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

LangChain provides Python building blocks — LLMChain, AgentExecutor, LCEL pipes — for constructing AI workflows locally. The Swarms API replaces this entire stack with a single REST endpoint: you describe your agents and workflow in JSON and the API handles orchestration, model routing, retries, and billing.

LangChain	Swarms API
`LLMChain(llm, prompt)`	Single agent completion via `/v1/agent/completions`
`SequentialChain([chain_a, chain_b])`	`SequentialWorkflow` via `/v1/swarms/completions`
`RunnableParallel({a: chain_a, b: chain_b})`	`ConcurrentWorkflow` via `/v1/swarms/completions`
`AgentExecutor(agent, tools)`	Agent with `"tools"` array
`ChatPromptTemplate.from_messages([...])`	`system_prompt` + `task` fields
`chain_a	chain_b` (LCEL pipe)	`SequentialWorkflow` with agents in order
`chain.invoke({"input": "..."})`	`POST` request with `"task": "..."`
`chain.stream({"input": "..."})`	Streaming endpoint (see Streaming)
`chain.batch([input1, input2])`	`ConcurrentWorkflow` or batch endpoint
`ConversationBufferMemory`	Stateless; manage conversation history externally
`Tool(name, func, description)`	`"tools"` array with built-in tool names
`ChatOpenAI(model="gpt-4o")`	`"model_name": "gpt-4o"` on agent spec

Side-by-Side: Simple LLMChain

LangChain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(model="gpt-4o", temperature=0.5)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant that explains complex topics simply."),
    ("human", "{topic}"),
])

chain = prompt | llm | StrOutputParser()
result = chain.invoke({"topic": "How does transformer attention work?"})
print(result)

Swarms API

import os
import requests

result = requests.post(
    "https://api.swarms.world/v1/agent/completions",
    headers={"x-api-key": os.environ["SWARMS_API_KEY"], "Content-Type": "application/json"},
    json={
        "agent_name": "explainer",
        "system_prompt": "You are a helpful assistant that explains complex topics simply.",
        "task": "How does transformer attention work?",
        "model_name": "gpt-4o",
        "max_loops": 1,
        "temperature": 0.5,
    },
    timeout=60,
).json()

print(result["outputs"])

Side-by-Side: SequentialChain (LCEL Pipe)

LangChain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(model="gpt-4o")

research_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a research specialist. Research the topic thoroughly."),
    ("human", "Research this topic: {topic}"),
])

summary_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a skilled summarizer. Create a concise summary."),
    ("human", "Summarize this research:\n\n{research}"),
])

research_chain = research_prompt | llm | StrOutputParser()
summary_chain = summary_prompt | llm | StrOutputParser()

full_chain = research_chain | (lambda x: {"research": x}) | summary_chain

result = full_chain.invoke({"topic": "Quantum computing applications in cryptography"})
print(result)

Swarms API

import os
import requests

result = requests.post(
    "https://api.swarms.world/v1/swarms/completions",
    headers={"x-api-key": os.environ["SWARMS_API_KEY"], "Content-Type": "application/json"},
    json={
        "name": "Research and Summarize",
        "description": "Research a topic then produce a concise summary",
        "swarm_type": "SequentialWorkflow",
        "task": "Research quantum computing applications in cryptography",
        "agents": [
            {
                "agent_name": "Researcher",
                "system_prompt": "You are a research specialist. Research the given topic thoroughly and return detailed findings with key facts, current developments, and important context.",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.3,
            },
            {
                "agent_name": "Summarizer",
                "system_prompt": "You are a skilled summarizer. Read the research provided and produce a clear, concise 3-paragraph summary that captures the essential points.",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.4,
            },
        ],
        "max_loops": 1,
    },
    timeout=120,
).json()

print(result["outputs"])

No lambdas or output-passing glue code needed — the sequential workflow passes each agent’s output to the next automatically.

Side-by-Side: RunnableParallel

LangChain

from langchain_core.runnables import RunnableParallel
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(model="gpt-4o")

pros_chain = (
    ChatPromptTemplate.from_template("List the pros of {topic}") | llm | StrOutputParser()
)
cons_chain = (
    ChatPromptTemplate.from_template("List the cons of {topic}") | llm | StrOutputParser()
)
examples_chain = (
    ChatPromptTemplate.from_template("Give real-world examples of {topic}") | llm | StrOutputParser()
)

parallel_chain = RunnableParallel(
    pros=pros_chain,
    cons=cons_chain,
    examples=examples_chain,
)

result = parallel_chain.invoke({"topic": "remote work"})
print(result["pros"])
print(result["cons"])
print(result["examples"])

