awesome-repositories.com
Blog
awesome-repositories.com

Descoperă cele mai bune repository-uri open source cu căutare AI.

ExploreazăCăutări recomandateAlternative open-sourceSoftware self-hostedBlogHartă site
ProiectDespreCum realizăm clasamentulPresăServer MCP
LegalConfidențialitateTermeni
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

4 repository-uri

Awesome GitHub RepositoriesAI Model Discovery

Interfaces for searching and locating machine learning models, datasets, and spaces within AI hubs.

Distinct from Hugging Face: The candidates focus on model conversion, weight conversion, or security scanning, not the search and discovery of models.

Explore 4 awesome GitHub repositories matching artificial intelligence & ml · AI Model Discovery. Refine with filters or upvote what's useful.

Awesome AI Model Discovery GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • getpaseo/paseoAvatar getpaseo

    getpaseo/paseo

    9,118Vezi pe GitHub↗

    Paseo is an LLM coding agent orchestrator and multi-agent workflow manager designed to coordinate multiple AI agents across isolated git worktrees. It provides a unified control interface for managing these agents and their associated environments to execute complex programming tasks. The system distinguishes itself through a remote agent daemon that enables secure access to local coding agents via encrypted relays. It employs a git worktree environment manager to isolate parallel tasks into dedicated directories and branch-based server URLs, preventing file collisions and network port confli

    Maps coding agents to specific API endpoints or self-hosted environments via extensible provider definitions.

    TypeScriptadeagentsclaude-code
    Vezi pe GitHub↗9,118
  • agentops-ai/agentopsAvatar AgentOps-AI

    AgentOps-AI/agentops

    5,654Vezi pe GitHub↗

    AgentOps este o platformă de observabilitate și un toolkit pentru dezvoltatori, destinat monitorizării execuției, performanței și fiabilității agenților autonomi bazați pe modele de limbaj mari (LLM). Servește drept sistem pentru urmărirea comportamentului agenților AI, depanarea fluxurilor de lucru complexe și benchmarking-ul performanței modelelor. Platforma se distinge prin capacitatea de a vizualiza fluxurile de lucru multi-agent prin grafuri ale căilor de execuție și reluări de sesiune. Oferă instrumente specifice pentru calcularea costurilor financiare la diverși furnizori de modele de limbaj și suportă un stack de observabilitate self-hosted pentru utilizatorii care necesită control total asupra datelor lor pe hardware sau cloud privat. Sistemul acoperă un set larg de capabilități, inclusiv detectarea erorilor agenților, analiza utilizării instrumentelor și urmărirea metricilor de performanță personalizate prin etichetarea evenimentelor. Se integrează cu framework-uri AI pentru a captura telemetria și datele de performanță.

    Calculates execution spending by matching API usage volumes against a database of model provider pricing.

    Python
    Vezi pe GitHub↗5,654
  • searxng/searxng-dockerAvatar searxng

    searxng/searxng-docker

    3,157Vezi pe GitHub↗

    This project is a containerized search infrastructure designed to deploy a privacy-focused metasearch engine. It acts as a self-hosted search proxy that aggregates results from multiple external web, image, and academic search providers while anonymizing requests and stripping trackers to protect user identity. The system utilizes Docker to orchestrate the search instance, integrating caching mechanisms and reverse proxy support to ensure a private and efficient search environment. It employs a modular adapter-based integration to standardize diverse external API responses and a processing pi

    Provides the ability to locate models, datasets, and spaces via specific Hugging Face endpoints.

    dockerdocker-compose
    Vezi pe GitHub↗3,157
  • anomalyco/models.devAvatar anomalyco

    anomalyco/models.dev

    2,694Vezi pe GitHub↗

    models.dev is a directory and intelligence system for large language models that provides a standardized catalog of technical specifications, provider mappings, and pricing data. It serves as a central index for model metadata, including context windows, output limits, and release dates. The project functions as a capability index and pricing comparison tool, allowing for the analysis of token costs across different hosting providers. It maps generic model names to the specific API identifiers required by various third-party platforms and tracks support for functional features such as tool ca

    Maps generic model names to the specific API identifiers required by various third-party hosting platforms.

    TypeScript
    Vezi pe GitHub↗2,694
  1. Home
  2. Artificial Intelligence & ML
  3. AI Model Discovery

Explorează sub-etichetele

  • Provider Cost MappingsDatabases and logic that map AI model provider API usage to specific financial costs. **Distinct from Provider Mappings:** Distinct from Provider Mappings by focusing on pricing and costs rather than API identifiers and hosting platforms.
  • Provider MappingsMaps specific AI models to the hosting platforms that provide them and their required API identifiers. **Distinct from AI Model Discovery:** Focuses on the mapping between models and hosting providers, whereas AI Model Discovery focuses on searching for the models themselves.