OmniGlyph（万象文枢）

中文文档 / Chinese README

AI Agent 的全球符号真值层。
The Symbol Ground Truth Layer for AI Agents.

OmniGlyph is not a dictionary for human reading. It is a local-first, source-traceable, machine-computable symbol infrastructure for AI agents, automation systems, and future semantic operating layers.

Its core philosophy is:

字符即封装，语义即运算。

In the AGI era, agents need a deterministic substrate beneath probabilistic language models. OmniGlyph turns Unicode characters, scripts, multilingual terms, technical symbols, industry abbreviations, and eventually domain concepts into structured facts that agents can query, verify, and compute against.

Product Thesis

OmniGlyph is built around three layers that share the same deterministic foundation:

1. Global Symbol Ground Truth Layer

OmniGlyph gives agents a local, source-traceable physical truth layer for symbols and terms. It helps agents identify Unicode code points, suspicious homoglyphs, zero-width characters, Bidi controls, fullwidth/halfwidth forms, and private terminology before a probabilistic model starts reasoning.

This does not claim to eliminate every model hallucination. It reduces a concrete class of symbol-, character-, and terminology-layer failures by making the low-level text substrate inspectable and source-backed.

2. Strict Enterprise Guardrails

On top of the symbol truth layer, OmniGlyph can run as a deterministic MCP guardrail for enterprise workflows. Users can mount private Lexicon Packs for business terms, SKUs, material names, supplier terms, confidential vocabulary, and approved aliases.

Generated output can then be checked with validate_output_terms and enforce_grounded_output. Unknown, unapproved, or unsupported terms can be blocked or routed to review before they reach customers, ERP systems, quotes, or downstream tools.

3. Language-as-Code Security Gateway

OmniGlyph also treats natural language as a runtime attack surface. scan_language_input checks untrusted text for prompt-injection directives and hidden Unicode attacks, scan_output_dlp redacts sensitive outbound text, and enforce_intent validates action requests against deterministic intent manifests.

This layer does not execute shell commands or promise complete prompt-injection immunity. It gives host systems machine-readable allow, review, and block evidence so execution and delivery decisions can happen outside the model.

In one sentence:

OmniGlyph is a local Symbol Ground Truth Layer, deterministic enterprise guardrail, and language security gateway for AI agents.

Available on PyPI + MCP Registry

OmniGlyph is prepared as both a Python package and an MCP Registry server.

• Current source package version: omniglyph==0.7.0b0
• Latest published PyPI package: omniglyph==0.6.0b0
• MCP Registry server: io.github.aidi1723/omniglyph
• Transport: local stdio MCP server

Install the latest published PyPI package:

pip install omniglyph==0.6.0b0

Run the MCP server:

omniglyph-mcp

Quick MCP smoke test:

printf '{"jsonrpc":"2.0","id":1,"method":"tools/list"}\n' | omniglyph-mcp

The source branch is now versioned as 0.7.0b0 and exposes the v0.7 MCP tool set. PyPI publication for 0.7.0b0 is a separate release step.

Current source MCP tools: lookup_glyph, lookup_term, explain_glyph, explain_term, explain_code_security, normalize_tokens, list_namespaces, validate_lexicon_pack, validate_output_terms, enforce_grounded_output, scan_code_symbols, scan_unicode_security, scan_language_input, `scan_outpu

... View full README on GitHub