purpose, very cheap
DeepSeek Reasoner (R1)"deepseek/deepseek-reasoner"Hard reasoning, math, code, planning

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DeepSeek Chat — The Cheap Workhorse

For day-to-day agents where cost matters: 
from swarms import Agent

def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"{city}: 21°C, partly cloudy"

agent = Agent(
    agent_name="DeepSeek-Assistant",
    model_name="deepseek/deepseek-chat",
    tools=[get_weather],
    max_loops=3,
)

print(agent.run("What's the weather in Tokyo right now?"))

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DeepSeek Reasoner (R1) — Hard Reasoning

R1 is purpose-built for chain-of-thought reasoning. It’s slower than DeepSeek Chat but punches well above its price class on math, code, and planning tasks: 
from swarms import Agent

agent = Agent(
    agent_name="R1-Mathematician",
    model_name="deepseek/deepseek-reasoner",
    system_prompt="You are a mathematics tutor. Show your full reasoning before each answer.",
    max_loops=1,
)

print(agent.run(
    "Prove that the square root of 2 is irrational. Be rigorous and clear."
))
R1 is particularly strong on:
  • Mathematical proofs and derivations
  • Code refactoring and bug-finding across multiple files
  • Multi-step planning where each step constrains the next
  • Logic puzzles and constraint satisfaction

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Autonomous Loops with R1

R1 excels in autonomous loops where each iteration builds on the last: 
from swarms import Agent

agent = Agent(
    agent_name="R1-Researcher",
    model_name="deepseek/deepseek-reasoner",
    max_loops="auto",
    persistent_memory=True,
    context_compression=True,
    context_length=64_000,
)

agent.run(
    "Research the top 5 vector databases used in production, compare them on "
    "latency, recall, and pricing, and write the report to report.md."
)

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Streaming

from swarms import Agent

agent = Agent(
    agent_name="Streaming-DeepSeek",
    model_name="deepseek/deepseek-chat",
    streaming_on=True,
    max_loops=1,
)

agent.run("Explain how Raft consensus works.")

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Multi-Model Pipelines

A common pattern: use cheap DeepSeek Chat for I/O and the expensive R1 only when reasoning is needed: 
from swarms import Agent, SequentialWorkflow

gatherer = Agent(
    agent_name="Data-Gatherer",
    model_name="deepseek/deepseek-chat",     # cheap & fast
    system_prompt="Gather facts and quotes from the input. Don't reason.",
    max_loops=1,
)

reasoner = Agent(
    agent_name="R1-Reasoner",
    model_name="deepseek/deepseek-reasoner",  # expensive but smart
    system_prompt="Given the facts, reason step-by-step to the final answer.",
    max_loops=1,
)

pipeline = SequentialWorkflow(agents=[gatherer, reasoner], max_loops=1)
print(pipeline.run("Should we adopt CRDTs for our collaborative editor?"))

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Production Defaults

from swarms import Agent

agent = Agent(
    agent_name="Production-DeepSeek",
    model_name="deepseek/deepseek-chat",
    max_loops=1,
    persistent_memory=True,
    context_compression=True,
    context_length=64_000,
    autosave=True,
    retry_attempts=3,
    print_on=False,
)

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Next Steps