logos-router-distributed-reasoning

name	logos-router-distributed-reasoning
description	Deploy and configure Logos Router for distributed semantic reasoning with zero-drift consensus and adaptive thinking depth
triggers	["set up logos router for distributed AI reasoning","configure a reasoning mesh with multiple nodes","implement strict write discipline protocol","create adaptive depth reasoning workflows","configure multilingual semantic routing","debug consensus threshold issues in logos router","optimize reasoning node topology","integrate logos router with local inference engines"]

Logos Router — Distributed Reasoning Skill

Skill by ara.so — MCP Skills collection.

Overview

Logos Router is a distributed semantic reasoning gateway that fragments AI reasoning across local nodes with zero-drift consensus guarantees. Unlike traditional single-model inference pipelines, it distributes subproblems across a mesh of reasoning units that coordinate through a disciplined write protocol. The system maintains reasoning fidelity through cross-validation, preventing the drift common in long-context AI interactions.

Key differentiators:

• Zero-drift consensus: Strict Write Discipline (SWD) protocol validates every reasoning step
• Adaptive depth: Dynamically adjusts cognitive complexity based on problem difficulty
• Multilingual support: 16 languages with unified reasoning representation
• Causal audit trails: Every decision is traceable and verifiable
• Local-first: All reasoning runs on your infrastructure

Installation

Prerequisites

# Requires Python 3.11+
python --version

# Requires a local inference engine (VLLM, Ollama, llama.cpp, etc.)
# Example with Ollama:
curl -fsSL https://ollama.com/install.sh | sh
ollama pull opus-mini-4.8

Install Logos Router

# Clone the repository
git clone https://github.com/rak7777/mythic-mcp-proxy.git
cd mythic-mcp-proxy

# Install dependencies
pip install -r requirements.txt

# Verify installation
python -m logos.router --version

Configuration

Basic Mesh Configuration

Create a mesh_config.yaml file to define your reasoning topology:

mesh:
  name: local-reasoning-mesh
  consensus_threshold: 0.95  # Confidence required before write (0.0-1.0)
  
  nodes:
    - name: primary-node
      type: local
      engine: vllm
      model: opus-mini-4.8
      max_thinking_depth: 7
      memory_limit: 8GB
      
    - name: verification-node
      type: local
      engine: ollama
      model: llama-3.3-70b
      max_thinking_depth: 5
      memory_limit: 4GB
  
  languages:
    - en
    - zh
    - es
    - ar
    - hi
    - fr
  
  grail:
    storage_path: ./reasoning_store
    retention_days: 30
    
  chiron:
    routing_strategy: semantic_similarity
    load_balance: 
    
  
     
     

Environment Configuration

# Create .env file for sensitive configuration
cat > .env << EOF
LOGOS_MESH_CONFIG=./mesh_config.yaml
LOGOS_LOG_LEVEL=INFO
LOGOS_API_PORT=8080
LOGOS_WEBSOCKET_PORT=8081

# Inference engine endpoints
VLLM_ENDPOINT=http://localhost:8000
OLLAMA_ENDPOINT=http://localhost:11434

# Optional: Cross-mesh bridging
LOGOS_ENABLE_FEDERATION=false
LOGOS_FEDERATION_KEY=${FEDERATION_KEY}
EOF

Core Usage Patterns

Single-Query Reasoning

from logos import Router

# Initialize router with configuration
router = Router(config="mesh_config.yaml")

# Simple query with adaptive depth
response = router.query(
    prompt="Explain the relationship between recursion and self-reference in formal systems.",
    depth="adaptive",  # or specify 1-7
    language="en"
)

print(response.text)
print(f"Reasoning depth used: {response.depth}")
print(f"Nodes consulted: {response.nodes_involved}")
print(f"Consensus score: {response.consensus_score}")

# Access audit trail
for step in response.reasoning_trail:
    print(f"Step {step.index}: {step.action}")
    print(f"  Node: {step.node_id}")
    print(f"  Confidence: {step.confidence}")
    print(f"  Verified by: {step.verifiers}")

