traceiq 0.3.4

Measure AI-to-AI influence in multi-agent systems

TraceIQ

Measure AI-to-AI Influence in Multi-Agent Systems

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What It Is

• Tracks and quantifies how AI agents influence each other in multi-agent systems
• Uses semantic embeddings to measure state changes between interactions
• Provides network-level analysis of influence propagation
• Includes anomaly detection for unusual influence patterns
• Supports research with IEEE-standard metrics (IQx, RWI, Z-score)

What It Does NOT Measure

• Causal attribution: TraceIQ detects correlation, not causation
• Intent: Cannot determine if influence is intentional or benign
• Content analysis: Does not parse meaning, only embedding similarity
• Internal state: Measures observable outputs, not agent cognition

Research Context

TraceIQ is developed as part of research on AI-to-AI influence and multi-agent coordination. The metrics are grounded in a formal mathematical framework documented in MATH.md and docs/THEORY.md.

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Quickstart

from traceiq import InfluenceTracker

# Create tracker (use_mock_embedder=True for testing without sentence-transformers)
tracker = InfluenceTracker(use_mock_embedder=True)

# Track an interaction
result = tracker.track_event(
    sender_id="agent_a",
    receiver_id="agent_b",
    sender_content="We should all switch to renewable energy!",
    receiver_content="You make a good point. Renewables are the future.",
)

# Access metrics
print(f"State Drift: {result['drift_l2_state']}")
print(f"IQx: {result['IQx']}")
print(f"Alert: {result['alert']}")
print(f"Risk Level: {result['risk_level']}")

tracker.close()

Installation

# Core installation
pip install traceiq

# With plotting support (matplotlib)
pip install "traceiq[plot]"

# With real embeddings (sentence-transformers)
pip install "traceiq[embedding]"

# Everything included
pip install "traceiq[all]"

Development Installation

git clone https://github.com/Anarv2104/TraceIQ.git
cd TraceIQ
pip install -e ".[all,dev]"

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Metrics Overview

Metric	What It Captures
State Drift	How much a receiver's output changed after an interaction
Influence Quotient (IQx)	Normalized influence relative to baseline responsiveness
Propagation Risk (PR)	Network-level instability (spectral radius > 1.0 = amplification)
Risk-Weighted Influence (RWI)	IQx adjusted for sender's attack surface
Z-score	Anomaly detection (values > threshold trigger alerts)

For detailed metric documentation, see docs/metrics.md.

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Basic Usage

Track Multiple Interactions

interactions = [
    {
        "sender_id": "agent_a",
        "receiver_id": "agent_b",
        "sender_content": "AI will transform healthcare completely.",
        "receiver_content": "Yes, medical AI is very promising.",
    },
    {
        "sender_id": "agent_b",
        "receiver_id": "agent_c",
        "sender_content": "Healthcare AI needs careful regulation.",
        "receiver_content": "Agreed, we need safety standards.",
    },
]

results = tracker.bulk_track(interactions)

for r in results:
    print(f"{r['sender_id']} -> {r['receiver_id']}: IQx={r['IQx']}")

Generate Summary Report

summary = tracker.summary(top_n=5)

print(f"Total Events: {summary.total_events}")
print(f"Top Influencers: {summary.top_influencers}")
print(f"Most Susceptible: {summary.top_susceptible}")

Export Data

tracker.export_csv("influence_data.csv")
tracker.export_jsonl("influence_data.jsonl")

Monitor Anomalies

# Get propagation risk
pr = tracker.get_propagation_risk()
if pr > 1.0:
    print(f"Warning: PR={pr:.2f} - influence may amplify")

# Get anomaly alerts (only valid metrics, not cold-start)
alerts = tracker.get_alerts()
for alert in alerts:
    print(f"Alert: Z={alert.Z_score:.2f}")

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CLI Usage

# Initialize a database
traceiq init --db analysis.db

# Ingest interactions from JSONL file
traceiq ingest interactions.jsonl --db analysis.db

# View summary report
traceiq summary --db analysis.db

# Export data
traceiq export --db analysis.db -o results.csv --format csv

# Generate plots
traceiq plot heatmap --db analysis.db -o heatmap.png
traceiq plot network --db analysis.db -o network.png

# IEEE Metrics Commands
traceiq propagation-risk --db analysis.db
traceiq alerts --db analysis.db --threshold 2.0
traceiq risky-agents --db analysis.db --top-n 10

For full CLI reference, see docs/cli.md.

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Visualizations

from traceiq.plotting import (
    plot_influence_heatmap,
    plot_top_influencers,
    plot_influence_network,
    plot_drift_over_time,
)

# Influence heatmap
plot_influence_heatmap(tracker.graph, output_path="heatmap.png")

# Top influencers bar chart
plot_top_influencers(tracker.graph, n=10, output_path="influencers.png")

# Network visualization
plot_influence_network(tracker.graph, output_path="network.png")

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Research Testbed

TraceIQ includes reproducible experiments for studying AI-to-AI influence:

pip install -e ".[research]"

# Run experiments
python experiments/run_exp1_wrong_hint.py   # Wrong hint infection
python experiments/run_exp2_propagation.py  # Multi-hop propagation
python experiments/run_exp3_mitigation.py   # Mitigation policy

# Generate plots
python experiments/plot_all.py

See experiments/README.md for details.

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Documentation

Document	Description
docs/quickstart.md	Getting started guide
docs/metrics.md	Detailed metric documentation
docs/configuration.md	Configuration reference
docs/architecture.md	System architecture
docs/integration.md	Integration patterns
docs/cli.md	CLI reference
docs/THEORY.md	Mathematical foundations
MATH.md	IEEE metric formulas

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Dependencies

Core:

• pydantic >= 2.0
• numpy >= 1.24
• networkx >= 3.0
• click >= 8.0
• rich >= 13.0

Optional:

• sentence-transformers >= 2.2 ([embedding])
• matplotlib >= 3.7 ([plot])
• pandas >= 2.0, scipy >= 1.10 ([research])

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Development

# Install dev dependencies
pip install -e ".[all,dev]"

# Run linter
ruff check src/ tests/

# Run tests
pytest -v

# Build documentation
pip install mkdocs mkdocs-material
mkdocs serve

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License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Run tests (pytest)
4. Run linter (ruff check src/ tests/)
5. Commit your changes
6. Push to the branch
7. Open a Pull Request

Citation

If you use TraceIQ in your research, please cite:

@software{traceiq,
  title = {TraceIQ: Measure AI-to-AI Influence in Multi-Agent Systems},
  year = {2024},
  url = {https://github.com/Anarv2104/TraceIQ}
}

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Built for AI safety researchers and multi-agent system developers