经 AI Skill Hub 精选评估，Vitrus 获评「强烈推荐」。这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。
Vitrus 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
Vitrus 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/ahmetvural79/Vitrus
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"vitrus": {
"command": "npx",
"args": ["-y", "vitrus"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例： 用户: 请帮我用 Vitrus 执行以下任务... Claude: [自动调用 Vitrus MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入："列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"vitrus": {
"command": "npx",
"args": ["-y", "vitrus"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<img src="./assets/logo.svg?v=3" alt="Vitrus" width="440" />
vitrus api import <spec>), retrieve the right endpoint for a task (vitrus api search), and verify a call deterministically before running it (vitrus api verify → valid / missing_args / wrong_type / unknown_args / unknown_endpoint / deprecated) — the anti-hallucination gate. vitrus api call verifies, then executes. Also vitrus ingest rest --config <c.json> to pull any REST response into the brain. Visual API-integration drawer in the dashboard.vitrus onboard "<role>" builds a sourced, pedagogically-ordered learning path from the brain (who to ask + what's not documented yet); vitrus quiz "<topic>" generates recall questions graded deterministically by verify.--explain ranking attribution. vitrus search "<q>" --explain prints each hit's score factors: vector/bm25/entity ranks + tier/cosine and the new graph-adjacency / cross-source boosts.vector(1536) schema — no migration).entities, graph_query, get_node, chunks, attention, conflicts, api_search/api_verify/api_call, onboarding_path, quiz, …; content tools are ACL fail-closed).vitrus capture "<note>" (arg/file/stdin) + a watched inbox folder (vitrus ingest inbox <dir>) for mobile capture.vitrus skills list|install — a validated SKILL.md library that teaches agents how to use Vitrus.Three design rules hold everywhere:
- Glass-box. Every answer is known / unknown (gap) / sourced. - Ownable. The source of truth is Markdown + a typed-edge sidecar in git. The index is disposable and rebuildable; delete it and your knowledge loses nothing. - Agent-native. The same brain serves humans (CLI, dashboard) and agents (MCP, Agent Skills) from one permission-aware memory.
- Gap analysis — five deterministic kinds: missing (referenced but undocumented), contradiction (conflicting edges), stale (superseded), single-point (bus-factor risk), uncited (event with no source). Derived from graph structure and explicit text signals only. - Proactive attention — vitrus watch makes gap analysis temporal: stale knowledge, unresolved incidents and aging gaps surfaced without being asked. Deterministic, no LLM; schedule it (with nightly vitrus dream consolidation) for a standing radar over your memory. - Ops-map — vitrus ops (MCP ops_report) reads the company as a system and flags operational inefficiencies: unowned services, bus-factor (single-person) risk, bottlenecks (overloaded hubs), broken handoffs (depending on superseded ground), and redundant tools (embedding-similar services). Severity-ranked, each finding cites real nodes — evidence, not a consultant's guess. - Conflict resolution — vitrus conflicts / vitrus resolve (MCP resolve_conflict) detects contradictions and shows both sides; resolve by choosing the winner — the loser is superseded (marked stale) and the conflict closes. Nothing overwritten in silence. - Write-back loop — agents read before they act and write after they decide: MCP record_decision / capture_session + vitrus decide + vitrus hooks install (Claude/Cursor/Codex). Decisions persist with their sources, so the brain stays live; a decision that contradicts an existing one is flagged back to the agent. - Self-linking graph — [[type::slug]] typed edges, extracted without an LLM; bi-temporal — vitrus dashboard --graph --asof <ISO> (and getConnections/graphSnapshot) answer "what did we believe in March?" (time-travel on the edge graph; correct from real created_at/expired_at). - Hybrid retrieval — vector + BM25 + entity match, RRF-fused; optional reranker. - Provenance everywhere — every claim traces to node → chunk → source URI. - ACL, fail-closed — enforced at the index layer; unauthorized content never appears in results. - Confidence + freshness — every answer carries a confidence score and oldest-source age. - Durable job queue — vitrus agent run "…" && vitrus agent work && vitrus jobs (crash-recovering).
Pick your entry point:
```bash
bunx @vitrus/core init --pglite # creates ./.vitrus vitrus import ./brain # ingest markdown (embeds + self-linking graph) vitrus think "how was the outage resolved"
| Symptom | Fix |
|---|---|
vitrus: command not found | cd packages/core && bun link (or use bunx @vitrus/core) |
| Answers feel weak in non-English | Set a production embedder: VITRUS_EMBED_PROVIDER=openai + key (default hashing embedder is language-naive) |
pg errors on import | Team scale needs bun add pg and a reachable VITRUS_PG_URL (PGLite needs neither) |
| MCP server not showing tools | Check claude mcp list; for HTTP, the endpoint is /mcp and auth is Authorization: Bearer <token> |
| "is my index stale?" | The index is disposable: vitrus reset && vitrus import ./brain rebuilds everything from Markdown |
| Anything else | vitrus doctor first — it reports backend, providers and health without leaking secrets |
Vitrus是一个高质量的开源MCP工具，提供了强大的知识图谱和智能决策支持功能
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
AI Skill Hub 点评：Vitrus 的核心功能完整，质量优秀。对于Claude Desktop / Claude Code 用户来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | Vitrus |
| Topics | agent-memorycompany-brainknowledge-graphmcpopen-coretypescript |
| GitHub | https://github.com/ahmetvural79/Vitrus |
| License | Apache-2.0 |
| 语言 | TypeScript |
收录时间：2026-06-20 · 更新时间：2026-06-20 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型，复制安装指令后粘贴到对应客户端