Ross ROSS = Recommend OSS · open-source software intelligence for agents

shiwenwen/hope-agent

🦭 会记忆、能持续推进目标、会动态编排多 Agent 的跨端桌面 AI 助手,也可服务化常驻 NAS / 云端 | A cross-device desktop AI agent with memory, autonomous goals, dynamic workflows, and headless deployment observed · 2026-08-28

github.com/shiwenwen/hope-agent · Rust · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

82/100

  • Activity 99
  • Release rhythm 99
  • Longevity 12

Flags: young

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: 2.0
  • age_days: 173
  • days_rel: 7
  • days_push: 7
  • n_releases_24m: 43

Full methodology

Adoption not part of the score

1501 stars · 140 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Hope Agent is a local-first, cross-device desktop AI assistant built with Rust and Tauri that features long-term memory, autonomous goal pursuit, and dynamic multi-agent workflow orchestration. It can also run headless as a persistent service on a NAS or cloud server via Docker.

Use cases

  • run a personal AI agent with long-term memory on my desktop
  • hand off AI agent sessions across my devices
  • deploy an AI assistant as a headless service on my NAS
  • orchestrate multi-agent workflows dynamically
  • use Claude, OpenAI, or Gemini models in one desktop assistant
  • have an AI agent continue working on goals while I'm away
  • self-host a personal AI assistant with Docker
  • connect MCP tools to a desktop AI agent

When to choose

  • you want a polished, installable desktop AI agent rather than a CLI tool
  • you need cross-device session handoff and long-term memory
  • you want one assistant that can also run headless on a server or NAS
  • you prefer a local-first, MIT-licensed agent supporting multiple LLM providers

When to avoid

  • you only need a simple chatbot without memory or autonomous goals
  • you require production-grade stability on Linux or Windows, which are still experimental
  • you need a lightweight library to embed agent capabilities in your own app
  • you want a fully open model stack with no cloud LLM dependency

Facets

application · maturity active

agent-framework chatbot chat-interface mcp llm-inference gui self-hosted large-language-models chatbots desktop-applications self-hosted developer-tools windows cross-platform rust self-hosted personal-ai-assistant local-first tauri cross-device-handoff autonomous-goals long-term-memory multi-agent-orchestration headless-deployment openai anthropic gemini ai-agents automation macos linux desktop docker web-server

1 source

Member repositories

RepositoryRoleHealth v2
shiwenwen/hope-agentmain82

For agents

markdown · JSON · MCP: product_card(name="shiwenwen/hope-agent")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem