Semi Agent is an open-source Python library that lets developers create autonomous AI agents by combining language models with custom tools, memory storage, and stepwise planning. It simplifies LLM integration, tool invocation, and stateful memory to orchestrate complex workflows.
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Workflow Automation
Tool Libraries
#AI agent development
#contextual memory
#modular architecture
#tool integration
#step-by-step planning
#logging and debugging
#Open-source AI
#Python customization
#workflow automation
#multi-step processes
#Language Model Support
#API integration
#conversational AI
#task-oriented agents
#AI for developers
#agent optimization
#custom tools
#lightweight AI solutions
#software extensibility
#agent behavior management
...
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Semi Agent
Semi Agent is an open-source Python library that lets developers create autonomous AI agents by combining language models with custom tools, memory storage, and stepwise planning. It simplifies LLM integration, tool invocation, and stateful memory to orchestrate complex workflows.
AI Platforms & Frameworks
Workflow Automation
Tool Libraries
#AI agent development
#contextual memory
#modular architecture
#tool integration
#step-by-step planning
#logging and debugging
#Open-source AI
#Python customization
#workflow automation
#multi-step processes
#Language Model Support
#API integration
#conversational AI
#task-oriented agents
#AI for developers
#agent optimization
#custom tools
#lightweight AI solutions
#software extensibility
#agent behavior management
...
Featured
What is Semi Agent?
Semi Agent provides a modular architecture for building AI agents that can plan, execute actions, and remember context over time. It integrates with popular language models, supports tool definitions for custom functionality, and maintains conversational or task-oriented memory. Developers can define step-by-step plans, connect external APIs or scripts as tools, and leverage built-in logging to debug and optimize agent behavior. Its open-source design and Python basis allow easy customization, extensibility, and integration into existing pipelines.
Who will use Semi Agent?
- Developers
- Data scientists
- AI researchers
- Automation engineers
- Technical hobbyists
How to use the Semi Agent?
- Step1: Install via pip install semi-agent
- Step2: Import Agent and Tool classes in your Python script
- Step3: Define your custom tools with execute methods
- Step4: Initialize the Agent with an LLM, memory store, and tools
- Step5: Provide prompts or goals to the agent instance
- Step6: Call agent.run() to start planning and execution
- Step7: Inspect agent logs and memory to refine workflows
Platform
- mac
- windows
- linux
Semi Agent's Core Features & Benefits
The Core Features
- LLM integration (OpenAI, HuggingFace)
- Custom tool execution
- Contextual memory management
- Stepwise planning module
- Detailed logging and debugging
The Benefits
- Accelerates AI agent development
- Open-source and customizable
- Supports complex multi-step workflows
- Lightweight and Python-native
- Easy integration with APIs and scripts
Semi Agent's Main Use Cases & Applications
- Automated customer support bots
- Data analysis and reporting agents
- Research assistants for literature summarization
- Personal productivity and scheduling agents
- Integration with monitoring and alerting pipelines
FAQs of Semi Agent
Semi Agent Company Information
Semi Agent Reviews
5/5
Semi Agent's Main Competitors and alternatives?
- LangChain Agents
- AutoGPT
- LlamaIndex
- ReACT
- OpenAI Functions
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