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Triform is a visual platform for building, running, and monitoring AI-powered systems. Users compose logic in the Builder workspace using a Canvas with Nodes (Agents, Flows, Actions) connected by Edges. Triton, the built-in AI assistant, helps build, modify, and debug Projects through natural conversation. The Chat workspace provides a conversational interface for using and interacting with completed agents.
Core Concepts
Projects are complete, deployable AI systems containing Actions, Agents, and Flows. Agents are LLM-powered components with prompts, model configs, and tools. Actions are atomic Python functions with typed inputs/outputs. Flows are orchestration graphs connecting components with edges defining data flow.
Important notes:
- The Builder workspace contains the Canvas, Chat Panel (Triton), and Properties Panel for building
- The Chat workspace is for conversing with and using your agents (like ChatGPT)
- All components live within Organizations ā Projects ā Library hierarchy
- Executions are tracked with logs, traces, and metrics for debugging
- Projects deploy to staging/production and expose API endpoints
- Dependencies must be pure Python (no binary installs)
Getting Started
- Quickstart: 5-minute guide to sign in, create a Flow with Triton, and use it in Chat
- Workspace Overview: Builder workspace, Chat workspace, and navigation
- Login: Authentication via Discord or GitHub
Core Concepts
- Agents: LLM logic with prompts, tools, and observability patterns
- Flows: Graph building blocks (Input/Output nodes, edges) and patterns (linear, branching, parallel)
- Actions: Python structure, I/O contracts, and workflow
- Glossary: Comprehensive definitions of all Triform terms
- Projects: Organization, structure, and deployment
- Executions: Running components, viewing results, and debugging
- Payloads: JSON input data format and schema matching
- Triggers: Webhooks, schedules, and manual execution
Triton AI Assistant
Workspace Interface
Builder Workspace (For Building)
Chat Workspace (For Using Agents)
Tutorials
API Reference
Optional
About this file
This page follows the llms.txt specification - a proposed standard for providing LLM-friendly information to help AI assistants use website documentation at inference time. The plain text version is available at /llms.txt.