FlowRunner

FlowRunner

Midnight Coders
+
+

Related Products

  • BAND
    3 Ratings
    Visit Website
  • StackAI
    53 Ratings
    Visit Website
  • Creatio
    570 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    985 Ratings
    Visit Website
  • Retool
    584 Ratings
    Visit Website
  • NeuBird
    2 Ratings
    Visit Website
  • Pipefy
    592 Ratings
    Visit Website
  • LM-Kit.NET
    29 Ratings
    Visit Website
  • Daylight
    10 Ratings
    Visit Website
  • Docket
    59 Ratings
    Visit Website

About

FlowRunner is a visual workflow automation platform with native AI agent orchestration. Agents run workflows autonomously and pause for human judgment where it is required. The difference is how oversight works. On most platforms, approval is a step the workflow passes through. In FlowRunner, an agent invokes a human as a callable tool at runtime: it assembles the context and choices, routes to a named person by email, Slack, WhatsApp, or phone, and resumes with the answer. A run can stay suspended for up to a year, so a decision that takes three days does not time out. Audit trails, RBAC, and SLA tracking arrive at mid-tier, not in a custom enterprise contract, and a BAA is available. Every tier includes unlimited users, unlimited workflows, and the entire integration catalog, with nothing gated by seat or connector. Billing is per workflow run, not per action step. Agents use your own AI provider keys, with no markup on inference. Cloud-hosted or self-hosted.

About

Model Context Protocol (MCP) is an open protocol designed to standardize how applications provide context to large language models (LLMs). It acts as a universal connector, similar to a USB-C port, allowing LLMs to seamlessly integrate with various data sources and tools. MCP supports a client-server architecture, enabling programs (clients) to interact with lightweight servers that expose specific capabilities. With growing pre-built integrations and flexibility to switch between LLM vendors, MCP helps users build complex workflows and AI agents while ensuring secure data management within their infrastructure.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Small and mid-market teams automating work that still needs a human decision. Built by technical ops leads, automation engineers, and consultants. Owned by ops, finance, and compliance leaders.

Audience

Developers and businesses looking for a standardized way to integrate LLMs with various data sources and tools to build scalable AI systems

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

$45/month
Free Version
Free Trial

Pricing

Free
Open source
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5

Pros & Cons from Real Users

Pros

  • MCP is one of the most important pieces of AI infrastructure right now because it gives agents a standard way to plug into the outside world. Instead of every AI app needing a custom integration for every database, SaaS tool, repo, file system, or internal API, MCP creates a common connection layer. That matters a lot. It makes AI agents feel less like isolated chat windows and more like real software that can read context, call tools, retrieve data, and take useful actions. I also like that MCP has momentum across the ecosystem. It is not just an Anthropic-only idea anymore. The fact that major AI tools and developer environments are adding MCP support makes it feel like a real protocol, not just another vendor feature.

Cons

  • MCP also raises the stakes. Once agents can access tools and data, security becomes a much bigger deal. Permissions, authentication, logging, prompt injection, tool poisoning, and accidental data exposure all need to be handled carefully. It can also get messy if teams expose too many tools without structure. An agent with a giant pile of vague tools is not automatically smarter. Good MCP servers need clean design, clear scopes, strong descriptions, and thoughtful permissions.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Midnight Coders
Founded: 2007
United States
flowrunner.ai

Company Information

Anthropic
Founded: 2021
United States
modelcontextprotocol.io

Alternatives

Alternatives

Oz

Oz

Warp
HiClaw

HiClaw

AgentScope
Flowise

Flowise

Flowise AI

Categories

Categories

Integrations

Claude
Microsoft Foundry
Arcade
Bijira
Bluebricks
Bond
Butterbase
CMEM Cloud
Chutes
Ekamoira
Griptape
InsForge
Jotform
Koi
MCP360
Papr
Qwen3.8-27B
Smarty
Taddoo
Telerivet

Integrations

Claude
Microsoft Foundry
Arcade
Bijira
Bluebricks
Bond
Butterbase
CMEM Cloud
Chutes
Ekamoira
Griptape
InsForge
Jotform
Koi
MCP360
Papr
Qwen3.8-27B
Smarty
Taddoo
Telerivet
Claim FlowRunner and update features and information
Claim FlowRunner and update features and information
Claim Model Context Protocol (MCP) and update features and information
Claim Model Context Protocol (MCP) and update features and information