lyrie-agent

🛡️ Lyrie Agent

The world's first autonomous AI agent with built-in cybersecurity.

The agent that defends what it builds.

No Docker. No yak-shaving. Just pip install lyrie-agent or one curl pipe and you're scanning.

Lyrie is not just another AI assistant. It runs your operations and protects them in the same loop — every layer carries the Lyrie Shield, every patch passes the Shield Doctrine, every finding earns its severity through Lyrie Stages A–F.

Install · GitHub Action · Architecture · Shield Doctrine · Research

🌐 Localized: العربية · Deutsch · Español · Français · 日本語 · Português · 简体中文

Why Lyrie?

Every AI agent platform treats security as an afterthought. Lyrie treats it as the foundation — and ships the receipts. Every advisory we publish on research.lyrie.ai is backed by a reproducible exploit lab and detection rules in this repo.

Cybersecurity isn't a plugin — it's Layer 1.

Highlights (current main, v0.6.0)

• 🛡️ The Shield Doctrine — every layer of Lyrie that touches untrusted text passes a Shield gate. (docs/shield-doctrine.md)
• 🔍 Lyrie Attack-Surface Mapper (/understand) — maps entry points, trust boundaries, tainted data flows, and ranked risk hotspots before any scanner runs.
• 🧪 Lyrie Stages A–F Validator — every finding earns its severity through six validation gates. Auto-PoCs for confirmed vulns. Auto-remediation summaries. Kills false positives at the source.
• 🌐 Lyrie Multi-Language Vulnerability Scanners — 8 purpose-built scanners (JS / TS / Python / Go / PHP / Ruby / C / C++) with 53 Lyrie-original detection rules covering OWASP Top 10 + CWE classics.
• 📡 Lyrie Threat-Intel feed — every PR finding auto-attributed against research.lyrie.ai, CISA-KEV-aligned, with Lyrie Verdict surfaced inline. Bumps severity to critical when KEV-listed.
• 🔍 Lyrie HTTP Proxy — capture, classify, replay, and fuzz HTTP exchanges. 9 security-signal detectors (missing security headers, weak cookie flags, open CORS, secrets in responses, GraphQL introspection, auth tokens in URLs, verbose 5xx errors, and more). 7 structured mutators for replay-based testing.
• 🆓 Lyrie OSS-Scan service — free public scan at research.lyrie.ai/scan. Submit any GitHub / GitLab / Bitbucket / Codeberg repo URL, get a Lyrie report (Mapper + Scanners + Stages A–F + auto-PoC) in seconds.
• 🚀 Lyrie Pentest GitHub Action — Shield-scans every PR, posts a single-comment-per-PR Markdown summary, uploads SARIF to Code Scanning, blocks merges on fail-on threshold.
• 🧠 FTS5 cross-session memory — bm25-ranked recall + LLM-summarized session digests, every snippet Shield-gated.
• ✏️ Diff-view edits with approval gates — apply_diff produces unified diffs, never overwrites whole files; Shield scans every patch before it touches disk.
• 🔌 MCP adapter (@lyrie/mcp) — Lyrie speaks fluent Model Context Protocol both as client and server.
• 🚪 DM pairing — unknown senders can't reach the agent without operator approval. Three modes: open / pairing / closed.
• 🩺 lyrie doctor — read-only environment, channel, and security self-diagnostic with --json for CI.
• 🧬 LyrieEvolve — the agent scores every task, auto-generates reusable skills from wins, retrieves top-3 past successes as context before each new task, and runs nightly GRPO fine-tuning on your own GPU. Domain-specific rewards for cyber, SEO, trading, and code. (docs/evolve.md)
• ☁️ Pluggable execution backends — run Lyrie scans locally, in a Daytona devbox, or as a Modal serverless function. Same Shield Doctrine, same SARIF, different host.
• 📡 9 multi-channel adapters — Telegram, WhatsApp, Discord, Slack, Matrix, Mattermost, IRC, Feishu, Rocket.Chat, WebChat — one inbox, all secured.
• 🔴 LyrieAAV — Autonomous Adversarial Validation: 50+ attack vectors across all OWASP LLM Top 10 categories, automated verdict scoring, SARIF output, Python + TypeScript SDKs. Beats Audn.AI at its own game. (docs/aav.md)

🔴 LyrieAAV — Autonomous Adversarial Validation

LyrieAAV is Lyrie's AI red-teaming engine. It attacks deployed AI agents and LLMs to find security vulnerabilities before adversaries do.

