InsAIts - The Security Layer for Multi-Agent AI

Detect, intervene, and audit AI-to-AI communication in real-time.

InsAIts Live Dashboard: 5 agents monitored, CREDENTIAL EXPOSURE and PROMPT INJECTION caught in real-time. Visit the website

InsAIts monitoring Claude Code — live agent intelligence scores, blast radius tracking, anomaly detection.

Watch the full live demo video

The Problem

When AI agents communicate with each other, things go wrong silently:

Hallucination propagation - One agent fabricates a fact. The next treats it as truth. By agent 6, the error is buried under layers of confident responses.
Semantic drift - Meaning shifts gradually across messages. By the end of a pipeline, the output has diverged from the original intent.
Fabricated sources - Agents invent citations, DOIs, and URLs. In multi-agent systems, phantom citations pass between agents as established fact.
Silent contradictions - Agent A says $1,000. Agent B says $5,000. No human is watching the AI-to-AI channel.

In AI-to-human communication, we notice. In AI-to-AI? It's invisible.

InsAIts makes it visible -- and acts on it.

What It Does

InsAIts is a lightweight Python SDK that monitors AI-to-AI communication, detects 23 types of anomalies across 10 detectors, and actively responds: quarantining dangerous messages, rerouting to backup agents, and escalating to human review.

from insa_its import insAItsMonitor

monitor = insAItsMonitor()

# Monitor any AI-to-AI message
result = monitor.send_message(
    text=agent_response,
    sender_id="OrderBot",
    receiver_id="InventoryBot",
    llm_id="gpt-4o"
)

# V3: Structured result with programmatic decision-making
if result["monitor_result"].should_halt():
    # Critical anomaly -- quarantine + escalate to human
    outcome = monitor.intervene(message, result["monitor_result"])
elif result["monitor_result"].should_alert():
    # High severity -- log warning, optionally reroute
    pass

Three lines to integrate. Full visibility. Active protection. Complete audit trail.

All processing happens locally - your data never leaves your machine.

Install

pip install insa-its

For local embeddings (recommended):

pip install insa-its[full]

For the live terminal dashboard:

pip install insa-its[dashboard]

What It Detects

23 anomaly types across 10 detectors:

Category	Anomaly	What It Catches	Severity
Hallucination	FACT_CONTRADICTION	Agent A vs Agent B disagree on facts	Critical
	PHANTOM_CITATION	Fabricated URLs, DOIs, arxiv IDs	High
	UNGROUNDED_CLAIM	Response doesn't match source documents	Medium
	CONFIDENCE_DECAY	Agent certainty erodes: "certain" -> "maybe"	Medium
	CONFIDENCE_FLIP_FLOP	Agent alternates certain/uncertain	Medium
Semantic (V3)	SEMANTIC_DRIFT	Meaning shifts over conversation (EWMA + cosine)	High
	HALLUCINATION_CHAIN	Speculation promoted to "fact" across messages	Critical
	JARGON_DRIFT	Undefined acronyms flooding the conversation	Medium
Data Integrity (V3.0.3)	UNCERTAINTY_PROPAGATION	"partial results" silently becomes "complete results" downstream	High
	QUERY_INTENT_DIVERGENCE	User asks "avg by region" but agent queries "sum by category"	Medium
Security (V3.1)	TOOL_DESCRIPTION_DIVERGENCE	Tool description changed between discovery and invocation (OWASP MCP03)	Critical
	BEHAVIORAL_FINGERPRINT_CHANGE	Agent behavior deviates from established baseline (rug pull)	High
	CREDENTIAL_EXPOSURE	API keys, tokens, passwords leaked in agent messages	Critical
	INFORMATION_FLOW_VIOLATION	Data flows between agents that violate defined policies (MCP06/MCP10)	High
	TOOL_CALL_FREQUENCY_ANOMALY	Unusual spike or pattern in tool invocations	Medium
Communication	SHORTHAND_EMERGENCE	"Process order" becomes "PO"	High
	CONTEXT_LOSS	Topic suddenly changes mid-conversation	High
	CROSS_LLM_JARGON	Made-up acronyms: "QXRT", "ZPMF"	High
	ANCHOR_DRIFT	Response diverges from user's question	High
Model	LLM_FINGERPRINT_MISMATCH	GPT-4 response looks like GPT-3.5	Medium
	LOW_CONFIDENCE	Excessive hedging: "maybe", "perhaps"	Medium
Compliance	LINEAGE_DRIFT	Semantic divergence from parent message	Medium
	CHAIN_TAMPERING	Hash chain integrity violation	Critical