Swarms API

import os
import requests

result = requests.post(
    "https://api.swarms.world/v1/swarms/completions",
    headers={"x-api-key": os.environ["SWARMS_API_KEY"], "Content-Type": "application/json"},
    json={
        "name": "Parallel Analysis",
        "description": "Three perspectives on remote work in parallel",
        "swarm_type": "ConcurrentWorkflow",
        "task": "Analyze remote work from your specific perspective.",
        "agents": [
            {
                "agent_name": "pros_analyst",
                "system_prompt": "You analyze ONLY the pros and benefits of the given topic. List them clearly with brief explanations.",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.4,
            },
            {
                "agent_name": "cons_analyst",
                "system_prompt": "You analyze ONLY the cons and drawbacks of the given topic. List them clearly with brief explanations.",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.4,
            },
            {
                "agent_name": "examples_analyst",
                "system_prompt": "You provide ONLY real-world examples and case studies related to the given topic. Be specific with company names and outcomes.",
                "model_name": "gpt-4o",
                "max_loops": 1,
                "temperature": 0.3,
            },
        ],
        "max_loops": 1,
    },
    timeout=90,
).json()

outputs = result["outputs"]
print(outputs["pros_analyst"])
print(outputs["cons_analyst"])
print(outputs["examples_analyst"])

Side-by-Side: AgentExecutor with Tools

LangChain

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.tools import tool

llm = ChatOpenAI(model="gpt-4o", temperature=0)
search = TavilySearchResults(max_results=3)

@tool
def get_word_length(word: str) -> int:
    """Returns the number of characters in a word."""
    return len(word)

tools = [search, get_word_length]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant."),
    ("human", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
])

agent = create_openai_tools_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = executor.invoke({"input": "What is the current population of Japan?"})
print(result["output"])

Swarms API

import os
import requests

result = requests.post(
    "https://api.swarms.world/v1/agent/completions",
    headers={"x-api-key": os.environ["SWARMS_API_KEY"], "Content-Type": "application/json"},
    json={
        "agent_name": "research_assistant",
        "system_prompt": "You are a helpful research assistant. Use available tools to find accurate, up-to-date information.",
        "task": "What is the current population of Japan?",
        "model_name": "gpt-4o",
        "tools": ["browser"],
        "max_loops": 3,
        "temperature": 0.2,
    },
    timeout=90,
).json()

print(result["outputs"])

See Available Tools for the full list of built-in tools.

Side-by-Side: Streaming

LangChain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o", streaming=True)
prompt = ChatPromptTemplate.from_template("Tell me a short story about {topic}")
chain = prompt | llm

for chunk in chain.stream({"topic": "a robot learning to paint"}):
    print(chunk.content, end="", flush=True)

Swarms API

import os
import requests

with requests.post(
    "https://api.swarms.world/v1/agent/completions/stream",
    headers={"x-api-key": os.environ["SWARMS_API_KEY"], "Content-Type": "application/json"},
    json={
        "agent_name": "storyteller",
        "system_prompt": "You are a creative storyteller.",
        "task": "Tell me a short story about a robot learning to paint.",
        "model_name": "gpt-4o",
        "max_loops": 1,
        "temperature": 0.7,
        "stream": True,
    },
    stream=True,
    timeout=120,
) as response:
    for chunk in response.iter_content(chunk_size=None):
        print(chunk.decode(), end="", flush=True)

See the Streaming guide for full details.

Prompt Templates → System Prompts

LangChain’s ChatPromptTemplate separates system messages from human messages. In the Swarms API, system instructions go in system_prompt and the user’s request goes in task.

LangChain

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an expert {role}. Always respond in {language}."),
    ("human", "{question}"),
])

chain = prompt | llm | StrOutputParser()
result = chain.invoke({
    "role": "Python developer",
    "language": "English",
    "question": "What is a decorator?",
})

Swarms API

result = requests.post(
    "https://api.swarms.world/v1/agent/completions",
    headers={"x-api-key": os.environ["SWARMS_API_KEY"], "Content-Type": "application/json"},
    json={
        "agent_name": "python_expert",
        "system_prompt": "You are an expert Python developer. Always respond clearly and concisely in English.",
        "task": "What is a decorator?",
        "model_name": "gpt-4o",
        "max_loops": 1,
        "temperature": 0.3,
    },
    timeout=60,
).json()

print(result["outputs"])

Variables that were filled in via ChatPromptTemplate are simply inlined into the system_prompt string.

Structured Output

LangChain

from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field

class Sentiment(BaseModel):
    label: str = Field(description="positive, negative, or neutral")
    score: float = Field(description="confidence score 0-1")
    reasoning: str = Field(description="brief explanation")

llm = ChatOpenAI(model="gpt-4o")
structured_llm = llm.with_structured_output(Sentiment)

result = structured_llm.invoke("I absolutely love this product!")
print(result.label, result.score)

Swarms API

import os, json, requests

result = requests.post(
    "https://api.swarms.world/v1/agent/completions",
    headers={"x-api-key": os.environ["SWARMS_API_KEY"], "Content-Type": "application/json"},
    json={
        "agent_name": "sentiment_analyzer",
        "system_prompt": "You are a sentiment analysis model. Always respond with valid JSON only.",
        "task": "Analyze the sentiment of: 'I absolutely love this product!'",
        "model_name": "gpt-4o",
        "max_loops": 1,
        "temperature": 0.1,
        "response_format": {"type": "json_object"},
    },
    timeout=60,
).json()

sentiment = json.loads(result["outputs"])
print(sentiment["label"], sentiment["score"])

See Structured Outputs for full details.

