linkedin-outreach-agent

linkedin-outreach-agent

An MCP server that enables agent-driven LinkedIn outreach across multiple accounts, with a safety gate for human approval and LLM-powered personalization.

Category
Visit Server

README

linkedin-outreach-agent

License: Apache 2.0

Status: scaffold. This repo currently holds the foundation only. Domain types, the two locked interfaces, and the database schema are built out; every other package is an empty stub for later work.

A self-hosted, agent-driven framework for running LinkedIn outreach across multiple accounts. A control plane plans campaigns and enforces safety; per-account runners drive a real browser to carry out actions. An LLM personalizes messages and classifies replies. Everything an account does is written to an append-only audit log.

How it's driven (bring your own agent)

The framework is an MCP server. It is the hands plus a server-side safety gate, not the brain. The brain can come from two places:

  • Driven mode (primary): an external agent, Claude Code or Codex running on your own model subscription, connects to the MCP server as the client. It calls the Observe tools, writes the copy itself, and calls the gated Act tools. No LLM key and no per-token cost on the framework side.
  • Autonomous mode (fallback): with no external agent attached, the framework runs its own loop and calls an LLM through a key. The internal LLM is optional and selected by which key is set (OpenRouter, else Anthropic, else an offline fake), and used only in this mode.

Both are safe the same way: the autonomy and approval gate is enforced server-side regardless of which brain drives. Under supervised autonomy every send and reply queues to human approval.

See docs/DRIVING.md for the topology and the driver playbook, and docs/SCHEDULING.md to run driven mode on a schedule.

Docs

Repo shape

This is an npm workspaces monorepo. Packages are scoped @loa/*.

linkedin-outreach-agent/
  control-plane/
    mcp/            @loa/mcp          MCP server exposing control-plane tools to the agent
    orchestrator/   @loa/orchestrator campaign state machine and action planning
    scheduler/      @loa/scheduler    time and budget aware action queue
    agent/          @loa/agent        LLM-driven decision loop
    safety/         @loa/safety       SafetyGate implementation and account state machine
  account-runner/   @loa/account-runner per-account browser runner (session, safety, executor, detector as folders)
  shared/           @loa/shared       domain types, enums, locked interfaces, Drizzle schema
  infra/            @loa/infra        deployment, migrations, proxy and vault wiring

account-runner is a single package. Its session, safety, executor, and detector concerns live as folders under src/, not as separate workspace packages, to keep the runner's internals cohesive. Note that the control-plane SafetyGate contract lives in @loa/safety; the runner's safety folder is only a local pre-flight mirror.

The two locked interfaces

Every package implements or consumes these. They live in @loa/shared and should not change shape without a coordinated migration.

interface SafetyGate {
  canAct(acct: Account, action: Action): Decision;   // allow | defer(until) | deny(reason)
  onSignal(acct: Account, sig: Signal): Transition;
  budget(acct: Account): DailyBudget;
}

interface LLMProvider {
  personalize(ctx: TargetContext): Promise<Draft>;
  classifyReply(msg: Message): Promise<Intent>;
  draftReply(thread: Thread, intent: Intent): Promise<Draft>;
}

Development

Requires Node 24+.

npm install        # install all workspaces
npm run typecheck  # tsc -b across every package
npm run build      # tsc -b, emits dist/ per package
npm test           # vitest run

Database schema lives in shared/src/db/schema.ts and is driven by Drizzle Kit from the repo root:

npm run db:generate   # generate SQL migrations into infra/migrations
npm run db:migrate     # apply them (needs DATABASE_URL)

Copy .env.example to .env and fill it in before running anything that touches the database or an external API.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured