The open-source XBOW alternative —
built on a real swarm.

Dozens of AI agents attack your target concurrently — and find real, proven vulnerabilities at machine speed.

_{Built for pentesters · bug bounty hunters · red teamers · security researchers}

📖 Documentation · Quick Start · vs. XBOW · Swarm vs. Multi-Agent · How It Works · Roadmap

Hack at machine speed.

📖 New here? Start with the documentation — quickstart, provider setup, CLI reference, and troubleshooting. (Or run pentestswarm docs to open it.)

XBOW proved a point: AI can top the bug-bounty leaderboards. But XBOW is a closed, hosted SaaS — one agent, on their cloud, on their model, at their price. We think the future of offense is open, self-hosted, and a swarm — so we built it.

Pentest Swarm vs. XBOW

	Pentest Swarm AI	XBOW
Open source	✅ AGPL, fork it	❌ closed
Self-hosted	✅ your infra, your model, $0 floor	❌ their cloud, their bill
A real swarm	✅ dozens of agents, concurrent	❌ single agent

Three structural wins — and the swarm is the one that compounds. This isn't a single planner LLM walking recon → classify → exploit → report down a fixed line. Dozens of agents work your surface at once, coordinating through a shared stigmergic blackboard: the instant a finding lands it wakes whichever agent it's relevant to, so a 1,000-subdomain target gets hit in parallel — attacking at machine speed. And unlike a scanner that spits out unverified "maybes," the swarm exploits what it finds, proves it with captured evidence, then writes the report.

Who it's for: pentesters covering a whole scope overnight · bug bounty hunters racing to first-blood on a fresh target · red teamers who need breadth fast · researchers pushing autonomous offense.

Run it on any model — Claude, anything OpenAI-compatible (incl. Together AI's hosted Llama/Qwen/DeepSeek), the new security-tuned open models (Pentest-R1 and the wave behind it), or fully local Ollama / LM Studio. We don't compete with those models — we're the harness that gives them hands: real tools, swarm coordination, scope safety, and evidence-backed reports. Air-gapped, zero API cost, and not one byte of your data leaving your box.

For authorized testing only — see the disclaimer below.

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Credits & Inspiration

This project stands on the shoulders of giants. We credit and thank these projects for pioneering AI-powered offensive security:

• PentestGPT — the OG that proved LLMs can pentest
• PentAGI — fully autonomous agent architecture
• Strix — AI hackers that find and fix vulns
• CAI — cybersecurity AI framework, 3600x faster than humans
• HackingBuddyGPT — LLM hacking in 50 lines of code
• Shannon — white-box AI pentester
• BlacksmithAI — multi-agent pentest framework
• PentestAgent — black-box AI security testing

Their open-source contributions made tools like this possible.

Legal Disclaimer: Pentest Swarm AI is designed exclusively for authorized security testing, bug bounty programs, CTF competitions, and educational research. You must obtain explicit written permission from the target system owner before running any scan. Unauthorized access to computer systems is illegal under the Computer Fraud and Abuse Act (CFAA), the Computer Misuse Act, and equivalent laws worldwide. The authors and contributors of this project accept no liability for misuse, damage, or any illegal activity conducted with this tool. By using this software, you agree that you are solely responsible for ensuring your use complies with all applicable laws and regulations. Do not use this tool against systems you do not own or have explicit authorization to test.

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What makes this a swarm?

Most "multi-agent" pentesting tools are a single planner LLM dispatching to specialist agents in a fixed order — recon → classify → exploit → report. That's a pipeline, not a swarm.

Pentest Swarm AI is built around three swarm-intelligence primitives:

• Stigmergy — agents coordinate by reading and writing findings on a shared blackboard, not by a central planner telling them what to do. A finding's pheromone weight biases other agents toward it and decays over time, so stale paths die naturally.
• Emergence — attack chains appear that no single agent planned. A recon finding wakes the classifier; a high-severity classification wakes the exploit agent; exploit results feed back into the board and wake the report agent. Order isn't prescribed — it emerges from the blackboard state.
• Decentralization — each agent runs its own trigger predicate. Add a new agent with its own predicate and it joins the swarm without anyone rewriting the orchestrator.

Why it wins: a pipeline is sequential (one stage at a time), can't fold a mid-run discovery back into recon, and is bounded by its slowest stage. A swarm works your surface concurrently, reacts to every new finding, lets attack chains emerge instead of scripting them, and lets stale paths decay and die. The payoff on stage is breadth — a long list of proven findings, because many agents chew on the surface at once.

📖 Full write-up: Architecture — Swarm vs. Pipeline (the blackboard, pheromone decay, emergent chains, and why concurrency stays cheap). Diagrams also live in docs/.

We built this because the category was empty. Every tool marketed as "swarm" was actually a pipeline. If you find a counter-example, open an issue — we'll add them to the comparison table.

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Quick Start

One command to install. One command to run. No API key, no cloud, no bill to start.

1 · Install (pick one)

npm install -g @armurai/pentestswarm                                       # npm (Node ≥16)
curl -fsSL https://raw.githubusercontent.com/Armur-Ai/Pentest-Swarm-AI/main/scripts/install.sh | sh   # any macOS/Linux
brew install Armur-Ai/tap/pentestswarm                                     # Homebrew
go install github.com/Armur-Ai/Pentest-Swarm-AI/cmd/pentestswarm@latest    # Go toolchain
docker run --rm ghcr.io/armur-ai/pentestswarm:latest --help                # Docker

2 · Run it

pentestswarm run

That's it. run opens the interactive TUI launcher — no flags to memorize:

• Pick your AI provider and paste a key right in the UI — Together AI (hosted Llama / Qwen / DeepSeek), Claude, OpenAI, Gemini, or fully local Ollama / LM Studio (no key at all).
• Point it at a target — a URL, or a bundled vulnerable lab (crAPI, Juice Shop, VAmPI, DVGA) that spins up, gets attacked, and tears down after. Legal, safe, zero setup.
• Choose your live view — a web dashboard on localhost:7777 and a full-screen terminal TUI with live charts, a swarm-topology diagram, and a graded findings stream. Both, by default.
• Then launch and watch the swarm work at machine speed.

A readiness check runs right in the launcher (Go, Docker, tools, provider) — it never blocks; anything missing is shown as a note you can fix or ignore.

Prefer flags? (scripting / CI)

run just wraps scan, so everything is scriptable too:

# watch it find a real vuln in ~2 min — bundled lab, local model, no key
pentestswarm scan --lab --lab-target crapi --provider ollama --swarm --tui

# a real target, cloud model for max quality
export PENTESTSWARM_ORCHESTRATOR_API_KEY=your-key-here
pentestswarm scan <authorized-target> --scope <target> --swarm --follow

New here? pentestswarm demo plays the whole campaign offline — that's the GIF above. Running inside GitHub Actions? See deploy/github-action/example-workflow.yml.

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How the swarm works

Key behaviours:

1. Agents are independent. Any one of them can be removed, replaced, or added without rewiring the others.
2. Pheromones decay per-finding-type. A PORT_OPEN stays hot for hours; a SESSION for minutes. Config-driven half-lives.
3. Scope is enforced at the tool layer and again at the executor. Defence in depth — --scope is not bypassable.
4. Cleanup is always registered before execution. SIGINT, crashes, and budget exhaustion all trigger reverse-order cleanup. See internal/pipeline/cleanup_memory.go and cleanup.go.
5. Prompt caching on Claude cuts cost and latency on repeated system prompts (enabled by default for recon + classifier).

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Comparison

How we position vs. the rest of the ecosystem. We'll ship real benchmark numbers in a future release (see the benchmarks roadmap).

Tool	Open / self-host	Architecture	Executes vs. suggests	Memory	Tools wired	MCP	Swarm?
Pentest Swarm AI	✅ open, self-hosted	Stigmergic blackboard	Executes	pgvector + pheromones	8 ProjectDiscovery + nmap; sqlmap / Burp MCP / Metasploit in roadmap	Yes	✅ real
XBOW	❌ closed SaaS	Autonomous agent (hosted)	Executes	Hosted	Managed	No public API	No
PentestGPT	✅ open	Single-agent ReAct	Suggests	None	None native	No	No
HackingBuddyGPT	✅ open	Single-agent	Executes	Run logs	Shell passthrough	No	No
PentAGI	✅ open	4 agents + planner	Executes	pgvector	40+ via MCP/shell	Partial	Pipeline
Shannon	✅ open	White-box + browser	Executes	Session state	Browser DOM	No	Pipeline
HexStrike	✅ open	MCP tool wrapper	Delegates to client LLM	None (stateless)	150+ via MCP	Yes	No
Pentest-R1	✅ open (model)	RL-tuned LLM	Executes	Trajectory	CTF-scope	No	No

If any entry here is wrong or out of date, please open a PR — we want this table to stay honest.

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Feature status

Honesty labels: stable means shipped + tested, beta means works but rough edges, alpha means experimental, planned means in the roadmap.

Feature	Status	Notes
Sequential 5-phase runner	stable	Default mode; battle-tested core
Stigmergic swarm scheduler	alpha	--swarm flag; memory-backed blackboard wired
ProjectDiscovery toolchain	stable	subfinder, httpx, nuclei, naabu, katana, dnsx, gau
nmap adapter	stable	XML parsed; scope-validated
Cleanup registry	stable	Always runs on SIGINT / exit / budget-cancel
Claude prompt caching	stable	Enabled for recon + classifier by default
--strict LLM mode	stable	Promotes LLM errors to fatal
CVSS v3.1 scoring	stable	FIRST spec
Jev false-positive filter	beta	--jev; optional second-opinion pass via TypeSafe's Jev — off by default, fails open. Docs
Postgres blackboard backend	beta	Migration shipped; runner uses memory-board for now
MCP server	beta	pentestswarm mcp serve
VS Code extension	beta	deploy/vscode/
GitHub Action	beta	deploy/github-action/action.yml with SARIF
Swarm playbooks (5)	beta	playbooks/{bug-bounty,external-asm,ci-cd,internal-network,ctf-solver}.yaml
Live dashboard	alpha	web/; UI built, wiring to live campaigns in progress
Burp MCP bridge	planned	Wave 2
Metasploit / ZAP / sqlmap adapters	planned	Wave 2
Fine-tuned Pentest-Swarm model	planned	Wave 3 (Pentest-R1 recipe)
Cybench / AutoPenBench benchmarks	planned	Wave 3

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CLI

pentestswarm run                                        # ⭐ Interactive TUI — pick options + target, no flags
pentestswarm scan <target> --scope <scope> --swarm      # Scriptable: stigmergic swarm scheduler
pentestswarm scan <target> --scope <scope> --tui        # Full-screen live TUI (charts + swarm topology)
pentestswarm scan --lab --lab-target crapi              # Attack a bundled vulnerable lab, no setup
pentestswarm playbook run <name> --target <t>           # Run a community playbook
pentestswarm doctor                                     # System health check
pentestswarm install-tools                              # Fetch the recon/exploit toolchain
pentestswarm mcp serve                                  # MCP server for Claude/Cursor
pentestswarm serve                                      # Start API server + dashboard

pentestswarm run is the front door — an interactive launcher (provider + key entry, target or bundled lab, live-view choice, readiness checks) so you never have to remember flags. The scan form stays fully scriptable for CI.

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LLM Providers

All agents inherit from a single provider config. Set one key, the entire swarm works.

Bring your own model — we're the harness, not the model. A new wave of open models is topping the cyber-offense benchmarks — GLM (5.3), Qwen (3.x), DeepSeek, and security-tuned fine-tunes like Pentest-R1. Pentest Swarm turns any of them — or a frontier model, or a fully-local one — into an operating pentester: real tools, swarm coordination, scope enforcement, and evidence-backed reports. The model does the reasoning; the swarm does the work.

Together AI is first-class — pick together (in pentestswarm run or --provider together) and just add your key; the endpoint is handled for you. Any other OpenAI-API-compatible endpoint (OpenAI, DeepSeek, Groq, …) works via openai + a base-URL, plus first-party Gemini. The only hard requirement is native tool/function calling, which GLM, Qwen, and DeepSeek all support.

Provider	provider:	Setup	Privacy	Best for
Claude (default)	claude	export PENTESTSWARM_ORCHESTRATOR_API_KEY=...	Cloud	Best quality, zero setup, prompt caching
Together AI	together	Just set the key — endpoint auto-configured	Cloud	Open cyber-benchmark leaders: GLM zai-org/GLM-5.3, Qwen Qwen/..., DeepSeek, Kimi
OpenAI-compatible	openai	Set key + the vendor's /v1 endpoint	Cloud	OpenAI, DeepSeek, Groq, or any Chat-Completions API
Gemini	gemini	export PENTESTSWARM_ORCHESTRATOR_API_KEY=AIza...	Cloud	Large context, free tier
Ollama	ollama	Install Ollama + pull models	100% local	Full privacy, air-gapped (GLM / Qwen builds available)
LM Studio	lmstudio	Load model, enable server	100% local	GUI model management
OrcaRouter	orcarouter	export PENTESTSWARM_ORCHESTRATOR_API_KEY=sk-orca-...	Cloud	One endpoint for Claude/GPT + other frontier models, gateway-level security

Together AI example — run the whole swarm on GLM 5.3:

orchestrator:
  provider: "together"             # first-class — endpoint defaults to Together's API
  model: "zai-org/GLM-5.3"        # or Qwen/..., deepseek-ai/..., etc. — see together.ai/models
  api_key: ""                      # or export PENTESTSWARM_ORCHESTRATOR_API_KEY
  context_window: 128000

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Tech Stack

Component	Technology	Why
Platform	Go 1.24	Single binary, goroutine concurrency, native security tools
CLI	Cobra + bubbletea	Beautiful TUI with multi-panel agent view
LLM	Claude / Together AI (GLM · Qwen · DeepSeek) / Gemini / OrcaRouter / Ollama / LM Studio	Best quality cloud + open cyber-bench leaders + full privacy local
Security Tools	subfinder · httpx · nuclei · naabu · katana · dnsx · gau · nmap	ProjectDiscovery Go libs + nmap subprocess
Blackboard	Postgres 16 + pgvector	Transactional writes, vector similarity, pheromone decay in SQL
Cache	Redis 7	Rate limiting, session state
Dashboard	Next.js 15 + shadcn/ui + tremor	Dark-first, chart-heavy
MCP	JSON-RPC stdio	Claude Desktop + Cursor integration

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Development

git clone https://github.com/Armur-Ai/Pentest-Swarm-AI.git
cd Pentest-Swarm-AI
./scripts/setup.sh    # Install tools, start Postgres/Redis/Ollama
make build            # Compile binary
make test             # Run tests
make dev              # Hot-reload development

Regenerate the demo GIF after any CLI change:

brew install vhs      # one-off
vhs docs/demo-flashy.tape

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Roadmap

• Wave 1 (in flight): real swarm architecture (done), dashboard wire-up, Burp MCP
• Wave 2: sqlmap / Metasploit / ZAP adapters, bug-bounty + ASM + CI/CD playbook polish, official GitHub Action in Marketplace
• Wave 3: fine-tuned Pentest-Swarm model (Pentest-R1 recipe), Cybench / AutoPenBench / CVE-Bench numbers, agent-memory poisoning hardening (MINJA / MemoryGraft defences)

