Draft:Inthon

Submission declined on 18 July 2026 by ChrysGalley (talk). This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content. LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. This draft's references do not show that the subject meets Wikipedia's criteria for inclusion. The draft requires multiple published secondary sources that: provide significant coverage: discuss the subject in detail, not just brief mentions or routine announcements; are reliable: from reputable outlets with editorial oversight; are independent: not connected to the subject, such as interviews, press releases, the subject's own website, or sponsored content. Please add references that meet all three of these criteria. If none exist, the subject is not yet suitable for Wikipedia. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by ChrysGalley 24 days ago. Last edited by ChrysGalley 24 days ago. Reviewer: Inform author. Resubmit Please note that if the issues are not fixed, the draft will be declined again.

• Comment: We can't accept AI assisted drafts. In addition there is not sufficient sourcing for notability here, the sources are primary/non independent/self published. ChrysGalley (talk) 13:23, 18 July 2026 (UTC)

Inthon is a domain-specific, Python-hosted programming language designed for artificial intelligence agents, tool orchestration, and sandboxed execution. Developed in 2026 by Harsha Vardhan of HarVa DeepLabs, Inthon provides a compile-ready, statically checked syntax for expressing agent goals, tool calls, memory persistence, safety policies, and human-in-the-loop approval checkpoints.^[1]

The language is designed to reduce the prompt token footprint of large language model (LLM) agents, validate interfaces prior to execution, and provide security boundaries when executing tools. Inthon compiles to Python code, JSON tool-call graphs, or directed acyclic graph (DAG) execution plans depending on the target runtime.^[2] In July 2026, the language implementation was presented at the BangPypers (Bangalore Python Users Group) community meetup.^[3]

Traditional autonomous agent architectures rely on Large Language Models executing workflows through natural language prompts or structured JSON or YAML schemas. This approach can introduce token overhead and execution errors due to non-deterministic parsing. Additionally, executing arbitrary Python or shell commands generated by an LLM exposes the host system to security risks.

Inthon was designed to address these limitations through three main components:

• Token efficiency: A minimal, context-free grammar written in EBNF, designed to decrease the number of tokens required for an agent to express a plan.^[2]
• Capability-based sandbox: A security model that blocks system calls, unauthorized network connections, and private attribute access by default.^[4]
• Deterministic execution tracing: Output logs formatted in JSON to allow execution steps and tool invocations to be audit-logged and replayed.

Inthon utilizes lexical block scoping. Variables declared inside a block are restricted to that scope. Mutable variables are declared using the let keyword, while constants are declared with const. Types are optionally annotated using a colon.

let name: str = "INTHON"
let version: float = 1.0
const max_retries: int = 3
let models: list[str] = ["gpt-4o", "gemini-3.5"]

The language features a gradual type system. Standard primitive types (str, int, float, bool, bytes, none, any) are supported alongside agent-specific types:

• Goal: High-level task description.
• Plan: Ordered execution steps.
• ToolCall and ToolResult: Records for tool execution tracking.
• Trace: Representation of the execution trace.
• MemoryRef: Persistent memory handle.
• Approval: Record of human-in-the-loop decisions.

The execution lifecycle of an agent is defined inside an agent block. This container encapsulates the agent's goal, input/output interfaces, runtime policies, and execution instructions.

agent Researcher {
    goal "Retrieve papers on room-temperature superconductors"
    inputs {
        query: str
        limit: int
    }
    outputs {
        papers: list[dict]
    }
    
    use tool web.search
    
    policy {
        max_tool_calls: 10
        max_cost_usd: 0.05
    }
    
    plan {
        let raw_results = web.search(query: query, count: limit)
        return raw_results
    }
}

Inthon supports conditional statements (if-else), loops (while, for-in), and functions (fn). Block-ending expressions that lack a semicolon or return keyword are implicitly returned.

fn multiplier(factor: int) -> fn(int) -> int {
    fn inner(x: int) -> int {
        x * factor
    }
    return inner
}

• Approval gateways: Halts script execution to await human approval before executing sensitive operations.
• Episodic memory: Integrates a local SQLite database to persist semantic statements, querying them via cosine similarity search on vector embeddings.
• Retry loops: Supports automated retry blocks with exponential backoff and randomized jitter to handle transient network errors.

Inthon restricts operations using a capability-based security model. When interacting with the host Python environment, the language utilizes a security system called PyBridge. This system enforces security at two levels:

1. Import hook interception: PyBridge installs a custom meta-importer hook in Python's sys.meta_path. The hook intercepts and blocks imports of modules outside an allowlist. Standard operating system, shell, and network modules (such as os, sys, subprocess, and socket) are blocked by default.
2. Namespace proxying: Allowed libraries are wrapped in proxy objects (InthonPyObject) that override attribute access (__getattribute__, __setattr__). This prevents programs from accessing private module properties or traversing namespaces to execute shell commands.

The compilation pipeline comprises a Lark-based concrete syntax parser, a semantic analyzer for type checking, and an intermediate representation (IR) builder.

Programs are executed using one of two modes:

• AST interpreter: Parses the concrete syntax tree into an abstract syntax tree (AST) and evaluates statements sequentially.
• InthonVM: A stack-based bytecode virtual machine. The compilation pipeline lowers the AST into a JSON-compatible bytecode format, which is executed by the virtual machine.

• Official website
• Inthon GitHub repository
• Inthon on Esolang Wiki