Strix vs pentagi
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
pentagi
Autonomous multi-agent penetration testing system with sandboxed security tools
Verdict
Strix ranks higher, 74 to 55. Of the 3 score parts we measure for both, Strix leads on 1, pentagi on 1 and 1 is even.
- AdoptionStrix leads by 77 points, 92 to 15: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score pentagi on this; Strix has 77.
- Easy to runWe do not score pentagi on this; Strix has 50.
- Agent-readypentagi leads by 10 points, 40 to 30.
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 pentagi if you are
Security engineers, pentesters and red teams doing authorized testing
- Runs 20+ pentest tools (nmap, Metasploit, sqlmap) in an isolated Docker sandbox
- Supports 10+ LLM providers, custom OpenAI-compatible endpoints, and local Ollama or vLLM
| What we compare | Strix | pentagi |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 92, widely used | 15, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 77, fair | Not scored for this kind |
| Easy to run | 50, easy | Not scored for this kind |
| Agent-ready | 30, minimal | 40, minimal |
| Facts from GitHub and the README | ||
| Stars | 68.6k | 25.3k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Go |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| 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
pentagi
PentAGI takes a target and goal in plain language, then uses a team of specialized agents (orchestrator, researcher, developer, executor) to plan and run 20+ tools such as nmap, Metasploit and sqlmap inside an isolated Docker sandbox. Findings are stored in PostgreSQL with pgvector, with an optional Neo4j/Graphiti knowledge graph, and it produces vulnerability reports. It works with 10+ LLM providers, any OpenAI-compatible endpoint, or local models via Ollama or vLLM.
Who it is for: Security engineers, pentesters and red teams doing authorized testing
Strengths
- Runs 20+ pentest tools (nmap, Metasploit, sqlmap) in an isolated Docker sandbox
- Supports 10+ LLM providers, custom OpenAI-compatible endpoints, and local Ollama or vLLM
- Persistent memory in PostgreSQL with pgvector, optional Neo4j knowledge graph via Graphiti
- REST and GraphQL APIs with Bearer tokens; Langfuse, Grafana and Prometheus integrations
Weaknesses
- Full stack spans many containers (Postgres, Neo4j, Langfuse, ClickHouse, Grafana), heavy to self-host
- Agent profiles, plugins, MCP servers and attack-chain view are Enterprise-only
- Not a BAS or adversary emulation tool; no predefined campaigns
- Report export limited to web, Markdown and PDF; no JSON export
- GPU optional
- Docker + Compose
- Needs Docker, Docker Compose, PostgreSQL with pgvector, Neo4j (optional), Langfuse (optional), Grafana/Prometheus (optional)
- Models: OpenAI, Anthropic, Google Gemini, AWS Bedrock, Ollama
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