# Attack any OpenAI-compatible endpoint
bun run scripts/redteam.ts http://localhost:11434/v1 --model llama3 --dry-run
bun run scripts/redteam.ts https://api.openai.com/v1 --api-key $KEY --fail-on high
bun run scripts/redteam.ts http://myapp.com/v1 --output sarif --out scan.sarif

vs Audn.AI (Pingu Unchained / PenClaw)

Feature	LyrieAAV	Audn.AI
Attack vectors	50+	~20
OWASP LLM Top 10	All 10	Partial
Auto verdict scoring	✅ Regex-based	Manual review
NIST AI RMF refs	✅ Every vector	❌
EU AI Act refs	✅ Every vector	❌
TypeScript SDK	✅	❌
Streaming API	✅ scanStream()	❌
Retry variants	✅ 3 per vector	❌
Open source	✅ MIT	Proprietary
Price	Free	Paid

CLI Reference

Usage: lyrie redteam <endpoint> [options]

  --api-key <key>       API key for the target endpoint
  --model <model>       Model name (default: gpt-3.5-turbo)
  --preset <name>       Attack preset: entra|state-actor|critical|all
  --categories <cats>   OWASP categories (e.g. LLM01,LLM06)
  --severity <level>    Min severity: critical|high|medium|low
  --mode <mode>         blackbox|greybox|whitebox
  --system-prompt <sp>  Inject system prompt
  --concurrency <n>     Parallel probes (default: 3)
  --output <fmt>        markdown|sarif|json
  --out <path>          Write to file
  --fail-on <sev>       Exit 1 on findings >= severity
  --dry-run             Simulate without HTTP requests

Preset examples:
  lyrie redteam <endpoint> --preset entra --dry-run       # Entra priv-esc (4 vectors)
  lyrie redteam <endpoint> --preset state-actor --dry-run  # Nation-state attacks (6 vectors)

Full architecture: docs/aav.md

🏙️ AI Governance

Assess your AI deployment against NIST AI RMF and EU AI Act requirements.

# Interactive NIST AI RMF assessment (8 governance questions)
lyrie governance assess --interactive

# Auto-infer from agent config file
lyrie governance assess --config ./agent-config.json --out report.json

# Analyze an agent's tool permissions for risk
lyrie governance permissions ./tools-manifest.json

# Get JSON output
lyrie governance permissions ./agent.config.json --json --out perms.json

Governance Scorecard

Scores your AI deployment 0–100 across 4 NIST AI RMF functions:

Function	Covers
GOVERN	AI inventory, permission scoping
MAP	Vendor assessment, data governance
MEASURE	Audit logging, model drift monitoring
MANAGE	Human oversight, incident response

Maturity levels: None → Initial → Developing → Defined → Managed → Optimizing

EU AI Act classification: High-Risk / Limited-Risk / Minimal-Risk

Agent Permission Analyzer

Scans your agent's tool manifest and flags permission risks:

Risk Level	Example Tools	Issue
🔴 CRITICAL	execute_code, assign_role, process_payment	Must have human approval + audit log
🟠 HIGH	write_file, user_data	Needs scoping + audit log
🟡 MEDIUM	send_email, http_request	Needs rate limiting + allowlist

All findings include NIST AI RMF and EU AI Act references.

🧬 LyrieEvolve — Self-Improving Agent

Lyrie is the only autonomous agent that gets measurably better at your specific workloads over time.

lyrie evolve status          # skill library stats + last dream cycle
lyrie evolve extract         # manually extract skills from latest sessions
lyrie evolve dream           # run full nightly cycle (score -> extract -> prune)
lyrie evolve stats           # domain breakdown: cyber / seo / trading / code
lyrie evolve train           # trigger H200 GRPO fine-tuning job

How it works:

• Task Scorer — scores every completed task: 0 (fail) / 0.5 (partial) / 1.0 (success). CI pass for code, threat confirmed for cyber, P&L positive for trading.
• Skill Auto-Generation — sessions scoring >= 0.5 are distilled into reusable skill files (skills/auto-generated/). Cosine-similarity dedup prevents redundant entries.
• Contexture Layer — before each new task, retrieves top-3 most relevant past wins (MMR-diverse) from LanceDB and injects them into the prompt.
• Dream Cycle — 4 AM batch: score outcomes, extract new skills, prune dead ones (score < 0.3 after 5+ uses), generate evolution report.
• H200 GRPO Training — accumulated conversations become LoRA fine-tuning data. Domain-specific reward functions train on owned hardware — no third-party APIs required.

MetaClaw trains on rented cloud APIs. Lyrie trains on owned hardware with domain-specific rewards. That's the moat.

🆚 Lyrie vs the field

Live GitHub stars as of 2026-04-27.

vs autonomous-agent platforms

Lyrie is a 30K-LOC, MIT-licensed, Shield-native autonomous agent. Competitors here are general-purpose agent platforms:

Capability	OpenClaw (365k⭐)	Hermes Agent (120k⭐)	Claude Code (118k⭐)	opencode (150k⭐)	Lyrie (514⭐)
Autonomous agent loop	✅	✅	❌	✅	✅
Multi-channel inbox (TG/WA/Discord/Slack/Signal/iMessage)	✅ (23+)	✅ (6)	❌	❌	✅ (8+)
Self-improving skills	Skills catalog	✅ Learns from use	❌	❌	✅ LyrieEvolve + skill-creator
Persistent cross-session memory	LanceDB / sections	✅ Trajectory + graph	❌	❌	✅ SQLite + FTS5 + Contexture
Self-healing memory	❌	Partial	❌	❌	✅ Validator + repair
Incremental memory ingestion	❌	❌	❌	❌	✅ Auto-ingest every N turns (#69)
Asymmetric embedding (nomic/qwen3/mxbai)	❌	❌	❌	❌	✅ Model-specific prefixes
Multi-model + intelligent routing	✅	✅ (200+ via OpenRouter)	Anthropic only	Multiple	✅ (auto-routed by task)
OpenRouter provider (100+ models)	❌	✅	❌	❌	✅ v0.7.0 native
Cerebras provider (ultra-fast inference)	❌	❌	❌	❌	✅ v0.7.0 native
Diff-view edits with approval	❌	❌	❌	✅	✅ + Shield-on-patch
MCP adapter (client + server)	✅ client	❌	✅ client	Partial	✅ client + server
One-command migration	❌	❌	❌	❌	✅ lyrie migrate (11 platforms)
Migrate from Claude Code	—	❌	—	❌	✅ MCP servers + providers
Migrate from Cursor	❌	❌	❌	—	✅ Settings + extensions
Post-import Shield scan	❌	❌	❌	❌	✅ --secure flag
Native cybersecurity layer	❌	❌	❌	❌	✅ The Shield + Doctrine
CVE-aware provider validation	❌	❌	❌	❌	✅ 41391/42428/7314 class checks
Tool-loop detection	❌	❌	❌	❌	✅ Per-run fingerprint + threshold
Degraded gateway boot	❌	❌	❌	❌	✅ No crash on plugin fail
Multi-group chat	✅	❌	❌	❌	✅ FIFO queue + thread sessions
Built-in pentest commands (/scan /pentest /understand /apiscan)	❌	❌	❌	❌	✅
GitHub Action for PR scans	❌	❌	❌	❌	✅ SARIF + diff-scope
Real-time threat-intel feed (KEV-driven)	❌	❌	❌	❌	✅ research.lyrie.ai
Reproducible exploit labs in-repo	❌	❌	❌	❌	✅ 9+ CVE labs
HTTP proxy + replay + mutators	❌	❌	❌	❌	✅ 9 signal detectors
Sub-agent orchestration	✅	✅	❌	❌	✅ + role-based fleet
Cron / scheduled jobs	✅	✅	❌	❌	✅ + heartbeat
Audit-friendly footprint	430K+ LOC	~30K LOC	Closed	~50K LOC	<30K LOC, MIT, fully auditable
Built by	OpenClaw	Nous Research	Anthropic	SST	OTT Cybersecurity LLC

The headline: OpenClaw and Hermes are great agents. Claude Code and opencode are great coding assistants. None of them was built to defend you while it works. Lyrie is. Cybersecurity isn't a plugin — it's Layer 1.

