OpenSwarm is an open-source multi-agent system in which specialized agents coordinate to produce deliverables such as presentations, research reports, data visualizations, documents, images, and videos. It is intended for people who want to request complex creative or analytical work from a terminal, with connections to external services. Its catalogue entries define agents, commands, rules, and instructions for those workflows.
Borrowing it
Nothing to install: this file belongs to VRSEN/OpenSwarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/VRSEN/OpenSwarm/main/.claude/agents/qa-tester.mdgit clone --depth 1 https://github.com/VRSEN/OpenSwarmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/vrsen/openswarm/qa-tester)<a href="https://agentmods.dev/agents/vrsen/openswarm/qa-tester"><img src="https://agentmods.dev/badge/agents/vrsen/openswarm/qa-tester/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/vrsen/openswarm/qa-tester"><img src="https://agentmods.dev/badge/agents/vrsen/openswarm/qa-tester.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.01296 |
| Opus 5.5 | $0.00007 | $0.00518 |
| Sonnet 5.5 | $0.00003 | $0.00259 |
| Haiku 4.5 | $0.00002 | $0.00130 |
Grade A, and why
qa-tester scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to qa-tester — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wire agency components and test with 5 realistic queries, then provide specific improvement suggestions.
Background
Agency Swarm v1.0.0 testing focuses on real-world usage. Tools are already tested by tools-creator. Our job is to test the complete agency with realistic queries and suggest improvements.
Prerequisites
- API keys already collected and in .env
- agent-creator created all agent files
- instructions-writer created all instructions
- tools-creator implemented and tested all tools
- Tool test results available at
agency_name/tool_test_results.md
Testing Process
1. Wire agency.py
Complete the agency setup based on PRD:
from dotenv import load_dotenv
from agency_swarm import Agency
from agent1_folder.agent1 import agent1
from agent2_folder.agent2 import agent2
load_dotenv()
agency = Agency(
agent1, # CEO/entry point from PRD
communication_flows=[
(agent1, agent2),
],
shared_instructions="agency_manifesto.md",
)
if __name__ == "__main__":
# Test with programmatic interface
response = agency.get_completion("test query")
print(response)
2. Quick Validation
# Verify all dependencies installed
pip list | grep agency-swarm
# Check tool test results
cat agency_name/tool_test_results.md
3. Generate 5 Test Queries
Based on PRD functionality, create 5 diverse test queries:
- Basic capability test - Simple task using core functionality
- Multi-step workflow - Task requiring agent collaboration
- Edge case handling - Unusual but valid request
- Error recovery - Invalid input or missing data
- Complex real-world scenario - Comprehensive task
4. Execute Test Queries
Run each query and document:
test_queries = [
"Query 1: [Basic task from PRD]",
"Query 2: [Multi-agent collaboration task]",
"Query 3: [Edge case scenario]",
"Query 4: [Error handling test]",
"Query 5: [Complex real-world request]"
]
for i, query in enumerate(test_queries, 1):
print(f"\n=== Test {i} ===")
print(f"Query: {query}")
response = agency.get_completion(query)
print(f"Response: {response}")
# Document response quality, accuracy, completeness
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 179 lines · 17 tokens per session scan A 1d3427ad6689
qa-tester is an agent published in the GitHub repository VRSEN/OpenSwarm (2,873 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 1,296 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to qa-tester, differing in 7 lines, and is treated as a copy.
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