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Flock

Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent teams, powered by LangGraph, Langchain, FastAPI, and NextJS.(Flock 是一个基于workflow工作流的低代码平台,用于快速构建聊天机器人、RAG、Agent和Muti-Agent应用,采用 LangGraph、Langchain、FastAPI 和 NextJS 构建。)

Flock

Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent teams, powered by LangGraph, Langchain, FastAPI, and NextJS.(Flock 是一个基于workflow工作流的低代码平台,用于快速构建聊天机器人、RAG、Agent和Muti-Agent应用,采用 LangGraph、Langchain、FastAPI 和 NextJS 构建。)

Author Avatar Theme by onelevenvy
Github Stars Github Stars: 950
Last Commit Last Commit: Aug 4, 2025 -
First Commit Created: Aug 8, 2025 -
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Overview

MseeP.ai has launched an impressive suite of tools known as Flock (Flexible Low-code Orchestrating Collaborative-agent Kits), designed to enable seamless integration and automation within workflows. With its rich features and continuous updates, Flock aims to provide developers and businesses with the flexibility to craft autonomous agents capable of reasoning and executing tasks efficiently.

The recent updates highlight MseeP.ai’s commitment to enhancing user experience. With the addition of innovative nodes and tools, the platform allows for complex workflows that are not only easy to configure but also integrate seamlessly with existing systems. This establishes Flock as a compelling choice for organizations looking to optimize their automated systems.

Features

  • Autonomous Agents: Create agents that can reason, plan, and execute various tasks independently, enhancing workflow autonomy.

  • Multimodal Chat Support: Supports image modality in chat, with plans to add more modalities, expanding communication capabilities.

  • MCP Tools Integration: Integrate Model Context Protocol tools, allowing for dynamic load and conversion of tools specifically for LangChain agents.

  • Parameter Extraction: Automatically extract structured information from text and output it in JSON format, simplifying data handling.

  • Human-in-the-loop: Involve humans for reviewing and validating model outputs or tool calls to ensure high accuracy and contextual relevance.

  • Conditional Logic: The If-Else node introduces advanced conditional logic options, enabling complex decision-making pathways within workflows.

  • Python Code Execution: Execute Python scripts directly within workflows for custom data transformations and processing, providing greater flexibility.

  • Subgraph Node: Encapsulate and reuse complex workflows as independent subgraph nodes, enhancing modularity and maintainability of workflows.