insa-its 2.0.0

Multi-LLM shorthand detection & deciphering for agent systems

InsAIts - Making Multi-Agent AI Trustworthy

Monitor what your AI agents are saying to each other.

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The Problem

When AI agents communicate with each other, strange things happen:

• Shorthand emergence - "Verify customer identity" becomes "Run CVP"
• Context loss - Agents suddenly switch topics mid-conversation
• Jargon creation - Made-up acronyms that mean nothing to humans
• Hallucination chains - One agent's error propagates through the system
• Anchor drift - Responses diverge from the user's original question

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

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The Solution

InsAIts is a lightweight Python SDK that monitors AI-to-AI communication in real-time.

from insa_its import insAItsMonitor

monitor = insAItsMonitor()

# V2: Set anchor for context-aware detection
monitor.set_anchor("What is quantum computing?")

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

if result["anomalies"]:
    # V2: Trace the root cause
    for anomaly in result["anomalies"]:
        trace = monitor.trace_root(anomaly)
        print(trace["summary"])

3 lines of code. Full visibility.

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What's New in V2

Anchor-Aware Detection (Phase 1)

Stop false positives by setting the user's query as an anchor:

# Set user's question as anchor
monitor.set_anchor("Explain quantum computing")

# Responses using "QUBIT", "QPU" won't trigger jargon alerts
# because they're relevant to the query
result = monitor.send_message("Quantum computers use qubits...", "agent1", llm_id="gpt-4o")

Forensic Chain Tracing (Phase 2)

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))

Domain Dictionaries (Phase 4)

Load domain-specific terms to reduce false positives:

# Load finance terms (EBITDA, WACC, DCF, etc.)
monitor.load_domain("finance")

# Available domains: finance, healthcare, kubernetes, machine_learning, devops, quantum

# Import/export custom dictionaries
monitor.export_dictionary("my_team_terms.json")
monitor.import_dictionary("shared_terms.json", merge=True)

# Auto-expand unknown terms with LLM
monitor.auto_expand_terms()  # Requires Ollama

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What It Detects

Anomaly Type	What It Catches	Severity
SHORTHAND_EMERGENCE	"Process order" -> "PO now"	High
CONTEXT_LOSS	Marketing meeting -> Recipe discussion	High
CROSS_LLM_JARGON	Undefined acronyms like "QXRT"	High
ANCHOR_DRIFT	Response diverges from user's question	High
LLM_FINGERPRINT_MISMATCH	GPT-4 response that looks like GPT-3.5	Medium
LOW_CONFIDENCE	Hedging: "maybe", "I think", "perhaps"	Medium

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Quick Start

Install

pip install insa-its

For local embeddings (recommended):

pip install insa-its[full]

Or from GitHub:

pip install git+https://github.com/Nomadu27/InsAIts.git

Use

from insa_its import insAItsMonitor

monitor = insAItsMonitor(session_name="my-agents")

# V2: Set anchor for smarter detection
monitor.set_anchor("Process customer refund request")

# Monitor your agent conversations
result = monitor.send_message(
    text="Process the customer order for SKU-12345",
    sender_id="OrderBot",
    receiver_id="InventoryBot",
    llm_id="gpt-4o-mini"
)

# Check for issues
if result["anomalies"]:
    for anomaly in result["anomalies"]:
        print(f"[{anomaly['severity']}] {anomaly['type']}")
        # V2: Get forensic trace
        trace = monitor.trace_root(anomaly)
        print(f"Root cause: {trace['summary']}")

# Get session health
print(monitor.get_stats())

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Features

Real-Time Terminal Dashboard

from insa_its.dashboard import LiveDashboard

dashboard = LiveDashboard(monitor)
dashboard.start()
# Live visualization of all agent communication

LangChain Integration

from insa_its.integrations import LangChainMonitor

monitor = LangChainMonitor()
monitored_chain = monitor.wrap_chain(your_chain, "MyAgent")

CrewAI Integration

from insa_its.integrations import CrewAIMonitor

monitor = CrewAIMonitor()
monitored_crew = monitor.wrap_crew(your_crew)

Decipher Mode

Translate AI-to-AI jargon for human review:

deciphered = monitor.decipher(message)
print(deciphered["expanded_text"])  # Human-readable version

Uses local Phi-3 via Ollama - no cloud, no data leaves your machine.