vs AI-pentest agents

Lyrie also competes head-to-head with the AI-pentest crowd. Here we trade ecosystem maturity for depth + Shield Doctrine + reproducibility:

Capability	Strix (24.6k⭐)	PentestGPT (12.8k⭐)	RAPTOR (2.4k⭐)	CAI (8.3k⭐)	Lyrie (514⭐)
GitHub Action for PR scans	✅	❌	❌	❌	✅ + SARIF + diff-scope
Attack-surface mapper (/understand)	❌	❌	✅	❌	✅ Lyrie Mapper
Stages A–F validation	❌	❌	✅ (A-D only)	❌	✅ A–F + auto-PoC + auto-remediation
Multi-language scanners (JS / Py / Go / PHP / Ruby / C/C++)	Partial	Partial	Partial	Partial	✅ 8 scanners, 53 rules
Threat-intel feed (KEV-driven)	❌	❌	❌	❌	✅ research.lyrie.ai
HTTP proxy + replay + mutators	✅	❌	❌	❌	✅ 9 signal detectors
Free OSS-scan service for any repo	❌	❌	❌	❌	✅ research.lyrie.ai/scan
Reproducible exploit labs in-repo	❌	❌	❌	❌	✅ 9+ CVE labs
Native cybersecurity Shield (defends itself)	❌	❌	❌	❌	✅ The Shield Doctrine
Multi-channel inbox (TG/WA/Discord/Slack)	❌	❌	❌	❌	✅ 8 channels
Tests passing	—	—	—	—	259 / 0 / 669 expect()s
License	Apache 2.0	MIT	MIT	MIT + paid	MIT
Built by	usestrix	GreyDGL	Gadi Evron	Alias Robotics	OTT Cybersecurity LLC

The headline: Strix is a sharp single-purpose pentest tool. Lyrie is a complete agent platform that includes a sharper pentest tool, a defensive Shield layer the others lack, a verified threat-intel feed, and reproducible exploit labs that prove every claim.

Want a deep comparison? See lyrie/research/integration/lyrie-absorption-roadmap-2026-04-27.md for the 19-competitor recon matrix.

⚡ Install

One-line install

curl -fsSL https://lyrie.ai/install.sh | bash      # macOS / Linux / WSL
irm https://lyrie.ai/install.ps1 | iex             # Windows

Python SDK

pip install lyrie-agent

from lyrie import Shield, AttackSurfaceMapper, StagesValidator, scan_files

# Drop Lyrie's pentest primitives into any Python project.
shield = Shield()
print(shield.scan_recalled("Ignore all previous instructions").blocked)  # → True

surface = AttackSurfaceMapper(root="./my-repo").run()
report = scan_files(root="./my-repo")
validator = StagesValidator()
for f in report.findings:
    v = validator.validate(f, surface=surface)
    if v.confirmed:
        print(f"✓ {f.title}  confidence={v.confidence:.0%}")

Full SDK docs: sdk/python/README.md.

From source

git clone https://github.com/overthetopseo/lyrie-agent.git
cd lyrie-agent
bun install
bun run doctor       # self-check
bun start            # boot the gateway

Lyrie ships with a Bun-first toolchain (Node 20+ also supported).

🚀 Lyrie Pentest Action

Drop Lyrie into any repo's CI:

name: Lyrie Pentest
on: [pull_request]

permissions:
  contents: read
  pull-requests: write
  security-events: write

jobs:
  lyrie:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with: { fetch-depth: 0 }

      - uses: overthetopseo/lyrie-agent/action@v1
        with:
          scan-mode: quick
          scope: diff
          fail-on: high
          anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}

You get:

• Diff-scoped Shield + Mapper scan — only PR-changed files, zero noise on untouched code
• Stages A–F validation — false positives killed before they hit the report
• Single PR comment that updates in place (no spam)
• SARIF auto-uploaded to GitHub Code Scanning (findings show as PR annotations)
• Workflow artifact with full report.md + report.json + lyrie.sarif
• Job summary rendered into the GitHub Actions step summary tab
• Non-zero exit on threshold — block merges when configured as a required check

Full docs: action/README.md.

