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5 repositorios

Awesome GitHub RepositoriesBranching Reasoning Explorations

Systematic exploration of multiple potential decision paths using search strategies like Monte Carlo Tree Search.

Distinct from Exploration Strategies: Existing candidates focus on recording paths or conversation editing, not the algorithmic exploration of reasoning branches for optimization.

Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Branching Reasoning Explorations. Refine with filters or upvote what's useful.

Awesome Branching Reasoning Explorations GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • datawhalechina/so-large-lmAvatar de datawhalechina

    datawhalechina/so-large-lm

    7,400Ver en GitHub↗

    This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of large language models. It provides a guided progression through the theory, architecture, training, and deployment of these models. The curriculum includes specialized guides on transformer architecture, model training tutorials, and frameworks for designing autonomous agents. It also provides dedicated resources for studying model safety and ethics. The material covers a wide range of technical capabilities, including distributed training strategies, parameter-efficient fine-tu

    Explores multiple reasoning paths using search strategies to select the most promising route for problem solving.

    Ver en GitHub↗7,400
  • airweave-ai/airweaveAvatar de airweave-ai

    airweave-ai/airweave

    6,453Ver en GitHub↗

    Airweave is a unified AI knowledge base platform that syncs data from external APIs into a searchable layer for retrieval-augmented generation. It provides a pre-built data connector library and a framework for building custom connectors, enabling the extraction, transformation, and synchronization of structured and unstructured data from SaaS applications. The platform includes a hybrid vector retrieval system that combines semantic, neural, and keyword search strategies to deliver grounded context for AI agents. The platform distinguishes itself through an agentic search engine that iterati

    Searches a knowledge base by iteratively exploring, reading, and navigating content hierarchies.

    Pythonagent-infrastructureaiai-agents
    Ver en GitHub↗6,453
  • openmanus/openmanus-rlAvatar de OpenManus

    OpenManus/OpenManus-RL

    3,916Ver en GitHub↗

    OpenManus-RL is a reinforcement learning framework and distributed training pipeline designed to train large language models as agents. It serves as an agentic reasoning optimizer and reward model trainer, providing the infrastructure to improve model decision-making through reward-based policy optimization. The project distinguishes itself through a distributed architecture that supports parameter sharding across multiple compute nodes and a coordinated rollout system for collecting interaction trajectories. It incorporates advanced reasoning strategies, such as Tree-of-Thoughts and Monte Ca

    Systematically explores branching decision paths using Tree-of-Thoughts and Monte Carlo Tree Search.

    Python
    Ver en GitHub↗3,916
  • fareedkhan-dev/all-agentic-architecturesAvatar de FareedKhan-dev

    FareedKhan-dev/all-agentic-architectures

    3,670Ver en GitHub↗

    This project is a comprehensive framework for building, evaluating, and connecting autonomous agent systems. It provides a library of standardized architectural patterns for implementing complex agent workflows, including multi-agent orchestration, iterative reasoning, and memory management. By offering a unified interface for model providers, the framework allows for consistent agent execution across different artificial intelligence services. The framework distinguishes itself through a focus on rigorous benchmarking and deterministic control. It includes a suite of tools for evaluating age

    Supports exploring multiple decision trees and reasoning trajectories to select optimal answers through sampling and reward-based search methods.

    Jupyter Notebookagentic-aiai-agentslangchain
    Ver en GitHub↗3,670
  • 1517005260/graph-rag-agentAvatar de 1517005260

    1517005260/graph-rag-agent

    2,240Ver en GitHub↗

    This project is a comprehensive framework for constructing, managing, and evaluating knowledge graphs through multi-agent reasoning and deep search capabilities. It provides an end-to-end pipeline that ingests multi-format documents, extracts entities and relationships based on configurable schemas, and maintains structured knowledge bases to support evidence-based retrieval. The system distinguishes itself through its multi-agent orchestration, which decomposes complex queries into parallel research steps and synthesizes long-form reports. It leverages advanced graph-based techniques, includ

    Performs information retrieval using local neighborhood, global community, and deep reasoning search patterns.

    Pythonagentic-ragchain-of-explorationdeepresearch
    Ver en GitHub↗2,240
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  • Search-Based Reasoning Strategies1 sub-etiquetaExploration of multiple reasoning paths using search strategies like Monte Carlo Tree Search to select the most promising route. **Distinct from Branching Reasoning Explorations:** Distinct from Branching Reasoning Explorations: focuses on the search strategy aspect of tree-of-thought reasoning, not just branching exploration.