coldforge

coldforge

Enables personalized cold email outreach through research, drafting, template management, and deliverability checks, all locally with no required API keys.

Category
Visit Server

README

coldforge

Honest, local-first cold outreach. Research a prospect → personalize the opener → run a safe, sequenced campaign → auto-follow-up only the people who didn't reply. One shared Python core, two front-ends: a CLI and an MCP server you can drive from Claude.

No SaaS, no account, no required API keys. Your prospect data and your .env never leave your machine — it's a single SQLite file you can delete and rebuild.

git clone https://github.com/Makeph/coldforge && cd coldforge
pip install -e ".[all]"            # or `pip install -e .` for the zero-dep core
coldforge init
coldforge leads import examples/leads.csv
coldforge research --all
coldforge campaign create --name q3 --sequence examples/sequence.yml
coldforge campaign activate q3 --leads examples/leads.csv
coldforge campaign preview q3      # review the whole timeline first
coldforge tick --dry-run           # see exactly what would send

Not on PyPI yet — install from source as above. (pip install coldforge will be the one-liner once it's published.)


Why this exists

It's a synthesis of the best ideas from a pile of open-source outreach tools, rebuilt small and honest:

Idea Borrowed from How coldforge does it
Local SQLite sending engine, sequences, A/B, safe tick worker cold-cli (Go) Re-implemented lean in Python
Web-search + scrape to personalize prospect-research-mcp research command + research_prospect MCP tool, zero-key DuckDuckGo fallback
LLM-personalized emails ProspectAI ~150 lines, not 137k; always degrades to template fill
Curated reply-driving templates, silent-reply follow-up, SPF/DKIM/DMARC check coldflow Original template pack + doctor + reply→cancel rule

Everything degrades gracefully: no Anthropic key → deterministic template fill; no Tavily key → DuckDuckGo + site scrape; no SMTP → dry-run only. You can run the entire pipeline end-to-end with an empty .env.

Two front-ends, one core

            ┌──────────────────────────────────────────────┐
            │  shared core (research · personalize ·        │
            │  templates · sequence · sender · db)          │
            └───────────────┬───────────────┬──────────────┘
                            │               │
                      coldforge CLI     MCP server
                  research→send→follow   research_prospect
                                         draft_email

CLI

coldforge templates list                 # browse the pack
coldforge templates show sales_pain_point
coldforge research alex@acme.io          # store a personalization signal
coldforge draft -l alex@acme.io -t sales_pain_point --research
coldforge doctor acme.io                 # SPF / DKIM / DMARC, 0–100 score
coldforge reply mark alex@acme.io        # records a reply → cancels follow-ups
coldforge stats q3

MCP (drive it from Claude)

pip install "coldforge[mcp]"
coldforge mcp        # stdio server

Register it with any MCP client (e.g. Claude Desktop / Claude Code):

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

Tools exposed: research_prospect, draft_email, list_templates, show_template, check_deliverability. Now you can ask Claude "research Alex at Acme and draft a pain-point cold email" and it uses the same engine the CLI does.

Sequences

A sequence is a list of steps in YAML:

- template: sales_pain_point   # concrete opener from the lead's data + research
  wait_days: 0
  condition: always
- template: followup_bump      # one soft bump, same thread, only if no reply
  wait_days: 3
  condition: no_reply

On activate, every step is pre-scheduled for every lead. The tick worker (run it from cron / Task Scheduler) sends what's due and enforces the guardrails:

  • Send window & days — nothing sends outside COLDFORGE_SEND_WINDOW / COLDFORGE_SEND_DAYS (default 09:00–17:00, Mon–Fri).
  • Daily capCOLDFORGE_DAILY_LIMIT (default 40) per account.
  • Jittered pacing — randomised gap between real sends.
  • Reply → cancel — a no_reply step is skipped and the rest of that lead's sequence canceled the moment a reply is recorded (manually or via IMAP).
# typical cron line — runs the worker every 15 min during the day
*/15 9-17 * * 1-5  coldforge tick --scan-replies

Configuration

Everything is optional — copy .env.example to .env and fill what you need.

Variable Purpose Without it
ANTHROPIC_API_KEY Claude-personalized drafts deterministic template fill
TAVILY_API_KEY high-quality research DuckDuckGo + site scrape
SMTP_*, COLDFORGE_FROM_* actually send mail dry-run only
IMAP_* auto-detect replies mark replies manually
COLDFORGE_DAILY_LIMIT / SEND_WINDOW / SEND_DAYS guardrails sane defaults

Templates

Nine curated, plaintext, reply-tested templates across sales, recruiting, partnership, warm-intro, networking, follow-up — each under ~120 words with one CTA and deliverability notes baked into the front-matter. Add your own by dropping a .md file into ~/.coldforge/templates/.

Install from source

git clone https://github.com/Makeph/coldforge
cd coldforge
pip install -e ".[dev]"
pytest          # offline test suite, no keys needed

Responsible use

coldforge is a precision tool, not a spam cannon: low daily caps, send windows, one-bump follow-ups, an explicit "say no and I'll stop" CTA in the templates, and a doctor check so you authenticate your domain before sending. Only email people you have a legitimate reason to contact, honour unsubscribes, and follow the law that applies to you (CAN-SPAM, GDPR, etc.).

License

MIT © 2026 Aurore Biakou

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