Agentled MCP

@agentled/mcp-server

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

$ agentled create "Outbound to fintech CTOs in Europe"

Loading workspace context from Knowledge Graph... ✦ ICP loaded ✦ 3 prior campaigns ✦ 847 contacts in KG

Creating campaign with 3 workflows...

━━ Workflow 1: Prospect Research linkedin · hunter · clearbit ✓ LinkedIn: CTO + fintech + EU → 189 profiles ✓ Enriched via Hunter + Clearbit → 156 matched ✓ ICP scoring → 43 high-intent leads

━━ Workflow 2: Signal Detection web-scraper · crunchbase ✓ Job postings → 12 hiring devops ✓ Crunchbase → 8 recently funded ✓ Cross-match: hiring + funded → 5 hot leads

━━ Workflow 3: Outreach email · linkedin · kg ✓ Personalized emails from context ✓ LinkedIn requests with custom notes ✓ 43 leads saved to Knowledge Graph

Campaign saved. Scheduled: every 48h Credits used: 720 → https://www.agentled.app/your-team/fintech-cto-outbound

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

claude mcp add --transport stdio --scope user agentled \

-e AGENTLED_API_KEY=wsk_... \ -- npx -y @agentled/mcp-server

--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:

/plugin marketplace add Agentled/mcp-server

/plugin install agentled@agentled

Then set your API key in the shell Claude Code runs from:

export AGENTLED_API_KEY=wsk_...

Codex plugin (one-step install)

The same repository is a Codex marketplace. Install the Agentled plugin with:

codex plugin marketplace add Agentled/mcp-server

codex plugin add agentled@agentled

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:

claude --plugin-dir ./plugins/agentled     # load from source

claude plugin validate ./plugins/agentled # check manifest + structure

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.

cd agentled-mcp-server

npm run build

claude mcp add --transport stdio agentled_local \ --env AGENTLED_API_KEY=wsk_... \ --env AGENTLED_URL=http://localhost:8080 \ -- node /absolute/path/to/agentsled-front/agentled-mcp-server/dist/index.js

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.

Workflows That Learn

Other automation tools start from zero every run. Agentled's Knowledge Graph remembers across executions — what worked, what didn't, what humans corrected. Scoring workflows can use compact row-level scoring_profile summaries and bounded scoring-memory retrieval so every run compounds on the last without dumping raw history into prompts.

Run 1:  Investor scoring → 62% accuracy (cold start)

Run 5: → 78% (learning from IC feedback) Run 12: → 89% (compound learning from outcomes, zero manual tuning)

Intelligent Orchestration

Unlike trigger-action tools, Agentled workflows have AI reasoning at every step. Multi-model support (Claude, GPT-4, Gemini, Mistral, DeepSeek, Moonshot), adaptive execution, and human-in-the-loop approval gates when needed.

Agent Teams

Agent Teams let you run multiple AI specialists in a single workflow step. Pick a preset and describe what you need — the team handles coordination, delegation, and synthesis.

"Add an Agent Team step that researches the company and produces an investment memo"

Six built-in presets cover the most common patterns:

When creating Agent Team steps via MCP, include preset metadata so the step opens correctly in the builder:

{

"id": "analyze", "type": "agentOrchestrator", "name": "Agent Team", "orchestratorConfig": { "pattern": "supervisor", "workers": [ { "id": "researcher", "name": "Researcher", "systemPrompt": "Research {{input.company_url}} — team, funding, market position" }, { "id": "analyst", "name": "Analyst", "systemPrompt": "Analyse the research. Identify risks and growth signals." } ] }, "metadata": { "agentTeamPreset": "research-and-summarize", "agentTeamMode": "simple", "agentTeamUxVersion": 1 }, "next": { "stepId": "milestone" } }

Existing steps created with raw orchestratorConfig and no metadata continue to work — they open in advanced mode in the builder without errors.

Analytics vs ROI semantics

When describing workflow outcomes, keep these terms separate:

• pipeline.analyticsConfig = business metrics (execution outcome stats shown in Business Metrics cards/charts).
• pipeline.metadata.roi = ROI assumptions/rollups (time saved and cost-value estimates).

If you update one without the other, name exactly what changed (e.g. "business metrics configured" vs "ROI assumptions configured").

CLI parity guard

The repository includes an automated parity guard so MCP tool additions do not silently drift from the CLI surface.

• Test: __tests__/cli/cli-mcp-parity.test.ts
• Docs: docs/CLI_MCP_PARITY.md

Run it with:

yarn test:node -- cli-mcp-parity.test.ts

What Can You Build?

Lead Enrichment & Sales Automation

"Find fintech CTOs in Europe, enrich via LinkedIn + Hunter, score by ICP fit,

draft personalized outreach, save everything to the Knowledge Graph"

Content & Media Production

"Scrape trending topics in our niche, generate 5 LinkedIn posts with AI,

create thumbnail images, schedule publishing for the week"

Company Research & Intelligence

"Research this company from its URL — team, funding, market position, competitors.

Generate an investment memo. Store in KG for future reference."

VC Investor Matching (real case study)

"Match this startup against our 2,000+ investor database. Score by sector focus,

stage preference, check size, and portfolio synergy. Compare with last round's outcomes."

3,000+ profiles processed. IC-ready reports. Prediction vs outcome learning — accuracy went from 62% to 89% over 12 runs with zero manual tuning.

Built-in Skills And Integrations

Media Production: Video generation, image generation, text-to-speech, auto-captions, media assembly

AI Intelligence: Multi-model AI (Claude, GPT-4, Gemini, Mistral, DeepSeek, Moonshot, xAI), K