LazyLLM

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LazyLLM is an open-source Python framework designed to simplify the development of intelligent agents powered by large language models. It provides an easy-to-use programmatic interface for creating, testing, and deploying agents with features like conversation memory, tool usage, and customizable workflows. With LazyLLM, developers can orchestrate complex agent behaviors, integrate external tools, and maintain conversation context seamlessly, accelerating the creation of practical AI-driven applications.
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May 06 2025
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LazyLLM

LazyLLM

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LazyLLM
LazyLLM is an open-source Python framework designed to simplify the development of intelligent agents powered by large language models. It provides an easy-to-use programmatic interface for creating, testing, and deploying agents with features like conversation memory, tool usage, and customizable workflows. With LazyLLM, developers can orchestrate complex agent behaviors, integrate external tools, and maintain conversation context seamlessly, accelerating the creation of practical AI-driven applications.
Added on:
Social & Email:
Platform:
May 06 2025
--
Featured

What is LazyLLM?

LazyLL external APIs or custom utilities. Agents execute defined tasks through sequential or branching workflows, supporting synchronous or asynchronous operation. LazyLLM also offers built-in logging, testing utilities, and extension points for customizing prompts or retrieval strategies. By handling the underlying orchestration of LLM calls, memory management, and tool execution, LazyLLM enables rapid prototyping and deployment of intelligent assistants, chatbots, and automation scripts with minimal boilerplate code.

Who will use LazyLLM?

  • AI developers
  • Software engineers
  • Data scientists
  • R&D teams
  • Automation engineers

How to use the LazyLLM?

  • Step1: Install via pip with `pip install lazyllm`.
  • Step2: Import LazyLLM modules and configure your LLM provider API key.
  • Step3: Define memory components and register custom tools or APIs.
  • Step4: Create an Agent class inheriting BaseAgent and implement its run logic.
  • Step5: Instantiate the agent, call agent.run(), and monitor output.
  • Step6: Use built-in testing utilities to validate behavior and iterate as needed.

Platform

  • web
  • mac
  • windows
  • linux

LazyLLM's Core Features & Benefits

The Core Features

  • Modular agent architecture
  • LLM provider abstraction
  • Conversation memory management
  • Tool and API integration
  • Synchronous and asynchronous workflows
  • Logging and testing utilities
  • Prompt customization

The Benefits

  • Rapid agent prototyping
  • Reduced boilerplate
  • Flexible LLM integrations
  • Enhanced conversation context
  • Easy tool orchestration
  • Improved maintainability

LazyLLM's Main Use Cases & Applications

  • Customer support chatbots
  • Data analysis assistants
  • Automated report generation
  • Workflow automation scripts
  • Personal virtual assistants
  • Task management bots

FAQs of LazyLLM

LazyLLM Company Information

Analytic of LazyLLM

Visit Over Time

Monthly Visits
2.8k
Avg Visit Duration
00:00:32
Page Per Visit
2.55
Bounce Rate
81.13%
Sep 2025 - Nov 2025 All Traffic

Geography

Top 1 Regions
China
100%
Sep 2025 - Nov 2025 Worldwide Desktop Only

Traffic Sources

Direct
73.01%
Referrals
12.19%
Search
10.06%
Social
3.27%
Paid Referrals
0.79%
Mail
0.69%
Sep 2025 - Nov 2025 Desktop Only

LazyLLM Reviews

5/5
Do You Recommend LazyLLM? Leave a Comment Below!

LazyLLM's Main Competitors and alternatives?

  • LangChain
  • Microsoft Semantic Kernel
  • Haystack
  • Jina AI

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