MARFT provides an integrated framework to define, train, and deploy multiple collaborating AI agents via reinforcement fine-tuning. It offers role management, memory modules, environment simulation, and evaluation pipelines. Developers and researchers can orchestrate multi-agent tasks, automate workflows, and benchmark performance with ease.
AI Platforms & Frameworks
Development Environment
Workflow Automation
#AI experimentation
#collaborative AI
#Prototyping Tools
#reinforcement learning
#multi-agent systems
#modular architecture
#workflow automation
#environment simulation
#custom reward functions
#evaluation metrics
#Python AI frameworks
#reproducible research
#agent orchestration
#Rapid Development
#AI model integration
#user memory management
#scalable AI solutions
#development efficiency
#research reproducibility
#Data-Driven AI
...
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MARFT
MARFT provides an integrated framework to define, train, and deploy multiple collaborating AI agents via reinforcement fine-tuning. It offers role management, memory modules, environment simulation, and evaluation pipelines. Developers and researchers can orchestrate multi-agent tasks, automate workflows, and benchmark performance with ease.
AI Platforms & Frameworks
Development Environment
Workflow Automation
#AI experimentation
#collaborative AI
#Prototyping Tools
#reinforcement learning
#multi-agent systems
#modular architecture
#workflow automation
#environment simulation
#custom reward functions
#evaluation metrics
#Python AI frameworks
#reproducible research
#agent orchestration
#Rapid Development
#AI model integration
#user memory management
#scalable AI solutions
#development efficiency
#research reproducibility
#Data-Driven AI
...
Featured
What is MARFT?
MARFT is a Python-based LLMs, enabling reproducible experiments and rapid prototyping of collaborative AI systems.
Who will use MARFT?
- Machine learning engineers
- AI researchers
- Software developers
- R&D teams
- Product managers
How to use the MARFT?
- Step1: Clone the repository or pip install marft.
- Step2: Define agent roles and memory schema in a YAML or Python file.
- Step3: Configure environment simulation and reward functions.
- Step4: Run marft train to launch multi-agent RL fine-tuning.
- Step5: Monitor metrics and adjust hyperparameters as needed.
- Step6: Evaluate agents with marft eval and export trained models.
- Step7: Deploy agents via marft deploy or integrate into your application.
Platform
- mac
- windows
- linux
MARFT's Core Features & Benefits
The Core Features
- Multi-agent orchestration API
- Reinforcement fine-tuning pipelines
- Role and memory management
- Environment simulation toolkit
- Customizable reward functions
- Built-in evaluation metrics
The Benefits
- Accelerates prototyping of collaborative AI
- Ensures reproducible RL experiments
- Modular and extensible architecture
- Seamless integration with major LLMs
- Comprehensive workflow automation
MARFT's Main Use Cases & Applications
- Virtual customer support with multiple specialized agents
- Automated negotiation and decision-making simulations
- Research prototypes for multi-agent coordination
- Task automation in business workflows
- Complex problem solving with role-based agents
FAQs of MARFT
MARFT Company Information
MARFT Reviews
5/5
MARFT's Main Competitors and alternatives?
- Ray RLlib
- LangChain Agents
- OpenAI Multi-Agent Toolkit
- Stable Baselines3
- RLlib MultiAgent
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