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LAgent

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Category:AI Agent Development Frameworks

Overview

A modular Python framework for building agents with model calls, memory and executable tools.

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Best For Professions:

software developersAI researchersdata scientistsmachine learning engineers

Lagent from InternLM gives Python developers components for agent messages, memory, model wrappers, output parsers and action execution. The examples range from a simple conversation to multi-step research and chart generation using web search and Python execution.

Developers choose the model, implement or enable tools, define the message flow and set execution limits. It suits teams that want to inspect and customize agent behaviour in Python. Hosting, access controls and the safe execution of generated code remain part of the application you build. The video introduces an earlier version of Lagent; use the current repository examples for setup.

Product overview

Capabilities, pricing and fit

A modular Python framework for building agents with model calls, memory and executable tools.

Information as of October 13, 2026

What it can do

  • Modular agent code

    Compose memory, parsers, messages and action executors.[1], [2]

  • Model choice

    Use documented OpenAI or vLLM model wrappers.[1], [2]

  • Multi-step examples

    Run configured research and chart-generation workflows.[1], [2]

Where it fits best

  • Building inspectable Python agent workflows[1], [2]
  • Combining research and code execution[1], [2]
  • Experimenting with model and tool configurations[1], [2]

Pricing and availability

Apache-2.0 source is available without a software subscription. You supply model access or local inference hardware and the resources needed to run tools.[1], [2]

Access: Developer-managed application or research environment.[1], [2]

Sources (2)
  1. github.com — ProductOfficial site · checked 2026-10-13
  2. internlm.readthedocs.io — Documentation and product detailsOfficial site · checked 2026-10-13

Autonomy level

74/100

Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.

Reasoning: Lagent enables configured agents to call tools, execute code and pass results through multi-step or multi-agent workflows. Developers choose the model, actions, prompts, message routing and stopping limits, and review the outputs. This score reflects the workflows enabled by the framework, not a ready-made independently operating service.

Comparisons

Custom Comparisons

Some of the use cases of LAgent:

  • Building inspectable Python agent workflows
  • Combining research and code execution
  • Experimenting with model and tool configurations

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Pricing model:

free

Code access:

open-source

Popularity level: 64%

Industries:

Software DevelopmentArtificial IntelligenceResearch

Tags:

open-source frameworkLLM-based agentsPythonmodular architecturetool integration

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