TreeInstruct is an open-source Python library that allows developers to define, execute, and manage decision-tree based prompt workflows. It streamlines multi-step LLM interactions by providing conditional branching, reusable nodes, and logging for robust, modular orchestration.
AI Platforms & Frameworks
Workflow Automation
Digital Workers
#decision tree
#Prompt Engineering
#Workflow-Management
#LLM Integration
#conditional logic
#error handling
#logging
#Customizable Prompts
#dynamic branching
#multi-turn interactions
#Scalable Workflows
#modular design
#AI prompt optimization
#flexible architecture
#transparency in AI
#complex decision-making
#Prompt Reuse
#function nodes
#workflow orchestration
#real-time execution
...
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TreeInstruct
TreeInstruct is an open-source Python library that allows developers to define, execute, and manage decision-tree based prompt workflows. It streamlines multi-step LLM interactions by providing conditional branching, reusable nodes, and logging for robust, modular orchestration.
AI Platforms & Frameworks
Workflow Automation
Digital Workers
#decision tree
#Prompt Engineering
#Workflow-Management
#LLM Integration
#conditional logic
#error handling
#logging
#Customizable Prompts
#dynamic branching
#multi-turn interactions
#Scalable Workflows
#modular design
#AI prompt optimization
#flexible architecture
#transparency in AI
#complex decision-making
#Prompt Reuse
#function nodes
#workflow orchestration
#real-time execution
...
Featured
What is TreeInstruct?
TreeInstruct provides a framework to build hierarchical, decision-tree based prompting pipelines for large language models. Users can define nodes representing prompts or function calls, set conditional branches based on model output, and execute the tree to guide complex workflows. It supports integration with OpenAI and other LLM providers, offering logging, error handling, and customizable node parameters to ensure transparency and flexibility in multi-turn interactions.
Who will use TreeInstruct?
- AI developers
- Prompt engineers
- NLP researchers
- Software architects
How to use the TreeInstruct?
- Step1: Install via pip install treeinstruct
- Step2: Import Tree and Node classes in your Python script
- Step3: Create nodes with prompts or function calls
- Step4: Define branches using conditions on node outputs
- Step5: Assemble nodes into a Tree object
- Step6: Execute tree.run() to start the workflow
- Step7: Inspect logs and outputs for each node
Platform
- mac
- windows
- linux
TreeInstruct's Core Features & Benefits
The Core Features
- Hierarchical decision-tree based prompt workflows
- Conditional branching on LLM outputs
- Reusable prompt and function nodes
- Integration with OpenAI and other LLM APIs
- Execution logging and error handling
The Benefits
- Modular design for complex multi-step interactions
- Improved prompt reusability and maintainability
- Enhanced decision-making via dynamic branches
- Transparent execution with detailed logs
- Scalable orchestration of LLM workflows
TreeInstruct's Main Use Cases & Applications
- Building interactive, decision-tree based chatbots
- Designing multi-stage Q&A assistants
- Automating data extraction pipelines with conditional logic
- Creating domain-specific tutorial or guidance systems
- Orchestrating LLM-powered form processing workflows
FAQs of TreeInstruct
TreeInstruct Company Information
TreeInstruct Reviews
5/5
TreeInstruct's Main Competitors and alternatives?
- LangChain
- LlamaIndex
- Microsoft PromptFlow
- Haystack
- OpenAI Function Calling
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