Swarms API Documentation - Build AI Agents & Multi-Agent Systems

The Swarms API exposes an OpenAI-compatible POST /v1/chat/completions endpoint. If your application already uses the OpenAI SDK, you can switch to Swarms by changing two lines — the base_url and api_key — and everything else works unchanged. Under the hood, every request is routed through the full Swarms agent infrastructure: model routing, token counting, billing, and logging all apply exactly as they do for the native /v1/agent/completions endpoint.

Endpoint Information

URL: /v1/chat/completions
Method: POST
Authentication: Required (x-api-key header or Authorization: Bearer <key>)
Rate Limiting: Subject to tier-based rate limits

Authentication

Two authentication methods are supported. Both work on all Swarms API endpoints.

Method	Header	Example
API key header	`x-api-key: <key>`	`x-api-key: sk-abc123`
Bearer token	`Authorization: Bearer <key>`	`Authorization: Bearer sk-abc123`

The Bearer token method is what the OpenAI SDK sends by default, so it works out of the box.

Get your API key at swarms.world/platform/api-keys.

Request Schema

ChatCompletionRequest Object

Parameter	Type	Required	Default	Description
`model`	`string`	Yes	—	Model to use for completion (e.g. `gpt-4o`, `claude-sonnet-4-20250514`, `gpt-4o-mini`). Any model supported by the Swarms API is accepted
`messages`	`List[ChatMessage]`	Yes	—	A list of messages comprising the conversation (see ChatMessage Object)
`temperature`	`float`	No	`0.5`	Sampling temperature (0.0 – 2.0). Lower values produce more deterministic output
`max_tokens`	`integer`	No	`8192`	Maximum number of tokens to generate in the response
`max_completion_tokens`	`integer`	No	—	Alternative to `max_tokens`. Takes precedence if both are set
`stream`	`boolean`	No	`false`	If `true`, returns Server-Sent Events (SSE) in the OpenAI chunk format
`top_p`	`float`	No	—	Nucleus sampling parameter. An alternative to temperature sampling
`presence_penalty`	`float`	No	—	Penalize tokens based on whether they have appeared in the text so far
`frequency_penalty`	`float`	No	—	Penalize tokens based on how frequently they appear in the text so far
`n`	`integer`	No	`1`	Number of completions to generate. Only `1` is supported — requests with `n > 1` are rejected
`user`	`string`	No	—	A unique identifier for the end-user, used for tracking
`max_loops`	`integer`	No	`1`	Swarms extension. Maximum number of agent reasoning loops. `1` = single pass (default). Higher values let the agent iterate on its own output. Pass via `extra_body` in the OpenAI SDK

ChatMessage Object

Each message in the messages array:

Field	Type	Required	Description
`role`	`string`	Yes	One of `system`, `user`, or `assistant`
`content`	`string` or `List[ContentPart]`	Yes	Text content, or an array of content parts for multimodal input
`name`	`string`	No	An optional name for the participant

ContentPart (Multimodal)

When content is an array, each element is a content part: Text part:

{"type": "text", "text": "Describe this image."}

Image part:

{"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}}

The url field accepts both HTTPS URLs and base64-encoded data URIs (data:image/png;base64,...).

Validation Rules

At least one message with role: "user" is required
n must be 1 — multiple completions per request are not supported (send separate requests instead)
Requests with zero messages or only system messages are rejected

Example Request Body

{
  "model": "gpt-4o",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain quantum computing in simple terms."}
  ],
  "temperature": 0.5,
  "max_tokens": 1024,
  "stream": false,
  "max_loops": 1
}

Response Schema

ChatCompletionResponse Object (Non-Streaming)

Field	Type	Description
`id`	`string`	Unique completion identifier, prefixed with `chatcmpl-`
`object`	`string`	Always `"chat.completion"`
`created`	`integer`	Unix timestamp of when the completion was generated
`model`	`string`	The model that was used (echoes back the requested model name)
`choices`	`List[Choice]`	Array containing the completion result (always one element)
`usage`	`CompletionUsage`	Token usage counts for billing

Choice Object

Field	Type	Description
`index`	`integer`	Always `0` (single-choice responses)
`message`	`ChatMessage`	The assistant’s response with `role: "assistant"`
`finish_reason`	`string`	Why the model stopped generating — `"stop"` for normal completion

