Swarm Dao Mcp

Unified AI Agent Governance — One DAO core, multiple host adapters.

Swarm DAO unifies the governance systems from pi-swarm-dao and the legacy opencode-dao project into a single, extensible architecture.

Quick Start

# Clone
git clone https://github.com/guyghost/swarm-dao.git
cd swarm-dao

# Install dependencies (runs the workspace setup and build via `prepare`)
bun install

# Re-run the setup any time: workspace links, Pi extension, optional SDK stubs
bun run setup-workspace

# Start Pi — the extension is auto-discovered
pi

Inside Pi:

> dao_setup          # Initialize with 8 default agents
> dao_propose        # Create a proposal
> dao_deliberate     # Run swarm deliberation
> dao_check          # Quality gates
> dao_ship           # Ship approved proposal (checks deps)

Architecture

┌───────────────────────────────────────────────────────────────────────┐
│  Hosts                                                                │
│  ┌──────┐ ┌──────────┐ ┌─────────┐ ┌───────┐ ┌────────┐ ┌──────────┐ │
│  │  Pi  │ │ OpenCode │ │ Copilot │ │Claude │ │ Codex  │ │ CLI/MCP… │ │
│  └──┬───┘ └────┬─────┘ └────┬────┘ └───┬───┘ └───┬────┘ └────┬─────┘ │
│     │          │            │          │         │           │       │
│  ┌──┴──────────┴────────────┴──────────┴─────────┴───────────┴────┐  │
│  │                     Host Adapter Interface                    │  │
│  │      spawnAgent · spawnAgents · log · exec · readFile · …     │  │
│  └──────────────────────────────┬─────────────────────────────────┘  │
│                                 │                                     │
│  ┌──────────────────────────────┴─────────────────────────────────┐  │
│  │                        Swarm DAO Core                          │  │
│  │  ┌──────────┐ ┌────────────┐ ┌──────────┐ ┌────────┐          │  │
│  │  │Governance│ │Intelligence│ │ Delivery │ │Control │          │  │
│  │  │  (L1)    │ │   (L2)     │ │  (L3)    │ │ (L4)   │          │  │
│  │  └──────────┘ └────────────┘ └──────────┘ └────────┘          │  │
│  └──────────────────────────────────────────────────────────────┘  │
│                                 │                                     │
│  ┌──────────────────────────────┴─────────────────────────────────┐  │
│  │                Persistence (.dao/ local files)                │  │
│  └──────────────────────────────────────────────────────────────┘  │
└───────────────────────────────────────────────────────────────────────┘

The 4 layers above are the governance model (what the DAO does). Internally, @guyghost/swarm-dao-core is organized as a hexagonal functional core — pure domain/models (XState workflows), application use cases, narrow ports, instance-owned adapters, and presenters — with no ambient I/O in the business logic. See ADR-002 and models/README.md for that internal boundary.

Pi spawns sub-agents natively; OpenCode, Copilot, Claude Code, and Codex use manual/MCP-based dispatch (see Pi vs OpenCode Differences for the pattern all non-native hosts share).

Packages

Package	Description
@guyghost/swarm-dao-core	Pure business logic + shared host-tools handlers
@guyghost/swarm-dao-mcp	Swarm DAO as a stdio MCP server (24 tools)
@guyghost/swarm-dao-copilot-adapter	GitHub Copilot plugin (MCP + instructions)
@guyghost/swarm-dao-claude-adapter	Claude Code plugin (MCP + slash commands)
@guyghost/swarm-dao-codex-adapter	OpenAI Codex plugin (MCP + AGENTS.md)
@guyghost/swarm-dao-pi-adapter	Bridge to Pi coding agent
@guyghost/swarm-dao-opencode-adapter	Bridge to OpenCode
@guyghost/swarm-dao-tmux-adapter	tmux host — one watchable pane per agent (swarm-forge model)
@guyghost/swarm-dao-herdr-adapter	herdr host — each agent is a real coding agent in a herdr workspace
@guyghost/swarm-dao-cli	Standalone CLI (swarm-dao)

