Free trial
A free tier with 200 credits is offered for solo builders and internal teams, described as free to start. No time-limited trial terms are stated on the homepage.
Learn moreAgentX
Build, evaluate, deploy and observe AI agent workforces in one lifecycle platform
Advanced API
What is AgentX?
AgentX is an AI chat bot tool that builds evaluate, deploy and observe AI agent workforces in one lifecycle platform. An enterprise platform spanning the whole AI agent lifecycle: multi-agent workflow building, pre-deploy evaluation with CI/CD gating, one-click deployment to API, Slack, web widget, email or voice, and framework-agnostic tracing with 24/7 runtime evaluation.
AgentX is an enterprise platform for the full AI agent lifecycle: building multi-agent workflows, evaluating them against test sets, deploying them to production channels, and observing them in real time. The pitch is deliberately narrower than general no-code bot builders. It targets teams that have moved past demos and need evidence that an agent actually works before it touches customers. Building happens in a drag-and-drop canvas. An orchestrator agent sits above a workforce of member agents, each with its own role, instructions, model, memory and tools. Workflows support branching logic and human handoff, and teams can bring their own model or use the models AgentX provides. From there the platform adds the steps most builders skip. Test sets can be synthesized from your documents, knowledge bases and historical conversations, then curated. Each version runs against those sets with multi-run scoring, so consistency rather than a single lucky run decides the score, and a CI/CD gate blocks or promotes a deploy based on thresholds you set. Deployment is one click to an API, Slack, a web widget, email or voice, with versioning and one-click rollback, in the cloud or on-prem. Observability is framework-agnostic: instrument LangChain, CrewAI, OpenAI Agents SDK, Anthropic or Google ADK once, and every run streams into a single trace pipeline that captures prompts, model calls, tool calls, retrieved documents, hand-offs, timing and cost. Runtime evaluations score live traffic using LLM-as-judge around the clock and alert on prompt drift, dataset drift, rising hallucinations, broken tool calls and latency or cost spikes. This puts AgentX closer to an agent operations layer than to a simple chatbot product. Solo builders and internal teams can start on the free tier and move to a paid production plan; agencies and service teams get white-label deployment and dedicated client workspaces. It also overlaps with observability tools and evaluation frameworks, though AgentX bundles build, eval, deploy and trace in one place, which is convenient for teams that would rather not stitch three vendors together.
How do you use AgentX?
1Review the agent lifecycle and plan your build
Start on the product page to understand how build, evaluate and deploy connect. Decide whether you are shipping your own internal agent or one for clients, since that shapes the plan you pick.
2Build a multi-agent workflow
Use the drag-and-drop builder to set an orchestrator agent and its workforce members. Assign each agent a role, instructions, model, memory and tools, then add branching logic and human handoff points.
3Create test sets and run evaluations
Generate evaluation datasets from your documents, knowledge bases or past conversations, then curate them. Run multi-run scoring against each agent version and review failing cases before promoting a build.
4Deploy to a production channel
Publish with one click to an API, Slack, a web widget, email or voice. Each deploy is versioned, so a rollback is a single action if monitoring later flags a problem.
5Instrument tracing and set up monitoring
Follow the developer documentation to instrument your framework, whether LangChain, CrewAI, OpenAI Agents SDK, Anthropic or Google ADK. Traces then stream into the same evaluation and monitoring views as builder agents.6Choose a plan or scope a managed build
Compare the free and Solo tiers against the white-label plans if you serve clients. Teams that want AgentX to run the workflow end to end can request a scoped engagement instead.
Pros and cons
Pros
- Covers build, evaluate, deploy and observe in one platform rather than stopping at a prototype builderAI
- Framework-agnostic tracing works with LangChain, CrewAI, OpenAI Agents SDK, Anthropic and Google ADK without a rebuildAI
- CI/CD gating with regression tracking and one-click rollback gives real safety around agent changesAI
- Deployment targets are practical (API, Slack, web widget, email, voice) with cloud or on-prem optionsAI
- Free tier with 200 credits and no per-seat pricing on the Solo plan keeps early experimentation cheapAI
Cons
- Pricing is only partly transparent: white-label and custom enterprise tiers require a sales conversationAI
- LLM-as-judge scoring is only as good as the rubrics and test sets your team curates, which is real ongoing workAI
- Feature depth (evaluation, tracing, governance) assumes engineering resources that smaller teams may not haveAI
- No public detail on credit consumption per run, so cost forecasting at scale is difficultAI
- The listing hint frames it as a chatbot tool, but it is a heavier platform than users wanting a simple FAQ bot may expectAI
How much does AgentX cost?
Pricing
Three routes: a free tier with 200 credits and a Solo plan at $49/mo with no per-seat pricing; white-label plans at Professional $199 and Business $299/mo for agencies deploying under their own brand; and custom pricing scoped per process when AgentX builds and operates the workflow for you.
Learn moreSupport
Priority support is listed on the Professional plan. Enterprise buyers can use a contact route for demos and scoping, and developer documentation covers instrumentation and platform setup.
Learn moreIntegrations
The trace pipeline is compatible with LangChain, CrewAI, OpenAI Agents SDK, Anthropic and Google ADK, and any model can be brought or AgentX models used. Deployment channels include API, Slack, web widget, email and voice.
Learn moreFeatures
Drag-and-drop multi-agent workflows with an orchestrator plus workforce agents, each with role, instructions, model, memory and tools. Evaluation covers synthesized test sets, multi-run scoring, a CI/CD gate and runtime LLM-as-judge monitoring. Deploy to API, Slack, web widget, email or voice with versioning and rollback. Traces from major frameworks stream into one pipeline with logs, cost, timing and drift alerts.
Learn moreFrequently asked questions about AgentX
How much does AgentX cost?
Three routes: a free tier with 200 credits and a Solo plan at $49/mo with no per-seat pricing; white-label plans at Professional $199 and Business $299/mo for agencies deploying under their own brand; and custom pricing scoped per process when AgentX builds and operates the workflow for you.Does AgentX offer a free trial?
A free tier with 200 credits is offered for solo builders and internal teams, described as free to start. No time-limited trial terms are stated on the homepage.Does AgentX have an API?
Yes, AgentX offers an API.How do you use AgentX?
The walkthrough on this page covers 6 steps: 1. Review the agent lifecycle and plan your build 2. Build a multi-agent workflow 3. Create test sets and run evaluations 4. Deploy to a production channel 5. Instrument tracing and set up monitoring 6. Choose a plan or scope a managed build.What platforms does AgentX support?
AgentX is available on Web App.What does AgentX integrate with?
The trace pipeline is compatible with LangChain, CrewAI, OpenAI Agents SDK, Anthropic and Google ADK, and any model can be brought or AgentX models used. Deployment channels include API, Slack, web widget, email and voice.
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Added10/22/2026
Platforms
Web App
Pricing
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