T3MP3ST

// Overview

T3MP3ST is a multi-agent offensive-security framework built around a harness rather than a model. It does not ship its own LLM or require a new API key: you connect the coding agent already installed on your machine — Claude Code, Codex, Hermes, OpenCode, Oh My Pi — or point it at a local OpenAI-compatible server such as Ollama, LM Studio or vLLM, and T3MP3ST supplies the tooling, orchestration and reporting around it. Tool calling is driven over text, so models without native function-calling still work.

Operators drive it from a browser "War Room" on localhost or from the CLI: describe an authorised target in plain English and the framework runs recon, exploitation and reporting as a coordinated agent workflow. Coverage differs sharply by domain and the README's status table is explicit about which is which — web-app black-box testing and hint-free CTF solving are marked stable, as is the coordinated-disclosure pipeline for OSS vulnerability hunting and multi-language white-box source analysis; smart-contract work is reproduction rather than novel discovery; and the cloud/IaC, mobile and binary domains are static-detection scaffolding whose dynamic exploitation is not yet benchmarked.

This is an offensive tool, licensed AGPL-3.0, and the project is emphatic that it is for authorised testing, research and education only — point it at systems you own or have explicit written permission to test. Its other distinguishing habit is reproducibility: the project states that every headline number in its README re-derives from data committed under `bench/`, and ships `npm run verify-claims` so a reader can recompute them instead of taking them on trust. It reports 90.1% pass@1 on XBOW's 104-challenge suite by that method; treat that as a self-reported figure you can check yourself.

// What it does

• Keyless by default — the coding agent already signed in on your machine is the reasoning backbone, with no second subscription or cloud tenant
• Fully offline operation against Ollama, LM Studio, vLLM or llama.cpp, with text-driven tool calling so native function-calling is not required
• Browser War Room on 127.0.0.1 plus a CLI, both driving the same recon → exploit → report workflow
• Domain coverage marked by maturity in the README: web apps and CTF stable, OSS/robotics coordinated disclosure and white-box source analysis stable, cloud IaC, mobile and binary at static-detection scaffolding
• Re-derivable benchmarks — `npm run verify-claims` recomputes every headline number from committed data under `bench/`
• Docker deployment that binds the API to localhost only, plus documented library/SDK, HTTP API and MCP usage
• Session-reuse and timeout guards, including an opt-in flag for reusing a Claude Code session that is off by default because a resumed session can retain ambient tool authority

// Getting started

Before anything else: only point this at systems you own or have explicit, written permission to test. The fastest path is the keyless War Room, which takes a couple of minutes to stand up.

npm install
npm run server        # War Room → http://127.0.0.1:3333/ui/

export OPENROUTER_API_KEY=...   # or ANTHROPIC_API_KEY / OPENAI_API_KEY / VENICE_API_KEY
export XAI_API_KEY=...
export NOVITA_API_KEY=...

ollama serve && ollama pull llama3
export TEMPEST_LOCAL_BASE_URL=http://localhost:11434/api   # LM Studio: http://localhost:1234/v1
export TEMPEST_LOCAL_MODEL=llama3
npm run build
npx tempest                 # → "Change default provider" → local

npm run verify-claims

cp .env.example .env
docker compose up -d
docker compose logs -f

curl http://localhost:3333/api/health

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Run an authorised black-box web-application assessment end to end, driven by a coding agent you already pay for
• Practise on CTF challenges with a sandbox-jailed, hint-free agent loop rather than a hand-assembled tool chain
• Hunt vulnerabilities in open-source projects through the coordinated-disclosure pipeline, with a refuter step to filter false positives
• Keep an engagement entirely on-premises by pointing the harness at a local model, so target data never leaves the machine

// How T3MP3ST compares

T3MP3ST alongside other open-source security agents tools AI/TLDR tracks, ranked by GitHub stars.