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AgentDB Simulation CLI Reference

Version: 2.0.0 Last Updated: 2025-11-30

Complete command-line reference for the AgentDB latent space simulation system. Covers all commands, options, and examples.

🎯 Command Overview

agentdb simulate [scenario] [options]
agentdb simulate --wizard
agentdb simulate --custom [component-options]
agentdb simulate --list
agentdb simulate --report [id]

Quick Reference

Command	Description	Example
`simulate [scenario]`	Run validated scenario	`agentdb simulate hnsw`
`simulate --wizard`	Interactive builder	`agentdb simulate --wizard`
`simulate --custom`	Custom configuration	`agentdb simulate --custom --backend ruvector`
`simulate --list`	List all scenarios	`agentdb simulate --list`
`simulate --report`	View past results	`agentdb simulate --report latest`

🎬 Scenario Commands

HNSW Graph Topology Exploration

agentdb simulate hnsw [options]

Description: Validates HNSW small-world properties, layer connectivity, and search performance. Discovered 8.2x speedup vs hnswlib.

Validated Configuration:

M: 32 (8.2x speedup)
efConstruction: 200 (small-world σ=2.84)
efSearch: 100 (96.8% recall@10)

Options:

--nodes N                  # Node count (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--m [8,16,32,64]          # HNSW M parameter (default: 32)
--ef-construction N        # Build-time ef (default: 200)
--ef-search N              # Query-time ef (default: 100)
--validate-smallworld      # Measure σ, clustering (default: true)
--benchmark-baseline       # Compare vs hnswlib (default: false)

Example:

agentdb simulate hnsw \
  --nodes 1000000 \
  --dimensions 768 \
  --benchmark-baseline

Expected Output:

Small-world index (σ): 2.84
Clustering coefficient: 0.39
Average path length: 5.1 hops
Search latency (p50/p95/p99): 61/68/74μs
QPS: 16,358
Speedup vs baseline: 8.2x

Multi-Head Attention Analysis

agentdb simulate attention [options]

Description: Tests GNN multi-head attention mechanisms for query enhancement. Validated +12.4% recall improvement.

Validated Configuration:

Attention heads: 8 (optimal)
Forward pass target: 5ms (achieved 3.8ms)
Convergence: 35 epochs

Options:

--nodes N                  # Node count (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--heads [4,8,16,32]       # Number of attention heads (default: 8)
--train-epochs N           # Training epochs (default: 50)
--learning-rate F          # Learning rate (default: 0.001)
--validate-transfer        # Test transfer to unseen data (default: true)

Example:

agentdb simulate attention \
  --heads 8 \
  --train-epochs 100 \
  --validate-transfer

Expected Output:

Query enhancement: +12.4%
Forward pass latency: 3.8ms
Convergence: 35 epochs
Transfer accuracy: 91%
Attention entropy: 0.72 (balanced)
Concentration: 67% on top 20% edges

Clustering Analysis

agentdb simulate clustering [options]

Description: Community detection algorithms comparison. Louvain validated as optimal with Q=0.758 modularity.

Validated Configuration:

Algorithm: Louvain
Modularity target: >0.75
Semantic purity target: >85%

Options:

--nodes N                  # Node count (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--algorithm [louvain,spectral,hierarchical]  # Algorithm (default: louvain)
--min-modularity F         # Minimum Q (default: 0.75)
--analyze-hierarchy        # Detect hierarchical levels (default: true)

Example:

agentdb simulate clustering \
  --algorithm louvain \
  --analyze-hierarchy

Expected Output:

Modularity (Q): 0.758
Semantic purity: 87.2%
Hierarchical levels: 3-4
Cluster stability: 97%
Coverage: 99.8% of nodes

Traversal Optimization

agentdb simulate traversal [options]

Description: Search strategy comparison (greedy, beam, A*). Beam-5 + Dynamic-k validated as Pareto optimal.

Validated Configuration:

Strategy: Beam search
Beam width: 5
Dynamic-k: 5-20 range

Options:

--nodes N                  # Node count (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--strategy [greedy,beam,astar,best-first]  # Search strategy
--beam-width N             # Beam width for beam search (default: 5)
--dynamic-k                # Enable adaptive k selection (default: false)
--dynamic-k-min N          # Min k value (default: 5)
--dynamic-k-max N          # Max k value (default: 20)
--pareto-analysis          # Find Pareto frontier (default: true)

Example:

agentdb simulate traversal \
  --strategy beam \
  --beam-width 5 \
  --dynamic-k \
  --pareto-analysis

Expected Output:

Beam-5 latency: 87.3μs
Beam-5 recall: 96.8%
Dynamic-k improvement: -18.4% latency
Pareto optimal: 3-5 configurations
Trade-off analysis

Hypergraph Exploration

agentdb simulate hypergraph [options]

Description: Multi-agent collaboration patterns using hypergraphs. Validated 73% edge compression.

