Agentfy is an extensible AI agent development framework that lets developers define agents in Python, integrate external APIs as tools, manage conversational memory, orchestrate multi-step workflows, and deploy agents on servers, edge devices, or containers with minimal configuration.
AI Platforms & Frameworks
Workflow Automation
Digital Workers
#AI Agents
#machine learning
#automated tasks
#modular architecture
#Open-source AI
#context management
#tool integrations
#Python development
#memory storage
#Docker deployment
#REST APIs
#AI Frameworks
#Developer Tools
#Cloud Solutions
#Hugging Face
#local utilities
#memory backends
#Task Automation
#software development
#AI technology
...
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Agentfy
Agentfy is an extensible AI agent development framework that lets developers define agents in Python, integrate external APIs as tools, manage conversational memory, orchestrate multi-step workflows, and deploy agents on servers, edge devices, or containers with minimal configuration.
AI Platforms & Frameworks
Workflow Automation
Digital Workers
#AI Agents
#machine learning
#automated tasks
#modular architecture
#Open-source AI
#context management
#tool integrations
#Python development
#memory storage
#Docker deployment
#REST APIs
#AI Frameworks
#Developer Tools
#Cloud Solutions
#Hugging Face
#local utilities
#memory backends
#Task Automation
#software development
#AI technology
...
Featured
What is Agentfy?
Agentfy provides a modular architecture for constructing AI agents by combining LLMs, memory backends, and tool integrations into a cohesive runtime. Developers declare agent behavior using Python classes, register tools (REST APIs, databases, utilities), and choose memory stores (local, Redis, SQL). The framework orchestrates prompts, actions, tool calls, and context management to automate tasks. Built-in CLI and Docker support enable one-step deployment to cloud, edge, or desktop environments.
Who will use Agentfy?
- AI/ML engineers
- Software developers
- Data scientists
- Startups building autonomous agents
- Research labs exploring agent architectures
How to use the Agentfy?
- Step1: Install Agentfy: `pip install agentfy`
- Step2: Define tools by subclassing Tool or using built-in adapters for APIs
- Step3: Configure a memory backend (e.g., Redis, SQLite)
- Step4: Create an Agent instance with your LLM provider, tools, and memory
- Step5: Call `agent.run(input_text)` to start the agent loop
- Step6: Deploy via `agentfy deploy` CLI or package in Docker for cloud/edge
Platform
- mac
- windows
- linux
Agentfy's Core Features & Benefits
The Core Features
- Agent class for orchestrating prompts, actions, and tools
- Pluggable tool integrations (APIs, databases, local utilities)
- Configurable memory backends (local, Redis, SQL)
- Multi-model support (OpenAI, Hugging Face, custom LLMs)
- CLI for project scaffolding and deployment
- Docker containerization for cloud/edge
The Benefits
- Accelerates AI agent development
- Modular and extensible architecture
- Seamless context and memory management
- Easy deployment across environments
- Open-source and community driven
Agentfy's Main Use Cases & Applications
- Customer support conversational agents
- Automated data extraction and processing bots
- Virtual research assistants for literature review
- Workflow automation in enterprise systems
- Personalized recommendation and scheduling agents
FAQs of Agentfy
Agentfy Company Information
- Agentfy-io
Agentfy Reviews
5/5
Agentfy's Main Competitors and alternatives?
- LangChain
- Microsoft Semantic Kernel
- AutoGPT
- Hugging Face Agents
- Haystack
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