Noema Declarative AI is an open-source Python framework that lets you define multi-step AI agent workflows in a declarative YAML/JSON format. It integrates custom tools, manages memory, and orchestrates complex reasoning pipelines without boilerplate code.
AI Platforms & Frameworks
Workflow Automation
Tool Libraries
#Declarative AI
#agent orchestration
#YAML configuration
#JSON workflows
#memory management
#API integration
#Rapid Prototyping
#automation scripts
#Chatbot Development
#reproducible research
#easy collaboration
#extensible plugins
#LLM support
#reduce boilerplate
#context handling
#structured results
#Simplify logic
#workflow automation
#Research experiments
...
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Noema Declarative AI
Noema Declarative AI is an open-source Python framework that lets you define multi-step AI agent workflows in a declarative YAML/JSON format. It integrates custom tools, manages memory, and orchestrates complex reasoning pipelines without boilerplate code.
AI Platforms & Frameworks
Workflow Automation
Tool Libraries
#Declarative AI
#agent orchestration
#YAML configuration
#JSON workflows
#memory management
#API integration
#Rapid Prototyping
#automation scripts
#Chatbot Development
#reproducible research
#easy collaboration
#extensible plugins
#LLM support
#reduce boilerplate
#context handling
#structured results
#Simplify logic
#workflow automation
#Research experiments
...
Featured
What is Noema Declarative AI?
Noema Declarative AI allows developers and researchers to specify AI agents and their workflows in a high-level, declarative manner. By writing YAML or JSON configuration files, you define agents, prompts, tools, and memory modules. The Noema runtime then parses these definitions, loads language models, executes each step of your pipeline, handles state and context, and returns structured results. This approach reduces boilerplate, improves reproducibility, and separates logic from execution, making it ideal for prototyping chatbots, automation scripts, and research experiments.
Who will use Noema Declarative AI?
- AI developers
- Machine learning engineers
- Software engineers
- Researchers
How to use the Noema Declarative AI?
- Step1: Install via pip: pip install noema
- Step2: Create a YAML or JSON config file defining agents, prompts, tools, and memory blocks
- Step3: Import Noema in your Python script and load the config: from noema import Runner; runner = Runner('config.yaml')
- Step4: Initialize and run: results = runner.run()
- Step5: Process and analyze the returned structured outputs
Platform
- mac
- windows
- linux
Noema Declarative AI's Core Features & Benefits
The Core Features
- Declarative agent definition via YAML/JSON
- Custom tool and API integration
- Built-in memory and context management
- Multi-step workflow orchestration
- Extensible plugin architecture
- Support for major LLM providers
The Benefits
- Reduces boilerplate code
- Enhances reproducibility
- Simplifies complex agent logic
- Separates definition from execution
- Facilitates rapid prototyping
- Improves collaboration
Noema Declarative AI's Main Use Cases & Applications
- Building chatbots with multi-turn reasoning
- Automating data extraction and processing pipelines
- Integrating LLMs into enterprise workflows
- Rapid prototyping of AI-driven applications
FAQs of Noema Declarative AI
Noema Declarative AI Company Information
Noema Declarative AI Reviews
5/5
Noema Declarative AI's Main Competitors and alternatives?
- LangChain
- Agentic
- ReACT
- OpenAI Function Calling
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