Search Results for "complex system"
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Showing 64 open source projects for "complex system"
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1
MiroFish
A Simple and Universal Swarm Intelligence Engine
MiroFish is a next-generation artificial intelligence prediction engine that leverages multi-agent technology and swarm-intelligence simulation to model, simulate, and forecast complex real-world scenarios. The system extracts “seed” information from sources such as breaking news, policy documents, and market signals to construct a high-fidelity digital parallel world populated by thousands of virtual agents with independent memory and behavior rules. Users can inject variables or conditions into this simulated environment from a “god’s eye view,” enabling iterative prediction of future trends under different assumptions, which can be useful for decision support, scenario planning, or creative exploration. ... -
2
WFGY 3.0
A tension reasoning engine over 131 S-class problems
...The system maps reasoning tension across a large set of complex problems spanning domains such as mathematics, science, climate, finance, and artificial intelligence behavior. -
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Youtu-GraphRAG
Vertically Unified Agents for Graph Retrieval-Augmented Reasoning
...These structures allow the system to perform multi-hop reasoning by decomposing complex questions into smaller queries that can be executed across different parts of the graph. The framework also incorporates hierarchical community detection algorithms that organize knowledge into clusters, improving both retrieval efficiency and reasoning performance. In addition to graph construction and retrieval, the system integrates iterative reasoning techniques that refine answers through multiple retrieval and reasoning cycles. -
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Hephaestus
Semi-Structured Agentic Framework. Workflows build themselves
Hephaestus is an open-source semi-structured agentic framework designed to orchestrate multiple AI agents working together on complex tasks. Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression, allowing workflows to evolve as new discoveries are made. ... -
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Opik
Debug, evaluate, and monitor your LLMapps, RAG systems, and agentic AI
Confidently evaluate, test, and monitor LLM applications. Opik is an open-source platform for evaluating, testing, and monitoring LLM applications. Built by Comet. Record, sort, search, and understand each step your LLM app takes to generate a response. Manually annotate, view, and compare LLM responses in a user-friendly table. Log traces during development and in production. Run experiments with different prompts and evaluate against a test set. Choose and run pre-configured evaluation... -
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OmAgent
Build multimodal language agents for fast prototype and production
...The framework provides abstractions and infrastructure for building AI agents that operate on text, images, video, and audio while maintaining a relatively simple interface for developers. Instead of forcing developers to implement complex orchestration logic manually, the system manages task scheduling, worker coordination, and node optimization behind the scenes. Its architecture uses a graph-based workflow engine where tasks are represented as nodes in a directed workflow, enabling modular composition of complex reasoning pipelines. The framework also includes support for various reasoning strategies commonly used in language agents, such as chain-of-thought prompting, self-consistency reasoning, and ReAct-style decision loops. -
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MiniMax-M2.5
State of the art LLM and coding model
MiniMax-M2.5 is a state-of-the-art foundation model extensively trained with reinforcement learning across hundreds of thousands of real-world environments. It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before generating code. The model supports full-stack development across web, mobile, and desktop platforms, covering the entire lifecycle from system design to testing and code review. ... -
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GPT-API-free
Free ChatGPT&DeepSeek API Key
...It offers a proxy-based approach that allows developers to interact with language models through a simplified interface, often requiring minimal configuration. The system is designed to lower barriers for developers who want to test or build applications using conversational AI without managing billing or complex authentication flows. It supports common API formats similar to widely used AI services, making it easier to integrate into existing projects. The project is particularly useful for rapid prototyping, educational purposes, and lightweight applications. ... -
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AI Powered Knowledge Graph Generator
AI Powered Knowledge Graph Generator
...These capabilities make knowledge graph platforms particularly useful for applications such as recommendation engines, enterprise knowledge management, and research data exploration. The system emphasizes structured data modeling and graph-based queries that allow users to explore relationships that would be difficult to identify using traditional relational databases. -
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ControlFlow
Take control of your AI agents
...The system integrates with common LLM providers and allows developers to create workflows that blend traditional software logic with AI-driven reasoning. Built on top of the Prefect ecosystem, the framework also includes observability and debugging capabilities that allow developers to monitor how tasks are executed. -
11
LISA
LISA: Reasoning Segmentation via Large Language Model
LISA is an open-source multimodal AI system designed to enable language models to perform pixel-level reasoning and segmentation tasks on images. The project introduces a framework where a large language model can interpret natural language instructions and produce segmentation masks that highlight relevant regions in an image. Instead of relying solely on predefined object categories, the model is capable of reasoning about complex textual queries and translating them into visual segmentation outputs. ... -
12
LLM-Aided OCR Project
Enhances Tesseract OCR output using LLMs (local or API)
LLM Aided OCR is an open-source system designed to improve optical character recognition accuracy by combining traditional OCR tools with large language models. The project addresses common OCR challenges such as distorted text, unusual fonts, historical documents, and complex layouts that often produce inaccurate results with standard OCR pipelines. The system first extracts raw text using OCR engines and then applies language models to analyze and correct recognition errors based on context. ... -
13
DocETL
A system for agentic LLM-powered data processing and ETL
