By SwarmDo
Session-as-skill browser automation: Playwright + RVF cognitive containers + swarmvector trajectories + AgentDB selector memory + AIDefence PII/injection gates
Probe a site's authentication flow for redirect leaks, missing CSRF, weak session cookies, and OAuth misconfiguration; produces an auth findings.md
Extract structured data via stored browser-templates or one-shot DOM queries, with mandatory AIDefence PII + prompt-injection gates before content reaches the model
Fill a web form by mapping field-name → value, with optional template lookup from browser-templates for known forms
Drive an authentication flow once, sanitize cookies through AIDefence, and vault a reusable cookie handle in browser-cookies for future sessions
Open a named, traced browser session into an RVF cognitive container with a swarmvector trajectory recording every action
Uses power tools
Uses Bash, Write, or Edit tools
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Based on the original, hugely popular ruflo — renamed, self-contained, and MIT-licensed. Full lineage in NOTICE.
An agent meta-harness for Claude Code and Codex.
Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work. Swarmdo is the harness — the execution layer around Claude Code and Codex that adds 100+ specialized agents, coordinated swarms, self-learning memory, federated comms across machines, and enterprise security guardrails. So agents don't just run, they collaborate.
One npx swarmdo init gives Claude Code a nervous system: agents self-organize into swarms, learn from every task, remember across sessions, and — with federation — securely talk to agents on other machines without leaking data. You keep writing code. Swarmdo handles the coordination.
Self-Learning / Self-Optimizing Agent Architecture
User --> Swarmdo (CLI/MCP) --> Router --> Swarm --> Agents --> Memory --> LLM Providers
^ |
+---- Learning Loop <-------+
New to Swarmdo? You don't need to learn 314 MCP tools or 26 CLI commands. After
init, just use Claude Code normally — the hooks system automatically routes tasks, learns from successful patterns, and coordinates agents in the background.
Swarmdo is now Swarmdo — named by
the upstream author, who loves Rust, flow states, and building things that feel inevitable. The "Ru" is the the upstream author. The "flo" is working until 3am. Underneath, powered byCognitum.Oneagentic architecture, running a supercharged Rust-based AI engine, embeddings, memory, and plugin system.
There are two different install paths with very different surface areas. Pick based on what you need (#1744):
| Claude Code Plugin | CLI install (npx swarmdo init) | |
|---|---|---|
| What it gives you | Slash commands + a few skills + agent definitions per-plugin | Full Swarmdo loop — 98 agents, 60+ commands, 30 skills, MCP server, hooks, daemon |
| Files in your workspace | Zero | .claude/, .swarmdo/, CLAUDE.md, helpers, settings |
| MCP server registered | No (memory_store, swarm_init, etc. unavailable to Claude) | Yes |
| Hooks installed | No | Yes |
| Best for | Try a single plugin's commands without committing to the full install | Production use — everything works as documented |
# Add the marketplace
/plugin marketplace add upstream/swarmdo
# Install core + any plugins you need
/plugin install swarmdo-core@swarmdo
/plugin install swarmdo-swarm@swarmdo
/plugin install swarmdo-rag-memory@swarmdo
/plugin install swarmdo-neural-trader@swarmdo
This adds slash commands and agent definitions only. The Swarmdo MCP server is NOT registered, so memory_store, swarm_init, agent_spawn, etc. won't be callable from Claude. For the full loop, use Path B below.
| Plugin | What it does |
|---|---|
| swarmdo-core | Foundation — server, health checks, plugin discovery |
| swarmdo-swarm | Coordinate multiple agents as a team |
| swarmdo-autopilot | Let agents run autonomously in a loop |
| swarmdo-loop-workers | Schedule background tasks on a timer |
| swarmdo-workflows | Reusable multi-step task templates |
| swarmdo-federation | Agents on different machines collaborate securely |
npx claudepluginhub swarmdo/swarmdo --plugin swarmdo-browserAI agent orchestration for Claude Code: swarm coordination, 314 MCP tools, 60+ agent types, persistent AgentDB memory with HNSW vector search, self-learning hooks, SPARC methodology, and GitHub automation
Ponytail for swarmdo — makes agents think like the laziest senior dev in the room: YAGNI, stdlib before dependencies, one line before fifty. Intensity levels lite/full/ultra plus audit, review, debt, and gain sub-skills. Vendored from DietrichGebert/sdo-ponytail (MIT).
Cross-installation agent federation with zero-trust security, peer discovery, consensus-based task routing, and per-call budget circuit breaker (ADR-097)
Self-learning vector database via npx [email protected] — HNSW, adaptive LoRA embeddings, code-graph clustering, hooks routing, brain/SONA, 103 MCP tools
ADR lifecycle management — create, index, supersede, check compliance, and link Architecture Decision Records to code via AgentDB hierarchical store + causal edges (supersedes/amends/depends-on/related)
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
A growing collection of Claude-compatible academic workflow bundles. Covers scientific figures, manuscript writing and polishing, reviewer assessment, citation retrieval, data availability, paper reading, literature search, response letters, paper-to-PPTX conversion, and evidence-grounded Chinese invention patent drafting. Rules are organized as reusable skill folders with explicit workflows and quality checks.
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review
Evidence-gated AI coding workflow: scan → analyze → plan → TDD → execute → fix → verify → review, powered by Codebase Memory MCP >= 0.9.0 with optional Serena LSP intelligence. Includes blast-radius planning, test/cycle gates, independent review, and Windows Git Bash hook auto-resolution.
Harness-native ECC operator layer - 67 agents, 278 skills, 94 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses