enhance_llm is a developer-centric Python library that enables you to create complex multi-step reasoning and agent workflows with LLMs. It offers utilities for chaining prompts, integrating tools, managing context, and handling dynamic decision-making across tasks. With enhance_llm, build flexible LLM pipelines for tasks like question answering, data extraction, and planning, enhancing reliability and enabling developers to customize workflows seamlessly.
AI Platforms & Frameworks
Development Environment
Workflow Automation
#Large Language Models
#Prompt Engineering
#API integration
#asynchronous processing
#memory management
#workflow automation
#error handling
#conversational AI
#modular framework
#data processing bots
#multi-step reasoning
#Custom Templates
#automated assistants
#debugging tools
#conditional logic
#software development
#Tech Stack
#User Interaction
#Agent-like Applications
#Extendable Workflows
...
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enhance_llm
enhance_llm is a developer-centric Python library that enables you to create complex multi-step reasoning and agent workflows with LLMs. It offers utilities for chaining prompts, integrating tools, managing context, and handling dynamic decision-making across tasks. With enhance_llm, build flexible LLM pipelines for tasks like question answering, data extraction, and planning, enhancing reliability and enabling developers to customize workflows seamlessly.
AI Platforms & Frameworks
Development Environment
Workflow Automation
#Large Language Models
#Prompt Engineering
#API integration
#asynchronous processing
#memory management
#workflow automation
#error handling
#conversational AI
#modular framework
#data processing bots
#multi-step reasoning
#Custom Templates
#automated assistants
#debugging tools
#conditional logic
#software development
#Tech Stack
#User Interaction
#Agent-like Applications
#Extendable Workflows
...
Featured
What is enhance_llm?
enhance_llm provides a modular framework for orchestrating large language model calls in defined sequences, allowing developers to chain prompts, integrate external tools or APIs, manage conversational context, and implement conditional logic. It supports multiple LLM providers, custom prompt templates, asynchronous execution, error handling, and memory management. By abstracting the boilerplate of LLM interaction, enhance_llm streamlines the development of agent-like applications—such as automated assistants, data processing bots, and multi-step reasoning systems—making it easier to build, debug, and extend sophisticated workflows.
Who will use enhance_llm?
- Python developers building LLM applications
- AI researchers prototyping agent workflows
- Machine learning engineers
- Data scientists automating text tasks
- Software engineers integrating LLMs
How to use the enhance_llm?
- Step1: Install via pip: pip install enhance-llm
- Step2: Import core classes: from enhance_llm import Chain, Node, LLMClient
- Step3: Configure your LLM provider credentials
- Step4: Define nodes or tasks with prompt templates and tool calls
- Step5: Link nodes into a Chain with conditional logic
- Step6: Execute the chain: result = chain.run(input_data)
- Step7: Handle and parse outputs for your application
Platform
- mac
- windows
- linux
enhance_llm's Core Features & Benefits
The Core Features
- Multi-step prompt chaining
- Tool and API integration
- Context and memory management
- Conditional logic and branching
- Asynchronous execution
- Error handling and retry
The Benefits
- Modular workflow design
- Improved LLM reliability
- Customizable prompt templates
- Streamlined debugging
- Easy extension with plugins
enhance_llm's Main Use Cases & Applications
- Automated question answering with context
- Data extraction and structured reporting
- Conversational AI assistants
- Automated planning and scheduling
- Multi-step text analysis pipelines
FAQs of enhance_llm
enhance_llm Company Information
- stay-leave
enhance_llm Reviews
5/5
enhance_llm's Main Competitors and alternatives?
- LangChain
- LlamaIndex
- Haystack
- AutoGen
- Steamship
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