CompletionUsage Object

Field	Type	Description
`prompt_tokens`	`integer`	Number of tokens in the input (system prompt + history + task)
`completion_tokens`	`integer`	Number of tokens in the generated response
`total_tokens`	`integer`	Sum of `prompt_tokens` and `completion_tokens`

Example Response

{
  "id": "chatcmpl-a1b2c3d4e5f6789012345678901",
  "object": "chat.completion",
  "created": 1711300000,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Quantum computing uses quantum bits (qubits) that can exist in multiple states simultaneously..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 42,
    "completion_tokens": 128,
    "total_tokens": 170
  }
}

Streaming Response Schema

When stream: true is set, the response is returned as Server-Sent Events (SSE). Each event is a data: line containing a JSON chunk.

StreamChunk Object

Field	Type	Description
`id`	`string`	Same `chatcmpl-` ID shared across all chunks in the stream
`object`	`string`	Always `"chat.completion.chunk"`
`created`	`integer`	Unix timestamp (same across all chunks)
`model`	`string`	The requested model name
`choices`	`List[StreamChoice]`	Array with one element containing the delta

StreamChoice Object

Field	Type	Description
`index`	`integer`	Always `0`
`delta`	`object`	Incremental content — see stream sequence below
`finish_reason`	`string` or `null`	`null` during streaming, `"stop"` on the final chunk

Stream Sequence

Order	`delta`	`finish_reason`	Purpose
First chunk	`{"role": "assistant"}`	`null`	Role declaration
Content chunks	`{"content": "..."}`	`null`	Incremental text content
Final chunk	`{}`	`"stop"`	Signals completion
Terminator	`data: [DONE]`	—	SSE stream end marker

Example Stream

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1711300000,"model":"gpt-4o","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1711300000,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":"Quantum"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1711300000,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" computing"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1711300000,"model":"gpt-4o","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]

Error Response Schema

Errors are returned in the standard OpenAI error format so the OpenAI SDK’s built-in error classes work correctly:

Error Object

Field	Type	Description
`error.message`	`string`	Human-readable error description
`error.type`	`string`	Error category (see table below)
`error.code`	`string` or `null`	Machine-readable error code
`error.param`	`string` or `null`	The parameter that caused the error

Error Types

HTTP Status	`type`	When
400	`invalid_request_error`	Malformed request, validation failure, missing required fields
401	`authentication_error`	Missing or invalid API key
403	`permission_error`	Insufficient permissions or subscription tier
429	`rate_limit_error`	Rate limit exceeded
500	`server_error`	Internal error during agent execution

Example Error Response

{
  "error": {
    "message": "At least one message with role 'user' is required.",
    "type": "invalid_request_error",
    "code": "invalid_request",
    "param": null
  }
}

Code Examples

Non-Streaming Completion

Python
TypeScript
Go
Rust
cURL

from openai import OpenAI

client = OpenAI(
    api_key="your-swarms-api-key",
    base_url="https://api.swarms.world/v1",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What are the key trends in renewable energy?"},
    ],
    max_tokens=1024,
    temperature=0.5,
)

print(response.choices[0].message.content)
print(f"Tokens used: {response.usage.total_tokens}")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-swarms-api-key",
  baseURL: "https://api.swarms.world/v1",
});

const response = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "What are the key trends in renewable energy?" },
  ],
  max_tokens: 1024,
  temperature: 0.5,
});

console.log(response.choices[0].message.content);
console.log(`Tokens used: ${response.usage?.total_tokens}`);

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
)

func main() {
    client := openai.NewClient(
        option.WithAPIKey("your-swarms-api-key"),
        option.WithBaseURL("https://api.swarms.world/v1"),
    )

    response, err := client.Chat.Completions.New(context.Background(),
        openai.ChatCompletionNewParams{
            Model: "gpt-4o",
            Messages: []openai.ChatCompletionMessageParamUnion{
                openai.SystemMessage("You are a helpful assistant."),
                openai.UserMessage("What are the key trends in renewable energy?"),
            },
            MaxTokens: openai.Int(1024),
            Temperature: openai.Float(0.5),
        },
    )
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(response.Choices[0].Message.Content)
    fmt.Printf("Tokens used: %d\n", response.Usage.TotalTokens)
}

use async_openai::{
    config::OpenAIConfig,
    types::{
        ChatCompletionRequestSystemMessageArgs,
        ChatCompletionRequestUserMessageArgs,
        CreateChatCompletionRequestArgs,
    },
    Client,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = OpenAIConfig::new()
        .with_api_key("your-swarms-api-key")
        .with_api_base("https://api.swarms.world/v1");

