logos-router-reasoning

Configure distributed semantic reasoning meshes with consensus verification across local inference nodes.

7|2|Updated May 16, 2026
One-click install
npx skills add https://github.com/reason-machines/mcp-skills --skill logos-router-reasoning-reason-machines
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: logos-router-reasoning
Source: https://github.com/reason-machines/mcp-skills/tree/main/skills/logos-router-reasoning
Command: npx skills add https://github.com/reason-machines/mcp-skills --skill logos-router-reasoning-reason-machines

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Coordinating multiple local AI inference engines for multi-step reasoning tasks is complex, and single-model outputs often suffer from hallucination chains and unverified conclusions. This Skill guides the deployment of a distributed reasoning router that fragments inference across nodes and enforces consensus before producing answers. ## Core Features & Use Cases - Zero-Drift Consensus: Requires peer verification across mesh nodes before writes, with configurable confidence thresholds and escalation rules. - Adaptive Reasoning Depth: Scales reasoning from 1 to 7 steps based on problem complexity, with streaming of intermediate steps and causal audit trails. - Multilingual Semantic Routing: Routes queries across 16 languages using a universal intermediate representation, with cross-language context processing. - Use Case: A team running VLLM and Ollama on separate GPU machines can configure a reasoning mesh where code review queries are verified by multiple nodes, with full audit trails showing which node produced each reasoning step. ## Quick Start Set up a Logos Router reasoning mesh with two local nodes and run a consensus-verified query explaining a technical concept.

Frequently Asked Questions about logos-router-reasoning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up a distributed reasoning mesh with multiple local AI nodes?▼

Define a config.yaml listing each node with its engine (VLLM, Ollama, llama.cpp), model, host, port, and max thinking depth. Initialize the Router with that config, and it coordinates queries across nodes with consensus verification.

How to enforce consensus verification across multiple LLM inference nodes?▼

Enable Strict Write Discipline in the config with a consensus_threshold such as 0.95 and a peer_verification_count. Queries then require multiple nodes to agree before a response is written, and failures can trigger escalation or fallback.

Can I route queries in Chinese and get responses in English?▼

Yes, multilingual routing supports 16 languages through a universal intermediate representation. Pass the input language and an output_language parameter, or supply mixed-language context documents with language set to auto.

Why does consensus fail on some reasoning queries?▼

Consensus fails when node agreement scores fall below the configured threshold, often due to model disagreement or ambiguous prompts. Enable debug mode to inspect failing nodes, disagreement reasons, and per-node verification scores.

What inference engines work with a distributed reasoning router?▼

The mesh supports local engines including VLLM, Ollama, and llama.cpp, each declared per node in the YAML configuration. Nodes can run on localhost for single-machine setups or on remote hosts for distributed GPU deployments.

When should I use single-node inference instead of a reasoning mesh?▼

Single-node inference fits latency-sensitive or low-stakes queries where consensus overhead is unjustified. The router supports forcing a specific node with consensus disabled as a fallback when mesh verification fails or times out.