经 AI Skill Hub 精选评估,智能聊天运维 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。
智能聊天运维 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。
智能聊天运维 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/papadopouloskyriakos/agentic-chatops
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"------": {
"command": "npx",
"args": ["-y", "agentic-chatops"]
}
}
}
# 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 智能聊天运维 执行以下任务... Claude: [自动调用 智能聊天运维 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": {
"______": {
"command": "npx",
"args": ["-y", "agentic-chatops"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
AI agents that triage infrastructure alerts, investigate root causes, and propose fixes — while a solo operator sleeps.
For the complete technical reference, see README.extensive.md.

Following Andrej Karpathy's LLM Knowledge Bases pattern: raw data from 7+ sources (117 memory files, 55 CLAUDE.md files, 33 incidents, 27 lessons, 101 OpenClaw memories, 17 skills, ~5,200 lab docs) is compiled into a browsable 72-article wiki with auto-maintained indexes, daily SHA-256 incremental recompilation, and contradiction detection. All articles embedded into RAG as the 3rd fusion signal.
git clone https://github.com/papadopouloskyriakos/agentic-chatops.git
cd agentic-chatops
cp .env.example .env # Add your credentials
See the Installation Guide for full setup.
The 2026-04-20 audit of openai/openai-agents-python flagged 11 gaps; 9 were implemented (issues IFRNLLEI01PRD-635..643). The system now has a versioned, typed, recoverable substrate the old string-based Matrix pipeline couldn't offer:
scripts/lib/schema_version.py) mirroring the SDK's RunState.CURRENT_SCHEMA_VERSION / SCHEMA_VERSION_SUMMARIES pattern. Writers stamp schema_version=CURRENT; readers check_row() fail-fast on future versions.session_events.py) in a new event_log table — tool_started/ended, handoff_requested/completed/cycle_detected/compaction, reasoning_item_created, mcp_approval_*, agent_updated, message_output_created, tool_guardrail_rejection, agent_as_tool_call. Replaces free-form Matrix strings with Grafana-queryable structured telemetry.session-start.sh, post-tool-use.sh, user-prompt-submit.sh, session-end.sh (new — the on_final_output equivalent) feeding a session_turns table with per-turn cost, tokens, duration, tool count.allow / reject_content / deny) in unified-guard.sh + audit-bash.sh + protect-files.sh. reject_content sends Claude a retry hint instead of a wall; deny hard-halts. Every rejection is a typed event.HandoffInputData envelope (scripts/lib/handoff.py) — zlib-compressed base64 payload carrying input_history, pre_handoff_items, new_items, run_context. 176 KB history → 752 B on the wire (0.43% ratio). Eliminates the "re-derive context via RAG" cost on escalation.scripts/compact-handoff-history.py) — opt-in per escalation. Local gemma3:12b with Haiku fallback; circuit-breaker aware.scripts/agent_as_tool.py) — wraps the 10 sub-agent definitions as callable tools so the orchestrator LLM can conditionally invoke them in the ambiguous-risk (0.4–0.6) band, complementing our deterministic routing.scripts/lib/handoff_depth.py) — handoff_depth >= 5 forces [POLL]; >= 10 hard-halts; any agent twice in the chain is refused and logged as handoff_cycle_detected.scripts/lib/snapshot.py) — a snapshot is captured BEFORE each mutating tool call (Bash, Edit, Write, Task; read-only tools skipped); rollback_to(id) restores any prior sessions row. 7-day retention.Four new SQLite tables (event_log, handoff_log, session_state_snapshot, session_turns) bring the total to 35. Migrations 006–011 apply idempotently on both fresh and legacy DBs. Two follow-ups since then — the A/B prompt patcher (IFRNLLEI01PRD-645, prompt_patch_trial + session_trial_assignment) and the CLI-session RAG capture pipeline (-646/-647/-648, no new tables; chunks + tool calls + knowledge rows tagged issue_id='cli-<uuid>' on the existing schema) — bring the live total to 39.
58 scenarios across 3 eval sets (22 regression + 20 discovery + 16 holdout) + 54 adversarial red-team tests. Prompt Scorecard grades 19 surfaces daily on 6 dimensions. Agent Trajectory scoring on 8 infra / 4 dev steps. A/B variant testing (react_v1 vs react_v2). CI eval gate blocks bad merges. Monthly eval flywheel cycle.
A deep audit against google/agents-cli flagged 6 skill-authoring dimensions where we trailed (phase-gate choreography, discoverability, anti-guidance, inline behavioral anti-patterns, governance/versioning, skill index). An 11-commit uplift (IFRNLLEI01PRD-712 umbrella, Phases A→J) closed every gap. 0 reverts.
