CyberStrikeAI
CyberStrikeAI is a Go-based agentic platform for automating authorized security operations by converting natural-language intent into governed, auditable actions through AI agents, security tools, and human oversight. It integrates MCP-native tools, knowledge retrieval, workflow orchestration, and operational context management for penetration testing, vulnerability assessment, and CTF scenarios.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | Ed1s0nZ/CyberStrikeAI |
| Owner | Ed1s0nZ |
| Primary language | Go |
| License | Apache-2.0 — OSI-approved |
| Stars | 5k |
| Forks | 813 |
| Open issues | 30 |
| Latest release | v1.6.51 (2026-07-06) |
| Last updated | 2026-07-08 |
| Source | https://github.com/Ed1s0nZ/CyberStrikeAI |
What CyberStrikeAI is
Built in Go with Eino-powered single/multi-agent orchestration (Deep, Plan-Execute, Supervisor modes), MCP tool execution (HTTP/stdio/SSE), YAML-based tool recipes, RAG knowledge base with MultiQuery rewrite and vector retrieval, graph workflow editor, WebShell management, built-in C2 framework, and SQLite persistence. Supports vision analysis via separate VL models and role-based tool/skill scoping.
Get the CyberStrikeAI source
Clone the repository and explore it locally.
git clone https://github.com/Ed1s0nZ/CyberStrikeAI.gitcd CyberStrikeAI# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Configure LLM providers (CloudWeGo/vendor APIs) and optional services (vector DB, reranking models, IM bots) before deployment. Audit logging and approval workflows require clear role/permission definitions.
- Deploy SQLite persistence with backup strategy; consider migration path to PostgreSQL if scaling beyond single-instance. WebShell and C2 features require strict network segmentation and monitoring.
- Set up MCP tool server(s) and review YAML tool recipes for your threat model. Test approval workflows and HITL audit agent in non-production before enabling on live engagements.
- Plan knowledge base ingestion (documents/attack frameworks) and RAG pipeline tuning (query rewrite, vector retrieval, reranking thresholds). Vision analysis requires separate VL model endpoint if screenshot analysis is needed.
- Establish audit log retention, evidence archival, and compliance reporting pipelines for regulated environments. Skill/role definitions and tool allowlists should be version-controlled and reviewed regularly.
When to avoid it — and what to weigh
- Unauthorized or Offensive Security Testing — CyberStrikeAI is explicitly designed for authorized engagements with governance and audit trails. Do not use for unauthorized access, lateral movement, or offensive operations outside a controlled lab environment.
- Standalone Web Application Without Integration Needs — If your use case requires only basic web UI without agent orchestration, tool federation, multi-agent workflows, or MCP integrations, the complexity may exceed your requirements.
- Zero-Trust or Highly Restricted Network Environments — Deployment requires integration with external LLMs (via MCP), optional vector DBs, reranking services, and IM bots. Network-restricted or air-gapped environments require significant adaptation.
- Non-Go Codebases Requiring Tight Integration — Core platform is Go; API is HTTP/WebSocket. If your stack is Python/Node.js-native and requires deep codebase coupling, consider wrapper approaches or alternatives.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), a permissive open-source license that allows commercial use, modification, and distribution with minimal restrictions (requires license notice and statement of changes).
Apache-2.0 explicitly permits commercial use, modification, and redistribution provided original license notice is retained. However, if bundling or reselling CyberStrikeAI as part of a proprietary security platform, consult legal counsel. No mention of enterprise support, SLAs, or vendor liability terms in provided data; requires review with vendor.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
Platform is purpose-built for authorized security operations with governance controls (approval workflows, audit logs, role-based tool/skill scoping). Audit trails and HITL review are built-in; however, security posture depends on: (1) LLM API and external service trust/TLS; (2) network isolation of WebShell/C2 lab environments; (3) secret management for LLM keys and MCP credentials; (4) audit log integrity and retention; (5) SQLite file permissions and backup encryption. No third-party security audit mentioned; requires internal review before production use. Vision analysis (screenshot handling) should be reviewed for data exfiltration risk if using external VL models.
Alternatives to consider
Metasploit Framework (Ruby)
Mature penetration testing automation without agentic orchestration. Lacks multi-agent workflows, RAG knowledge base, and human-in-the-loop governance; better for traditional module-based exploitation rather than AI-driven engagement planning.
OpenAI Assistants API + Custom Tools
Lightweight AI agent framework for tool execution, but requires significant custom development for multi-agent orchestration, MCP federation, attack-chain intelligence, approval workflows, and operational evidence management.
Julep or LangChain Agents + Security Integrations
Python-native agent frameworks with flexible tool integration, but lack out-of-the-box security domain models (tool recipes, vulnerability management, WebShell/C2, role-based testing) and would require extensive customization for pen-test workflows.
Build on CyberStrikeAI with DEV.co software developers
CyberStrikeAI combines AI agents, governed tool execution, and operational intelligence for authorized security assessments. Deploy on-premises, integrate with your LLM and MCP tools, and start orchestrating auditable workflows. Join the Discord community or review the docs to plan your pilot.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
CyberStrikeAI FAQ
Can I use CyberStrikeAI for real-world client penetration tests?
What LLMs does CyberStrikeAI support?
How does the knowledge base (RAG) work?
Is CyberStrikeAI suitable for air-gapped environments?
Custom software development services
Need help beyond evaluating CyberStrikeAI? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and mcp servers integrations — and maintain them long-term.
Ready to Automate Your Security Engagements?
CyberStrikeAI combines AI agents, governed tool execution, and operational intelligence for authorized security assessments. Deploy on-premises, integrate with your LLM and MCP tools, and start orchestrating auditable workflows. Join the Discord community or review the docs to plan your pilot.