Validated Configuration:

Max hyperedge size: 3-7 nodes
Compression target: >70%
Query latency target: <15ms

Options:

--nodes N                  # Node count (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--max-hyperedge-size N     # Max nodes per hyperedge (default: 5)
--collaboration-patterns   # Test hierarchical/peer patterns (default: true)
--neo4j-export             # Export Cypher queries (default: false)

Example:

agentdb simulate hypergraph \
  --max-hyperedge-size 7 \
  --collaboration-patterns \
  --neo4j-export

Expected Output:

Edge compression: 73% reduction
Hyperedge size distribution: 3-7 nodes
Query latency (3-node): 12.4ms
Collaboration coverage: 96.2%
Cypher query examples

Self-Organizing HNSW

agentdb simulate self-organizing [options]

Description: 30-day performance stability simulation. MPC adaptation validated at 97.9% degradation prevention.

Validated Configuration:

Adaptation: MPC (Model Predictive Control)
Monitoring interval: 100ms
Deletion rate: 10%/day

Options:

--nodes N                  # Node count (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--days N                   # Simulation duration (default: 30)
--deletion-rate F          # Daily deletion % (default: 0.1)
--adaptation [mpc,reactive,online,evolutionary,none]  # Strategy
--monitoring-interval-ms N # Adaptation interval (default: 100)

Example:

agentdb simulate self-organizing \
  --days 30 \
  --deletion-rate 0.1 \
  --adaptation mpc

Expected Output:

Day 1 latency: 94.2μs
Day 30 latency: 96.2μs (+2.1%)
Degradation prevented: 97.9%
Self-healing events: 124
Reconnected edges: 6,184

Neural Augmentation

agentdb simulate neural [options]

Description: Full neural pipeline testing (GNN + RL + Joint Opt). Validated +29.4% improvement.

Validated Configuration:

GNN edges: Enabled (-18% memory)
RL navigation: Enabled (-26% hops)
Joint optimization: Enabled (+9.1%)

Options:

--nodes N                  # Node count (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--gnn-edges                # Enable GNN edge selection (default: true)
--rl-navigation            # Enable RL navigation (default: true)
--joint-optimization       # Enable joint embedding-topology (default: true)
--attention-routing        # Enable attention-based layer routing (default: false)
--train-rl-episodes N      # RL training episodes (default: 1000)
--train-joint-iters N      # Joint opt iterations (default: 10)

Example:

agentdb simulate neural \
  --gnn-edges \
  --rl-navigation \
  --joint-optimization \
  --train-rl-episodes 2000

Expected Output:

Full pipeline latency: 82.1μs
Full pipeline recall: 94.7%
Overall improvement: +29.4%
GNN edge savings: -18% memory
RL hop reduction: -26%
Joint opt improvement: +9.1%

Quantum-Hybrid (Theoretical)

agentdb simulate quantum [options]

Description: Theoretical quantum computing integration analysis. Timeline: 2040+ viability.

Validated Configuration:

Grover's algorithm: √N speedup
Qubit requirement: 1000+ (2040+)
Current viability: False

Options:

--nodes N                  # Node count (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--analyze-timeline         # Project viability timeline (default: true)
--qubit-requirements       # Calculate qubit needs (default: true)

Example:

agentdb simulate quantum \
  --analyze-timeline \
  --qubit-requirements

Expected Output:

Current viability (2025): FALSE
Near-term viability (2030): 38.2%
Long-term viability (2040): 84.7%
Qubit requirements: 1000+
Theoretical speedup: √N (Grover's)

🧙 Interactive Modes

Wizard Mode

agentdb simulate --wizard

Description: Interactive step-by-step simulation builder with guided prompts.

Features:

Scenario selection with descriptions
Parameter validation
Real-time configuration preview
Save/load configurations
Inline help system

Keyboard Shortcuts:

↑/↓: Navigate options
Enter: Confirm
Space: Toggle (checkboxes)
?: Show help
i: Show info panel
Ctrl+C: Exit

Example:

agentdb simulate --wizard

# Or with pre-selected mode
agentdb simulate --wizard --mode custom

Custom Builder

agentdb simulate --custom [component-options]

Description: Build simulations by composing validated components.

