Describe your product. Define your target market. The AI finds the leads for you.
OpenOutreach is a self-hosted, open-source LinkedIn automation tool for B2B lead generation. Unlike other tools, you don't need a list of profiles to contact — you describe your product and your target market, and the system autonomously discovers, qualifies, and contacts the right people.
How it works:
- You provide a product description and a campaign objective (e.g. "SaaS analytics platform" targeting "VP of Engineering at Series B startups")
- The AI generates LinkedIn search queries to discover candidate profiles
- A Bayesian ML model (Gaussian Process Regressor on profile embeddings) learns which profiles match your ideal customer — using an explore/exploit strategy to balance finding the best leads now vs. learning what makes a good lead
- An LLM classifies each profile selected by the model; the GP learns from every decision to select better candidates over time
- Qualified leads are automatically contacted, and an AI agent manages multi-turn follow-up conversations
The system gets smarter with every decision. It starts by exploring broadly, then progressively focuses on the highest-value profiles as it learns your ideal customer profile from its own classification history.
Why choose OpenOutreach?
- 🧠 Autonomous lead discovery — No contact lists needed; AI finds your ideal customers
- 🛡️ Undetectable — Playwright + stealth plugins mimic real user behavior
- 💾 Self-hosted + full data ownership — Everything runs locally, browse your CRM in a web UI
- 🐳 One-command setup — Dockerized deployment, interactive onboarding
- ✨ AI-powered messaging — LLM-generated personalized outreach (bring your own model)
Perfect for founders, sales teams, and agencies who want powerful automation without account bans or subscription lock-in.
| # | What | Example |
|---|---|---|
| 1 | A LinkedIn account | Your email + password |
| 2 | An LLM API key | OpenAI, Anthropic, or any OpenAI-compatible endpoint |
| 3 | A product description + target market | "We sell cloud cost optimization for DevOps teams at mid-market SaaS companies" |
That's it. No spreadsheets, no lead databases, no scraping setup.
Pre-built images are published to GitHub Container Registry on every push to master.
git clone https://github.com/eracle/OpenOutreach.git
cd OpenOutreach
docker run --pull always -it -p 5900:5900 --user "$(id -u):$(id -g)" -v ./assets:/app/assets ghcr.io/eracle/openoutreach:latestThe interactive onboarding walks you through the three inputs above on first run. Your data persists in the local assets/ directory across restarts — the same database used by python manage.py.
Connect a VNC client to localhost:5900 to watch the browser live.
For Docker Compose, build-from-source, and more options see the Docker Guide.
For contributors or if you prefer running directly on your machine.
git clone https://github.com/eracle/OpenOutreach.git
cd OpenOutreach
# Install deps, Playwright browsers, run migrations, and bootstrap CRM
make setupmake runThe interactive onboarding will prompt for LinkedIn credentials, LLM API key, and campaign details on first run. Fully resumable — stop/restart anytime without losing progress.
OpenOutreach includes a full CRM web interface powered by DjangoCRM:
# Create an admin account (first time only)
python manage.py createsuperuser
# Start the web server
make adminThen open:
- Django Admin: http://localhost:8000/admin/
| Feature | Description |
|---|---|
| 🧠 Autonomous Lead Discovery | No contact lists needed — LLM generates search queries from your product description and campaign objective. |
| 🎯 Bayesian Active Learning | Gaussian Process model on profile embeddings learns your ideal customer via explore/exploit, selecting the most informative candidates for LLM qualification. |
| 🤖 Stealth Browser Automation | Playwright + stealth plugins mimic real user behavior for undetectable interactions. |
| 🛡️ Voyager API Scraping | Uses LinkedIn's internal API for accurate, structured profile data (no fragile HTML parsing). |
| 🔄 Stateful Pipeline | Tracks profile states (QUALIFIED → READY_TO_CONNECT → PENDING → CONNECTED → COMPLETED) in a local DB — fully resumable. |
| ⏱️ Smart Rate Limiting | Configurable daily/weekly limits per action type, respects LinkedIn's own limits automatically. |
| 💾 Built-in CRM | Full data ownership via DjangoCRM with Django Admin UI — browse Leads, Contacts, Companies, and Deals. |
| 🐳 One-Command Deployment | Dockerized setup with interactive onboarding and VNC browser view (localhost:5900). |
| ✍️ AI-Powered Messaging | Agentic multi-turn follow-up conversations — the AI agent reads history, sends messages, and schedules future follow-ups. |
The daemon runs a continuous task queue backed by a persistent Task model. Three task types self-schedule follow-on work:
| Task Type | What it does |
|---|---|
| Connect | Ranks qualified profiles by GP model probability, sends connection requests (daily + weekly limits). Triggers qualification and search via composable generators when the pool is empty. |
| Check Pending | Checks if a pending request was accepted (exponential backoff per profile) |
| Follow Up | Runs an AI agent that manages multi-turn conversations with connected profiles |
The qualification loop in detail:
Profiles discovered during navigation are automatically scraped and embedded (384-dim FastEmbed vectors). The connect task's backfill chain decides which profile to evaluate next using a balance-driven strategy:
- When negatives outnumber positives → exploit: pick the profile with highest predicted qualification probability (seek likely positives to fill the pipeline)
- Otherwise → explore: pick the profile with highest BALD (Bayesian Active Learning by Disagreement) score (seek the most informative label to improve the model)
All qualification decisions go through the LLM. The GP model selects which candidate to evaluate next and gates promotion from QUALIFIED to READY_TO_CONNECT (confidence threshold). Every LLM decision feeds back into the model, making candidate selection progressively smarter.