Memory and Conversation History

LangChain’s ConversationBufferMemory persists chat history between chain calls. The Swarms API is stateless — maintain history externally and pass it in the task field.

LangChain

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
from langchain_openai import ChatOpenAI

memory = ConversationBufferMemory()
chain = ConversationChain(llm=ChatOpenAI(model="gpt-4o"), memory=memory)

chain.predict(input="Hi, my name is Alice.")
chain.predict(input="What is my name?")

Swarms API

import os
import requests

conversation_history = []

def chat(user_message: str) -> str:
    conversation_history.append(f"User: {user_message}")
    context = "\n".join(conversation_history)

    result = requests.post(
        "https://api.swarms.world/v1/agent/completions",
        headers={"x-api-key": os.environ["SWARMS_API_KEY"], "Content-Type": "application/json"},
        json={
            "agent_name": "conversational_agent",
            "system_prompt": "You are a friendly conversational assistant. Use the conversation history provided to give contextually aware responses.",
            "task": f"Conversation so far:\n{context}\n\nRespond to the last user message.",
            "model_name": "gpt-4o",
            "max_loops": 1,
            "temperature": 0.5,
        },
        timeout=60,
    ).json()

    reply = result["outputs"]
    conversation_history.append(f"Assistant: {reply}")
    return reply

print(chat("Hi, my name is Alice."))
print(chat("What is my name?"))

Key Differences to Keep in Mind

Concern	LangChain	Swarms API
LCEL composition	`chain_a	chain_b` pipe syntax	`SequentialWorkflow` with agents in order
Memory	`ConversationBufferMemory`, `VectorStoreRetriever`	Stateless; manage externally
Streaming	`.stream()` / `.astream()`	Dedicated `/stream` endpoint
Callbacks	`callbacks=[...]` on chain/agent	Not needed; all outputs returned in response
Retry logic	`with_retry()`	Handled server-side
Fallbacks	`with_fallbacks([...])`	`model_name` can be swapped per agent
Output parsers	`StrOutputParser`, `PydanticOutputParser`	`response_format: {type: "json_object"}`
Vector stores / RAG	`VectorStoreRetriever`	Pass retrieved context directly in `task`
Embeddings	`OpenAIEmbeddings`, etc.	Not needed in the API; use external embedding service

Getting Started

Agent Capabilities

Multi-Agent Architectures

Applications

API Utilities

Migration Guides

Guides

Migrate from LangChain

Side-by-Side: Simple LLMChain

LangChain

Swarms API

Side-by-Side: SequentialChain (LCEL Pipe)

LangChain

Swarms API

Side-by-Side: RunnableParallel

LangChain

Swarms API

Side-by-Side: AgentExecutor with Tools

LangChain

Swarms API

Side-by-Side: Streaming

LangChain

Swarms API

Prompt Templates → System Prompts

LangChain

Swarms API

Structured Output

LangChain

Swarms API

Memory and Conversation History

LangChain

Swarms API

Key Differences to Keep in Mind

Getting Started

Agent Capabilities

Multi-Agent Architectures

Applications

API Utilities

Migration Guides

Guides

​Side-by-Side: Simple LLMChain

​LangChain

​Swarms API

​Side-by-Side: SequentialChain (LCEL Pipe)

​LangChain

​Swarms API

​Side-by-Side: RunnableParallel

​LangChain

​Swarms API

​Side-by-Side: AgentExecutor with Tools

​LangChain

​Swarms API

​Side-by-Side: Streaming

​LangChain

​Swarms API

​Prompt Templates → System Prompts

​LangChain

​Swarms API

​Structured Output

​LangChain

​Swarms API

​Memory and Conversation History

​LangChain

​Swarms API

​Key Differences to Keep in Mind

​Related Resources

Side-by-Side: Simple LLMChain

LangChain

Swarms API

Side-by-Side: SequentialChain (LCEL Pipe)

LangChain

Swarms API

Side-by-Side: RunnableParallel

LangChain

Swarms API

Side-by-Side: AgentExecutor with Tools

LangChain

Swarms API

Side-by-Side: Streaming

LangChain

Swarms API

Prompt Templates → System Prompts

LangChain

Swarms API

Structured Output

LangChain

Swarms API

Memory and Conversation History

LangChain

Swarms API

Key Differences to Keep in Mind

Related Resources