Multi-Step Workflow

from logos import Router, Workflow

router = Router(config="mesh_config.yaml")

# Create a multi-step reasoning workflow
workflow = router.create_workflow(
    name="code_analysis",
    description="Analyze code for security and performance issues"
)

# Define workflow steps
workflow.add_step(
    name="extract_structure",
    prompt="Analyze the code structure and identify key components",
    depth=3
)

workflow.add_step(
    name="security_review",
    prompt="Identify potential security vulnerabilities",
    depth=5,
    requires_consensus=True
)

workflow.add_step(
    name="performance_analysis",
    prompt="Analyze performance characteristics and bottlenecks",
    depth=4
)

workflow.add_step(
    name="generate_report",
    prompt="Synthesize findings into a comprehensive report",
    depth=6,
    synthesis_mode="harmonized"
)

# Execute workflow with input
with open("target_code.py", "r") as f:
    code_content = f.read()

result = workflow.execute(input_data={"code": code_content})

# Access workflow results
for step_name, step_result in result.steps.items():
    print(f"\n{step_name}:")
    print(step_result.text)
    print(f"Consensus: {step_result.consensus_score}")

Document Analysis Workflow

from logos import Router

router = Router(config="mesh_config.yaml")

# Create document analysis task
task = router.create_workflow("analyze_research_paper.pdf")

# Chain analysis steps
task.extract_claims()  # Depth 4
task.verify_claims(consensus=True)  # Depth 6, requires 0.95 consensus
task.cross_reference(sources=["./references/"])  # Depth 5
task.generate_summary(style="academic", length=500)  # Depth 4

# Execute with progress tracking
result = task.execute(progress_callback=lambda step, progress: 
    print(f"{step}: {progress * 100:.1f}%")
)

print(result.summary)
print(f"Claims verified: {len(result.verified_claims)}")
print(f"Total reasoning time: {result.execution_time}s")

Multilingual Reasoning

from logos import Router

router = Router(config="mesh_config.yaml")

# Query in Chinese, get response in English
response = router.query(
    prompt="解释量子纠缠的基本原理",  # Chinese input
    depth="adaptive",
    source_language="zh",
    target_language="en"  # Output in English
)

print(response.text)  # English explanation

# Or keep same language
response_zh = router.query(
    prompt="解释量子纠缠的基本原理",
    depth=5,
    language="zh"  # Input and output in Chinese
)

Streaming Reasoning Steps

from logos import Router

router = Router(config="mesh_config.yaml")

# Stream reasoning steps in real-time
for step in router.query_stream(
    prompt="Design a distributed consensus algorithm",
    depth=7
):
    print(f"Node {step.node_id}: {step.text}")
    print(f"  Confidence: {step.confidence:.3f}")
    print(f"  Verification: {step.verification_status}")
    
    # Access intermediate reasoning
    if step.type == "deliberation":
        print(f"  Considering: {step.alternatives}")

Advanced Configuration

Custom Reasoning Profile

Create a custom reasoning profile for specialized domains:

# profiles/scientific_reasoning.yaml
profile:
  name: scientific-reasoning
  max_depth: 7
  
  heuristics:
    - name: hypothesis_generation
      trigger: "problem requires speculation"
      depth_escalation: +2
      
    - name: empirical_verification
      trigger: "factual claim detected"
      requires_consensus: true
      min_verifiers: 3
      
    - name: mathematical_rigor
      trigger: "quantitative reasoning"
      precision_threshold: 0.99
  
  synthesis:
    style: academic
    citation_format: apa
    contradiction_policy: flag_and_escalate

Load custom profile:

from logos import Router

router = Router(
    config="mesh_config.yaml",
    profile="profiles/scientific_reasoning.yaml"
)

response = router.query(
    prompt="Design an experiment to test quantum entanglement",
    use_profile=True
)

Node Capability Manifests

Define specialized node capabilities:

# nodes/math_specialist.yaml
node:
  name: math-reasoning-node
  type: local
  engine: vllm
  model: minerva-540b
  
  capabilities:
    - mathematical_reasoning
    - symbolic_manipulation
    - proof_verification
    
  specializations:
    - domain: calculus
      confidence_boost: 0.15
    - domain: linear_algebra
      confidence_boost: 0.12
      
  resource_limits:
    max_concurrent_queries: 4
    memory_limit: 16GB
    gpu_memory: 24GB

Register specialized node:

from logos import Router

router = Router(config="mesh_config.yaml")

# Register specialized node
router.register_node("nodes/math_specialist.yaml")

# Query will route to specialized node for math problems
response = router.query(
    prompt="Prove that the square root of 2 is irrational",
    prefer_capabilities=["mathematical_reasoning", "proof_verification"]
)

Graceful Degradation Configuration

mesh:
  name: production-mesh
  
  degradation:
    enabled: true
    
    triggers:
      - metric: memory_usage
        threshold: 90%
        action: reduce_depth
        
      - metric: latency
        threshold: 15s
        action: reduce_concurrency
        
      - metric: consensus_failures
        threshold: 3
        action: escalate_and_notify
    
    depth_reduction:
      strategy: proportional  # or "aggressive", "conservative"
      min_depth: 3
      notify_user: true

CLI Usage

Start Router Server

# Start with default config
python -m logos.router serve

# Start with custom config
python -m logos.router serve --config mesh_config.yaml --port 8080

# Start with verbose logging
python -m logos.router serve --log-level DEBUG

# Start with specific node topology
python -m logos.router serve --nodes 4 --engines vllm,ollama

Query from CLI

# Single query
logos-query "Explain quantum entanglement" --depth adaptive

# With language specification
logos-query "¿Qué es la mecánica cuántica?" --language es

# Stream reasoning steps
logos-query "Design a consensus algorithm" --stream --depth 7

# Save reasoning trail
logos-query "Analyze this code" --input code.py --output analysis.json --trail

Mesh Management

# Check mesh status
logos-mesh status

# Add node to running mesh
logos-mesh add-node --config node_config.yaml

# Remove node
logos-mesh remove-node --id node-123

# View reasoning statistics
logos-mesh stats --timeframe 24h

# Export reasoning trail
logos-mesh export-trail --query-id abc123 --format json

REST API Integration

Start API Server

from logos import Router
from logos.api import create_app

router = Router(config="mesh_config.yaml")
app = create_app(router)

# Run with uvicorn
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8080)

API Endpoints

import requests
import json

# Query endpoint
response = requests.post(
    "http://localhost:8080/v1/query",
    json={
        "prompt": "Explain distributed consensus algorithms",
        "depth": "adaptive",
        "language": "en",
        "stream": False
    }
)

result = response.json()
print(result["text"])
print(result["consensus_score"])

# Workflow endpoint
workflow_response = requests.post(
    "http://localhost:8080/v1/workflow",
    json={
        "name": "code_review",
        "steps": [
            {
                "name": "analyze_structure",
                "prompt": "Analyze code structure",
                "depth": 3
            },
            {
                "name": "security_check",
                "prompt": "Check for security issues",
                "depth": 5,
                "requires_consensus": True
            }
        ],
        "input_data": {
            "code": "def factorial(n): return 1 if n == 0 else n * factorial(n-1)"
        }
    }
)

# Stream endpoint (SSE)
import sseclient

response = requests.post(
    "http://localhost:8080/v1/query/stream",
    json={"prompt": "Design a distributed system", : },
    stream=
)

client = sseclient.SSEClient(response)
 event  client.events():
    step_data = json.loads(event.data)
    ()

WebSocket Real-Time Streaming

import asyncio
import websockets
import json

async def stream_reasoning():
    uri = "ws://localhost:8081/v1/stream"
    
    async with websockets.connect(uri) as websocket:
        # Send query
        await websocket.send(json.dumps({
            "prompt": "Explain category theory",
            "depth": 6,
            "language": "en"
        }))
        
