GitHub topic

embedding GitHub repositories

Explore 9 tracked GitHub repositories tagged embedding. Compare their recorded star activity and implementation languages, then follow related topics to find other projects.

Snapshot: 2026-10-28. The collection gained 103 stars since the previous daily snapshot.

Tracked repositories: 9
Total stars: 91,029
Stars gained · 7 days: +674
Stars gained · 30 days: +4,341

Compare star activity

Up to five projects on this page, ordered by total stars. Open a project for its description and recorded star history.

Swipe to compare all star periods →

Star activity for embedding repositories on this page
Repository	Total stars	Today	7 days	30 days
TencentCloud/TencentDB-Agent-Memory	30.0K	+41	+322	+2,702
RyanCodrai/turbovec	17.4K	+2	+24	+180
modelscope/ms-swift	16.0K	+24	+68	+230
datawhalechina/all-in-rag	12.6K	+33	+248	+1,160
OpenBMB/UltraRAG	5.7K	+0	+3	+25

Explore repositories

Showing 1–9 of 9 tracked repositories.

TencentCloud/TencentDB-Agent-Memory

TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.

★ 30.0K+41 todayTypeScript

llm agent

View ↗

RyanCodrai/turbovec

A vector index built on TurboQuant, written in Rust with Python bindings

★ 17.4K+2 todayRust

python rag

View ↗

modelscope/ms-swift

Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).

★ 16.0K+24 todayPython

llm deepseek

View ↗

datawhalechina/all-in-rag

🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/

★ 12.6K+33 todayPython

llm ai

View ↗

OpenBMB/UltraRAG

A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines

★ 5.7K+0 todayPython

llm mcp

View ↗

infiniflow/infinity

The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.