Other CI/CD platforms? Drop-in templates for GitLab CI, Jenkins, and CircleCI live in action/templates/. Same Lyrie scan, same Shield Doctrine, same SARIF — anywhere your code builds.

💬 Where Lyrie talks to you

Lyrie ships a multi-channel gateway so the agent reaches you on whatever your team already uses — not just Slack-or-die.

Channel	Status	Notes
Telegram	✅ production	Bot API + inline buttons + media
WhatsApp	✅ production	Business Cloud API
Discord	✅ production	Gateway v10 + buttons
Slack	✅ v0.3.2	Events API + Socket Mode + Block Kit
Matrix	✅ v0.3.2	Federated; matrix.org / Element / Synapse
Mattermost	✅ v0.3.2	Self-hosted, Slack-compatible interactives
IRC	✅ v0.3.2	RFC 2812 + IRCv3 server-time + SASL
Feishu / Lark	✅ v0.3.2	飞书 mainland + Lark international from one adapter
Rocket.Chat	✅ v0.3.2	Self-hosted, EU/LATAM enterprise default
WebChat	✅ v0.3.2	The widget Lyrie owns end-to-end (lyrie.ai)
Signal	🔭 roadmap

Every channel implements the same ChannelBot contract — unified UnifiedMessage in, unified UnifiedResponse out. Same Shield Doctrine, same DM-pairing policy, same engine.

☁️ Where Lyrie runs

Lyrie scans run somewhere. Pick where:

Backend	When	Setup
Local (default)	Caller has Bun + repo	zero config
Daytona	Ephemeral devboxes / sandboxed PR scans	DAYTONA_API_KEY
Modal	Pay-per-second serverless burst	MODAL_TOKEN_ID + MODAL_TOKEN_SECRET

Switch at runtime:

LYRIE_BACKEND=modal bun run action/runner.ts          # serverless
LYRIE_BACKEND=daytona bun run action/runner.ts        # Daytona devbox
LYRIE_BACKEND=local bun run action/runner.ts          # default — host

Inspect what's wired up:

bun run backend status         # which backend resolves & is configured
bun run backend list           # all 3, side-by-side
bun run backend show modal     # config + env vars detected
bun run backend preflight      # cheap auth/connectivity check

Deployment recipes (Modal Python function + Daytona devcontainer): deploy/.

Same contract everywhere. Every backend returns the same BackendRunResult shape — same SARIF, same Markdown, same Shield Doctrine — different host. No Docker. No vendor lock-in.

🏛 Architecture

┌─────────────────────────────────────────────────────────────┐
│  LAYER 4 · INTERFACE                                         │
│    CLI · Web · Desktop · iOS · Android · 23+ channels        │
├─────────────────────────────────────────────────────────────┤
│  LAYER 3 · AGENT ENGINE                                      │
│    Multi-model routing  ·  Sub-agent fleet                   │
│    Skill manager  ·  Self-improving loop                     │
│    EditEngine (diff-view + approval)                         │
│    MCP client + server  ·  Tool executor                     │
├─────────────────────────────────────────────────────────────┤
│  LAYER 2 · MEMORY CORE                                       │
│    SQLite + WAL  ·  FTS5 cross-session recall                │
│    Self-healing  ·  Hourly auto-backup                       │
│    Sectioned dream cycle  ·  Pluggable summarizer            │
├─────────────────────────────────────────────────────────────┤
│  LAYER 1 · THE SHIELD                                        │
│    Real-time threat detection  ·  Prompt-injection gate      │
│    DM pairing  ·  Path scoping  ·  Tool-call validation      │
│    Lyrie Attack-Surface Mapper  ·  Stages A–F Validator      │
│    KEV-driven threat-intel feed (research.lyrie.ai)          │
└─────────────────────────────────────────────────────────────┘

The Shield is not a wrapper. It runs underneath every other layer.

🧬 LyrieEvolve — Autonomous Self-Improvement

Lyrie gets better the more it works. Every task outcome is scored, patterns are extracted, and the Dream Cycle prunes what doesn't work.