DocETL is an open-source system designed to build and execute data processing pipelines powered by large language models, particularly for analyzing complex collections of documents and unstructured datasets. The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. -
14
LlamaDeploy
Deploy your agentic worfklows to production
llama_deploy is an open-source framework designed to simplify the deployment and productionization of agent-based AI workflows built with the LlamaIndex ecosystem. The project provides an asynchronous architecture that allows developers to deploy complex multi-agent workflows as scalable microservices. It enables teams to move from experimental prototypes to production systems with minimal changes to existing LlamaIndex code, making it easier to operationalize AI agents. The system supports orchestrating multiple services, handling communication between agents, and managing workflow execution in distributed environments. ... -
15
OM1
Modular AI runtime for robots
...The framework integrates reasoning modules, planning strategies, and tool interfaces that allow agents to operate in dynamic environments. Developers can extend the system by connecting new tools, services, or data sources to the agent architecture. The platform also includes mechanisms for coordinating workflows and managing the state of ongoing tasks. -
16
Agentless
An agentless approach to automatically solve software development
Agentless is an open-source framework that applies large language models to automatically resolve software development issues without relying on complex autonomous agent systems. The project proposes an alternative approach to AI-driven code repair that avoids the overhead of multi-agent orchestration by using a structured pipeline for identifying and fixing bugs. When solving a problem, the system first performs localization to determine which files, functions, or code segments are most likely responsible for the issue. ... -
17
ReCall
Learning to Reason with Search for LLMs via Reinforcement Learning
ReCall is an open-source framework designed to train and evaluate language models that can reason through complex problems by interacting with external tools. The project builds on earlier work focused on teaching models how to search for information during reasoning tasks and extends that idea to a broader system where models can call a variety of external tools such as APIs, databases, or computation engines. Instead of relying purely on static knowledge stored inside the model, ReCall allows the language model to dynamically decide when it should retrieve information or invoke external capabilities during the reasoning process. ... -
18
super-agent-party
All-in-one AI companion! Desktop girlfriend + virtual streamer
Super Agent Party is an open-source experimental framework designed to demonstrate collaborative multi-agent AI systems interacting within a shared environment. The project explores how multiple specialized AI agents can coordinate to solve complex tasks by communicating with each other and sharing information. Instead of relying on a single monolithic model, the framework organizes agents with different roles or capabilities that cooperate to achieve goals. Each agent may handle different responsibilities such as planning, execution, reasoning, or knowledge retrieval, allowing the system to tackle more complex problems than a single agent might handle alone. ... -
19
Index
The SOTA Open-Source Browser Agent
Index is an open-source browser automation agent designed to autonomously perform complex tasks across websites by transforming web interfaces into programmable APIs. The system enables developers to instruct an AI agent to interact with web pages using natural language rather than traditional automation scripts. Instead of writing detailed browser automation code, users can describe the desired task and allow the agent to interpret the page structure, interact with elements, and complete multi-step workflows automatically. ... -
20
Skywork-R1V4
Skywork-R1V is an advanced multimodal AI model series
Skywork-R1V is an open-source multimodal reasoning model designed to extend the capabilities of large language models into vision-language tasks that require complex logical reasoning. The project introduces a model architecture that transfers the reasoning abilities of advanced text-based models into visual domains so the system can interpret images and perform multi-step reasoning about them. Instead of retraining both language and vision models from scratch, the framework uses a lightweight visual projection layer that connects a pretrained vision backbone with a reasoning-capable language model. ... -
21
Sparrow
Structured data extraction and instruction calling with ML, LLM
Sparrow is an open-source platform designed to extract structured information from documents, images, and other unstructured data sources using machine learning and large language models. The system focuses on transforming complex documents such as invoices, receipts, forms, and scanned pages into structured formats like JSON that can be processed by downstream applications. It combines several components, including OCR pipelines, vision-language models, and LLM-based reasoning modules to identify and extract meaningful data fields from heterogeneous document layouts. ... -
22
Reader LLM
Convert any URL to an LLM-friendly input with a simple prefix
...In addition to converting individual pages, the service can perform web searches and return relevant content that can be ingested directly by AI systems. The tool relies on specialized models and parsing techniques to handle complex HTML structures and extract meaningful content while preserving important context. -
23
LangBot
Production-grade platform for building agentic IM bots
...LangBot combines LLM capabilities with agent logic, knowledge base orchestration, and plugin infrastructure so that bots can perform complex tasks rather than simple conversational responses. The platform includes a web-based management interface that simplifies configuration, access control, and integration with external AI services. -
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Unstructured.IO
Open source libraries and APIs to build custom preprocessing pipelines
The unstructured library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of unstructured revolve around streamlining and optimizing the data processing workflow for LLMs. unstructured modular bricks and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and is efficient in transforming unstructured data into... -
25
UFO³
Weaving the Digital Agent Galaxy
UFO is an open-source framework developed by Microsoft for building intelligent agents that automate interactions with graphical user interfaces on the Windows operating system. The system allows users to issue natural language instructions that are translated into automated actions across multiple desktop applications. Using a dual-agent architecture, the framework analyzes both visual interface elements and system control structures in order to understand how applications should be manipulated. This enables the agent to navigate complex software environments and perform tasks that normally require manual interaction. ...
