LLMFlow is an open-source framework designed to orchestrate multi-step LLM workflows by chaining prompts, integrating external tools, and managing contextual memory. With modular nodes, developers can define tasks, create branching logic, and execute pipelines efficiently. Supports plugin architecture for custom modules and provides built-in adapters for popular LLM providers. Ideal for automating customer support, content generation, and data processing tasks.
LLMFlow is an open-source framework designed to orchestrate multi-step LLM workflows by chaining prompts, integrating external tools, and managing contextual memory. With modular nodes, developers can define tasks, create branching logic, and execute pipelines efficiently. Supports plugin architecture for custom modules and provides built-in adapters for popular LLM providers. Ideal for automating customer support, content generation, and data processing tasks.
LLMFlow provides a declarative way to design, test, and deploy complex language model workflows. Developers create Nodes which represent prompts or actions, then chain them into Flows that can branch based on conditions or external tool outputs. Built-in memory management tracks context between steps, while adapters enable seamless integration with OpenAI, Hugging Face, and others. Extend functionality via plugins for custom tools or data sources. Execute Flows locally, in containers, or as serverless functions. Use cases include creating conversational agents, automated report generation, and data extraction pipelines—all with transparent execution and logging.
Who will use LLMFlow?
AI engineers
Software developers
Data scientists
Product managers
Enterprises building LLM applications
How to use the LLMFlow?
Step1: Install the package via npm or yarn (npm install llmflow).
Step2: Define Nodes and Flows in a configuration file or TypeScript.
Step3: Configure provider credentials and environment variables.
Step4: Run llmflow dev to test interactions locally.
Step5: Deploy the Flow using Docker or as a serverless function.
Platform
mac
windows
linux
LLMFlow's Core Features & Benefits
The Core Features
Declarative LLM workflow chaining
Branching logic and conditional flows
Contextual memory management
External tool integration
Plugin architecture
Adapters for multiple LLM providers
Logging and monitoring support
Error handling and retry policies
The Benefits
Accelerates development of complex LLM pipelines
Modular and reusable workflow components
Transparent execution and debugging
Easy integration with existing tools
Scalable deployment options
LLMFlow's Main Use Cases & Applications
Building conversational AI assistants with multi-turn logic
Automating content generation and editing pipelines