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Agentled/mcp-server

Agentled/mcp-server

Workflow orchestration with long-term me
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add mcp-server -- npx -y mcp-server
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
3 ★ · Agentled/mcp-server · 取自 GitHub 搜索 API · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
评分构成
综合分 703 · 3 ★ · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 Agentled/mcp-server;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

@agentled/mcp-server

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

> The automation engine built for AI agents. Intelligent AI workflow orchestration with long-term memory, 100+ integrations, and unified credits.
What is Agentled?
Agentled is the automation engine built for AI agents.
It gives Claude, Codex, Cursor, Windsurf, and any MCP-compatible client direct access
to intelligent workflow orchestration, long-term memory, and 100+ integrations.
**Three things make it different:**
🧠 **Long-Term Memory** — A built-in Knowledge Graph stores insights across
workflow executions. Your agents get smarter over time — they remember past
research, lead scores, content performance, and business context.
⚡ **Unified Credits** — One API key, one credit system, 100+ services.
No need to sign up for LinkedIn, email, scraping, AI models, or video
generation separately. Connect once, use everything.
🎯 **Intelligent Orchestration** — AI reasons at every step. Workflows
aren't just "if this then that" — they understand context, make decisions,
and adapt to results.
See it in action
One prompt. Three workflows. LinkedIn enrichment, email finding, AI scoring, multi-channel outreach — all orchestrated, all stored in the Knowledge Graph for the next run.
Quick Start
--scope user registers the server in your user MCP config so it loads in **every** project (not only the repo where you ran the command). Use a distinct server name (e.g. agentled_my_workspace) if you add multiple workspaces. For team-shared config in git, use --scope project and .mcp.json instead (Claude Code MCP scopes).
Claude Code plugin (one-step install)
Prefer the plugin if you want the MCP server **and** the Agentled skill installed together. In Claude Code:
Then set your API key in the shell Claude Code runs from:
Codex plugin (one-step install)
The same repository is a Codex marketplace. Install the Agentled plugin with:
Then set AGENTLED_API_KEY=wsk_... in the shell that launches Codex and start a
new task. The plugin bundles the Agentled skills, starts the MCP server through
npx -y @agentled/mcp-server, and loads its lifecycle hooks from the standard
hooks/hooks.json path.
The plugin bundles the agentled skill (workflow-authoring guidance, namespaced agentled:agentled) and auto-starts the MCP server via npx -y @agentled/mcp-server. The same plugin directory carries both Claude Code and Codex manifests — one bundle, both hosts.
For Codex, the hook pack acts as in-session guidance around the CLI/MCP loop:
session start explains the Agentled/Codex business-loop split, prompt/tool hooks
add turn-level guidance when client needs, priorities, failures, or product gaps
appear, and stop hooks nudge implementation handoffs to include readiness,
validation, side effects, and next decision. Hooks do not store feedback, call
Agentled APIs, run automations, spend credits, or perform customer/workspace
writes. In Codex, run /hooks after installing or changing the plugin so the
local hook definitions are reviewed and trusted before they run.
Use Codex automations for outside-workspace FDE cadence such as Outlook/client
email follow-up, vendor replies, repo/build checks, and weekly operator reviews.
Use Agentled routines for Agentled workspace/runtime checks such as workflow
health, routine health, execution review, workspace summaries, and managed-agent
operations. Use submit_feedback_to_agentled or agentled feedback submit when
the user explicitly wants product feedback captured.
> **Pick one install path, not both.** If you previously ran claude mcp add agentled ... or --setup-skills, remove those before (or instead of) installing the plugin — otherwise you get two identical MCP server processes and the skill registered twice. Cleanup: claude mcp remove agentled and delete .claude/skills/agentled/ (or ~/.claude/skills/agentled/). --setup-skills now detects an installed plugin and refuses to double-register unless you pass --force.
To develop the plugin locally:
> plugins/agentled/skills/ is a generated mirror of skills/ (synced by publish.sh) — edit skills/agentled/SKILL.md, never the mirror.
Local development
Use the local built entrypoint when you want to test unpublished changes against a
local app. npx -y @agentled/mcp-server always uses the latest published npm package.
Getting your API key
1. Sign up at agentled.app
2. Open **Workspace Settings > Developer**
3. Generate a new API key (starts with wsk_)
Why Agentled MCP?
One API Key. One Credit System. 100+ Services.
No need to sign up for LinkedIn APIs, email services, web scrapers, video generators, or AI models separately. Agentled handles all integrations through a single credit system.
来源
awesome · https://github.com/Agentled/mcp-server
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