Get This Tool
swarm-test
Pricing
- Model
- Free
Summary
Chain fourteen agents at 95% reliability each and you get a system that's roughly 49% reliable end-to-end — and no single agent's logs will tell you why it keeps failing. swarm-test is a static analysis tool that inspects your agent topology before you deploy, surfacing cascade risks and single points of failure without making a single LLM call.
The tool works by analyzing the graph structure of your multi-agent system — how agents connect, depend on, and hand off to each other — rather than running live inference. It supports CrewAI, LangGraph, AutoGen, and custom agent graphs out of the box, per the project docs. Output includes interactive reports and Mermaid visualizations suited for CI/CD pipelines and GitHub Actions. The ceiling appears when your reliability concern is runtime behavior rather than topology: swarm-test cannot catch prompt drift, model degradation, or failures that only surface under real load. Teams with those requirements run this alongside live integration testing, not instead of it.
Bottom line: Use this to catch structural fragility in a multi-agent graph before it hits production — it will not catch the failures that only appear when the model starts behaving differently at 2am on a Tuesday.
Community Performance Report Card
No community ratings yet. Be the first to rate this tool!
Community Benchmarks Community
Sign in to submit a benchmarkNo community benchmarks yet. Be the first to share a real-world data point.
Pros
Sign in to edit- Zero-cost static analysis — no LLM API calls are made during inspection, so teams can run reliability checks in CI without per-execution API spend or rate-limit exposure.
- Graph-level cascade detection, so fragile dependency chains that look fine in per-agent unit tests get surfaced before they cause silent end-to-end failures in production.
- Native GitHub Actions support via a committed action.yml, which means structural checks can be added to a PR workflow without custom scripting.
- Framework-agnostic graph support for CrewAI, LangGraph, AutoGen, and custom topologies, so teams are not locked into a single agent framework to get pre-deployment analysis.
- Interactive Mermaid visualizations in generated reports, so a product manager or engineering lead can read the failure risk map without parsing raw JSON or log files.
Cons
Sign in to edit- Static analysis only covers the topology you define — it cannot detect failures caused by model behavior, prompt instability, or latency degradation at runtime. Teams whose primary reliability concern is what the model does under load will find the tool answers a different question than the one they are asking, and need a runtime observability tool instead.
- The project has 3 stars and 1 open issue at time of curation, with no indication of a commercial backer or active contributor community. Teams that hit an edge case in graph parsing have the MIT source to work from, but no support channel and precious little community precedent to draw on.
- Custom agent graph support requires defining your topology in a format the tool can parse — teams with bespoke orchestration logic that does not map cleanly to a supported framework spend time on schema translation before they get their first report, at which point some switch to writing their own topology-aware test harness rather than adapting an external tool.
Community Reviews
Sign in to write a reviewNo reviews yet. Be the first to share your experience.
About
- Platforms
- Python, CLI, GitHub Actions
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-25T13:17:10.267Z
Best For
Who it's for
- Developers building or maintaining multi-agent AI systems
- Teams needing static analysis without API costs
- Integration into GitHub Actions for automated checks
What it does well
- Pre-deployment reliability testing of multi-agent workflows
- Detecting single points of failure and cascade risks in agent graphs
- Generating interactive reports and visualizations for CI/CD pipelines
Integrations
Discussion Community
Sign in to commentNo discussion yet. Sign in to start the conversation.
Compare swarm-test
Spotted incorrect or missing data? Join our community of contributors.
Sign Up to ContributeCommunity Notes & Tips Community
Sign in to contributeBe the first to contribute. General notes, observations, gotchas, and tips from people who use this tool day-to-day.
Frequently Asked Questions
- Is swarm-test free?
- Yes — swarm-test is fully free to use. There is no paid tier.
- Is swarm-test open source?
- Yes. swarm-test is open source.
- Can I self-host swarm-test?
- Yes. swarm-test supports self-hosting on your own infrastructure.
- What platforms does swarm-test support?
- swarm-test is available on: Python, CLI, GitHub Actions.
Hours Saved & ROI Stories Community
Sign in to contributeBe the first to contribute. Concrete time/cost savings, with context. e.g. "Cut my code review backlog from 4h to 45m per week."
Best swarm-test alternatives →
Curated lists that include this category
Most multi-agent failures don’t live inside a single agent — they live in the connections. A dependency that looks fine in isolation becomes a single point of failure the moment three downstream agents rely on it. swarm-test addresses this by performing static analysis on your agent graph: it reads your topology definition, computes reliability propagation, and flags cascade risks and structural vulnerabilities. No live API calls are made, which means analysis runs fast and costs nothing per execution. The core workflow is a pip install, a YAML or code-defined graph, and a CLI invocation that produces a report.
The differentiating feature is the math it applies to graph structure. The project README demonstrates the core problem with a concrete calculation: fourteen agents at 95% individual reliability produce a system-level reliability of roughly 49%. swarm-test operationalizes that reasoning across your actual topology, identifying which nodes or edges are responsible for the steepest reliability drops — information that is invisible when you test agents in isolation.
The tool fits cleanly into CI/CD pipelines — the repo ships an action.yml for GitHub Actions integration, so structural checks can block a merge before a fragile graph reaches staging. Where it breaks: swarm-test is a static analyzer. It models the topology you define, not the runtime behavior of the models inside each agent. Prompt failures, latency spikes, model version drift, and failures that emerge only under concurrent load are outside its scope. Teams that need runtime reliability guarantees use this as a pre-flight check and pair it with live integration tests or an observability layer.
The project is MIT-licensed and self-hostable, with no API dependency and no external service calls required. Report output includes Mermaid graph visualizations, with example outputs committed directly to the repository for reference before adoption.