Showing 601-620 of 1,105 projects
All-in-one platform for search, recommendations, RAG, and analytics offered via API for vibe coders
A framework for building resilient language agents as graphs using TypeScript and AI tools.
Optimizes large language models for low-bit precision and sparsity, improving model compression techniques.
Project that provides a reasoning-based segmentation method using large language models.
A Python library that provides AI-powered conversation memory, allowing developers to build AI assistants that remember past interactions.
A Python library that provides user profile-based long-term memory for AI chatbot applications.
Achieve state-of-the-art inference performance on modern accelerators with this Kubernetes-based solution.
A curated list of awesome papers about unifying large language models (LLMs) and knowledge graphs (KGs).
A curated list of open-source models, datasets, and benchmarks for domain-specific language models.
Open-source agent-based simulation framework for large-scale AI societies and language model experiments
Reliable model swapping for local LLM servers - seamlessly switch between llama.cpp, vLLM, and compatible backends
Learn to build a Second Brain AI assistant with LLMs, agents, and fine-tuning techniques.
AI-powered web scraping and data gathering SDK for building intelligent agents and LLM apps
An official implementation of a time series forecasting model using large language models.
A web app for interacting with any LangGraph agent (PY & TS) via a chat interface.
RAGEN leverages reinforcement learning to train LLM reasoning agents in interactive, stochastic environments.
Zero-shot vulnerability discovery using large language models (LLMs) for security researchers.
A curated list of papers and resources on large language model (LLM) agents, covering methodologies, applications, and challenges.
Modular Python framework for building AI agents and workflows with chain-of-thought reasoning and tools.
Effortlessly run LLM backends, APIs, frontends, and services with one command.
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