Ranked by Attention Score: stars gained over the last 7 complete days
15 repositories by Attention Score · Oct 28, 2026 – Nov 3, 2026 (UTC)
Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.
The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.
Build Real-Time Knowledge Graphs for AI Agents
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory with small models for free
When Claude Code hits its usage limit, Codex picks the task up from the same memory. Sno Station puts Codex, Claude Code, Cursor, Hermes Agent and OpenClaw on one squad: they hand work to each other and review each other's code. Open source, runs on your computer.
#1 Persistent memory for AI coding agents based on real-world benchmarks
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
Code graph and memory for your coding agents, in one Rust program. For Claude Code, Codex, Cursor and any MCP client.
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.
World's first open-source enterprise world model.
Platform for stateful agents: AI with advanced memory that can learn and self-improve over time.
The ai agent workflow tool that scales from small, simple projects to large, complex codebases.
TranslatedNot a Q&A bot, but a digital twin that can actually get work done. Agents and project context are shared, so all project members can work together. At the same time, agents, like people, abstract and summarize experience from materials or work into the project context.
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