Cold start: With fewer than 2 labelled profiles, the model can't fit — candidates are selected in order and qualified via LLM. As labels accumulate, the GP becomes better at selecting high-value candidates.
Configure rate limits and behavior via Django Admin (LinkedInProfile + Campaign models).
├── assets/
│ ├── data/ # crm.db (SQLite)
│ ├── models/ # Persisted GP models (campaign_<id>_model.joblib)
│ └── templates/prompts/ # LLM prompt templates (qualify, search, follow-up agent)
├── docs/
│ ├── architecture.md # System architecture
│ ├── configuration.md # Configuration reference
│ ├── docker.md # Docker setup guide
│ ├── templating.md # Follow-up messaging guide
│ └── testing.md # Testing strategy
├── linkedin/
│ ├── actions/ # Browser actions (connect, message, status, search)
│ ├── agents/ # ReAct follow-up agent (multi-turn conversations)
│ ├── api/ # Voyager API client + parser + messaging package
│ ├── browser/ # Session management, login, navigation
│ ├── conf.py # Configuration loading (.env + defaults)
│ ├── daemon.py # Task queue worker loop
│ ├── db/ # CRM-backed CRUD (leads, deals, enrichment, chat)
│ ├── django_settings.py # Django/CRM settings (SQLite at assets/data/crm.db)
│ ├── management/setup_crm.py # Idempotent CRM bootstrap (Dept, Stages, Closing Reasons)
│ ├── ml/ # Bayesian qualifier (GPR), embeddings, profile text
│ ├── models.py # Django models (Campaign, LinkedInProfile, Task, etc.)
│ ├── onboarding.py # Interactive onboarding (campaign, credentials, LLM config)
│ ├── pipeline/ # Candidate sourcing, qualification, pool management
│ ├── setup/ # GDPR, self-profile, freemium campaign setup
│ └── tasks/ # Task handlers (connect, check_pending, follow_up)
├── manage.py # Entry point (no args = daemon, or Django commands)
├── local.yml # Docker Compose
└── Makefile # Shortcuts (setup, run, admin, test)
- Architecture
- Configuration
- Profile Lifecycle
- Docker Installation
- Follow-up Messaging
- Template Variables
- Testing
Join for support and discussions: Telegram Group
Got a specific use case, feature request, or questions about setup?
Book a free 15-minute call — I'd love to hear your needs and improve the tool based on real feedback.
This project is built in spare time to provide powerful, free open-source growth tools. Your sponsorship funds faster updates and keeps it free for everyone.
| Tier | Monthly | Benefits |
|---|---|---|
| ☕ Supporter | $5 | Huge thanks + name in README supporters list |
| 🚀 Booster | $25 | All above + priority feature requests + early access to new campaigns |
| 🦸 Hero | $100 | All above + personal 1-on-1 support + influence roadmap |
| 💎 Legend | $500+ | All above + custom feature development + shoutout in releases |
GNU GPLv3 — see LICENCE.md
Not affiliated with LinkedIn.
By using this software you accept the Legal Notice. It covers LinkedIn ToS risks, built-in self-promotional actions, automatic newsletter subscription for non-GDPR accounts, and liability disclaimers.
Use at your own risk — no liability assumed.
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