Lagent

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Lagent is an AI Agents & Automation tool that provides a lightweight framework for building LLM-based agents. It allows developers to create and experiment with multi-agent applications, offering flexible message passing and response formatting.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is lagent?

Lagent is a lightweight framework designed for building sophisticated LLM-based agents, inspired by the design philosophy of PyTorch. It simplifies the process of creating multi-agent applications by allowing users to focus on defining layers and message passing between them in a Pythonic way. Key features include agent-to-agent communication via AgentMessage, memory management for conversational context, and custom message aggregation. The framework also supports flexible response formatting and consistent tool calling through ActionExecutor. Lagent offers dual interfaces (synchronous and asynchronous) for debugging and large-scale inference, making it suitable for various development needs.

Best used for

Ideal for developers and data scientists who need to rapidly prototype and deploy LLM-based agents, manage complex agent interactions, and integrate custom tools. Especially valuable for those looking to build multi-agent applications with flexible message passing and robust memory management capabilities.

Common actions

build AI agents

manage agent communication

integrate LLM tools

develop multi-agent systems

"AI Agents"github copilotopen-sourceautomated workflowface swappingworkflowsdeepfakecollaborationlow-code/no-code

Capabilities

Key features

LLM-based agent framework
Agent-to-agent communication
Memory management
Custom message aggregation
Flexible response formatting
Synchronous/asynchronous interfaces
Tool calling consistency

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the core philosophy behind Lagent's design?

Lagent is inspired by PyTorch's design philosophy, aiming to make the workflow intuitive by treating agent components like neural network layers. Users can focus on creating layers and defining message passing, simplifying the development of complex LLM-based agents.

How does Lagent handle conversational memory?

Lagent automatically adds both input and output messages to an agent's memory in each forward pass. This memory can be inspected and cleared, allowing for persistent conversational context across interactions and session isolation.

Does Lagent support asynchronous operations?

Yes, Lagent adopts a dual interface design, providing both synchronous and asynchronous variants for almost every component, including LLMs, actions, and action executors. This allows for efficient large-scale inference by leveraging idle CPU and GPU resources.

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