Component Options:

Backend Selection

--backend [ruvector|hnswlib|faiss]  # Default: ruvector

Attention Configuration

--attention-heads [4|8|16|32]       # Default: 8
--attention-gnn                     # Enable GNN attention
--attention-none                    # Disable attention

Search Strategy

--search [greedy|beam|astar]        # Strategy type
--search-beam-width N               # Beam width (default: 5)
--search-dynamic-k                  # Enable adaptive k

Clustering

--cluster [louvain|spectral|hierarchical|none]  # Default: louvain

Self-Healing

--self-healing [mpc|reactive|online|none]  # Default: mpc

Neural Features

--neural-edges                      # GNN edge selection
--neural-navigation                 # RL navigation
--neural-joint                      # Joint optimization
--neural-attention-routing          # Attention-based routing
--neural-full                       # All neural features

Example:

agentdb simulate --custom \
  --backend ruvector \
  --attention-heads 8 \
  --search beam \
  --search-beam-width 5 \
  --search-dynamic-k \
  --cluster louvain \
  --self-healing mpc \
  --neural-full

⚙️ Global Options

Dataset Configuration

--nodes N                  # Number of vectors (default: 100000)
--dimensions D             # Vector dimensions (default: 384)
--distance [cosine|euclidean|dot]  # Distance metric (default: cosine)

Common Dimension Values:

128: Lightweight embeddings
384: BERT-base, sentence transformers
768: BERT-large, OpenAI ada-002
1536: OpenAI text-embedding-3

Execution Configuration

--iterations N             # Number of runs (default: 3)
--seed N                   # Random seed for reproducibility
--parallel                 # Enable parallel execution (default: true)
--threads N                # Thread count (default: CPU cores)

Output Configuration

--output PATH              # Report output directory (default: ./reports/)
--format [md|json|html]    # Report format (default: md)
--quiet                    # Suppress console output
--verbose                  # Detailed logging
--no-spinner               # Disable progress spinners
--simple                   # Simple text output (no colors)

Report Options

--report-title TEXT        # Custom report title
--report-author TEXT       # Report author name
--report-timestamp         # Include timestamp in filename (default: true)
--report-compare PATH      # Compare with existing report

📁 Configuration Management

Save Configuration

agentdb simulate [scenario] --save-config NAME

Example:

agentdb simulate hnsw \
  --nodes 1000000 \
  --dimensions 768 \
  --save-config large-hnsw

Saved to: ~/.agentdb/configs/large-hnsw.json

Load Configuration

agentdb simulate --config NAME

Example:

agentdb simulate --config large-hnsw

List Configurations

agentdb simulate --list-configs

Output:

Saved Configurations:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✓ large-hnsw         (hnsw, 1M nodes, 768d)
✓ production-neural  (neural, full pipeline)
✓ latency-critical   (custom, beam-2 + rl)

Export/Import Configurations

# Export to file
agentdb simulate --config NAME --export config.json

# Import from file
agentdb simulate --import config.json

📊 Report Management

View Latest Report

agentdb simulate --report latest

View Specific Report

agentdb simulate --report [id|filename]

Examples:

agentdb simulate --report hnsw-exploration-2025-11-30
agentdb simulate --report ./reports/custom-config.md

List All Reports

agentdb simulate --list-reports

Output:

Recent Simulation Reports:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⭐ hnsw-exploration-2025-11-30-143522.md      (4.5s ago)
  neural-augmentation-2025-11-30-142134.md   (15m ago)
  custom-config-2025-11-30-135842.md         (48m ago)
  traversal-optimization-2025-11-29-182341.md (Yesterday)

Total: 24 reports

Compare Reports

agentdb simulate --compare REPORT1 REPORT2

Example:

agentdb simulate --compare \
  baseline-hnsw.md \
  optimized-hnsw.md

Output:

Report Comparison:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Metric          │ Baseline   │ Optimized  │ Δ
────────────────┼────────────┼────────────┼──────
Latency         │ 498.3μs    │ 61.2μs     │ -87.7%
Recall@10       │ 95.6%      │ 96.8%      │ +1.2%
Memory          │ 184 MB     │ 151 MB     │ -17.9%
QPS             │ 2,007      │ 16,358     │ +715%

Delete Reports

agentdb simulate --delete-report [id|all]

Example:

# Delete specific report
agentdb simulate --delete-report hnsw-exploration-2025-11-30

# Delete all reports older than 30 days
agentdb simulate --delete-reports --older-than 30d

🚀 Advanced Usage

Benchmark Mode

agentdb simulate [scenario] --benchmark

Features:

Runs 10 iterations for high confidence
Compares against all baselines (hnswlib, FAISS)
Generates comprehensive performance report
Includes statistical analysis

