LLM-Blender-Agent enables developers to build modular, multi-agent AI systems by wrapping LLMs into collaborative agents. Each agent can access tools like Python execution, web scraping, SQL databases, and external APIs. The framework handles conversation memory, step-by-step reasoning, and tool orchestration, allowing tasks such as report generation, data analysis, automated research, and workflow automation. Built on top of LangChain, it’s lightweight, extensible, and works with GPT-3.5, GPT-4, and other LLMs.

At its core, AGIFlow provides an intuitive canvas where users can assemble AI agents into dynamic workflows, defining triggers, conditional logic, and data exchanges between agents. Each agent node can execute custom code, call external APIs, or leverage pre-built models for NLP, vision, or data processing tasks. With built-in connectors to popular databases, web services, and messaging platforms, AGIFlow streamlines integration and orchestration across systems. Version control and rollback features allow teams to iterate rapidly, while real-time logging, metrics dashboards, and alerting ensure transparency and reliability. Once workflows are tested, they can be deployed on scalable cloud infrastructure with scheduling options, enabling businesses to automate complex processes such as report generation, customer support routing, or research pipelines.

AgentIn is a Python-based AI agent framework designed to accelerate the development of conversational and task-driven agents. It offers built-in memory modules to persist context, dynamic tool integration to call external APIs or local functions, and a flexible prompt templating system for customized interactions. Multi-agent orchestration enables parallel workflows, while logging and caching improve reliability and auditability. Easily configurable via YAML or Python code, AgentIn supports major LLM providers and can be extended with custom plugins for domain-specific capabilities.