12 dépôts
Multi-step execution patterns designed for complex reasoning tasks where results propagate through steps.
Distinguishing note: Focuses on iterative reasoning steps rather than standard pipeline execution.
Explore 12 awesome GitHub repositories matching artificial intelligence & ml · Reasoning Chains. Refine with filters or upvote what's useful.
CL4R1T4S is a framework designed to orchestrate generative AI workflows and optimize language model outputs. It functions as a centralized utility for managing, versioning, and deploying structured system prompts and behavioral parameters to ensure consistent performance across complex tasks. The project distinguishes itself by implementing a structured pipeline that wraps model interactions to enforce behavioral constraints and sanitize inputs. This orchestration layer incorporates heuristic-based validation and stateful context management to maintain coherence and quality throughout multi-s
Structures tasks into sequential logical steps to improve accuracy through iterative verification.
Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr
Enables hybrid reasoning modes for structured step-by-step problem solving.
Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q
Sequences multiple reasoning units into complex workflows with conditional logic for multi-step problem solving.
gpt-oss is an open-weight large language model and reasoning engine designed for complex reasoning and agentic workflows. It functions as an AI agent framework and model serving API, allowing for local deployment and the hosting of standardized interfaces to expose model completions and internal reasoning processes. The project distinguishes itself as a quantized inference engine, utilizing tensor parallelism and weight quantization to run high-parameter models on limited hardware. It features a reasoning model that employs chain-of-thought processing to solve multi-step logical tasks. The s
Implements multi-step execution patterns that run Python code to solve problems during the chain-of-thought process.
DB-GPT is an AI-driven database management system that uses agentic reasoning to execute data tasks. It converts natural language prompts into executable database queries and combines structured database records with unstructured knowledge bases to provide grounded analysis. The system orchestrates multi-step reasoning chains that integrate database queries, custom scripts, and external tool calls. It allows for the packaging of domain knowledge into reusable analysis skills and executes generated code within sandboxed environments for system safety. The platform covers data orchestration ac
Implements multi-step reasoning chains to decompose complex data tasks into sequences of tool calls and queries.
Open Deep Research is an artificial intelligence framework designed to automate complex, multi-step research workflows. It functions as an autonomous agent that performs iterative web searches, analyzes retrieved data, and synthesizes information into structured reports. By decomposing broad queries into smaller sub-tasks, the system builds a comprehensive knowledge base to address open-ended questions. The platform distinguishes itself through an agentic loop that dynamically refines research strategies based on previous findings. It manages long-form data by compressing and summarizing cont
Decomposes complex research queries into sequential reasoning chains to build comprehensive knowledge bases.
Z-Image is an AI image editing engine and generation framework designed for photorealistic synthesis and the refinement of diffusion models. It functions as a multilingual text-to-image renderer and a system for training custom foundation models to generate and edit visuals using natural language instructions. The project distinguishes itself through a reasoning-based prompt enhancer that expands simple descriptions into detailed visual instructions using a structured reasoning chain. It also features specialized capabilities for rendering high-quality Chinese and English typography within ge
Converts simple user descriptions into detailed visual instructions using a structured reasoning chain before generation.
Ell is a Python library that treats prompts as callable functions, transforming a Python function into a language model program where the docstring defines the system message and the return value defines the user message. It provides a framework for writing language model programs that can accept and return images, audio, and text natively within message objects, and supports chaining multiple model calls into a single function for multi-step reasoning or test-time compute. The library decouples model selection from client instantiation through a registry that supports multiple provider backe
Ships a mechanism to chain several language model calls into a single function for multi-step reasoning.
Ce projet est un framework agentique et un orchestrateur Llama Stack utilisé pour construire des applications IA autonomes. Il coordonne l'inférence de modèles et l'exécution d'outils pour décomposer des objectifs complexes en chaînes de raisonnement multi-étapes et en boucles d'inférence continues. Le framework intègre un système de garde-fous de sécurité dédié qui filtre les entrées et sorties du modèle via des modèles de sécurité pour appliquer des restrictions de contenu au niveau du système. Il inclut également une couche d'intégration d'outils qui mappe les requêtes de fonctions générées par le modèle vers des définitions d'exécution externes pour effectuer des actions au-delà de la simple génération de texte. Le système fournit des capacités de génération augmentée par récupération (RAG) en extrayant des embeddings de documents depuis des bases de données vectorielles pour injecter des connaissances externes dans les prompts du modèle. Ces services sont exposés via une couche API unifiée qui prend en charge les scripts programmatiques et les interfaces de chat graphiques.
Decomposes high-level objectives into sequential reasoning chains for structured multi-step processing.
GLM-4.5 is a multimodal large language model and advanced reasoning system. It functions as an AI coding assistant, an autonomous AI agent, and a multimodal content generator capable of processing and generating text, images, audio, and video within a single unified system. The project is distinguished by its deep reasoning capabilities, utilizing chain-of-thought processing to solve complex mathematical, logical, and technical problems. It features an agentic architecture that allows for autonomous task execution, long-horizon goal planning, and the ability to interact with external tools an
Interleaves thinking steps between tool executions to interpret intermediate results and chain actions.
DataFlow is an agent-based workflow orchestrator and data pipeline designed to synthesize, clean, and augment large-scale datasets for training large language models. It functions as a synthetic data generator and text curation tool, utilizing an intelligent assistant to assemble modular processing operators into functional pipelines based on user requirements. The project distinguishes itself through a low-code approach, providing a web-based visual interface for designing and monitoring multi-stage execution flows. It features an operator-based registry system that allows for the integratio
Produces detailed chain-of-thought reasoning for translating natural language questions into SQL.
HunyuanImage-3.0 is a diffusion-based text-to-image tool and large language model image generator designed for creating high-fidelity, photorealistic visual content. It functions as an image-to-image synthesis framework and a multimodal visual reasoning engine. The system includes a prompt refinement system that automatically rewrites sparse user inputs into detailed descriptions to improve output precision. It also employs a reasoning chain architecture to analyze image inputs and prompts, decomposing complex editing tasks into structured sub-tasks. The project covers a range of synthesis c
Employs a reasoning-chain architecture to decompose complex image editing requests into structured sub-tasks.