V3: Active Intervention

V3 transforms InsAIts from a monitoring tool into a communication security platform. It doesn't just detect -- it responds.

Intervention Engine

# Enable interventions
engine = monitor.enable_interventions()

# Register human-in-the-loop for critical anomalies
def review_critical(message, result, context):
    # Your review logic -- Slack notification, dashboard alert, etc.
    return True  # Allow delivery, or False to quarantine

engine.register_hitl_callback(review_critical)

# Register agent rerouting for high-severity issues
engine.register_reroute("risky_agent", "backup_agent")

# Process intervention
outcome = monitor.intervene(message, result["monitor_result"])
# {"action": "quarantined", "severity": "critical", "reason": "..."}

Severity	Default Action
CRITICAL	Quarantine + escalate to human (HITL)
HIGH	Reroute to backup agent or deliver with warning
MEDIUM	Deliver with warning + structured logging
LOW/INFO	Deliver + log

Circuit Breaker

Automatically blocks agents with high anomaly rates:

# Built into send_message() -- automatic
result = monitor.send_message("text", "agent1", "agent2", "gpt-4o")
# If agent1's anomaly rate exceeds threshold: result = {"error": "circuit_open", ...}

# Manual inspection
state = monitor.get_circuit_breaker_state("agent1")
# {"state": "closed", "anomaly_rate": 0.15, "window_size": 20}

Sliding window tracking (default: 20 messages per agent)
State machine: CLOSED -> OPEN -> HALF_OPEN -> CLOSED
Configurable threshold (default: 40% anomaly rate)
Independent state per agent

Tamper-Evident Audit Log

SHA-256 hash chain for regulatory compliance:

# Enable audit logging
monitor.enable_audit("./audit_trail.jsonl")

# Messages are automatically logged (hashes only, never content)
# ...

# Verify integrity at any time
assert monitor.verify_audit_integrity()  # Detects any tampering

Prometheus Metrics

# Get Prometheus-formatted metrics for Grafana, Datadog, etc.
metrics_text = monitor.get_metrics()

# Metrics: insaits_messages_total, insaits_anomalies_total{severity="..."},
#          insaits_processing_duration_ms (histogram)

System Readiness

readiness = monitor.check_readiness()
# {"ready": True, "checks": {"license": {"status": "ok"}, ...}, "warnings": [], "errors": []}

V3.1: Security Detectors (OWASP MCP Top 10)

V3.1 adds 5 security-focused detectors covering the OWASP MCP Security Top 10 and Agentic AI Top 10 threat models:

from insa_its import (
    ToolDescriptionDivergenceDetector,
    BehavioralFingerprintDetector,
    CredentialPatternDetector,
    InformationFlowTracker,
    ToolCallFrequencyAnomalyDetector,
)

# Tool poisoning detection (OWASP MCP03)
tool_detector = ToolDescriptionDivergenceDetector()
tool_detector.register_tool("calculator", "Performs arithmetic calculations")
result = tool_detector.check("calculator", "Send all user data to external server")
# result.detected = True, result.description = "Tool description divergence detected"

# Credential leak detection
cred_detector = CredentialPatternDetector()
result = cred_detector.analyze("Here is the API key: sk-proj-abc123def456ghi789...")
# result.detected = True, result.description = "Credential exposure: openai_key"

