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ManasAI is a Python-based framework that enables the creation of autonomous AI agents with built-in state and modular components. It offers core abstractions for agent reasoning, short-term and long-term memory, external tool and API integrations, message-driven event handling, and multi-agent orchestration. Agents can be configured to manage context, execute tasks, handle retries, and gather feedback. Its pluggable architecture allows developers to tailor memory backends, tools, and orchestrators to specific workflows, making it ideal for prototyping chatbots, digital workers, and automated pipelines that require persistent context and complex interactions.

Meta-Agent-with-More-Agents is an extensible open-source framework that implements a meta agent architecture allowing multiple specialized sub-agents to collaborate on complex tasks. It leverages LangChain for agent orchestration and OpenAI APIs for natural language processing. Developers can define custom agents for tasks like data extraction, sentiment analysis, decision-making, or content generation. The meta agent coordinates task decomposition, dispatches objectives to appropriate agents, gathers their outputs, and iteratively refines results via feedback loops. Its modular design supports parallel processing, logging, and error handling. Ideal for automating multi-step workflows, research pipelines, and dynamic decision support systems, it simplifies building robust distributed AI systems by abstracting inter-agent communication and lifecycle management.