swarm-test 0.2.4

The first reliability testing framework for multi-agent AI systems

swarm-test

The first reliability testing framework for multi-agent AI systems.

swarm-test builds a NetworkX interaction graph of your agent swarm and runs 5 automated chaos tests to surface cascade failures, context leakage, intent drift, collusion, and blast radius risks — all from a 3-line API.

from swarm_test import SwarmProbe

probe  = SwarmProbe(crew)
report = probe.run_all()
report.print_summary()

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Features

Test	What it checks
Cascade Failure	Which agents, if they fail, bring down the most of the swarm
Context Leakage	Sensitive data (credentials, PII) crossing agent boundaries
Intent Drift	Agents acting outside their role; prompt injection; goal hijacking
Collusion Detection	Dense cliques, echo chambers, orchestrator-bypass cycles
Blast Radius	Single points of failure, critical path, redundancy score

---

Installation

pip install swarm-test
# or with framework extras:
pip install "swarm-test[crewai]"
pip install "swarm-test[langgraph]"
pip install "swarm-test[langchain]"

From source:

git clone https://github.com/surajkumar811/swarm-test
cd swarm-test
pip install -e ".[dev]"

---

Quick Start

With a CrewAI crew

from crewai import Crew, Agent, Task
from swarm_test import SwarmProbe

researcher = Agent(role="researcher", goal="...", backstory="...")
writer     = Agent(role="writer",     goal="...", backstory="...")
crew = Crew(agents=[researcher, writer], tasks=[...])

probe  = SwarmProbe(crew, swarm_name="my-crew")
report = probe.run_all()
report.print_summary()
report.to_html("report.html")   # D3 graph visualization

With a LangGraph workflow

from langgraph.graph import StateGraph
from swarm_test import SwarmProbe

graph = StateGraph(dict)
graph.add_node("researcher", researcher_fn)
graph.add_node("writer", writer_fn)
graph.add_edge("researcher", "writer")
compiled = graph.compile()

probe  = SwarmProbe(compiled, swarm_name="my-langgraph")
report = probe.run_all()
report.print_summary()
report.to_json("report.json")   # Structured JSON with stable finding IDs

Static graph (no live swarm)

from swarm_test import SwarmProbe, AgentNode, InteractionEvent, EventType

a = AgentNode(name="Fetcher", role="researcher")
b = AgentNode(name="Summarizer", role="writer")

probe = SwarmProbe(
    swarm_name="my-swarm",
    agents=[a, b],
    events=[InteractionEvent(
        source_agent_id=a.id,
        target_agent_id=b.id,
        event_type=EventType.TASK_DELEGATE,
    )],
)
report = probe.run_all()
report.print_summary()

CLI

# Run against a Python script containing a `crew` variable
swarm-test probe my_crew.py --output report.html --fail-on-critical

# Static scan from the command line
swarm-test scan \
  --agents Researcher --agents Analyst --agents Writer \
  --edges "Researcher:Analyst" --edges "Analyst:Writer" \
  --output report.html

---

Architecture

swarm_test/
├── core/
│   ├── models.py       # Pydantic models (AgentNode, Finding, SwarmReport, …)
│   ├── graph.py        # NetworkX SwarmGraph
│   ├── interceptor.py  # Monkey-patch agent methods, sensitive-data scanner
│   └── probe.py        # SwarmProbe — main entry point
├── attacks/
│   ├── cascade.py          # Cascade failure simulation
│   ├── context_leakage.py  # Sensitive-data boundary check
│   ├── intent_drift.py     # Role violations + goal hijacking
│   ├── collusion.py        # Clique/echo-chamber/cycle detection
│   └── blast_radius.py     # Topological SPOF + redundancy analysis
├── integrations/
│   ├── base.py             # BaseAdapter
│   └── crewai_adapter.py   # CrewAI Crew ingestion
├── reporters/
│   ├── console.py          # Rich terminal output
│   └── html.py             # D3 force-directed graph report
└── cli.py                  # Click CLI

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Report Output

Terminal (Rich)

─────────────────── SWARM-TEST RELIABILITY REPORT ───────────────────

 Summary
 Swarm: research-crew-demo    Framework: crewai
 Agents: 4   Edges: 6
 Risk Score: 45/100
 Duration: 12ms

╭─────────────────── Test Results ─────────────────────╮
│ Test                  Status   Findings  Critical  High │
│ cascade_failure       FAILED       2         1       1  │
│ context_leakage       PASSED       0         0       0  │
│ intent_drift          PASSED       0         0       0  │
│ collusion_detection   PASSED       0         0       0  │
│ blast_radius          FAILED       1         1       0  │
╰───────────────────────────────────────────────────────╯

HTML Report

Interactive D3.js force-directed graph showing agent nodes, interaction edges, and color-coded findings.

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Extending

Custom attack

from swarm_test.attacks.base import BaseAttack
from swarm_test.core.models import Finding, Severity, TestResult

class MyCustomAttack(BaseAttack):
    name = "my_custom_attack"

    def run(self, graph):
        findings = []
        # ... analyze graph.graph, graph.events ...
        return TestResult(test_name=self.name, findings=findings)

Custom adapter

from swarm_test.integrations.base import BaseAdapter

class MyFrameworkAdapter(BaseAdapter):
    framework_name = "my-framework"

    def _ingest_impl(self, swarm, graph):
        for raw_agent in swarm.my_agents:
            node = self._make_agent_node(raw_agent.name, raw_agent.role)
            graph.add_agent(node)

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Development

pip install -e ".[dev]"
pytest tests/ -v --cov=swarm_test
ruff check swarm_test/
black swarm_test/

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License

MIT — see LICENSE.