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swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop

Simple agents are good for 1-to-1 retrieval system. For more complex task we need multi steps reasoning loop. In a reasoning loop the agent can break down a complex task into subtasks and solve them step by step while maintaining a conversational memory.

更新:2026/10/12 05:35:05首次收录:2026/10/12 05:35:05原始来源
language
Jupyter Notebook
stars
4
forks
3
license
null
homepage
source_updated_at
2026-07-28T23:49:29Z

详细说明:

Simple agents are good for 1-to-1 retrieval system. For more complex task we need multi steps reasoning loop. In a reasoning loop the agent can break down a complex task into subtasks and solve them step by step while maintaining a conversational memory.

描述来源标记:原始/编辑整理

如何使用:

在 GitHub 打开仓库 swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop,按 README 安装与使用。Stars:4。

同类对比:

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LangChain相关The agent engineering platform.与「swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop」相关,可按场景对照选用
LlamaIndex相关LlamaIndex is the document processing platform for AI与「swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop」相关,可按场景对照选用
MCP 官方示例服务器集合相关Model Context Protocol 官方维护的参考 MCP Server 集合。与「swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop」相关,可按场景对照选用
ComfyUI相关AI 相关工具/产品:ComfyUI。与「swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop」相关,可按场景对照选用
AutoGen相关AI 相关工具/产品:AutoGen。与「swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop」相关,可按场景对照选用
NousResearch/hermes-agent相关The agent that grows with you与「swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop」相关,可按场景对照选用
firecrawl/firecrawl相关Supercharge your AI agents with data from the web and beyond. Building the library for superintelligence. 🔥与「swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop」相关,可按场景对照选用
huggingface/transformers相关🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. 与「swastikmaiti/LlamaIndex-Agent-with-Reasoning-Loop」相关,可按场景对照选用

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