    let client = Client::with_config(config);

    let request = CreateChatCompletionRequestArgs::default()
        .model("gpt-4o")
        .messages(vec![
            ChatCompletionRequestSystemMessageArgs::default()
                .content("You are a helpful assistant.")
                .build()?
                .into(),
            ChatCompletionRequestUserMessageArgs::default()
                .content("What are the key trends in renewable energy?")
                .build()?
                .into(),
        ])
        .max_tokens(1024_u32)
        .temperature(0.5)
        .build()?;

    let response = client.chat().create(request).await?;

    if let Some(choice) = response.choices.first() {
        if let Some(content) = &choice.message.content {
            println!("{}", content);
        }
    }

    if let Some(usage) = &response.usage {
        println!("Tokens used: {}", usage.total_tokens);
    }

    Ok(())
}

curl -X POST https://api.swarms.world/v1/chat/completions \
  -H "Authorization: Bearer your-swarms-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What are the key trends in renewable energy?"}
    ],
    "max_tokens": 1024,
    "temperature": 0.5
  }'

Streaming Completion

Python
TypeScript
Go
Rust
cURL

from openai import OpenAI

client = OpenAI(
    api_key="your-swarms-api-key",
    base_url="https://api.swarms.world/v1",
)

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a haiku about AI agents."}],
    stream=True,
)

for chunk in stream:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)
print()

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-swarms-api-key",
  baseURL: "https://api.swarms.world/v1",
});

const stream = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Write a haiku about AI agents." }],
  stream: true,
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) process.stdout.write(content);
}
console.log();

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
)

func main() {
    client := openai.NewClient(
        option.WithAPIKey("your-swarms-api-key"),
        option.WithBaseURL("https://api.swarms.world/v1"),
    )

    stream := client.Chat.Completions.NewStreaming(context.Background(),
        openai.ChatCompletionNewParams{
            Model: "gpt-4o",
            Messages: []openai.ChatCompletionMessageParamUnion{
                openai.UserMessage("Write a haiku about AI agents."),
            },
        },
    )

    for stream.Next() {
        chunk := stream.Current()
        if len(chunk.Choices) > 0 {
            fmt.Print(chunk.Choices[0].Delta.Content)
        }
    }

    if err := stream.Err(); err != nil {
        log.Fatal(err)
    }
    fmt.Println()
}

use async_openai::{
    config::OpenAIConfig,
    types::{
        ChatCompletionRequestUserMessageArgs,
        CreateChatCompletionRequestArgs,
    },
    Client,
};
use futures::StreamExt;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = OpenAIConfig::new()
        .with_api_key("your-swarms-api-key")
        .with_api_base("https://api.swarms.world/v1");

    let client = Client::with_config(config);

    let request = CreateChatCompletionRequestArgs::default()
        .model("gpt-4o")
        .messages(vec![
            ChatCompletionRequestUserMessageArgs::default()
                .content("Write a haiku about AI agents.")
                .build()?
                .into(),
        ])
        .build()?;

    let mut stream = client.chat().create_stream(request).await?;

    while let Some(result) = stream.next().await {
        match result {
            Ok(response) => {
                for choice in &response.choices {
                    if let Some(ref content) = choice.delta.content {
                        print!("{}", content);
                    }
                }
            }
            Err(e) => eprintln!("Error: {}", e),
        }
    }
    println!();

    Ok(())
}

curl -X POST https://api.swarms.world/v1/chat/completions \
  -H "Authorization: Bearer your-swarms-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Write a haiku about AI agents."}],
    "stream": true
  }' \
  --no-buffer -N

Multi-Turn Conversation

Python
TypeScript
Go
Rust

from openai import OpenAI

client = OpenAI(
    api_key="your-swarms-api-key",
    base_url="https://api.swarms.world/v1",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a math tutor."},
        {"role": "user", "content": "What is the derivative of x^2?"},
        {"role": "assistant", "content": "The derivative of x^2 is 2x."},
        {"role": "user", "content": "What about x^3?"},
    ],
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-swarms-api-key",
  baseURL: "https://api.swarms.world/v1",
});

const response = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: "You are a math tutor." },
    { role: "user", content: "What is the derivative of x^2?" },
    { role: "assistant", content: "The derivative of x^2 is 2x." },
    { role: "user", content: "What about x^3?" },
  ],
});

console.log(response.choices[0].message.content);

