AgentLoader

Overview

The AgentLoader class provides a unified interface for instantiating Agent objects from on-disk definitions. It supports three file formats and dispatches automatically based on the file extension.

Format	Extension	Backing loader
Markdown	`.md`	`load_agents_from_markdown`
YAML	`.yaml`	`create_agents_from_yaml`
CSV	`.csv`	`CSVAgentLoader`

Use this when you want agent definitions to live as data — checked into git, edited by humans, generated by tools — rather than hard-coded in Python.

Installation

pip install -U swarms

Constructor

from swarms import AgentLoader

loader = AgentLoader(concurrent=True)

bool

default:"True"

Default for concurrent loading when multiple files are passed. Individual load_* methods accept their own concurrent override.

Methods

auto()

Dispatch to the right loader based on file extension. The simplest entry point.

def auto(self, file_path: str, *args, **kwargs) -> List[Agent]

Parameters:

file_path (str): Path to .md, .yaml, or .csv file.
*args, **kwargs: Forwarded to the underlying loader.

Returns: List[Agent] Raises: ValueError if the file extension is not .md, .yaml, or .csv.

load_single_agent()

Alias for auto() — loads a single file by dispatching on extension.

def load_single_agent(self, *args, **kwargs) -> List[Agent]

load_multiple_agents()

Apply auto() to a list of files. Each file may be a different format.

def load_multiple_agents(
    self,
    file_paths: List[str],
    *args,
    **kwargs,
) -> List[List[Agent]]

Parameters:

file_paths (List[str]): Paths to agent definition files.

Returns: List of agent lists, one per input file.

load_agent_from_markdown()

Load a single agent from a Markdown file.

def load_agent_from_markdown(
    self,
    file_path: str,
    **kwargs,
) -> Agent

load_agents_from_markdown()

Load multiple agents from one or more Markdown files.

def load_agents_from_markdown(
    self,
    file_paths: Union[str, List[str]],
    concurrent: bool = True,
    max_file_size_mb: float = 10.0,
    **kwargs,
) -> List[Agent]

Union[str, List[str]]

Single path or list of paths to Markdown agent files.

bool

default:"True"

Load files in parallel.

float

default:"10.0"

Files above this size are skipped.

load_agents_from_yaml()

Load agents from a single YAML file.

def load_agents_from_yaml(
    self,
    yaml_file: str,
    return_type: ReturnTypes = "auto",
    **kwargs,
) -> List[Agent]

str

Path to the YAML definitions file.

ReturnTypes

default:"\"auto\""

Controls the return shape from the underlying YAML loader.

load_many_agents_from_yaml()

Load agents from a list of YAML files with per-file return type control.

def load_many_agents_from_yaml(
    self,
    yaml_files: List[str],
    return_types: List[ReturnTypes] = ["auto"],
    **kwargs,
) -> List[Agent]

load_agents_from_csv()

Load agents from a CSV file via CSVAgentLoader.

def load_agents_from_csv(
    self,
    csv_file: str,
    **kwargs,
) -> List[Agent]

parse_markdown_file()

Lower-level: parse a Markdown file via MarkdownAgentLoader directly, using the host’s CPU count as worker count.

def parse_markdown_file(self, file_path: str) -> List[Agent]

Usage Examples

Auto-Dispatch on Extension

The shortest path — let auto() figure out the format.

from swarms import AgentLoader

loader = AgentLoader()

# Each file uses its own format; auto() picks the right loader
research_agents = loader.auto("agents/research_team.yaml")
support_agents = loader.auto("agents/support_squad.md")
sales_agents = loader.auto("agents/sales_reps.csv")

Load Many Files in One Call

from swarms import AgentLoader

loader = AgentLoader()

all_agents = loader.load_multiple_agents([
    "agents/research_team.yaml",
    "agents/support_squad.md",
    "agents/sales_reps.csv",
])

# all_agents is a list-of-lists; one entry per file
for file_agents in all_agents:
    for agent in file_agents:
        print(agent.agent_name)

Markdown with Concurrency Control

from swarms import AgentLoader

loader = AgentLoader()

agents = loader.load_agents_from_markdown(
    file_paths=[
        "agents/researcher.md",
        "agents/writer.md",
        "agents/editor.md",
    ],
    concurrent=True,
    max_file_size_mb=5.0,
)

Compose with a Swarm

from swarms import AgentLoader, SequentialWorkflow

loader = AgentLoader()
agents = loader.auto("agents/research_pipeline.yaml")

workflow = SequentialWorkflow(agents=agents, max_loops=1)
result = workflow.run("Summarize the current state of multi-agent research")

Source Code

View the source on GitHub.

Core Classes

Multi-Agent Harnesses

Tools & Utilities

Deployment

Overview

Installation

Constructor

Methods

auto()

load_single_agent()

load_multiple_agents()

load_agent_from_markdown()

load_agents_from_markdown()

load_agents_from_yaml()

load_many_agents_from_yaml()

load_agents_from_csv()

parse_markdown_file()

Usage Examples

Auto-Dispatch on Extension

Load Many Files in One Call

Markdown with Concurrency Control

Compose with a Swarm

Source Code

​Overview

​Installation

​Constructor

​Methods

​auto()

​load_single_agent()

​load_multiple_agents()

​load_agent_from_markdown()

​load_agents_from_markdown()

​load_agents_from_yaml()

​load_many_agents_from_yaml()

​load_agents_from_csv()

​parse_markdown_file()

​Usage Examples

​Auto-Dispatch on Extension

​Load Many Files in One Call

​Markdown with Concurrency Control

​Compose with a Swarm

​Source Code

Overview

Installation

Constructor

Methods

auto()

load_single_agent()

load_multiple_agents()

load_agent_from_markdown()

load_agents_from_markdown()

load_agents_from_yaml()

load_many_agents_from_yaml()

load_agents_from_csv()

parse_markdown_file()

Usage Examples

Auto-Dispatch on Extension

Load Many Files in One Call

Markdown with Concurrency Control

Compose with a Swarm

Source Code