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Build controllable, stateful, reusable AI workflows with a graph-native language.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ainativelang" yet — see the docs or source repo.
Use AINL to design a customer support ticket workflow: receive the user issue, classify intent, query the knowledge base, call an external ticketing tool when needed, generate a reply, and persist context state and handling logs. Provide the structured flow definition and node explanations.
A structured AI workflow definition including nodes, state, tool calls, and validation logic.
I currently have a content moderation process stitched together with long prompts. Help me refactor it into a repeatable AINL graph structure with input validation, risk assessment, human-review branches, and final output constraints.
A more maintainable, reusable flow graph definition with branch control and output constraints.
Use AINL to design a research assistant workflow: take a research question, break it into tasks, retrieve sources, summarize findings, store stage memory, and reuse prior state in later rounds. Output the node graph and state model.
A research workflow supporting task decomposition, retrieval, persistent memory, and multi-turn execution.
Create a portable AI identity with memory, persona, and judgment.
Generate, validate, and manage PRDs with persistent memory and platform awareness.
Modernize Java enterprise development workflows with AI-native, human-in-the-loop automation.
Build self-hosted visual AI workflows with agents, RAG, HITL, and observability.
Build a two-tier memory system so AI understands team context and shorthand.
A native Python coding agent with hierarchical memory for faster assistance.