4-Layer Governance

Layer	Purpose	Key Concepts
L1 Governance	Decide what enters the roadmap	Proposals, voting, quorum, state machine, amendments
L2 Intelligence	Produce analysis and recommendations	7 specialized agents, parallel deliberation, synthesis
L3 Delivery	Convert decisions into execution	Plans, tasks, execution, verification, artefacts
L4 Control	Reduce risk before publication	Quality gates, audit trail, checklists

The 8 Default Agents

Agent	Weight	Role
Product Strategist	3	Vision, objectives, hypotheses
Research Agent	2	Market, competition, user signals
Solution Architect	3	Technical options, tradeoffs
Critic / Risk Agent	3	Risk scoring, objections, guardrails
Prioritization Agent	2	Impact/cost/risk scoring, roadmap fit
Spec Writer	1	PRD, user stories, acceptance criteria
Delivery Agent	1	Implementation plan, tasks, CI/CD
UX/UI Designer	2	UX/UI critique, design directions, accessibility

Proposal Lifecycle

open ──► deliberating ──► approved ──► controlled ──► executed
                       ╲              ╲              ╲
                     rejected       rejected        failed

CLI Usage

# Install (Homebrew or npm)
brew install guyghost/tap/swarm-dao
npm install -g @guyghost/swarm-dao-cli

# Initialize DAO storage
swarm-dao init

# Setup with default agents
swarm-dao setup

# Create proposal
swarm-dao propose --title "Add dark mode" --type product-feature \
  --description "Implement dark theme for the app"

# List proposals
swarm-dao list
swarm-dao list --status open
swarm-dao list --type security-change

# Show proposal details
swarm-dao show 1

# Cast a vote
swarm-dao vote 1 --position for --reasoning "Low risk, high impact" --weight 3

# Ship (execute) a proposal
swarm-dao ship 1
swarm-dao ship 1 --cascade   # also ship unexecuted dependencies first
swarm-dao ship 1 --force     # skip dependency checks

# Configure GitHub integration (auth via the gh CLI; --issues tracks proposals as GitHub issues)
swarm-dao github-config --owner myorg --repo myrepo

# Create a branch for a proposal
swarm-dao github-branch 1

# Open a pull request for a proposal
swarm-dao github-pr 1 --head-branch dao/1-add-dark-mode

# View audit trail
swarm-dao audit
swarm-dao audit --proposal 1

# View DAO status
swarm-dao status

# View configuration
swarm-dao config

# Run an improvement loop series (any project; gates in a bounded container)
REF=$(echo -n "$(git rev-parse HEAD)" | shasum -a 256 | cut -d' ' -f1)
swarm-dao improve init --series-id s1 --scope ci-health --reference-hash "$REF"
swarm-dao improve once --series-id s1 --sandbox container --image node:22-bookworm
swarm-dao improve status --series-id s1

Full CLI reference — installation, configuration, every command and flag — in packages/cli/README.md.

Pi Usage

The Pi extension is auto-discovered from .pi/extensions/ or ~/.pi/agent/extensions/.

# Initialize
> dao_setup

# Create proposal
> dao_propose title="Add dark mode" type="product-feature" \
    description="Implement dark theme"

# Deliberate (automatic swarm dispatch)
> dao_deliberate proposalId=1

# Check quality gates
> dao_check proposalId=1

# Ship (execute, with dependency check)
> dao_ship proposalId=1

# View audit
> dao_audit proposalId=1

OpenCode Usage

# Initialize
> dao_setup

# Create proposal
> dao_propose title="Add dark mode" type="product-feature" \
    description="Implement dark theme"

# Get dispatch plan (manual sub-agent spawning)
> dao_deliberate proposalId=1

# Record outputs after collecting from sub-agents
> dao_record_outputs proposalId=1 outputs=[...]

# Control gates
> dao_control proposalId=1

# Execute
> dao_execute proposalId=1

Copilot, Claude Code, and Codex Usage

These hosts don't load a native extension/plugin — they speak MCP. Each has a dedicated adapter package that bundles the MCP server plus native config and instructions:

