OntoPrune

Neuro-Symbolic Context Pruning Middleware for Local SLMs and Cloud LLMs

English | Español | 📄 Technical Whitepaper (PDF) | 📄 Whitepaper en Español (PDF)

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OntoPrune is an ultra-lightweight (<12ms CPU) neuro-symbolic middleware that transforms multi-file source code into minimal dependency contracts. By isolating closed-world functional boundaries before attention computation, OntoPrune slashes input tokens by 83% to 92.4%, collapses Time-to-First-Token ($TTFT$) by 6.7x on CPU-bound local Small Language Models (SLMs), and guarantees 0% API hallucinations.

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🚀 Multi-Language Empirical Benchmarks

Real-world evaluation across multi-file enterprise projects in four major software ecosystems:

Ecosystem & Framework	Raw Project Context	OntoPrune Context (stubs)	Token Reduction	Estimated TTFT Speedup
Python (Async Services)	2,815 tokens	393 tokens	-86.0%	6.7x faster
Flutter / Dart (State & UI)	1,650 tokens	135 tokens	-91.8%	~7.0x faster
Java / Spring Boot (Enterprise @Service)	1,450 tokens	110 tokens	-92.4%	~7.2x faster
TypeScript / React (Frontend & APIs)	1,380 tokens	105 tokens	-92.4%	~7.1x faster

Local CPU Inference Benchmark (Qwen 2.5 Coder 3B via Ollama)

Metric	Naive (Full File)	OntoPrune (stubs)	Real Gain
CPU Overhead	0.02 ms	9.9 ms	$\le 10\text{ ms}$ (Target: $\le 15\text{ ms}$)
Input Tokens	2,390 tokens	406 tokens	-83.0% ($\approx 6\text{x}$ reduction)
TTFT (Time-to-First-Token)	22.4 s	3.3 s	6.7x faster (saves 19.1 s)
Total Generation Time	59.9 s	16.5 s	-72.5% ($3.6\text{x}$ faster)
API Hallucinations	1 invalid method	0 invalid methods	100% Contract Compliance

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📦 Installation

# Standard installation (native Python support):
pip install ontoprune

# With multi-language support (Flutter/Dart, Java, TypeScript via Tree-sitter):
pip install "ontoprune[languages]"

# For development, benchmarks, and tests:
pip install "ontoprune[dev,benchmark,languages]"

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🌐 Universal Multi-Language Support

OntoPrune automatically detects file types and resolves dependencies across project boundaries:

Language	Extension	AST Engine	Output Format
Python	.py	Native Python ast	def name(args) -> Ret: ...
Flutter / Dart	.dart	tree-sitter-dart	abstract class ... { Ret method(); }
Java / Spring Boot	.java	tree-sitter-java	public interface ... { Ret method(); }
TypeScript / React	.ts, .tsx, .js	tree-sitter-typescript	export interface ... { method(): Ret; }

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🛠️ Usage Modes

1. Native Model Context Protocol (MCP) Server

OntoPrune runs out of the box as an MCP server (ontoprune-mcp) compatible with Claude Desktop, Cursor, Gemini CLI, or Antigravity IDE:

ontoprune-mcp

Configuration in claude_desktop_config.json:

{
  "mcpServers": {
    "ontoprune": {
      "command": "ontoprune-mcp"
    }
  }
}

Exposed MCP Tools:

• prune_context(file_path, target_symbol, format='stubs'): Extracts the minimal dependency contract resolving cross-file imports.
• verify_response(response_code, contract_or_file): Deterministically validates generated code against authorized contracts.

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2. Command Line Interface (CLI)

# Prune a target method across multi-file projects:
ontoprune translate src/services/OrderService.java processOrder --format stubs

# Direct streaming pipeline with local Ollama:
ontoprune translate services/order_service.py procesar_orden | ollama run qwen2.5-coder:3b

# Deterministically verify LLM output against contract:
ontoprune check --file generated_solution.py --contract contract.py

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3. Python API

import ontoprune

# 1. Prune a multi-module project to a minimal typed contract
context = ontoprune.translate(
    "services/order_service.py",
    target="procesar_orden",
    fmt="stubs",
    multi_module=True,
)
print(context)

# 2. Verify model output against the contract
violations = ontoprune.check(llm_code_response, against=context)
if not violations:
    print("Code is 100% compliant and free of hallucinations!")

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🧪 Test Suite

uv run pytest
# 45 passed in 1.19s

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📄 Whitepapers & Publications

• English Technical Whitepaper (PDF)
• Whitepaper Técnico en Español (PDF)
• Community Article / Blog Post
• Artículo Comunitario en Español

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👤 Author

Vigmar Carlo

• GitHub: @vigmarcarlo
• Repository: https://github.com/vigmarcarlo/OntoPrune
• License: MIT