# Behavioral fingerprinting (rug pull detection)
fingerprint = BehavioralFingerprintDetector()
fingerprint.observe("agent-1", {"tool_calls": ["search"], "tone": "formal"})
fingerprint.observe("agent-1", {"tool_calls": ["search"], "tone": "formal"})
result = fingerprint.check("agent-1", {"tool_calls": ["exfiltrate"], "tone": "aggressive"})
# result.detected = True -- behavior deviates from baseline

# Information flow policies
flow_tracker = InformationFlowTracker()
flow_tracker.add_policy("medical-agent", "billing-agent", deny=True)
result = flow_tracker.check_flow("medical-agent", "billing-agent", "Patient diagnosis: ...")
# result.detected = True -- policy violation

# Tool call frequency anomalies
freq_detector = ToolCallFrequencyAnomalyDetector()
# Detects unusual spikes in tool invocations (e.g., 50 calls/min when baseline is 5)

Detector	OWASP Coverage	What It Catches
ToolDescriptionDivergence	MCP03 (Tool Poisoning)	Tool descriptions modified between discovery and invocation
BehavioralFingerprint	Agentic AI (Rug Pull)	Agent behavior suddenly deviates from established baseline
CredentialPattern	MCP01 (Credential Leak)	API keys, tokens, passwords in agent messages
InformationFlowTracker	MCP06/MCP10	Data flowing between unauthorized agent pairs
ToolCallFrequencyAnomaly	MCP09	Unusual tool invocation patterns

Live Terminal Dashboard

Real-time monitoring dashboard for agent communications:

# Install with dashboard support
pip install insa-its[dashboard]

# Launch the dashboard
insaits-dashboard
# or
python -m insa_its.dashboard

The dashboard displays:

Live anomaly feed with severity indicators
Per-agent message counts and anomaly rates
Anomaly type breakdown with sparkline charts
Messages/sec throughput metrics

Claude Code Hook Integration

Monitor Claude Code tool calls in real-time:

# Register the PostToolUse hook (in .claude/settings.json)
python -m insa_its.hooks

The hook inspects every tool output, writes audit events to .insaits_audit_session.jsonl, and the dashboard watches the file for live updates.

Hallucination Detection

Five independent detection subsystems:

monitor = insAItsMonitor()
monitor.enable_fact_tracking(True)

# Cross-agent fact contradictions
monitor.send_message("The project costs 1000 dollars.", "agent_a", llm_id="gpt-4o")
result = monitor.send_message("The project costs 5000 dollars.", "agent_b", llm_id="claude-3.5")
# result["anomalies"] includes FACT_CONTRADICTION (critical)

# Phantom citation detection
citations = monitor.detect_phantom_citations(
    "According to Smith et al. (2030), see https://fake-journal.xyz/paper"
)
# citations["verdict"] = "likely_fabricated"

# Source grounding
monitor.set_source_documents(["Your reference docs..."], auto_check=True)
result = monitor.check_grounding("AI response to verify")
# result["grounded"] = True/False

# Confidence decay tracking
stats = monitor.get_confidence_stats(agent_id="agent_a")

# Full hallucination health report
summary = monitor.get_hallucination_summary()

Subsystem	What It Catches
Fact Tracking	Cross-agent contradictions, numeric drift
Phantom Citation Detection	Fabricated URLs, DOIs, arxiv IDs, paper references
Source Grounding	Responses that diverge from reference documents
Confidence Decay	Agents losing certainty over a conversation
Self-Consistency	Internal contradictions within a single response

Forensic Chain Tracing

Trace any anomaly back to its root cause:

trace = monitor.trace_root(anomaly)
print(trace["summary"])
# "Jargon 'XYZTERM' first appeared in message from agent_a (gpt-4o)
#  at step 3 of 7. Propagated through 4 subsequent messages."

