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pilottai

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Basic Information

PilottAI is a Python framework designed to build, orchestrate, and run autonomous multi-agent systems for scalable AI applications. It provides a structured, hierarchical agent model with manager and worker roles, intelligent job routing, context-aware processing, and specialized agent implementations. The project targets production use with support for asynchronous processing, dynamic scaling, load balancing, fault tolerance, and comprehensive logging. It includes memory capabilities for semantic storage and job history, integrations with multiple LLM providers, document processing tools, WebSocket support, and custom tool integration. The repository contains core components, agent implementations, memory and orchestration modules, tool integrations, and example specialized agents for common use cases. The package is installable via pip and aims to help teams deploy enterprise-ready multi-agent workflows and job orchestration in Python.

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App URL
https://github.com/pilottai/pilottai
Github Repository
https://github.com/pilottai/pilottai/blob/main/README.md

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App Details

Features
The README highlights a hierarchical agent system with manager and worker hierarchies, intelligent job routing, and context-aware processing. Production-ready features include asynchronous processing, dynamic scaling, load balancing, fault tolerance, and comprehensive logging to support robust deployments. Advanced memory features provide semantic storage, job history tracking, context preservation, and knowledge retrieval to maintain state and context across jobs. Integrations listed include multiple LLM providers such as OpenAI, Anthropic, and Google, document processing capabilities, WebSocket support, and hooks for custom tools. The project provides ready-to-use specialized agents for customer service, document processing, email handling, learning and research, marketing, sales, social media, and web search to accelerate common applications.
Use Cases
PilottAI helps developers and teams create and operate complex AI-driven workflows by providing reusable orchestration, agent lifecycle management, and built-in resilience features. It reduces the engineering effort needed to coordinate multiple autonomous agents by offering intelligent routing, load balancing, and fault tolerance primitives. Memory management features enable agents to store and retrieve semantic context and job histories, improving continuity and relevance of agent actions. Built-in integrations to major LLM providers and document processing utilities simplify connecting models and tools to agents. Example specialized agents and practical code snippets demonstrate common patterns for document processing, customer support, research analysis, marketing automation, and job orchestration, helping users prototype and deploy production-ready agent systems more quickly.

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