Search Results for "complex"
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Showing 17 open source projects for "complex"
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CrewAI
Framework for orchestrating role-playing, autonomous AI agents
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. The power of AI collaboration has too much to offer. CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. -
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AutoGen
An Open-Source Programming Framework for Agentic AI
AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns. AutoGen provides... -
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MetaGPT
The Multi-Agent Framework
The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo. Assign different roles to GPTs to form a collaborative software entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories / competitive analysis/requirements/data structures / APIs / documents, etc. Internally, MetaGPT includes product managers/architects/project managers/engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. -
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Langroid
Harness LLMs with Multi-Agent Programming
Given the remarkable abilities of recent Large Language Models (LLMs), there is an unprecedented opportunity to build intelligent applications powered by this transformative technology. The top question for any enterprise is: how best to harness the power of LLMs for complex applications? For technical and practical reasons, building LLM-powered applications is not as simple as throwing a task at an LLM system and expecting it to do it. Effectively leveraging LLMs at scale requires a principled programming framework. In particular, there is often a need to maintain multiple LLM conversations, each instructed in different ways, and "responsible" for different aspects of a task. -
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Ax
Build LLM powered Agents and "Agentic workflows"
Build intelligent agents quickly — inspired by the power of "Agentic workflows" and the Stanford DSPy paper. Seamlessly integrates with multiple LLMs and VectorDBs to build RAG pipelines or collaborative agents that can solve complex problems. Advanced features streaming validation, multi-modal DSPy, etc. We've renamed from "llmclient" to "ax" to highlight our focus on powering agentic workflows. We agree with many experts like "Andrew Ng" that agentic workflows are the key to unlocking the true power of large language models and what can be achieved with in-context learning. ... -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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Simultra
Multiagent simulator of road traffic in Qt/C++ and OpenStreetMap.
Simultra is an open-source, hybrid road traffic simulator designed to handle large roadmaps in real-time. It combines microscopic and mesoscopic simulations into one multiagent hybrid simulator. The large-scale maps are modelled mesoscopically in real-time, and the complex traffic interactions benefit from detailed agent-based microscopic simulations. To resolve the concurrency issues within the maps representation and the meso-micro transitions, Simultra combines an event-based mesoscopic model of the maps with a more detailed physical engine. -
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EVO is a simulation platform for co-evolving the morphology and the behavior of 3D artificial agents in a complex environment. This project directly follows the original work of Karl Sims. It puts together complex creatures, a GA & Open Dynamics Engine.
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BitBang
Agent-based framework for Artificial Life and Complex Systems
BitBang is an agent-based simulation framework, best suited for complex systems or artificial life research. It is developed with ease of integration in mind, allowing the interplay with existing 3D and physics engines. -
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...The program simulates a 2D world populated with "animals" which struggle for food, reproduce and may also eat each other. The subject of evolution is their behavior, which may become quite complex. There is an updated video tutorial at http://www.youtube.com/watch?v=nPkZvyVDWJU !
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Elysia is an attempt to study the complex process behind decision making in neural systems. An evolved entity approach is used to converge on an 'intelligence' solution through genetic algorithms as well as adaptable models of distributed 'thinking'.
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Trinity is mobile agent framework for creation of wide range of mobile agent types. Main goal of project is to provide a tool for rapid development of non-complex agents. Trinity is implemented in .NET v3.5 with usage of Windows Workflow Foundation and W
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EU-funded R&D project that aims to develop and validate a platform for composition of government services into complex process definitions (covering life events/business episodes) enabling semantic interoperability of particular e-Government services.
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videonetwork
A simulator for self-organizing video replication and more
Multimedia consumption at social events - if available - can be considered as a complex problem. People use their smart phones and tablets to take pictures and videos, leading to a high number of production places. If we enable those visitors to share their content at the event, new challenges emerge. Visitors move, go online and offline, and build a dynamic network. One can say a complex adaptive network emerges, with all its problems. -
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This project aims to develop various tools for artificial life and complex systems simulations: from the simulator itself to generic generation tools.
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Procedural content generation of deterministic and complex entities, with properties and other entities inside, defined by an editable XML file, along with a framework to simulate actions, compositions, and interactions of entities.
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ADSKIT
Agents design supporting system for simulation models
Main task is automating the creation of simulation models of complex model. The originalities of proposed software are explicit representation of conceptual model in the concept – attribute – relation form and applying expert system approach to transform designed ontology into agent model. Researchers develop the structure and behavior of agents using declarative approach. Web: https://84.237.20.67/RFFI/Main.php
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