The first reliability testing framework for multi-agent AI systems
Project description
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.
CrewAI, LangGraph, AutoGen — one tool.
GitHub Action
Drop swarm-test into your CI as a reliability gate on every PR. If your agent system's reliability drops below the configured threshold, the build fails.
# .github/workflows/swarm-test.yml
on: [pull_request]
jobs:
swarm-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: surajkumar811/swarm-test@v0.3.0
with:
script: my_crew.py
fail-on-severity: high
Findings appear inline on the PR as ::error:: / ::warning:: / ::notice::
annotations, and a swarm-score summary is posted to the workflow's job summary.
Add a badge to your repo:
[](https://github.com/surajkumar811/swarm-test)
See .github/workflows/swarm-test-example.yml
for a fully-annotated reference workflow.
from swarm_test import SwarmProbe
probe = SwarmProbe(crew)
report = probe.run_all()
report.print_summary()
# First line of output:
# Swarm Score: 72/100 — NEEDS IMPROVEMENT (3 critical, 2 high findings)
Output Modes
Every command (probe, scan, run) leads with a single headline verdict
line that tells you the swarm's reliability at a glance — perfect for CI logs
and dashboards.
Swarm Score: 92/100 — EXCELLENT (no findings)
Swarm Score: 72/100 — NEEDS IMPROVEMENT (3 critical, 2 high findings)
Swarm Score: 8/100 — CRITICAL (5 critical, 11 high findings)
| Flag | Output |
|---|---|
--quiet / -q |
Only the headline verdict (one line). Ideal for if checks in CI scripts. |
| (default) | Headline + test results table + CRITICAL / HIGH findings + SPOFs. |
--verbose / -V |
Headline + every finding (including LOW / INFO), graph metrics, full health & redundancy tables. |
swarm-test run my_crew.py --quiet # CI-friendly: one line out
swarm-test run my_crew.py # default summary
swarm-test run my_crew.py --verbose # full report
The same setting is available in .swarmtest.yml:
output_verbosity: normal # quiet | normal | verbose
Interactive HTML Report
--output-format html renders a self-contained dashboard with a sticky
navigation bar, a live D3 force-directed agent graph (drag, zoom, click to
highlight neighbours), an NxN interaction heatmap, sortable health and
redundancy tables, and collapsible findings cards filterable by severity.
swarm-test run my_crew.py --output-format html --output-path report.html --open
--open launches the report in your default browser as soon as it's
generated. Single-points-of-failure pulse red on the graph; cells in the
heatmap that have findings are tinted red so you can jump straight to the
offending edge.
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 |
Redundancy Scoring
Every agent gets a 0-100 redundancy score that quantifies how replaceable it is:
| Score | Level | Meaning |
|---|---|---|
| 0-20 | IRREPLACEABLE | Single point of failure — removing this agent breaks the swarm |
| 21-40 | LOW | Few or no peers can cover for this agent |
| 41-60 | MODERATE | Some overlap with peers; monitor |
| 61-80 | HIGH | Multiple peers can pick up the work |
| 81-100 | FULLY REDUNDANT | Failure is invisible — graph survives with no degradation |
The score is composed from five factors: path redundancy (30%), role uniqueness (25%), tool coverage (20%), betweenness centrality (15%), and degree ratio (10%). Agents detected as articulation points (SPOFs) are capped below 20.
Console output
Agent Redundancy
╭───────────────────────────────────────────────────────────────╮
│ Agent Score Level Risk │
│ Orchestrator 8/100 IRREPLACEABLE SPOF │
│ Writer 45/100 MODERATE Monitor │
│ Researcher 65/100 HIGH Safe │
│ Reviewer 82/100 FULLY REDUNDANT Safe │
╰───────────────────────────────────────────────────────────────╯
JSON output
{
"overall_redundancy": 50.0,
"redundancy_scores": [
{
"agent_id": "abc-123",
"agent_name": "Orchestrator",
"agent_role": "orchestrator",
"score": 8.0,
"level": "IRREPLACEABLE"
},
{
"agent_id": "def-456",
"agent_name": "Researcher",
"agent_role": "researcher",
"score": 65.0,
"level": "HIGH"
}
]
}
Supported Frameworks
| Framework | Adapter | Status |
|---|---|---|
| CrewAI | CrewAIAdapter |
Stable |
| LangGraph | LangGraphAdapter |
Stable |
| AutoGen | AutoGenAdapter — GroupChat, GroupChatManager, ConversableAgent |
Stable |
| Generic / static graph | BaseAdapter |
Stable |
Installation
pip install swarm-test
# or with framework extras:
pip install "swarm-test[crewai]"
pip install "swarm-test[langgraph]"
pip install "swarm-test[langchain]"
pip install "swarm-test[autogen]"
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
With an AutoGen GroupChat
from autogen import ConversableAgent, GroupChat, GroupChatManager
from swarm_test import SwarmProbe
planner = ConversableAgent(name="Planner", system_message="...")
coder = ConversableAgent(name="Coder", system_message="...")
reviewer = ConversableAgent(name="Reviewer", system_message="...")
groupchat = GroupChat(
agents=[planner, coder, reviewer],
allowed_or_disallowed_speaker_transitions={
planner: [coder],
coder: [reviewer],
reviewer: [planner],
},
speaker_transitions_type="allowed",
)
manager = GroupChatManager(groupchat=groupchat)
probe = SwarmProbe(manager, swarm_name="my-autogen")
report = probe.run_all()
report.print_summary()
From the CLI:
swarm-test run autogen_app.py # auto-detects `groupchat` / `manager`
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
Configuration
swarm-test supports a YAML config file for repeatable runs and CI gates. Copy the example and edit it to taste:
cp .swarmtest.example.yml .swarmtest.yml
A minimal .swarmtest.yml:
fail_on_severity: high # critical | high | medium | low | info | none
max_blast_radius: 0.5 # 0.0 - 1.0 — findings above this threshold fail
disabled_tests: # skip individual tests
- collusion
sensitive_patterns: # extra regexes added to the sensitive-data scanner
- "INTERNAL-[A-Z0-9]+"
output_format: html # console | json | markdown | html
output_path: ./swarm.html
quick_scan: false
timeout_seconds: 30
strict: false # treat ANY finding as a failure
Run with the new run subcommand:
swarm-test run --config .swarmtest.yml
swarm-test run -a "A,B,C" -e "A>B,B>C" --strict
swarm-test run my_crew.py --config custom-config.yml --output-format json
Auto-discovery. With no --config flag, swarm-test discovers
.swarmtest.yml, .swarmtest.yaml, or swarmtest.yml in the project root,
falling back to a [tool.swarmtest] table in pyproject.toml.
CLI flags always override config-file values. Exit codes from run:
0 (passed), 1 (findings exceed thresholds), 2 (config or runtime error).
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
│ ├── langgraph_adapter.py # LangGraph StateGraph / CompiledGraph ingestion
│ └── autogen_adapter.py # AutoGen GroupChat / ConversableAgent ingestion
├── reporters/
│ ├── console.py # Rich terminal output
│ └── html.py # D3 force-directed graph report
└── cli.py # Click CLI
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.
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)
Integrations
swarm-test exports (agent_health scores and structural findings) can feed runtime risk gates. Each integration has its own page under docs/integrations/:
- Black_Wall — pre-action risk gate; consumes
agent_healthas a downside-only prior.
Development
pip install -e ".[dev]"
pytest tests/ -v --cov=swarm_test
ruff check swarm_test/
black swarm_test/
License
MIT — see LICENSE.
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