        # Receive reasoning steps
        async for message in websocket:
            step = json.loads(message)
            
            if step["type"] == "reasoning_step":
                print(f"Node {step['node_id']}: {step['text']}")
                print(f"Confidence: {step['confidence']:.3f}")
                
            elif step["type"] == "consensus_check":
                print(f"Consensus: {step['score']:.3f}")
                
            elif step["type"] == "final_response":
                print(f"\nFinal: ")
                

asyncio.run(stream_reasoning())

Troubleshooting

Low Consensus Scores

from logos import Router

router = Router(config="mesh_config.yaml")

# Enable diagnostic mode
router.enable_diagnostics()

response = router.query(
    prompt="Your query here",
    depth=5
)

# Check consensus details
diagnostics = router.get_diagnostics()
for step in diagnostics.consensus_failures:
    print(f"Step {step.index} failed consensus:")
    print(f"  Primary node score: {step.primary_score}")
    print(f"  Verifier scores: {step.verifier_scores}")
    print(f"  Reason: {step.failure_reason}")
    
# Adjust consensus threshold if needed
router.set_consensus_threshold(0.90)  # Lower from 0.95

High Latency Issues

from logos import Router

router = Router(config="mesh_config.yaml")

# Profile query performance
response = router.query(
    prompt="Your query",
    depth=5,
    profile=True
)

# Analyze bottlenecks
print(f"Total time: {response.metrics.total_time}s")
print(f"Node selection: {response.metrics.routing_time}s")
print(f"Inference: {response.metrics.inference_time}s")
print(f"Consensus: {response.metrics.consensus_time}s")
print(f"Synthesis: {response.metrics.synthesis_time}s")

# Optimize by reducing depth or adding nodes
if response.metrics.inference_time > 10.0:
    print("Consider adding more inference nodes")

Memory Pressure

# mesh_config.yaml - Enable aggressive memory management
mesh:
  nodes:
    - name: constrained-node
      memory_limit: 4GB
      swap_to_disk: true
      cache_strategy: aggressive_eviction
      
  grail:
    compression: true
    prune_old_trails: true
    max_trail_size: 100MB

Node Failures

from logos import Router

router = Router(config="mesh_config.yaml")

# Enable automatic failover
router.configure_resilience(
    auto_failover=True,
    health_check_interval=10,  # seconds
    max_retries=3
)

# Monitor node health
for node_id, health in router.get_node_health().items():
    if health.status != "healthy":
        print(f"Node {node_id}: {health.status}")
        print(f"  Last seen: {health.last_seen}")
        print(f"  Error: {health.error_message}")

Debugging Reasoning Trails

from logos import Router

router = Router(config="mesh_config.yaml")

response = router.query(
    prompt="Complex reasoning query",
    depth=7,
    save_trail=True
)

# Export full reasoning trail for inspection
trail = response.export_trail(format="json")

with open("reasoning_trail.json", "w") as f:
    json.dump(trail, f, indent=2)

# Visualize reasoning DAG
from logos.visualization import plot_reasoning_dag

plot_reasoning_dag(
    trail,
    output="reasoning_graph.svg",
    show_consensus=True,
    show_alternatives=True
)

Best Practices

1. Start with adaptive depth: Let the router determine optimal reasoning complexity
2. Use consensus for critical decisions: Enable requires_consensus=True for high-stakes reasoning
3. Monitor consensus scores: Scores below 0.90 indicate potential reasoning issues
4. Profile before scaling: Use diagnostic mode to identify bottlenecks before adding nodes
5. Cache semantic patterns: Enable semantic caching for repeated query patterns
6. Version reasoning profiles: Track profile changes alongside code changes
7. Audit critical trails: Export and review reasoning trails for important decisions

Performance Optimization

from logos import Router

# Production-optimized configuration
router = Router(
    config="mesh_config.yaml",
    cache_enabled=True,
    cache_ttl=3600,  # 1 hour
    parallel_verification=True,
    max_concurrent_queries=10
)

# Batch queries for efficiency
queries = [
    "Query 1",
    "Query 2",
    "Query 3"
]

responses = router.batch_query(
    prompts=queries,
    depth=4,
    max_parallel=3
)

for i, response in enumerate(responses):
    print(f"Query {i+1}: {response.text}")