.claude/skills/chatops-workflow/SKILL.md codifies the Phase 0→6 incident lifecycle (triage → drift-check → context → propose → approve → execute → post-incident). Force-injected into every Runner session's Build Prompt (marker-delimited for surgical removal; rollback anchor preserved at /tmp/runner-pre-IMMUTABLE.json).scripts/render-skill-index.py emits a drift-gated docs/skills-index.md from all SKILL.md + agent frontmatter. Guarded by test-656-skill-index-fresh.sh, refreshed as a pre-step of the daily 04:30 UTC wiki-compile cron.version: 1.x.0 + requires: {bins, env}. scripts/audit-skill-requires.sh + a Prometheus exporter feed two new alerts (SkillPrereqMissing, SkillMetricsExporterStale). scripts/audit-skill-versions.sh walks git history for body-changed-without-bump cases; semver convention at docs/runbooks/skill-versioning.md.memory/feedback_*.md with source citations) — behavioral inoculation at the surface where the model is about to act.check_evidence() in scripts/classify-session-risk.py emits an evidence_missing risk signal that forces [POLL] when CONFIDENCE ≥ 0.8 but the reply carries no tool output / code fence. Mirrored in the Runner's Prepare Result node to strip unearned [AUTO-RESOLVE] markers and prepend a GUARDRAIL EVIDENCE-MISSING: banner.config/user-vocabulary.json (20 entries: "the firewall" → nl-fw01;gr-fw01, "xs4all" → "budget" post-2026-04-21 rename, etc.) scanned by the prompt-submit hook; every match emits a typed vocabulary event to event_log.Scorecard delta: 3.94 → 4.94 average; 13/16 dimensions at 5/5 (was 9/16). Full memo: docs/scorecard-post-agents-cli-adoption.md. E2E hardened in the same batch via a J1–J5 pass: live vocabulary event captured by firing the real prompt-submit hook, promtool test rules executed inside the live Prometheus pod, force-injection proven by a real Runner session whose first tool call grepped for Phase 0 in the injected skill body.
scripts/qa/run-qa-suite.sh runs 51 suite files (~3–5 min) with JSON scorecard + summary output, guarded by a per-suite QA_PER_SUITE_TIMEOUT wrapper (IFRNLLEI01PRD-724) that caps any slow/wedged suite at 120 s and emits a synthetic FAIL record so the orchestrator never hangs silently:
INSERTs into a versioned table is asserted to stamp schema_version=1; same for all 5 n8n-workflow INSERT sites.handoff_depth.bump() calls with no-lost-updates assertion. Surfaced and fixed a real race condition.scripts/qa/lib/mock_http.py) — stdlib-only fake ollama/anthropic endpoints for testing successful compaction offline.---
agentic-chatops 是一个 AI 代理项目,旨在自动化基础设施告警的处理、调查根源原因以及提出修复方案。它基于 Karpathy-Style 编译知识库的模式,集成了来自多个来源的数据,形成一个可浏览的 72 篇文章的 wiki。
环境依赖与系统要求中文说明
首先,使用 Git 克隆项目代码,接着创建一个 .env 文件并添加你的凭证信息。详细的安装步骤请参阅 [Installation Guide](docs/installation.md)。
使用 agentic-chatops 的快速入门方法是克隆项目代码,创建 .env 文件并添加凭证信息。详细的使用教程请参阅 [Quick Start](README.md)。
agentic-chatops 使用一个结构化的代理基座(Structured Agentic Substrate),它基于 OpenAI 的 Agents-Python 库。该基座提供了多项功能,包括版本控制、类型检查和恢复机制。它还支持 9 个 OpenAI Agents-Python 库的特性,包括 Schema 版本控制和类型检查。
agentic-chatops 提供了多个 API 接口,包括 Agentic 设计模式、Claude 认证架构和 API 参考文档。详细的 API 接口请参阅 [API References](README.md)。
agentic-chatops 的工作流程包括一个正式评估管道,包含 58 个场景和 54 个红队测试。它还提供了 Prompt 分数卡和 Agent 轨迹评分功能。详细的工作流程请参阅 [Formal Evaluation Pipeline](README.md)。
agentic-chatops 提供了一个 QA 套件,包含 533+ 个已知通过的测试和 51 个测试文件。详细的 QA 套件请参阅 [QA Suite](README.md)。
高质量的开源MCP工具,实现了多种聊天运维模式
该工具未明确声明开源协议,商业使用前请联系原作者确认授权范围,避免侵权风险。
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。
AI Skill Hub 点评:智能聊天运维 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 | agentic-chatops |
| 原始描述 | 开源MCP工具:3-tier agentic ChatOps (n8n + GPT-4o + Claude Code) implementing all 21 patterns。⭐105 · Python |
| Topics | agentic-aichatopsclaude-codedevopsinfrastructure-automation |
| GitHub | https://github.com/papadopouloskyriakos/agentic-chatops |
| 语言 | Python |
收录时间:2026-06-09 · 更新时间:2026-06-11 · License:未公布 · 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