AgentX Inc (United States) offers a platform that covers building, evaluating, and deploying AI agents in one place. Without writing code, you can combine several agents ─ each with its own role, instructions, model, memory, and tools ─ and run them as a single workflow. Agents you build can be deployed to a website chat widget, Slack, Discord, WhatsApp, or an API, so the same foundation covers everything from internal process automation to customer-facing chatbots. In recent years the platform has expanded features aimed at production use ─ pre-release evaluation (LLM-as-judge), execution tracing, and version control with rollback.
Key Features
- No-code multi-agent setup: Instead of a single bot, you can have several agents with separate roles cooperate on complex work. Each agent gets its own instructions, LLM, memory, and set of available tools
- Multi-channel deployment: The same agent can be published as a website chatbot or inline widget, or through Slack, Discord, WhatsApp, and the API. Additional channels such as Instagram, SMS, and email are being added over time
- Knowledge bases with RAG: You can build a vector database from websites, files such as PDF, Word, text, and images, or manually entered text, so agents answer based on your own material. OCR reading of scanned PDFs is supported
- Built-in business actions: Lead capture is a standard action (email and name are the default fields) and can be connected to HubSpot, Wix CRM, or any webhook. Schedule management through Google Calendar integration is also provided
- Tool connections via the MCP hub: You can pick Model Context Protocol (MCP) servers from a list and connect them, giving agents external capabilities such as search, maps, file operations, and database queries. Your own MCP servers can be added as well
- Evaluation, observability, and versioning: The platform includes a way to evaluate agent responses before going live, plus tracing that follows the execution path. Version history is kept, so you can roll back to an earlier state if something breaks
- White-label and client workspaces: Higher plans allow delivery under your own brand and separate workspaces per customer, aimed at studios and agencies that build and hand over agents for clients
Pricing
| Plan | Price | What’s included |
|---|---|---|
| Free | $0 | 1 workspace, up to 5 agents, 200 credits (one-time), 1 seat, multi-agent workflows, API access |
| Solo Builder | $49/month ($490/year) | Unlimited workspaces, up to 25 agents, 5,000 credits/month, 1 seat, production deployment, evaluation mode |
| Professional | $199/month ($1,490/year) | Up to 25 agents, 10,000 credits/month, 2 seats, white-label deployment, client workspaces |
| Business | $299/month ($2,990/year) | Unlimited agents, 20,000 credits/month, 2 seats, priority support, priority SLA |
| Enterprise | Contact sales | Unlimited workspaces, agents, and seats, SSO, dedicated infrastructure, on-premise deployment, enterprise SLA |
Additional credits cost $10 per 1,000 credits, and extra seats are $10 per month each.
Pricing is current as of August 2026. Check the official pricing page for the latest details.
Pros & Cons
✅ Pros
- You can assemble a multi-agent setup without writing code, which keeps the learning curve low as an entry point into agent development
- Deployment targets are broad ─ web, Slack, Discord, WhatsApp, and API ─ so internal and customer-facing agents can share one foundation
- RAG over your own documents is built in, so a support chatbot can be stood up in a short time
- Actions tied directly to sales and support work, such as lead capture and CRM integration, are available out of the box
- MCP support leaves room to add external tools later on
- The free plan allows up to 5 agents, so you can try the real thing before deciding
⚠️ Cons
- The credit-based model makes costs harder to predict as usage grows, and buying extra credits tends to become the norm
- The entry paid plan starts at $49 per month, which is not cheap for individual use
- White-label deployment and client workspaces require the $199/month Professional plan or above
- Some channels and business actions are still listed as upcoming, so you need to confirm in advance that the features you need are available
- As a no-code platform, fine-grained control is more limited than with code-first frameworks
Comparison with Similar Services
| Item | AgentX | Dify | Botpress | CrewAI |
|---|---|---|---|---|
| Main use | Building multi-agent systems and deploying them to customers | Development platform for LLM apps and agents | Building conversational bots and automating work | Code-first multi-agent development |
| How you build | Mainly no-code | No-code combined with code | Visual flows plus code extensions | Written in Python |
| Main deployment targets | Web, Slack, Discord, WhatsApp, API | Web embed, API | Web, various messaging channels | Embedded in your own app |
| White-label | Available on higher plans | Partially available | Available | Not applicable |
| Self-hosting | On-premise on Enterprise | Possible with the open-source edition | Cloud-centric | Possible, as it is open source |
Who Is It For
- Staff who want to start automating internal inquiry handling or back-office work with no code
- Teams that want to launch an AI chatbot on their own site or in Slack without spending development resources
- Studios and agencies that build and deliver AI agents for clients
- Anyone who wants to try cooperation between several role-separated agents rather than a single chatbot
- People who want agents that touch business data by connecting to an existing CRM or MCP-compatible tools
Summary
AgentX stands out for letting you assemble multi-agent systems without code and deploy them straight to the surfaces where they will actually be used ─ web, Slack, Discord, and WhatsApp. It covers the basics well: RAG over your own knowledge, business actions such as lead capture, and external tool connections through MCP, which makes it approachable as an entry point into running agents. On the other hand, pricing is credit-based, and agency-oriented features such as white-labeling sit on the higher plans. A reasonable approach is to build one agent on the free plan, confirm that your expected usage and channels fit your requirements, and then consider a paid plan.