Host	Package	Bundles
GitHub Copilot	@guyghost/swarm-dao-copilot-adapter	.mcp.json, copilot-instructions.md
Claude Code	@guyghost/swarm-dao-claude-adapter	.mcp.json, CLAUDE.md, /dao-* slash commands
OpenAI Codex	@guyghost/swarm-dao-codex-adapter	config.toml snippet, AGENTS.md
Any other MCP host	@guyghost/swarm-dao-mcp	stdio MCP server only

Install the adapter for your host (npm install @guyghost/swarm-dao-<host>-adapter) and follow its README for the exact config file to copy. Deliberation over MCP is always manual, the same pattern as OpenCode above: dao_deliberate returns a dispatch plan, the host spawns the sub-agents, then dao_record_outputs feeds the results back in.

Configuration

Per-project config in .dao/config.json:

{
  "mode": "suggest",
  "criticalPaths": [
    "src/auth/**",
    "src/payment/**",
    ".env*"
  ],
  "agentOverrides": {
    "researcher": { "enabled": false },
    "critic": { "weight": 5 }
  },
  "execution": {
    "isolation": "worktree",
    "worktreeRoot": ".dao/worktrees",
    "baseBranch": "main"
  }
}

mode declares intent (opt-in (default), suggest, enforce) and criticalPaths declares the paths that matter. agentOverrides filters and overrides agents on every dao_deliberate call; mode and criticalPaths are enforced through the edit gate — agents call dao_check_edit with the files they are about to touch before editing:

• opt-in: everything is allowed; critical paths are flagged informationally.
• suggest: everything is allowed; uncovered critical paths produce a non-blocking nudge toward dao_propose.
• enforce: a critical path is only editable when an approved, controlled, or executed proposal declares it in affectedPaths; otherwise the gate blocks the edit and explains how to get approval.

Paths are lexically normalized before matching (absolute paths, backslashes, ./ prefixes, and a/../b segments are refused or resolved), so the gate cannot be evaded by spelling variants — an unmatchable path is refused in every mode. Requests over 200 paths are rejected outright.

dao_check_edit is exposed on every agent-facing host (MCP and the Copilot/Claude/Codex adapters, Pi, OpenCode). It is a deterministic, read-only decision — the gate never edits files and never transitions proposal state.

Ship audit challenge (AUDIT_REQUIRED)

Opt-in, per project:

{ "ship": { "auditChallenge": true } }

The swarm-forge AUDIT_REQUIRED pattern adapted to shipping: the first dao_ship / swarm-dao ship call for a proposal does not execute — it returns AUDIT_REQUIRED and records the decision fingerprint (votes, gates, scope). Only a second, unchanged call executes; any change re-issues the challenge. --force / force=true is an explicit, audited human bypass. Snapshots persist in .dao/ship-audits/. No AI role exists in the model — confirmation is deterministic (models/ship-audit.md).

Execution isolation (worktrees)

When execution.isolation is "worktree" (default "none"), executing or shipping a proposal first carves an isolated git worktree — branch dao/<id>-<slug> checked out under execution.worktreeRoot (default .dao/worktrees, gitignored with the rest of .dao/). The pattern comes from swarm-forge: concurrent executions never step on each other in the shared checkout, work happens on a dedicated branch, and merging back stays a deliberate action (via the existing GitHub PR flow or a plain git merge).

Preparation is idempotent (re-running dao_execute reattaches to the existing branch), and a failed preparation leaves the proposal controlled — no state transition happens without the workspace. The execution snapshot and audit entry record the real branch. Available on every host surface: dao_execute (Pi, OpenCode, MCP hosts) and swarm-dao ship (CLI).

Sequential (Pipeline) Deliberation (advanced, opt-in)

By default the swarm deliberates in parallel — every agent analyzes the proposal independently, which maximizes vote independence. Projects that prefer deeper deliberation can opt into the swarm-forge-style pipeline:

{
  "deliberation": {
    "strategy": "sequential",
    "charsPerAgent": 1500
  }
}

Agents run in order, one at a time (registry order: strategist → researcher → architect → critic → prioritizer → spec-writer → delivery), and each agent receives a ## Prior Analyses section built from the agents before it — analyses only, never votes or reasoning (extractAnalysis strips everything from the ## Vote heading on, and each excerpt is capped at charsPerAgent characters). The deterministic tally is unchanged and stays independent: this is orchestration, not authority — no proposal state, transition, or AI boundary moves. Manual hosts (MCP, OpenCode) receive the pipeline protocol in their dispatch plan and feed analyses forward the same way before recording all outputs together.