🧠 The Adaptive Swarm — headline releases

Today the swarm reacts to findings and runs verified attack playbooks (BOLA/IDOR, mass assignment, NoSQL injection, excessive data exposure) end-to-end. Next, we make it think — three major capabilities, each shipping as its own release:

• ① Runtime reaction to discoveries (planned) — stigmergic emergence: the swarm mines every response for object references (ids, UUIDs, emails), writes them to the blackboard, and other agents react by probing those objects across endpoints. Find one leaked id and the swarm turns it into cross-user BOLA/IDOR attacks nobody scripted.
• ② Self-correcting attacks (planned) — closed-loop replanning: a failed step (401/403/415, an odd response body) feeds back into the planner, which adjusts the request and retries. Attacks heal themselves instead of dead-ending.
• ③ On-demand specialist sub-agents (planned) — the swarm spawns purpose-built agents at runtime (an auth agent to hold a session, a fuzzing agent for a discovered parameter, a chain-builder for a specific API) and tears them down when done.

Together these turn a reactive swarm into an adaptive one — attack surface it has never seen, handled without anyone writing a plan. Generalization beyond curated targets and full LLM-driven chaining ride on top of these.

Follow the GitHub Project board for live status.

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Why "Swarm"?

Single agents are tools. Pipelines dressed up as agents are slightly fancier tools. A swarm is different: agents share an environment, each agent's writes influence other agents' behaviour, and the useful work is emergent rather than prescribed. That's what lets a swarm handle a 1,000-subdomain target without anyone writing a plan for it.

One agent is a tool. A swarm is a platform.

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Community

Join the swarm on Discord → 🐝

We're building the first real open-source pentest swarm in the open — come build it with us. In Discord you can share findings, request a tool adapter, argue about stigmergy and pheromone decay, get help running your first scan, or grab a good first issue and ship a PR. Researchers, red-teamers, and the AI-security-curious all welcome.

⭐ If a real open-source swarm is something you want to exist, drop a star. Star velocity is the fuel that keeps this shipping — it's the single biggest thing you can do in ten seconds.

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Who's using Pentest Swarm?

Running Pentest Swarm — internally, on client engagements, in CI, or embedded in your own workflow? Add your org to ADOPTERS.md with a quick PR — it helps others trust the project and helps us prioritize what to build. Not ready to be listed publicly? A hello in Discord still helps.

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Enterprise & Commercial Support

Pentest Swarm AI is free and open source (AGPL-3.0), and always will be. If your team wants a hand getting it into production, Armur AI — the team behind the project — offers commercial services:

• Managed deployment on your infrastructure — cloud, on-prem, or fully air-gapped (no data leaves your environment)
• Integration & customization — wire it into your SIEM, ticketing, and CI, or build custom tools and playbooks for your stack
• Priority support & SLAs — a direct line to the maintainers
• Training & onboarding — get your security team productive fast

Especially useful for startups and enterprises that want the control of self-hosting without doing the plumbing themselves.

📧 akhil@armur.ai — tell us your setup and what you're trying to do.

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License

GNU Affero General Public License v3.0 (AGPL-3.0) — see LICENSE.

What this means for you

Use case	Allowed?
Run Pentest Swarm on your own infrastructure (CI, laptop, internal red team)	✅ yes, no obligations
Use it on authorized bug-bounty programs / pentests	✅ yes, no obligations
Fork it for your own private experiments	✅ yes, no obligations
Distribute a modified binary	✅ yes — must share your modifications under AGPL
Run a modified version as a paid SaaS or network service	✅ yes — must share your modifications under AGPL

The AGPL exists specifically to prevent the SaaS-fork loophole: anyone who improves Pentest Swarm and offers it commercially must share their improvements with the community. We made it open source; we want it to stay open source even as it scales.

If you have a use case the table doesn't cover, open an issue and ask.

Built by Armur AI.