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
)

func main() {
    client := openai.NewClient(
        option.WithAPIKey("your-swarms-api-key"),
        option.WithBaseURL("https://api.swarms.world/v1"),
    )

    response, err := client.Chat.Completions.New(context.Background(),
        openai.ChatCompletionNewParams{
            Model: "gpt-4o",
            Messages: []openai.ChatCompletionMessageParamUnion{
                openai.SystemMessage("You are a math tutor."),
                openai.UserMessage("What is the derivative of x^2?"),
                openai.AssistantMessage("The derivative of x^2 is 2x."),
                openai.UserMessage("What about x^3?"),
            },
        },
    )
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(response.Choices[0].Message.Content)
}

use async_openai::{
    config::OpenAIConfig,
    types::{
        ChatCompletionRequestAssistantMessageArgs,
        ChatCompletionRequestSystemMessageArgs,
        ChatCompletionRequestUserMessageArgs,
        CreateChatCompletionRequestArgs,
    },
    Client,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = OpenAIConfig::new()
        .with_api_key("your-swarms-api-key")
        .with_api_base("https://api.swarms.world/v1");

    let client = Client::with_config(config);

    let request = CreateChatCompletionRequestArgs::default()
        .model("gpt-4o")
        .messages(vec![
            ChatCompletionRequestSystemMessageArgs::default()
                .content("You are a math tutor.")
                .build()?
                .into(),
            ChatCompletionRequestUserMessageArgs::default()
                .content("What is the derivative of x^2?")
                .build()?
                .into(),
            ChatCompletionRequestAssistantMessageArgs::default()
                .content("The derivative of x^2 is 2x.")
                .build()?
                .into(),
            ChatCompletionRequestUserMessageArgs::default()
                .content("What about x^3?")
                .build()?
                .into(),
        ])
        .build()?;

    let response = client.chat().create(request).await?;

    if let Some(choice) = response.choices.first() {
        if let Some(content) = &choice.message.content {
            println!("{}", content);
        }
    }

    Ok(())
}

Error Handling

Python
TypeScript
Go
Rust

from openai import (
    OpenAI,
    APIError,
    AuthenticationError,
    BadRequestError,
    PermissionDeniedError,
    RateLimitError,
)

client = OpenAI(
    api_key="your-swarms-api-key",
    base_url="https://api.swarms.world/v1",
)

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}],
    )
    print(response.choices[0].message.content)
except AuthenticationError:
    print("Missing API key (401)")
except PermissionDeniedError:
    print("Invalid API key or insufficient permissions (403)")
except BadRequestError as e:
    print(f"Validation error (400): {e.message}")
except RateLimitError:
    print("Rate limited — back off and retry")
except APIError as e:
    print(f"API error ({e.status_code}): {e.message}")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-swarms-api-key",
  baseURL: "https://api.swarms.world/v1",
});

try {
  const response = await client.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: "Hello" }],
  });
  console.log(response.choices[0].message.content);
} catch (error) {
  if (error instanceof OpenAI.AuthenticationError) {
    console.error("Missing API key (401)");
  } else if (error instanceof OpenAI.PermissionDeniedError) {
    console.error("Invalid API key or insufficient permissions (403)");
  } else if (error instanceof OpenAI.BadRequestError) {
    console.error(`Validation error (400): ${error.message}`);
  } else if (error instanceof OpenAI.RateLimitError) {
    console.error("Rate limited — back off and retry");
  } else if (error instanceof OpenAI.APIError) {
    console.error(`API error (${error.status}): ${error.message}`);
  }
}

package main

import (
    "context"
    "errors"
    "fmt"
    "log"
    "net/http"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
)

func main() {
    client := openai.NewClient(
        option.WithAPIKey("your-swarms-api-key"),
        option.WithBaseURL("https://api.swarms.world/v1"),
    )