Delegated Facet Investigation (advanced, opt-in)

Any agent can declare delegates (facet + archetype) so it can hand off a narrow sub-question to a child agent mid-deliberation. Disabled by default — opt in per project:

{
  "delegation": {
    "enabled": true,
    "maxDepth": 1,
    "maxChildrenPerParent": 3,
    "foldTimeoutMs": 30000
  }
}

• Children are spawned via the same HostAdapter.spawnAgent, one level deep (a child cannot itself delegate).
• The child's output is folded into the parent's content under a ## Delegated Facets section — it never touches the parent's ## Vote.
• Governed by two pure state machines (coordinator budget + per-request gate/fold lifecycle) in packages/core/src/governance/delegation.machine.ts.

Graph Engineering (deterministic change control)

A repository-local change-control overlay for Codex work. It moves a change run from an explicit reviewed model to a verified implementation through an XState machine, typed AI signals, an exact human-approved model hash, six frozen deterministic anchors, and durable evidence — so model approval, implementation authorization, verification, retries, and terminal outcomes are all deterministic.

• Executable model: packages/core/src/models/graph-engineering.machine.ts; spec: models/graph-engineering.md.
• Boundary: it owns only the state of a change run. It never emits proposal events and never writes .dao/. A run may carry an immutable proposalId correlation, but that value grants no permission and causes no transition in either machine.
• AI workers (modeler, implementer) produce signals only; the human owner approves the exact model hash, and tools enforce the six success anchors (model-contract, graph-tests, architecture-contract, repository-ci, runtime-scenario, regression).
• Evidence lives in evidence/graph-runs/ and is not committed.

# Initialize / inspect / submit a signal for a change run
bun run graph:init -- --run-id <id>
bun run graph:status -- --run-id <id>
bun run graph:submit -- --run-id <id> --signal <file>

# Validate the reviewed model hash and graph contract
bun run graph:validate

# End-to-end reference scenario + the six-anchor regression counter-check
bun run graph:demo
bun run graph:regression

Improvement Loop (self-improvement cycle)

A self-improvement layer that sits above the proposal lifecycle and Graph Engineering. Each cycle pairs an optimizing metric with a required counter-metric (Goodhart pairing), audits the metric for drift, arbitrates any conflict between the paired signals deterministically, and only succeeds when six ground-contact anchors pass. A cycle can never succeed on AI judgment alone, and a metric can never travel without its counter-metric.

• Executable model: packages/core/src/models/improvement-loop.machine.ts; spec: models/improvement-loop.md.
• Boundary: it owns only the state of an improvement cycle run (proposalStateAuthority: "none"). It never changes proposal or Graph Engineering status; correlation is immutable and one-way.
• AI workers (sensor, counter-sensor, drift-auditor) emit signals only; a deterministic arbitrator and anchor-verifier decide outcomes, and the human owner owns reference (target) values and the frozen set.
• Evidence lives in evidence/improvement-cycles/ and is not committed.

# Initialize (reference hash required) / inspect / submit a signal for a cycle
bun run improvement:init -- --cycle-id <id> --reference-hash <hash> [--scope <s>]
bun run improvement:status -- --cycle-id <id>
bun run improvement:submit -- --cycle-id <id> --signal <file>

# Validate the reviewed model hash; run the frozen ground-contact anchors
bun run improvement:validate
bun run improvement:anchors

# End-to-end reference scenario + arbitration / frozen-set / regression tests
bun run improvement:demo
bun run improvement:regression

Artefacts

Auto-generated for every approved proposal:

Artefact	Description
Decision Brief	Executive summary with key votes
ADR	Architecture Decision Record
Risk Report	Risks, permissions, guardrails
PRD Lite	User stories, scope, metrics
Implementation Plan	Phases, tasks, critical path
Test Plan	Unit, integration, E2E tests
Release Packet	Changelog, checklist, rollback

GitHub Integration

Available on every host surface: the CLI (swarm-dao github-config / github-branch / github-pr), the MCP tools (dao_config_github, dao_github_create_branch, dao_github_open_pr — exposed by @guyghost/swarm-dao-mcp and the Copilot/Claude/Codex adapters), and the Pi extension and OpenCode plugin (registered as native tools).