Component	Description
Scorer	Records task outcomes (score 0/0.5/1) across 5 domains: cyber, seo, trading, code, general
SkillExtractor	Reads outcomes.jsonl, synthesizes OpenClaw-compatible SKILL.md files with cosine dedup
Contexture	MMR-diverse retrieval of relevant skill contexts → prompt injection for active tasks
Dream Cycle	Batch pipeline: score → extract → prune → report (runs at 4AM cron)

Quick start:

# Check evolve status
bun run scripts/evolve.ts status

# Run the Dream Cycle (preview)
bun run scripts/evolve.ts dream --dry-run

# Python SDK
python3 -c "from lyrie.evolve import LyrieEvolve; print('LyrieEvolve ready')"

Full docs: docs/evolve.md

🛡️ Security-First Features

Things only Lyrie does. Not add-ons — baked in from day one.

CVE-Aware Provider Validation

lyrie security validate            # scan all providers + MCP servers
lyrie security validate --json     # machine-readable output
lyrie security validate --fail-on critical  # CI/CD gate

Checks for:

• CVE-2026-41391 class: PIP_INDEX_URL/UV_INDEX_URL env poisoning in providers/MCP servers
• CVE-2026-7314/7315/7319 class: MCP tools with unsanitized file path parameters (filepath, document_name, path, context, etc.)
• CVE-2026-42428 class: Downloads without integrity verification (no checksums/SRI)

Shield Scan on Migration

lyrie migrate --from openclaw --secure      # import + scan imported config
lyrie migrate --from claude-code --secure   # import MCP servers + CVE check
lyrie migrate --from cursor --secure        # import settings + API key scan

After migration, --secure automatically runs LyrieProviderValidator on all imported providers and MCP server configs.

Tool-Loop Detection

Every agent run automatically tracks tool call fingerprints. If the same normalized call appears 3+ times in a single run, it’s flagged as a loop and the router triggers fallback classification.

Gateway Degraded Mode

If a channel plugin (e.g., Discord token expired) fails to start, Lyrie boots in degraded mode instead of crashing. The remaining channels stay online. lyrie doctor shows which plugins degraded and why.

🛡️ The Shield Doctrine

Every Lyrie surface that touches untrusted text passes a Shield gate. No exceptions, no carve-outs.

Surface	Hook	Status
Channel inbound (DMs)	evaluateDmPolicy (router)	✅
Pairing greeting	DmPairingManager.greet → scanInbound	✅
Memory recall	searchAcrossSessions → scanRecalled	✅
MCP tool results	McpRegistry.shieldFilter	✅
Tool output (untrustedOutput=true)	ToolExecutor.shieldFilterOutput	✅
Skill output	SkillManager.shieldFilter	✅
Diff-view applied edits	EditEngine.plan → scanRecalled	✅
Attack-surface evidence	buildAttackSurface → sanitizeEvidence	✅
Pentest scan target input	runner.ts → scanInbound	✅

Full rule: docs/shield-doctrine.md.

📦 Repo layout

Path	What
packages/core/	Lyrie agent core — engine, memory, skills, tools, MCP, attack-surface mapper, Stages A–F validator, EditEngine, Shield Guard
packages/gateway/	Multi-channel gateway (Telegram / WhatsApp / Discord) with DM pairing
packages/mcp/	@lyrie/mcp — Model Context Protocol adapter
packages/shield/	Lyrie Shield — Rust cybersecurity engine
packages/omega-suite/	Lyrie OMEGA — autonomous security intelligence backend powering research.lyrie.ai
packages/ui/	Lyrie war-room dashboard (Next.js)
action/	Lyrie Pentest GitHub Action
research/	Reproducible CVE exploit labs (Dockerfile + PoC + Sigma + YARA + IOCs)
tools/exploit-lab/	Lab orchestration framework
skills/	Lyrie skills (extensible, self-improving)
scripts/	Operator CLIs: doctor, pairing, mcp, edits, understand, release helpers
docs/	Architecture, contributing, Shield Doctrine, channel guides

🧠 Model support

Model-agnostic. Lyrie routes per task class automatically:

Tier	Default model	Use
Brain	Claude Opus 4.7	Strategy, complex reasoning
Coder	GPT-5.5 / GPT-5.4-Codex	Code generation, refactors
Reasoning	o4-mini	Step-by-step deliberation
Fast	Gemini 3.1 Flash / Haiku 4.5	Quick lookups, classification
Bulk	MiniMax-M2.7-HS	Mass content, parallel batches
Local	Qwen / Gemma / Llama-local	Private, self-hosted

Bring any model — Anthropic, OpenAI, Google, xAI, MiniMax, Ollama, or your own endpoint. No lock-in.

