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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.
Standardize planning, memory, verification, and review across AI coding agents.
Create a portable AI identity with memory, persona, and judgment.
Generate, validate, and manage PRDs with persistent memory and platform awareness.
Search Ainu resources, edit glossaries, and convert scripts with an MCP server.
Helps AI learn from sessions and persist rules automatically.
Modernize Java enterprise development workflows with AI-native, human-in-the-loop automation.