Strix vs shannon
Two of the top projects in Security, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Strix
Autonomous AI pentesting agents that validate vulnerabilities with working exploits
shannon
Autonomous pentester that exploits web apps and APIs using source code analysis
Verdict
Strix ranks higher, 74 to 67. Of the 5 score parts we measure for both, Strix leads on 1, shannon on 2 and 2 are even.
- AdoptionStrix leads by 57 points, 91 to 34: a wide gap.
- FreshnessEven at 100.
- Maintenanceshannon leads by 16 points, 93 to 77: a wide gap.
- Easy to runEven at 50.
- Agent-readyshannon leads by 25 points, 55 to 30: a wide gap.
Pick Strix if you are
Developers and security teams pentesting their own apps and CI pipelines
- Validates findings with working PoCs rather than only static-analysis pattern matches
- Targets code, live URLs, GitHub repos and OpenAPI/Postman specs from one CLI
Pick shannon if you are
Developers and security teams pentesting apps they own, locally or in CI
- Reports only exploits with a reproducible proof of concept, which cuts speculative scanner noise
- Outputs PDF, Markdown, JSON and SARIF 2.1.0; GitHub Action and GitLab CI can gate on exploited findings
| What we compare | Strix | shannon |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 91, widely used | 34, known |
| Freshness | 100, active | 100, active |
| Maintenance | 77, fair | 93, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 30, minimal | 55, partly |
| Facts from GitHub and the README | ||
| Stars | 68.6k | 48.9k |
| License | Apache-2.0 (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | TypeScript |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Strix
Strix runs teams of LLM-driven agents against a local codebase, repo URL, web app or API spec, using a Caido HTTP proxy, browser automation, shell and a Python exploit runtime inside a Docker sandbox. Findings come with proof-of-concept exploits and are written to strix_runs/, viewable with a local dashboard via `strix view`. It also runs headless in CI such as GitHub Actions and connects to user-defined MCP servers.
Who it is for: Developers and security teams pentesting their own apps and CI pipelines
Strengths
- Validates findings with working PoCs rather than only static-analysis pattern matches
- Targets code, live URLs, GitHub repos and OpenAPI/Postman specs from one CLI
- Headless mode exits non-zero on findings, with a documented GitHub Actions workflow
- Works with many LLM providers via LiteLLM, including local models through LLM_API_BASE
Weaknesses
- Needs a running Docker daemon and an LLM API key or ChatGPT subscription
- SSO, compliance reports and VPC deployment are Enterprise-only
- One-click autofix and PR reviews are listed under the paid Cloud offering
- Scan quality and cost depend on the chosen model; README gives no cost or hardware figures
- no GPU
- Docker
- Needs Docker, LLM API key, Caido, LiteLLM
- Models: OpenAI, Anthropic, Google Vertex AI, DeepSeek, Moonshot
shannon
Shannon reads a web application's source, maps attack paths, then uses browser automation and command-line tools to run real exploits against the running app and its APIs. Only findings with a working proof of concept reach the PDF, Markdown, JSON or SARIF 2.1.0 report. It runs from an npx launcher with an ephemeral Docker worker, and also ships a GitHub Action and a GitLab CI component.
Who it is for: Developers and security teams pentesting apps they own, locally or in CI
Strengths
- Reports only exploits with a reproducible proof of concept, which cuts speculative scanner noise
- Outputs PDF, Markdown, JSON and SARIF 2.1.0; GitHub Action and GitLab CI can gate on exploited findings
- Bring your own key: Anthropic, OpenAI, xAI, Bedrock, gateways, or local Ollama, vLLM and LM Studio
- Supports authenticated scans (login flows, TOTP) and resumable workspaces
Weaknesses
- Needs source code of the target; black-box testing is only in paid Pro and Enterprise
- Actively executes exploits, so it must not be pointed at production systems
- Anthropic and OpenAI cyber safeguards can interrupt scans unless you complete their verification
- Claude Code subscriptions unsupported after version 1.9.0; model API costs are yours
- no GPU
- Docker + Compose
- Needs Docker, Node.js 18+, AI provider API key or subscription
- Models: Anthropic, OpenAI, xAI, AWS Bedrock, Ollama
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