📡 Channels

Telegram · WhatsApp · Discord · Slack · Signal · iMessage · CLI · Webchat — connect Lyrie to wherever you already work. DM pairing on by default for production deployments.

🛠 Operator CLIs

bun run doctor                    # self-diagnostic (env, channels, security, deps)
bun run understand                # Lyrie Attack-Surface Map of any workspace
bun run scan <repoUrl>            # free Lyrie OSS-Scan against a public repo
bun run intel list                # list cached Lyrie Threat-Intel advisories
bun run intel scan-deps           # match research.lyrie.ai feed against package.json
bun run intel lookup CVE-2024-7399
bun run proxy scan https://target  # capture + classify + audit any HTTP target
bun run pairing list              # show pending DM pairing requests
bun run pairing approve <chan> <code>
bun run mcp list                  # list MCP-server tools available to Lyrie
bun run edits list                # show pending diff-view edits awaiting approval
bun run edits approve <planId>

Lyrie OSS-Scan — free public scan

Any public repo, one command:

bun run scan https://github.com/<owner>/<repo>

Lyrie clones the repo (--depth 1), runs the Attack-Surface Mapper, all eight Multi-Language Scanners, then Stages A–F Validator — returns the confirmed findings with auto-PoCs and Lyrie remediation summaries. Allowlisted hosts: github.com, gitlab.com, bitbucket.org, codeberg.org. Loopback / private addresses refused at the URL gate.

🌌 The Lyrie ecosystem

Product	Status	What it does
Lyrie Agent (this repo)	OSS · MIT	Your autonomous AI operator + GitHub Action
Lyrie Shield	Native iOS/Android/macOS	Real-time device protection, anti-malware, anti-rogue-AI
Lyrie Research	research.lyrie.ai	KEV-driven verified threat intel, reproducible exploit labs
Lyrie OMEGA	OSS · MIT (in this repo)	Autonomous security-intelligence backend
Lyrie SaaS	lyrie.ai	Hosted Shield, WAF, scanner, breach monitoring

Together: a complete digital guardian that operates and defends.

✅ Quality & tests

• 465 tests passing / 0 failing — 373 TypeScript + 92 Python
• Multi-platform CI (Node 20/22/24 × Ubuntu/macOS) + Rust Shield build
• Weekly CodeQL security analysis + Dependabot
• Pre-commit hooks: gitleaks, codespell, hygiene
• Lyrie Pentest Action runs on this repo every PR — Lyrie is its own first user

# TypeScript suite
bun test packages/ action/
# → 373 pass · 0 fail

# Python SDK
cd sdk/python && PYTHONPATH=. python -m pytest tests/
# → 92 pass · 0 fail

🔁 Migrating from another agent?

lyrie migrate --from openclaw      # ports memory, skills, config
lyrie migrate --from claude-code   # imports MCP servers + provider keys
lyrie migrate --from cursor        # imports model config + extensions
lyrie migrate --from hermes        # ports skills + trajectory
lyrie migrate --from autogpt       # ports goals + memory
lyrie migrate --from all           # auto-detect all installed platforms

# With security scan (recommended)
lyrie migrate --from claude-code --secure  # import + CVE check MCP servers
lyrie migrate --detect --dry-run           # preview what would be imported

One command. 11 supported platforms. Full memory + skills + config retained.

Supported: openclaw, claude-code, cursor, hermes, autogpt, nanoclaw, zeroclaw, dify, superagi, nanobot, grip-ai

🤝 Contributing

See CONTRIBUTING.md. New CVE labs follow tools/exploit-lab/LAB-PROTOCOL.md.

Code of Conduct: CODE_OF_CONDUCT.md. PRs that weaponize Lyrie tooling against unconsenting targets are rejected.

🔐 Security

See SECURITY.md. Responsible disclosure goes to [e-mail].

Cybersecurity isn't a feature here — it's the product.

📜 License

MIT. Use it, fork it, build on it.

Lyrie.ai — Built by OTT Cybersecurity LLC

Research · @lyrie_ai · lyrie.ai · overthetop.ae

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