V2: Domain Dictionaries

# See available domains
print(monitor.get_available_domains())
# ['finance', 'healthcare', 'kubernetes', 'machine_learning', 'devops', 'quantum']

# Load one or more
monitor.load_domain("kubernetes")
monitor.load_domain("devops")

# Terms like K8S, HPA, CI/CD won't trigger false positives

V2: Forensic Chain Visualization

# Get ASCII visualization of anomaly chain
viz = monitor.visualize_chain(anomaly, include_text=True)
print(viz)

# Output:
# ============================================================
# FORENSIC CHAIN TRACE: CROSS_LLM_JARGON
# ============================================================
#
# [Step 1]
#   agent_a -> agent_b (gpt-4o)
#   Words: 15
#   Text: "Let's discuss the implementation..."
#      |
#      v
# [Step 2]
#   agent_b -> agent_a (claude-3.5)
#   Words: 20
#      |
#      v
# [Step 3] >>> ROOT <<< ANOMALY
#   agent_a -> agent_b (gpt-4o)
#   Words: 8
#   Text: "Use XYZPROTO for this..."
#
# ------------------------------------------------------------
# SUMMARY:
# Jargon 'XYZPROTO' first appeared in message from agent_a (gpt-4o)
# at step 3 of 3. Propagated through 0 subsequent messages.
# ============================================================

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Pricing

Lifetime Deals - First 100 Users Only!

Plan	Price	What You Get
LIFETIME STARTER	EUR99 one-time	10K msgs/day forever
LIFETIME PRO	EUR299 one-time	Unlimited forever + priority support

Buy Lifetime (Gumroad):

• Lifetime Starter (EUR99)
• Lifetime Pro (EUR299)

Buy Lifetime (Stripe):

• Lifetime Starter (EUR99)
• Lifetime Pro (EUR299)

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Monthly Plans

Tier	Messages/Day	Price	Best For
Free	100	$0	Testing & evaluation
Starter	10,000	$49/mo	Indie devs & small teams
Pro	Unlimited	$79/mo	Production workloads

Buy Monthly (Gumroad):

• Monthly Starter ($49/mo)
• Monthly Pro ($79/mo)

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

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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
Healthcare	Critical multi-agent systems where errors matter
Research	Ensuring scientific integrity in AI experiments

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Demo

Try it yourself:

git clone https://github.com/Nomadu27/InsAIts.git
cd InsAIts
pip install -e .[full] rich

# Run the dashboard demo
python demo_dashboard.py

# Run marketing team simulation
python demo_marketing_team.py

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Architecture

Your Multi-Agent System              InsAIts V2
         |                              |
         |-- user query --------------> |-- set_anchor() [NEW]
         |                              |
         |-- message -----------------> |
         |                              |-- Anchor similarity check [NEW]
         |                              |-- Semantic embedding (local)
         |                              |-- Pattern analysis
         |                              |-- Anomaly detection
         |                              |
         |<-- anomalies, health --------|
         |                              |
         |-- trace_root() ------------> |-- Forensic chain [NEW]
         |<-- summary, visualization ---|

Privacy First:

• Local embeddings (nothing leaves your machine)
• No raw messages stored in cloud
• API keys hashed before storage
• GDPR-ready

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Documentation

Resource	Link
Installation Guide	installation_guide.md
API Reference	insaitsapi-production.up.railway.app/docs
Privacy Policy	PRIVACY_POLICY.md
Terms of Service	TERMS_OF_SERVICE.md

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Support

• Email: [e-mail]
• GitHub Issues: Report a bug
• API Status: insaitsapi-production.up.railway.app

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License

Proprietary Software - All rights reserved.

Free tier available for evaluation. Commercial use requires a paid license. See LICENSE for details.

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InsAIts V2 - Making AI Collaboration Trustworthy
Now with Anchor-Aware Detection, Forensic Chain Tracing, and Domain Dictionaries.