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QRev

AI-driven sales agents that replace costly CRM platforms

Customer Relationship Management (CRM)

QRev provides AI‑driven sales agents that automate lead handling, outreach, and pipeline management, delivering a customizable, cost‑effective alternative to traditional CRM platforms.

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Overview

Overview

QRev is designed for sales teams and developers who want an AI‑first CRM without the expense and rigidity of traditional solutions. By modeling Salesforce with modern AI agents, it offers role‑based assistance for SDRs, BDRs, account executives, and sales leadership.

Capabilities & Architecture

The platform centers on a super‑agent (Qai) that delegates tasks to a fleet of digital workers, leveraging Langchain for LLM integration, ChromaDB for vector storage, MongoDB for application data, and optional SQLite/SQLAlchemy for relational AI data. Its TypeScript/NodeJS stack enables rapid customization and extension.

Deployment

QRev runs as three components—client, app server, and AI server—each installable via npm and standard Node.js tooling. After cloning the repository, configuring environment variables, and provisioning MongoDB, the services can be started locally with npm start. Community Slack and the open source repository provide ongoing support and contribution pathways.

Highlights

Super‑agent Qai with role‑based permissions
Modular digital workers powered by Langchain
Vector and relational databases for AI context
Fully customizable TypeScript/NodeJS codebase

Pros

  • Significantly lower cost than enterprise CRMs
  • Deep AI integration from the ground up
  • Open source flexibility for custom workflows
  • Active community and extensible architecture

Considerations

  • Early‑stage project with evolving features
  • Limited out‑of‑the‑box third‑party integrations
  • Self‑hosting requires technical expertise
  • Performance depends on chosen LLM and infrastructure

Managed products teams compare with

When teams consider QRev, these hosted platforms usually appear on the same shortlist.

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Flexible, data-driven CRM platform designed for startups and relationship-focused workflows

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Inside sales CRM with calling, email, and SMS built in

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Copper

CRM for Google Workspace users to manage leads and pipelines

Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.

Fit guide

Great for

  • Small to mid‑size sales teams seeking AI automation
  • Developers who want to tailor a CRM to specific processes
  • Organizations with constrained budgets for CRM software
  • Teams comfortable with Node.js and TypeScript environments

Not ideal when

  • Enterprises requiring guaranteed SLA and vendor support
  • Non‑technical users unwilling to manage self‑hosted services
  • Companies needing extensive pre‑built integrations out of the box
  • Teams that must meet strict compliance certifications not yet addressed

How teams use it

AI‑powered lead qualification

Automatically triage inbound leads, routing high‑potential prospects to sales reps for faster pipeline conversion.

Personalized outreach campaign generation

Ingest CSV contact lists and produce tailored email sequences, boosting response rates and engagement.

Role‑specific sales assistance

Provide SDRs and account executives with real‑time guidance, task automation, and performance insights.

Unified workflow integration

Connect QRev with existing tools like Google Workspace to synchronize data and streamline daily sales operations.

Tech snapshot

JavaScript50%
Python28%
TypeScript14%
SCSS6%
Dockerfile1%
HTML1%

Tags

aisalesforceagentssalesforce-developers

Frequently asked questions

What programming languages does QRev use?

The client is built with TypeScript, the backend with Node.js, and the AI server uses Python.

Which databases are required?

MongoDB stores application data, ChromaDB handles vector embeddings, and SQLite (or any SQLAlchemy‑compatible DB) is used for relational AI data.

Is there a hosted version of QRev?

Currently QRev is self‑hosted; the community may provide hosted options in the future.

What large language models can I use?

Any model compatible with Langchain can be integrated, giving flexibility to choose OpenAI, Anthropic, Llama, etc.

How can I contribute to the project?

Fork the repository, submit pull requests, and join the community Slack for discussion and support.

Project at a glance

Stable
Visit siteView repo
Stars
271
Watchers
271
Forks
40
LicenseAGPL-3.0
Repo age1 year old
Last commit6 months ago
Primary languageJavaScript

Last synced 2 days ago