OpenLeads

OpenLeads

Describe a customer. Get the people, with every email labeled by how we know it.

OpenLeads searches free public sources for the businesses and people you describe, finds their work emails, and marks each one *found*, *pattern* or *guessed*. Then it works them like Apollo does: lists, ICP scoring, sequences, tasks and a deal pipeline. It runs on your machine, with no API keys and nothing to pay.

Website | Docs | Live benchmark | Changelog

pipx install "openleads[all]"          # or: pip install "openleads[all]", or: npx openleads

openleads find "dentists in Austin" # ten leads, every email labeled openleads web # the workspace at http://127.0.0.1:8787

The local dashboard on the demo workspace (openleads demo, invented data).

Every email says how we know it

Most free finders call a first.last@ guess "valid". OpenLeads labels each address with where it came from, then decides separately whether it is safe to send:

Seven checks decide the tier, and most of them work without port 25, which home internet providers block: two DNS-over-HTTPS resolvers that must agree on the mail server, SPF and DMARC, disposable/role/free-mail lists, Gravatar, published addresses, learned company patterns, and SMTP with catch-all detection when port 25 is open. How the scoring works.

Measured against live data every night

Unit tests can't tell you whether "dentists in Austin" returns dentists, so a GitHub Action runs the same real searches against the live sources every night, asks for ten leads each, and commits what came back to BENCHMARK.md, empty nights included. Run it yourself with openleads bench.

What you can search

One sentence fans out across the sources that fit it. You never have to pick one, but -s name pins a source.

Companies are turned into named people from their team and about pages. When a search comes back short, OpenLeads says which source failed or ran out of time and what to try next. Need a source we don't have? A source is one Python file.

Work your leads

Everything you find lands in a local workspace: the same data in the dashboard, the terminal and your AI assistant.

Home. What is due today, how outreach is going, and the strongest leads you haven't contacted. Sequences. Email, call, LinkedIn and task steps with A/B variants and same-thread follow-ups. People leave when they reply, bounce or unsubscribe.

Deals. A drag-and-drop pipeline with a weighted forecast. Winning a deal makes the contact a customer. Everything else. People and companies with Apollo-style filters, lists, ICP fit from A to D, a task queue, templates, CSV import, duplicate merge and a ⌘K palette.

openleads people --fit A --never-contacted                 # best untouched leads

openleads lists add "Q4 dentists" --where dental openleads seq enroll "Q4 outbound" --list "Q4 dentists" openleads tasks --bucket today openleads import apollo-export.csv --list "Imported"

Workspace guide | Sequences guide | Moving from Apollo

Use it from Python

import openleads

leads = openleads.find("gyms in Sydney", count=20) for lead in leads: print(lead.email, lead.evidence, lead.tier) leads.write("gyms.csv") # .csv, .json or .jsonl print(leads.report.summary()) # which sources ran, what failed, why it stopped

Python API

Use it from Claude, Cursor or Claude Code

OpenLeads is an MCP server. Your assistant can find and verify leads mid-conversation, and work your lists, tasks and sequences:

claude mcp add openleads -- openleads mcp

{ "mcpServers": { "openleads": { "command": "openleads", "args": ["mcp"] } } }

*"Find 10 law firms in London with emails we can actually send to, save them to a list called London law, and give me a call task for each partner."*

The assistant can search and edit your workspace, but it never sends email itself.

Automate it

Workflows are n8n-style node graphs you draw in the dashboard: triggers (schedule, new leads, a reply), find, verify, filter and split by tier, write email, wait, send, add to a list or sequence, create tasks, and export. Test runs never send; every run shows what each node did. Sends stay inside your daily cap, warmup ramp and business hours, and stop for anyone who replies.

openleads workflow templates                       # 7 ready-made workflows

openleads workflow new dentists --from local-business-outreach # arrives paused openleads workflow run dentists # a test run: finds and writes, never sends

openleads recipe add miami "marketing agencies in Miami" --every "weekdays 9am" --send

openleads watch add new-dentists "dentists in Austin" # tell me only about new matches openleads schedule on # one agent: launchd, cron or Windows Task Scheduler openleads runs # what ran, what it found, what failed

Workflows | Writing emails | Timed runs. Exports go to CSV, Google Sheets, Notion, Airtable, HubSpot or any webhook. There is also a GitHub Action:

- uses: Samyrrrrrr990/openleads@v5.2.0

with: { query: "marketing agencies in Miami", count: 50 }

Install

pipx install "openleads[all]"     # recommended

pip install openleads # minimal: the standard-library engine and CLI npx openleads # Node: sets up a private Python environment on first run

openleads doctor    # checks your setup: DNS, port 25, mailbox

openleads demo # the dashboard on an invented workspace openleads # chat: type what you want

Python 3.8 or newer on macOS, Linux or Windows. Full walkthrough: quickstart.

Commands

# find

openleads find "50 fintech founders, verified only" -o leads.csv openleads find "accountants in Toronto" --explain # the plan, each source's status, hints openleads company stripe.com # tech stack, socials, phones, people openleads enrich my-list.csv # your names and companies, emails labeled openleads verify ada@acme.io

reach out (a dry run unless you add --live)

openleads write "rust developers in Berlin" --variants 2 --explain # drafts, scored, with the brief openleads run "rust developers in Berlin"

the workspace

openleads people | companies | lists | seq | tasks | deals openleads import | export | dedupe | stats | backup

set up and automate

openleads init | config | doctor | web | mcp | bench openleads recipe | watch | schedule | runs

openleads --help lists every command by group.

How it works

flowchart LR

Q["'dentists in Austin'"] --> P[Query parser] P --> F[Planner picks the sources] F --> S1[OpenStreetMap] F --> S2[YC, HN, GitHub, Hugging Face, OpenAlex, EDGAR, Wikidata, NPI] S1 & S2 --> D[People from team pages

• real-name check] D --> E[Email checks

harvest, pattern, MX, SMTP, Gravatar] E --> L[Leads with evidence and tier] L --> W[Workspace, sequences, exports] E <--> DB[(Local SQLite learned patterns, workspace)]

The engine is Python standard library only. Requests that fail transiently are retried, a search returns within its time budget with whatever finished, and every run produces a report of which sources answered, which fell back and why. Your leads, drafts, mailbox credentials, learned patterns and the whole workspace live in ~/.openleads. There is no hosted backend, no account and no telemetry. Architecture.

Responsible use

OpenLeads is for legitimate prospecting, recruiting and research. You are responsible for anti-spam law (CAN-SPAM, GDPR, CASL) and each source's terms. It ships the guardrails: dry runs by default, a suppression list, one-click unsubscribe, warmup caps and no tracking pixels. Responsible use.

License

Open source under AGPL-3.0. Using OpenLeads is free, including at your company. If you embed it in a closed-source product, or run a modified version as a service without publishing your changes, you need a [commercial license](./