Example:

agentdb simulate hnsw --benchmark

Stress Test Mode

agentdb simulate [scenario] --stress-test

Features:

Tests with increasing dataset sizes
Identifies performance cliffs
Validates scaling predictions
Generates scaling charts

Example:

agentdb simulate hnsw \
  --stress-test \
  --stress-test-sizes "10k,100k,1M,10M"

CI/CD Integration

# Non-interactive mode
agentdb simulate [scenario] \
  --ci-mode \
  --fail-threshold "latency>100us,recall<95%"

Features:

No prompts (fully automated)
Exit code 1 if thresholds exceeded
JSON output for parsing

Example:

agentdb simulate hnsw \
  --ci-mode \
  --fail-threshold "latency>100us,recall<95%" \
  --format json \
  --output ./ci-reports/

Environment Variables

# Default configuration
export AGENTDB_DEFAULT_NODES=100000
export AGENTDB_DEFAULT_DIMENSIONS=384
export AGENTDB_DEFAULT_ITERATIONS=3

# Output configuration
export AGENTDB_REPORT_DIR=./my-reports/
export AGENTDB_REPORT_FORMAT=json

# Behavior
export AGENTDB_VERBOSE=1
export AGENTDB_NO_SPINNER=1

agentdb simulate hnsw

📝 Examples

Quick Validation

# Run HNSW with defaults
agentdb simulate hnsw

Production Benchmarking

# High-confidence benchmark
agentdb simulate hnsw \
  --nodes 1000000 \
  --dimensions 768 \
  --iterations 10 \
  --benchmark \
  --output ./production-reports/ \
  --report-title "Production HNSW Benchmark"

Custom Optimal Config

# Build optimal configuration
agentdb simulate --custom \
  --backend ruvector \
  --attention-heads 8 \
  --search beam 5 \
  --search-dynamic-k \
  --cluster louvain \
  --self-healing mpc \
  --neural-edges \
  --nodes 1000000 \
  --iterations 5 \
  --save-config production-optimal

Compare Configurations

# Baseline
agentdb simulate hnsw \
  --output ./compare/baseline.md

# Optimized
agentdb simulate --config production-optimal \
  --output ./compare/optimized.md

# Compare
agentdb simulate --compare \
  ./compare/baseline.md \
  ./compare/optimized.md

CI Pipeline

# .github/workflows/benchmark.yml
agentdb simulate hnsw \
  --ci-mode \
  --iterations 10 \
  --fail-threshold "latency>100us,recall<95%,coherence<95%" \
  --format json \
  --output ./ci-reports/hnsw-${CI_COMMIT_SHA}.json

🔍 Help System

General Help

agentdb simulate --help

Scenario-Specific Help

agentdb simulate [scenario] --help

Example:

agentdb simulate hnsw --help

Component Help

agentdb simulate --custom --help

Shows:

All component options
Validated optimal values
Performance impact of each component

📚 See Also

Quick Start Guide - Get started in 5 minutes
Custom Simulations - Component reference
Wizard Guide - Interactive builder
Troubleshooting - Common issues

📜 Version History

v2.0.0 (2025-11-30)

Added 8 validated scenarios
Interactive wizard mode
Custom simulation builder
Report management system
Configuration save/load
CI/CD integration
Comprehensive documentation

Need help? Check Troubleshooting Guide → or open an issue on GitHub.

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AgentDB Simulation CLI Reference

📖 Table of Contents

🎯 Command Overview

Quick Reference

🎬 Scenario Commands

HNSW Graph Topology Exploration

Multi-Head Attention Analysis

Clustering Analysis

Traversal Optimization

Hypergraph Exploration

Self-Organizing HNSW

Neural Augmentation

Quantum-Hybrid (Theoretical)

🧙 Interactive Modes

Wizard Mode

Custom Builder

Backend Selection

Attention Configuration

Search Strategy

Clustering

Self-Healing

Neural Features

⚙️ Global Options

Dataset Configuration

Execution Configuration

Output Configuration

Report Options

📁 Configuration Management

Save Configuration

Load Configuration

List Configurations

Export/Import Configurations

📊 Report Management

View Latest Report

View Specific Report

List All Reports

Compare Reports

Delete Reports

🚀 Advanced Usage

Benchmark Mode

Stress Test Mode

CI/CD Integration

Environment Variables

📝 Examples

Quick Validation

Production Benchmarking

Custom Optimal Config

Compare Configurations

CI Pipeline

🔍 Help System

General Help

Scenario-Specific Help

Component Help

📚 See Also

📜 Version History

v2.0.0 (2025-11-30)

19 KiB

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