Authentication is delegated to the GitHub CLI: run gh auth login once and Swarm DAO never stores or transmits tokens. Pass issues=true if you want proposal modifications tracked as GitHub issues.

# Via an MCP host (Claude, Codex, Copilot, or a generic MCP client)
> dao_config_github owner="myorg" repo="myrepo" issues=true

# Create branch
> dao_github_create_branch proposalId=1

# Open PR
> dao_github_open_pr proposalId=1 headBranch="dao/1-add-dark-mode"

Persistence

DAO state stored in .dao/:

• state.json — monolithic state snapshot (single source of truth, including all proposals)
• decisions/NNN.json — compact decision summaries
• config.json — per-project configuration

Previously each proposal was also mirrored in .dao/proposals/NNN.json "sidecar" files; that redundant copy has been removed. On the first load after upgrading, any existing sidecars are imported into state.json and the proposals/ directory is removed.

herdr Host (real coding agents in herdr workspaces)

Each deliberation agent runs as a real coding agent (pi, claude, codex, grok, opencode…) inside an isolated herdr workspace — herdr tracks its lifecycle, you can attach to any agent pane live, and a blocked agent (approval/question UI) surfaces as an error output, never as a vote.

import { createHerdrHostAdapter } from "@guyghost/swarm-dao-herdr-adapter";
const adapter = createHerdrHostAdapter({ workDir: process.cwd(), kind: "pi" });

Prerequisites: herdr installed and its server running. See packages/herdr-adapter.

tmux Host (one watchable pane per agent)

The swarm-forge execution model for deliberation: each agent runs as its own detached tmux pane — watch it live with tmux attach, inspect scrollback after the fact. Configure the agent CLI in .dao/config.json:

{ "tmux": { "command": "claude -p \"$PROMPT\"", "timeoutMs": 300000 } }

$PROMPT carries the deliberation prompt; stdout is harvested as the agent's output and feeds the same deterministic tally as every other host. See packages/tmux-adapter.

Adding a New Host

See docs/EXTENSION-GUIDE.md.

Quick overview:

import type { HostAdapter } from "@guyghost/swarm-dao-core";

const myAdapter: HostAdapter = {
  hostId: "my-host",
  spawnAgent: async ({ agent, proposal, systemPrompt }) => { /* ... */ },
  spawnAgents: async ({ agents, proposal, maxConcurrent }) => { /* ... */ },
  log: async ({ level, message }) => { /* ... */ },
  getWorkingDirectory: () => process.cwd(),
  readFile: async (path) => { /* ... */ },
  writeFile: async (path, content) => { /* ... */ },
  exec: async (command, options) => { /* ... */ },
  hasCapability: (cap) => true,
};

Testing

# Run all tests
bun test

# Run specific package tests
bun test packages/core/tests
bun test packages/cli/tests

# Run integration tests
bun run test:integration

# Run performance benchmarks
bun run bench
bun run bench:ci

CI/CD

GitHub Actions workflow included (.github/workflows/ci.yml):

• Lint
• Type checking
• Test execution
• Build verification
• Pi extension npm package validation

Release workflow included (.github/workflows/publish.yml):

• Creates Changesets version PRs from pull_request_target
• Publishes to npm from main via GitHub OIDC trusted publishing

Documentation

• ADR-001: Unified Architecture
• ADR-002: Hexagonal Functional Core
• Behavioral Models Overview
• Graph Engineering Model
• Improvement Loop Model
• Command Registry
• MCP Host Integration Guide
• Extension Guide
• Usage Guide
• Agent Prompts
• XState Proposal Machine

License

MIT