    response, err := client.Chat.Completions.New(context.Background(),
        openai.ChatCompletionNewParams{
            Model: "gpt-4o",
            Messages: []openai.ChatCompletionMessageParamUnion{
                openai.UserMessage("Hello"),
            },
        },
    )
    if err != nil {
        var apiErr *openai.Error
        if errors.As(err, &apiErr) {
            switch apiErr.StatusCode {
            case http.StatusUnauthorized:
                log.Fatal("Missing API key (401)")
            case http.StatusForbidden:
                log.Fatal("Invalid API key or insufficient permissions (403)")
            case http.StatusBadRequest:
                log.Fatalf("Validation error (400): %s", apiErr.Message)
            case http.StatusTooManyRequests:
                log.Fatal("Rate limited — back off and retry")
            default:
                log.Fatalf("API error (%d): %s", apiErr.StatusCode, apiErr.Message)
            }
        }
        log.Fatal(err)
    }

    fmt.Println(response.Choices[0].Message.Content)
}

use async_openai::{
    config::OpenAIConfig,
    error::OpenAIError,
    types::{
        ChatCompletionRequestUserMessageArgs,
        CreateChatCompletionRequestArgs,
    },
    Client,
};

#[tokio::main]
async fn main() {
    let config = OpenAIConfig::new()
        .with_api_key("your-swarms-api-key")
        .with_api_base("https://api.swarms.world/v1");

    let client = Client::with_config(config);

    let request = CreateChatCompletionRequestArgs::default()
        .model("gpt-4o")
        .messages(vec![
            ChatCompletionRequestUserMessageArgs::default()
                .content("Hello")
                .build()
                .unwrap()
                .into(),
        ])
        .build()
        .unwrap();

    match client.chat().create(request).await {
        Ok(response) => {
            if let Some(choice) = response.choices.first() {
                if let Some(content) = &choice.message.content {
                    println!("{}", content);
                }
            }
        }
        Err(OpenAIError::ApiError(e)) => {
            eprintln!("API error: {}", e.message);
        }
        Err(e) => {
            eprintln!("Error: {}", e);
        }
    }
}

Multi-Loop Reasoning

By default the agent runs a single pass (max_loops=1). To let the agent iterate on its own output — useful for complex reasoning, self-correction, or multi-step tasks — pass max_loops via the OpenAI SDK’s extra_body parameter:

Python
TypeScript
cURL

from openai import OpenAI

client = OpenAI(
    api_key="your-swarms-api-key",
    base_url="https://api.swarms.world/v1",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a rigorous analyst. Think step by step, then review and refine your answer."},
        {"role": "user", "content": "What are the second-order effects of raising the federal funds rate by 50 basis points?"},
    ],
    max_tokens=2048,
    extra_body={"max_loops": 3},
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-swarms-api-key",
  baseURL: "https://api.swarms.world/v1",
});

const response = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: "You are a rigorous analyst. Think step by step, then review and refine your answer." },
    { role: "user", content: "What are the second-order effects of raising the federal funds rate by 50 basis points?" },
  ],
  max_tokens: 2048,
  // @ts-expect-error — Swarms extension field
  max_loops: 3,
});

console.log(response.choices[0].message.content);

curl -X POST https://api.swarms.world/v1/chat/completions \
  -H "Authorization: Bearer your-swarms-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a rigorous analyst. Think step by step, then review and refine your answer."},
      {"role": "user", "content": "What are the second-order effects of raising the federal funds rate by 50 basis points?"}
    ],
    "max_tokens": 2048,
    "max_loops": 3
  }'

max_loops is a Swarms extension field — it is not part of the OpenAI API spec. In the Python OpenAI SDK, use extra_body={"max_loops": N} to pass it. In cURL or raw HTTP, include it directly in the JSON body.

How It Maps to Swarms Internals

For users already familiar with the native Swarms API, here is how the OpenAI request fields map to AgentCompletion and AgentSpec:

OpenAI Field	Swarms Equivalent	Notes
`model`	`AgentSpec.model_name`	Passed through as-is
`messages` (system)	`AgentSpec.system_prompt`	Defaults to “You are a helpful assistant.” if absent
`messages` (last user)	`AgentCompletion.task`	The actual prompt the agent runs on
`messages` (prior turns)	`AgentCompletion.history`	User and assistant messages before the final user message
`messages` (image_url parts)	`AgentCompletion.img` / `imgs`	Extracted from multimodal content parts
`temperature`	`AgentSpec.temperature`	Defaults to 0.5
`max_tokens` / `max_completion_tokens`	`AgentSpec.max_tokens`	`max_completion_tokens` takes precedence; defaults to 8192
`top_p`	`AgentSpec.llm_args.top_p`	Passed through to the underlying LLM
`presence_penalty`	`AgentSpec.llm_args.presence_penalty`	Passed through to the underlying LLM
`frequency_penalty`	`AgentSpec.llm_args.frequency_penalty`	Passed through to the underlying LLM
`max_loops`	`AgentSpec.max_loops`	Defaults to 1. Higher values enable multi-loop reasoning
`stream`	Route dispatch	`true` returns `StreamingResponse` with SSE; `false` returns JSON

The agent is created with max_loops set to the requested value (defaults to 1 for single-turn) and streaming_on=False (the agent itself runs to completion; streaming is simulated at the HTTP layer by chunking the result).

