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Deepseek Chat

deepseek-chat

Deepseek Chat is available on Swarms Cloud through our Agent Completions API, no separate provider account or API key required. Run it as a standalone agent with the Agent Completions API, enable tools, structured outputs, and autonomous loops, or orchestrate it inside multi-agent swarms alongside models from other providers.

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Quick start

Get started with Deepseek Chat

1

Get your Swarms API key

Create an API key from your API keys dashboard or at swarms.world/platform/api-keys.

2

Set it in your environment

# .env
SWARMS_API_KEY="your-api-key"

# or export it in your shell
export SWARMS_API_KEY="your-api-key"
3

Run a single agent

Execute one agent with deepseek-chat via the Agent Completions API.

Single agent completion

curl -X POST 'https://api.swarms.world/v1/agent/completions' \
  -H 'x-api-key: '"$SWARMS_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "agent_config": {
    "agent_name": "Research Analyst",
    "description": "Expert in analyzing and synthesizing research data",
    "system_prompt": "You are a Research Analyst with expertise in data analysis and synthesis.",
    "model_name": "deepseek-chat",
    "max_loops": 1,
    "max_tokens": 8192,
    "temperature": 0.5
  },
  "task": "Analyze the impact of artificial intelligence on healthcare"
}'
4

Scale to a multi-agent swarm

Chain multiple agents on deepseek-chat with the Swarm Completions API.

Multi-agent swarm

curl -X POST 'https://api.swarms.world/v1/swarm/completions' \
  -H 'x-api-key: '"$SWARMS_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "name": "Research Swarm",
  "description": "A two-agent research and analysis pipeline",
  "swarm_type": "SequentialWorkflow",
  "task": "Research the latest developments in renewable energy and summarize the top 3 trends",
  "agents": [
    {
      "agent_name": "Researcher",
      "description": "Gathers and organizes source material",
      "system_prompt": "You are a meticulous researcher. Collect relevant facts and organize them clearly.",
      "model_name": "deepseek-chat",
      "max_loops": 1
    },
    {
      "agent_name": "Analyst",
      "description": "Synthesizes research into actionable insights",
      "system_prompt": "You are an analyst. Turn the research into a concise, actionable summary.",
      "model_name": "deepseek-chat",
      "max_loops": 1
    }
  ],
  "max_loops": 1
}'

Frequently asked questions

How do I use Deepseek Chat with the Swarms API?

Set your SWARMS_API_KEY environment variable, then send a POST request to /v1/agent/completions with agent_config.model_name set to "deepseek-chat". The API works from Python, TypeScript, cURL, or any HTTP client.

Can I use Deepseek Chat in a multi-agent swarm?

Yes. Pass "deepseek-chat" as the model_name of any agent in a POST to /v1/swarm/completions. You can mix it with other models across 17+ swarm architectures such as SequentialWorkflow, ConcurrentWorkflow, and HierarchicalSwarm.

How is Deepseek Chat billed on Swarms Cloud?

Usage is billed per input and output token. See the Swarms Cloud pricing page for the token cost calculator and current rates.

Do I need a separate provider API key to use Deepseek Chat?

No. A single Swarms API key gives you access to every model in the catalog through one agent completions API, no separate provider accounts required.