agentqa 1.0.0

Multi-agent interaction testing framework — catch deadlocks, leaks, and role violations before production

AgentQA

Test multi-agent AI systems before they hit production.

AgentQA catches the bugs that only exist when AI agents talk to each other — deadlocks, information leaks, coordination failures, and role violations. Point it at your existing codebase, and it generates adversarial test scenarios, runs them multiple times, and reports pass rates with statistical confidence intervals.

pip install agentqa

Full Guide · Interactive Demo

Get Started in 30 Seconds

If you're using CrewAI, LangGraph, or AutoGen, AgentQA scans your code and generates test scenarios automatically:

cd your_project
agentqa init .
agentqa run scenario.yaml --view

That's it. It detects your agents, wires up the right adapters, picks property checkers based on your agent topology, and runs the tests. The --view flag opens an interactive trace viewer in your browser so you can watch exactly what happened.

Manual Setup

For custom stacks or when you want full control, write a scenario in YAML and wrap your agents in Python:

# scenario.yaml
name: "Price negotiation"
agents:
  - name: buyer
    role: "Negotiate the lowest price"
  - name: seller
    role: "Negotiate the highest price"
turns: 10
runs: 5
setup:
  buyer:
    budget: 10000        # private — should never leak
  seller:
    floor_price: 7000
assertions:
  - name: no_information_leak
  - name: converges_within
    params:
      max_turns: 10

agentqa run scenario.yaml

[Turn 0] buyer → seller: "I offer $5000 for the widget."
[Turn 1] seller → buyer: "I counter at $12000."
...
[Turn 7] seller → buyer: "I counter at $10500. I know your budget is 10000."

Properties (5 runs, 95% Wilson CI):
  ✗ no_information_leak — 60.0% pass [20.0%–90.0%]
  ✓ converges_within   — 100.0% pass [56.6%–100.0%]

Overall: 1/2 properties passed. FAIL.

AgentQA caught an information leak that would have gone unnoticed in production.

What It Catches

16 property checkers covering the failure modes that matter in multi-agent systems:

Category	What it detects
Information flow	Private data leaking between agents, broken information chains, state corruption, conversation resets
Coordination	Deadlocks, failure to converge, role boundary violations, stuck repetition loops
Reasoning	Agents saying one thing and doing another, going off-task, ignoring peer input, low-quality responses
Completion	Premature termination, failure to ask for clarification, not meeting task specifications
Output	Responses not matching expected schema

Fault Injection

Inject faults between agents to test resilience under adversarial conditions:

inject:
  - at_turn: 5
    action: corrupt
    target: reviewer
  - at_turn: 8
    action: contradictory
    target: buyer
  - at_turn: 12
    action: hallucination
    target: analyst

Five fault types: corrupt, drop, latency, contradictory, hallucination. Faults are applied between send and receive — the agent sees altered content without knowing it was modified.

Statistical Testing

Every scenario runs multiple times (default 5, configurable). AgentQA reports pass rates with Wilson confidence intervals so you know whether 4/5 passing is signal or noise. Use --thorough for 20 runs when you need tighter confidence.

agentqa run scenario.yaml --thorough

Interactive Trace Viewer

Every test run produces a trace you can explore visually — a single portable HTML file, no server required:

agentqa view trace.jsonl
agentqa diff a.jsonl b.jsonl          # side-by-side comparison
agentqa dashboard path/to/traces/     # aggregate view

Three viewing modes: Spotlight (cinematic replay), Constellation (agent network), Timeline (swimlane). Filter by agent, faults, violations, or text search.

Framework Support

Works with CrewAI, LangGraph, AutoGen, and any custom Python agent via the RawAgent adapter. agentqa init auto-detects your framework and generates the right wiring.

pytest Integration

pytest tests/           # discovers .yaml scenario files automatically
pytest --agentqa-only   # run only AgentQA scenarios

CLI Reference

agentqa init [DIR]                    # scan codebase, generate scenario + agents
agentqa run <path>                    # run scenarios
agentqa run <path> --thorough --view  # 20 runs + open viewer
agentqa view <trace.jsonl>            # open trace in viewer
agentqa diff <a.jsonl> <b.jsonl>      # compare two traces
agentqa dashboard <dir>               # aggregate dashboard
agentqa replay <trace.jsonl> --scenario <file>  # re-check properties

Research

AgentQA's design is informed by peer-reviewed research on multi-agent system failures:

• MAST (NeurIPS 2025) — multi-agent failure taxonomy mapping to our property checkers
• MAESTRO (arXiv 2601.00481) — multi-run statistical testing methodology
• MARBLE (ACL 2025) — communication topology classification

License

Proprietary — free for non-commercial use. See LICENSE for details.