Supported Models

The model field accepts any model supported by the Swarms API. Common options:

Provider	Models
OpenAI	`gpt-4o`, `gpt-4o-mini`, `gpt-4.1`, `gpt-4.1-mini`, `gpt-4.1-nano`, `o3-mini`
Anthropic	`claude-sonnet-4-20250514`, `claude-3-7-sonnet-latest`
Groq	`groq/llama3-70b-8192`, `groq/deepseek-r1-distill-llama-70b`

For the full list, call GET /v1/models/available with your API key.

Differences from the OpenAI API

Behavior	OpenAI API	Swarms API
`n > 1`	Returns multiple choices	Rejected with error — send separate requests
Tool calling / function calling	Supported	Not supported on this endpoint. Use `/v1/agent/completions` with `tools_list_dictionary`
`logprobs`	Supported	Not supported
Response format (`json_object`)	Supported	Not supported on this endpoint. Use `/v1/agent/completions` with structured output
Streaming	True token-by-token streaming	Simulated — the agent runs to completion, then the result is delivered in chunks
`max_loops`	Not applicable	Swarms extension — multi-loop agent reasoning (pass via `extra_body`)

Billing

Usage is metered and billed identically to the native /v1/agent/completions endpoint:

Input tokens are counted from the combined system prompt, conversation history, and task
Output tokens are counted from the agent’s response
Credits are deducted automatically after each completion
The usage field in the response shows the exact token counts

Check your balance anytime with GET /v1/users/me/credits.

Getting Started

Clients

Agent Completions

Multi-Agent

Batch

Capabilities

Resources

OpenAI-Compatible Endpoint

Endpoint Information

Authentication

Request Schema

ChatCompletionRequest Object

ChatMessage Object

ContentPart (Multimodal)

Validation Rules

Example Request Body

Response Schema

ChatCompletionResponse Object (Non-Streaming)

Choice Object

CompletionUsage Object

Example Response

Streaming Response Schema

StreamChunk Object

StreamChoice Object

Stream Sequence

Example Stream

Error Response Schema

Error Object

Error Types

Example Error Response

Code Examples

Non-Streaming Completion

Streaming Completion

Multi-Turn Conversation

Error Handling

Multi-Loop Reasoning

How It Maps to Swarms Internals

Supported Models

Differences from the OpenAI API

Billing

Getting Started

Clients

Agent Completions

Multi-Agent

Batch

Capabilities

Resources

​Endpoint Information

​Authentication

​Request Schema

​ChatCompletionRequest Object

​ChatMessage Object

​ContentPart (Multimodal)

​Validation Rules

​Example Request Body

​Response Schema

​ChatCompletionResponse Object (Non-Streaming)

​Choice Object

​CompletionUsage Object

​Example Response

​Streaming Response Schema

​StreamChunk Object

​StreamChoice Object

​Stream Sequence

​Example Stream

​Error Response Schema

​Error Object

​Error Types

​Example Error Response

​Code Examples

​Non-Streaming Completion

​Streaming Completion

​Multi-Turn Conversation

​Error Handling

​Multi-Loop Reasoning

​How It Maps to Swarms Internals

​Supported Models

​Differences from the OpenAI API

​Billing

Endpoint Information

Authentication

Request Schema

ChatCompletionRequest Object

ChatMessage Object

ContentPart (Multimodal)

Validation Rules

Example Request Body

Response Schema

ChatCompletionResponse Object (Non-Streaming)

Choice Object

CompletionUsage Object

Example Response

Streaming Response Schema

StreamChunk Object

StreamChoice Object

Stream Sequence

Example Stream

Error Response Schema

Error Object

Error Types

Example Error Response

Code Examples

Non-Streaming Completion

Streaming Completion

Multi-Turn Conversation

Error Handling

Multi-Loop Reasoning

How It Maps to Swarms Internals

Supported Models

Differences from the OpenAI API

Billing