Pools

Learn how pools enable multi-agent collaboration and intelligent task routing.

A Pool coordinates multiple agents so they can work together on a task. It brings structure to multi-agent workflows by deciding how agents interact and how information flows between them.

• Each Agent focuses on a specific skill or responsibility and contributes its part of the work.

• A shared State lets all agents access and update the same context, allowing them to build on each other’s progress.

• The Router decides which agent should act next, using either round-robin or intelligent LLM-based routing.

Creating a Pool

Use create_pool() function to coordinate multiple agents. The agents parameter accepts a list of all the agents you want to include in the pool.

from peargent import create_agent, create_pool
from peargent.models import groq

# researcher agent and write agent

# Create pool
pool = create_pool(
    agents=[researcher, writer],
)

# Run the pool along with user input
result = pool.run("Research and write about quantum computing")

How a Pool Works

A Pool can be thought of as a controller that organizes multiple agents, maintains a shared State for them to collaborate through, and uses a Router to decide which agent should act next.

Model Selection

By default, the pool uses the model of the first agent. You can also provide a default_model for the pool. Any agent without an explicitly set model will use this default_model.

from peargent import create_pool
from peargent.models import openai

# researcher agent, analyst agent and writer agent

# Create pool
pool = create_pool(
    agents=[researcher, analyst, writer],
    default_model=openai("gpt-5")
)

All the available models are listed in Models.

Routing the Agents

By default, pools use round-robin routing, where Agents take turns in order. You can also plug in a custom router to make more intelligent decisions based on the task.

For all routing options, including round-robin, LLM-based routing, and custom router functions, see the Routers .

from peargent import create_pool

pool = create_pool(
    agents=[researcher, analyst, writer],
)

article = pool.run("Write an article about renewable energy trends")
# Executes: researcher → analyst → writer

Max Iterations

A pool runs for a fixed number of iterations, where each iteration represents one agent being routed, executed, and updating the state. Pools use a default limit of 5 iterations, but you can change this using the max_iter parameter.

from peargent import create_pool

pool = create_pool(
    agents=[researcher, analyst, writer],
    max_iter=10
)

article = pool.run("Write an article about renewable energy trends")
# Executes: researcher → analyst → writer → researcher → analyst → writer → ...

Parameters

For advanced configuration like history and tracing, see the Advanced Features.