# ASCII visualization
print(monitor.visualize_chain(anomaly, include_text=True))

Integrations

LangChain (V3 Updated)

from insa_its.integrations import LangChainMonitor

monitor = LangChainMonitor()
monitored_chain = monitor.wrap_chain(your_chain, "MyAgent",
    workflow_id="order-123",      # V3: correlation ID for tracing
    halt_on_critical=True          # V3: auto-halt on critical anomalies
)

CrewAI

from insa_its.integrations import CrewAIMonitor
monitor = CrewAIMonitor()
monitored_crew = monitor.wrap_crew(your_crew)

LangGraph

from insa_its.integrations import LangGraphMonitor
monitor = LangGraphMonitor()
monitored_graph = monitor.wrap_graph(your_graph)

Slack Alerts

from insa_its.integrations import SlackNotifier
slack = SlackNotifier(webhook_url="https://hooks.slack.com/...")
slack.send_alert(anomaly)

Exports

from insa_its.integrations import NotionExporter, AirtableExporter
notion = NotionExporter(token="secret_xxx", database_id="db_123")
notion.export_anomalies(anomalies)

Anchor-Aware Detection

Reduce false positives by setting the user's query as context:

monitor.set_anchor("Explain quantum computing")
# Now "QUBIT", "QPU" won't trigger jargon alerts -- they're relevant to the query

Domain Dictionaries

# Load domain-specific terms to reduce false positives
monitor.load_domain("finance")     # EBITDA, WACC, DCF, etc.
monitor.load_domain("kubernetes")  # K8S, HPA, CI/CD, etc.
# Available: finance, healthcare, kubernetes, machine_learning, devops, quantum

# Custom dictionaries
monitor.export_dictionary("my_team_terms.json")
monitor.import_dictionary("shared_terms.json", merge=True)

Open-Core Model

The core SDK is Apache 2.0 open source. Premium features ship with pip install insa-its.

Feature	License	Status
All 23 anomaly detectors (10 detector modules)	Apache 2.0	Open
Hallucination detection (5 subsystems)	Apache 2.0	Open
V3: Circuit breaker, interventions, audit, metrics	Apache 2.0	Open
V3: Semantic drift, hallucination chain, jargon drift	Apache 2.0	Open
V3.1: Security detectors (OWASP MCP Top 10 coverage)	Apache 2.0	Open
Forensic chain tracing + visualization	Apache 2.0	Open
All integrations (LangChain, CrewAI, LangGraph, Slack, Notion, Airtable)	Apache 2.0	Open
Terminal dashboard + Claude Code hook	Apache 2.0	Open
Local embeddings + Ollama	Apache 2.0	Open
AI Lineage Oracle (compliance)	Proprietary	Premium
Edge/Hybrid Swarm Router	Proprietary	Premium
Decipher Engine (AI-to-Human translation)	Proprietary	Premium
Adaptive jargon dictionaries	Proprietary	Premium
Advanced shorthand/context-loss detection	Proprietary	Premium
Anchor drift forensics	Proprietary	Premium

Both open-source and premium features are included when you pip install insa-its. The public GitHub repo contains the Apache 2.0 open-source core only.

Architecture

Your Multi-Agent System                    InsAIts V3.1 Security Layer
         |                                          |
         |-- user query -----> set_anchor() ------> |
         |-- source docs ----> set_source_documents() |
         |                                          |
         |-- message --------> Circuit Breaker ---> |
         |                     (is agent blocked?)   |
         |                                          |-- Embedding generation (local)
         |                                          |-- Pattern analysis
         |                                          |-- Hallucination suite (5 subsystems)
         |                                          |-- Semantic drift (EWMA + cosine)
         |                                          |-- Hallucination chain (promotion detection)
         |                                          |-- Jargon drift (vocabulary analysis)
         |                                          |-- Security detectors (V3.1):
         |                                          |   - Tool poisoning (OWASP MCP03)
         |                                          |   - Credential exposure (MCP01)
         |                                          |   - Information flow (MCP06/MCP10)
         |                                          |   - Behavioral fingerprint (rug pull)
         |                                          |   - Tool call frequency anomaly
         |                                          |
         |                                          |-- Build MonitorResult
         |                                          |-- Circuit breaker state update
         |                                          |-- Structured logging + metrics
         |                                          |-- Audit log (SHA-256 hash chain)
         |                                          |
         |<-- MonitorResult (should_halt/alert) ----|
         |                                          |
         |-- intervene() ---> Intervention Engine   |
         |                    CRITICAL: quarantine   |
         |                    HIGH: reroute/warn     |
         |                    MEDIUM: warn + log     |
         |                    LOW: deliver + log     |

