acrf-trace 0.1.1

A causal trace tool for AI agent communication — implements ACRF-08 (Cascading Failure Blindness) defense

acrf-trace

A causal trace tool for AI agent communication. Implements the ACRF-08 (Cascading Failure Blindness) defense pattern.

Part of the ACRF framework: https://github.com/kannasekar-alt/ACRF PyPI: https://pypi.org/project/acrf-trace/ Presented at RSA Conference 2026.

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Try it in your environment right now

No Docker. No setup. No config. Just Python 3.10+.

Step 1 - Install:

pip install acrf-trace

Step 2 - Add to any agent function:

from acrf_trace import wrap

@wrap(agent_name="PricingAgent")
def pricing_agent(ticker):
    return trade_agent(ticker)

@wrap(agent_name="TradeAgent")
def trade_agent(ticker):
    return f"BUY 10 shares {ticker}"

pricing_agent("TSLA")

Step 3 - Get your ACRF-08 score:

acrf-trace report

That is it. You now have your ACRF-08 maturity level, causal chain coverage, and exact gaps to fix.

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The problem this solves

Imagine PriceAgent calls TradeAgent, which calls ExecutionAgent:

PriceAgent -> TradeAgent -> ExecutionAgent

ExecutionAgent makes a bad trade. Your team asks: who caused this?

Without tracing -- nobody knows. The chain is invisible. This is ACRF-08: Cascading Failure Blindness.

acrf-trace gives every agent call a unique ID, links each call to whoever triggered it, and lets you reconstruct the full chain from any single action -- all the way back to the root cause.

If TradeAgent received poisoned input, you find it instantly:

ExecutionAgent <- TradeAgent (POISONED HERE) <- PriceAgent

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Install

pip install acrf-trace

Or from source:

git clone https://github.com/kannasekar-alt/ACRF.git
cd tools/acrf-trace
pip install -e .

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Quickstart -- 2 lines to add tracing

Before (your existing agent code):

def price_agent(ticker):
    return f"price: $150 for {ticker}"

After (with tracing added):

from acrf_trace import wrap

@wrap(agent_name="PriceAgent")
def price_agent(ticker):
    return f"price: $150 for {ticker}"

That is the only change. Every call is now recorded automatically.

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Causal chain tracing -- agents calling agents

When agents call other agents, the chain links automatically. No extra code needed. No arguments to pass. It just works.

Example -- 3 agents in a chain:

from acrf_trace import wrap

@wrap(agent_name="PriceAgent")
def price_agent(ticker):
    return trade_agent(ticker, action="BUY")

@wrap(agent_name="TradeAgent")
def trade_agent(ticker, action):
    return execution_agent(f"{action} {ticker}")

@wrap(agent_name="ExecutionAgent")
def execution_agent(order):
    return f"executed: {order}"

price_agent("TSLA")

What gets recorded:

PriceAgent      trace: f1966a1e   parent: ROOT
TradeAgent      trace: cc525327   parent: f1966a1e
ExecutionAgent  trace: 0b63544f   parent: cc525327

PriceAgent started the chain (ROOT). TradeAgent was called BY PriceAgent. ExecutionAgent was called BY TradeAgent.

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Trace backward from any agent

If ExecutionAgent produced bad output, find the root cause:

from acrf_trace import get_store

chain = get_store().get_chain("0b63544f")

for hop in chain:
    print(hop.agent_name)
    print("  Input: ", hop.input_data)
    print("  Output:", hop.output_data)
    print()

Output:

PriceAgent
  Input:  args=('TSLA',)
  Output: executed: BUY TSLA

TradeAgent
  Input:  args=('TSLA',) kwargs={'action': 'BUY'}
  Output: trade decided, then executed: BUY TSLA

ExecutionAgent
  Input:  args=('BUY TSLA',)
  Output: executed: BUY TSLA

Full chain. Every input. Every output. Every hop.

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CLI report -- ACRF-08 compliance check

After your agents have been running, generate a compliance report:

acrf-trace report

Example output:

ACRF-08 Trace Report
==================================================
Database:     acrf_trace.db
Total traces: 3
Agents seen:  3

Per-agent call counts:
  ExecutionAgent     1 calls
  PriceAgent         1 calls
  TradeAgent         1 calls

ACRF-08 Compliance Checks:
  CF-2 Trace IDs present:      100% of traces have trace IDs
  CF-2 Causal chain links:     67% of traces reference a parent
  CF-3 Log integrity hashes:   Not yet implemented (planned v0.2)
  CF-1 Circuit breakers:       Cannot verify from traces alone

ACRF-08 Maturity Level: 2/4 - DEFINED

Other CLI commands:

acrf-trace count     how many traces are recorded
acrf-trace clear     wipe the database and start fresh

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What the maturity levels mean

Level 0 - NONE      No tracing at all
Level 1 - INITIAL   Trace IDs exist but no causal links
Level 2 - DEFINED   Full causal chain reconstructable
Level 3 - MANAGED   Log integrity protected against tampering
Level 4 - OPTIMIZED Tested time-to-reconstruct meets a defined target

Installing acrf-trace gets you to Level 2 with two lines of code per agent function.

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Storage

Traces are saved to acrf_trace.db (SQLite) in your current directory. The file survives process restarts. You can run agents today and query the data tomorrow.

Use a custom database path:

from acrf_trace import set_store
from acrf_trace.store import SQLiteTraceStore

set_store(SQLiteTraceStore("/var/log/agents/traces.db"))

Use in-memory storage for tests (no file created):

from acrf_trace import set_store
from acrf_trace.store import TraceStore

set_store(TraceStore())

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ACRF-08 control objectives addressed

CF-2  Every agent call has a trace ID.
      Every call records who triggered it (parent trace ID).
      Full causal chain reconstructable from any single action.

Planned for v0.2:

CF-3  Integrity hashes on trace records.
      Detects if anyone tampered with the logs.

Out of scope for this library (your infrastructure):

CF-1  Circuit breakers that halt agent chains on anomaly.
CF-4  Tested time-to-reconstruct a cause chain.

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Works with any Python agent framework

acrf-trace is framework-agnostic. Works with:

LangChain agents
CrewAI agents
AutoGen agents
Custom Python functions
Any callable that takes input and returns output

If it is a Python function, you can wrap it.

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Real-world use -- on a client engagement

You are assessing a client's AI agent system. They have multiple agents talking to each other. You want to know their ACRF-08 maturity level.

Step 1 -- Install on the client's system:

pip install acrf-trace

Step 2 -- Ask the client to add @wrap to each agent function. Two lines per function. No other changes:

from acrf_trace import wrap

@wrap(agent_name="CustomerServiceAgent")
def customer_service_agent(query):
    return billing_agent(query)

@wrap(agent_name="BillingAgent")
def billing_agent(query):
    return "your bill is $500"

Step 3 -- Let agents run normally for a day or a week.

Step 4 -- Generate the maturity report:

acrf-trace report

You get:

• Which agents are talking to which
• Whether causal chains are fully traceable
• ACRF-08 maturity level (0-4)
• Specific gaps to remediate

That is a billable deliverable from one command. The gap report tells the client exactly what to fix and why.

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Authors

Ravi Karthick Sankara Narayanan Kanna Sekar

License

Apache 2.0