Visual workflow design
Model multi-step execution flows on a canvas with agents, conditions, outputs, and control logic. The visual structure makes workflows easier to review, maintain, and evolve.
Local-first AI automation platform
AI provider
Memory
Knowledge base
Integrations
Operational AI under your control
Build automations and AI agents that run within your own environment. Keep data, execution, and integrations under local or enterprise-controlled infrastructure.
Designed for local deployment, private data, and controlled execution.
Core capabilities
Core capabilities for operational AI workflows
Design, configure, validate, and operate AI-driven workflows in one environment. Built for teams that need visibility, control, and extensibility.
Full agent manager
Configure production-grade agents with prompts, memory, tools, external MCP servers and model settings in one place. Build assistants you can inspect, tune and ship.
Knowledge access under your control
Connect documents, files, and internal sources so agents can retrieve relevant context without moving sensitive material into shared cloud tooling.
Integration with existing systems
Work with files, Git repositories, databases, HTTP services, messaging platforms, and internal APIs. Texx is designed to fit enterprise environments rather than replace them.
Extensible with plugins
Use built-in building blocks, add plugins, or connect external MCP tools. Texx fits around your stack instead of forcing you into a fixed set of integrations.
Controlled execution and validation
Operate complex workflows with retries, branching, checks, validations, and captured outputs. Improve reliability when upstream services are slow, unstable, or partially unavailable.
Agent workspace
An execution environment for production-ready agents
Texx provides a visual workflow editor, plugin architecture, and a structured agent configuration interface. It is designed for teams that require controlled behavior, transparent configuration, and repeatable deployment.
Model the execution flow visually, connect tools and knowledge sources, and export the result as a runnable artifact. The same agent definition can be prepared in the desktop environment and deployed to a workstation, gateway, Raspberry Pi, or another edge device located near equipment.
Visual workflows
Plugins + MCP
Portable export
- Centralized agent configuration. Manage prompts, model routing, tool access, memory, captured outputs, and execution behavior from a single interface.
- Consistent deployment across environments. Export the runtime and operate it locally, from the command line, through an API, or behind a compact web interface on another device.
- Applicable to robotics and device-connected workflows. Integrate with local APIs, serial bridges, files, HTTP services, and custom plugins that communicate with controllers, sensors, test benches, and industrial equipment.
Deployment options
Export and run anywhere
Package workflow logic, integrations, and agent behavior into a portable runtime. Use the same definition across developer workstations, internal servers, support systems, or edge devices.
From the command line
Run exported agents with a single command for scripts, scheduled jobs, test automation, maintenance processes, and local operations.
As a web API
Expose the same agent behind an HTTP interface so internal services, dashboards, gateways, or device controllers can invoke it programmatically.
Built-in web interface
Provide a compact operator interface for execution, logs, and results when human oversight is required on a workstation or support system.
Integrations and operating scenarios
Designed for production environments
Works with established tools and infrastructure
OpenAI
Anthropic
Ollama
GitHub
PostgreSQL
Slack
Telegram
Filesystem
Web APIs
MCP and custom tools
Representative use cases
- AI agents with memory, tools, and controlled access to enterprise data
- Internal workflow automation without adding a large orchestration stack
- Scheduled tasks, one-off scripts, and background operational jobs
- Document retrieval and answer workflows with local or private knowledge sources
- Resilient automations with retries, validations, and structured outputs
- Auditable workflows for regulated, security-sensitive, or customer-facing processes
- Portable runtime packages that can be shared across teams or environments
- Automated test, integration, and support workflows with captured execution logs
- Conversational interfaces that trigger deterministic backend actions
- Edge agents near robots, benches, gateways, or device controllers
- Multi-step data processing where AI is applied selectively and transparently