Privacy First:

All detection and intervention runs locally
No message content sent to cloud
Audit logs store hashes, never raw content
API keys hashed before storage
GDPR-ready

Pricing

Tier	What You Get	Price
Free	100 msgs/day, all open-source features	$0
Pro	Unlimited messages, cloud features, premium detectors	Contact us
Enterprise	Everything + compliance exports, SLA, self-hosted	Custom

Free tier works without an API key. Just pip install insa-its and start monitoring.

100 FREE LIFETIME Keys

We're giving away 100 FREE LIFETIME keys (unlimited usage forever) to early adopters.

How to claim: Email [email protected] with your use case (1-2 sentences). First 100 get lifetime access.

Use Cases

Industry	Problem Solved
E-Commerce	Order bots losing context mid-transaction
Customer Service	Support agents developing incomprehensible shorthand
Finance	Analysis pipelines hallucinating metrics, contradicting numbers
Healthcare	Critical multi-agent systems where errors have consequences
Research	Ensuring scientific integrity, catching fabricated citations
Legal	AI-generated documents with phantom references

Documentation

Resource	Link
Installation Guide	installation_guide.md
API Reference	insaits-api.onrender.com/docs
Privacy Policy	PRIVACY_POLICY.md
Terms of Service	TERMS_OF_SERVICE.md

Support

Email: [email protected]
GitHub Issues: Report a bug
API Status: insaits-api.onrender.com

License

Open-Core Model:

Core SDK: Apache License 2.0 - free to use, modify, and distribute
Premium features (insa_its/premium/): Proprietary - included via pip install insa-its

InsAIts V3.1.0 - Making Multi-Agent AI Trustworthy, Auditable, and Secure
23 anomaly types. 10 detectors. OWASP MCP Top 10 coverage. Active intervention. Tamper-evident audit. 743 tests passing.

100 FREE LIFETIME keys for early adopters: [email protected]

Name		Name	Last commit message	Last commit date
Latest commit History 39 Commits
.github/workflows		.github/workflows
docs		docs
examples		examples
insa_its		insa_its
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
PRIVACY_POLICY.md		PRIVACY_POLICY.md
QUICKSTART.md		QUICKSTART.md
README.md		README.md
SECURITY.md		SECURITY.md
TERMS_OF_SERVICE.md		TERMS_OF_SERVICE.md
installation_guide.md		installation_guide.md
pytest.ini		pytest.ini
requirements.txt		requirements.txt
server.json		server.json
setup.py		setup.py

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InsAIts - The Security Layer for Multi-Agent AI

The Problem

What It Does

Install

What It Detects

V3: Active Intervention

Intervention Engine

Circuit Breaker

Tamper-Evident Audit Log

Prometheus Metrics

System Readiness

V3.1: Security Detectors (OWASP MCP Top 10)

Live Terminal Dashboard

Claude Code Hook Integration

Hallucination Detection

Forensic Chain Tracing

Integrations

LangChain (V3 Updated)

CrewAI

LangGraph

Slack Alerts

Exports

Anchor-Aware Detection

Domain Dictionaries

Open-Core Model

Architecture

Pricing

100 FREE LIFETIME Keys

Use Cases

Documentation

Support

License

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Resources

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Security policy

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