GenAI Development Stack
Archon orchestration + SPARC methodology + Multimodal RAG — backed by GitHub & Linear
Project Backbone
source of truth
📋
Linear
Issue tracking, cycles, roadmap
Issues = atomic work units for agents
Cycles = sprint boundaries
Labels: agent/auto human/review parallel-ok
Status automations synced with GitHub PRs
Linear API
Webhooks
GitHub sync
🐙
GitHub
Code, PRs, Actions, review gates
Repos = all project codebases
PRs = agent output review surface
Actions = CI/CD + agent trigger pipelines
Branch protection: agents can't merge to main
gh CLI
Actions
Webhooks
Bidirectional sync: Linear issue created → triggers GitHub Action → agent picks up work → opens PR → PR merge auto-closes Linear issue → cycle progresses. Magic words in commits (Fixes LIN-123) link everything.
↓
Linear issue assigned to agent → webhook fires → Archon picks up
↓
Orchestration Layer
Archon OS
brain
🏛️
Archon
Agent orchestrator + knowledge backbone
Planner: Reasoning LLM (o3-mini / Claude) decomposes Linear issue into SPARC phases
Router: Assigns phases to specialized agents, detects parallelizable work
Knowledge: Surfaces docs, codebase context, and RAG results to each agent
Feedback loop: Reviews agent output, triggers refinement if quality gate fails
LangGraph
Pydantic AI
MCP
Multi-provider LLM
🧠
Multimodal RAG
Cognee + Honcho + Hermes
Feeds codebase knowledge to agents
FTS5 / LanceDB / FalkorDB triple backend
Honcho personalizes per-agent context
→ See RAG deployment diagram
Cognee SDK
D1 + R2
FalkorDB
↓
Archon decomposes → assigns SPARC phases to coding agents
↓
Execution Methodology
SPARC by ruvnet
S
Specification
Define objectives, requirements, constraints, edge cases from the Linear issue
→ Researcher agent
P
Pseudocode
High-level plan, algorithm design, API contracts before writing real code
→ Planner agent
A
Architecture
System design, data flows, file structure, dependency mapping from RAG context
→ Architect agent
R
Refinement
Iterative implementation, TDD, linting, type-checking, review loops
→ Coder agent(s)
C
Completion
Tests pass, docs written, PR opened, Linear issue updated, deploy readiness
→ Deployer agent
Boomerang pattern: Refined code loops back to Architecture validation. If structural drift detected, Archon restarts from phase A. Each phase gate is a quality checkpoint — code only advances if tests, lint, and type checks pass.
↓
SPARC phases execute via coding agents — parallel when independent
↓
Coding Agents
execution
parallelizable
⚡
Claude Code
Primary coding agent
CLI agentic coding with MCP tools
Deep codebase understanding
SPARC-compatible via system prompts
CLI
MCP
Opus / Sonnet
🦘
Roo Code
SPARC-native coding agent
Native SPARC mode integration
Boomerang task orchestration
VS Code embedded execution
VS Code
SPARC modes
🔮
Cursor / Cline
Secondary coding agents
IDE-integrated AI coding
Good for UI/frontend work
SPARC prompts via .cursorrules
IDE
Multi-model
Parallel Execution — Independent Work Streams
Lane A — Claude Code
Bug fix: auth token refresh race condition (LIN-142)
Branch: fix/auth-token-142
running
Lane B — Claude Code
Bug fix: rate limiter not respecting burst config (LIN-143)
Branch: fix/rate-limiter-143
running
Lane C — Roo Code
Feature: add FTS5 search to settings page (LIN-144)
Branch: feat/fts5-settings-144
running
Lane D — Queued
Refactor: extract shared utils (LIN-145) — depends on A + B merging
Blocked: waits for fix/ branches
queued
Parallelism rule: Archon checks file-level overlap between issues. If no shared files → parallel lanes. If overlap → sequential with dependency. Each lane = separate git branch, separate agent instance.
↓
agents push branches → open PRs → GitHub Actions CI → human review
↓
Quality Gates
automated + human
✅
GitHub Actions CI
Automated quality checks
Lint + type-check + unit tests
Integration tests (non-overlapping = parallel)
Security scan (SAST/dependency audit)
SPARC Completion gate: all phases validated
Actions
pytest / vitest
ruff / eslint
🔄
Archon Feedback Loop
Auto-refinement on CI failure
CI fails → Archon reads error logs
Routes back to SPARC Refinement phase
Agent fixes → re-pushes → CI re-runs
Max 3 retries before escalating to human
Webhook
Auto-retry
👁️
Human Review
Final merge authority
PR review with AI-generated summary
Approve → merge → Linear auto-closes
Request changes → back to Archon
Agents never merge to main directly
PR review
CODEOWNERS
End-to-End: Issue → Merged Code
1
Issue Created
Linear issue with description, acceptance criteria, labels
Linear
2
Archon Picks Up
Webhook → decompose into SPARC phases → check for parallel opportunities
Archon
3
RAG Context
Cognee retrieves relevant code, docs, graph context for the task
RAG stack
4
S → P → A
Spec → Pseudocode → Architecture phases produce implementation plan
SPARC agents
5
Code (parallel)
Claude Code / Roo Code implement on separate branches. Non-overlapping = parallel
Coding agents
6
CI + Refinement
Actions run tests. Fail → auto-retry via Archon (max 3x). Pass → PR ready
GitHub Actions
7
Review + Merge
Human reviews PR, approves → merge → Linear auto-closes → cycle updates
GitHub + Linear
| Integration |
Direction |
Protocol |
Trigger |
Purpose |
| Linear → Archon |
webhook |
HTTPS |
Issue created / updated |
Agent picks up new work, detects priority changes |
| Archon → GitHub |
API |
gh CLI / REST |
SPARC phase starts |
Create branch, push commits, open PR |
| GitHub → Archon |
webhook |
HTTPS |
CI pass/fail, PR review |
Trigger refinement loop or mark complete |
| GitHub ↔ Linear |
bidirectional |
Native sync |
PR merge / commit keywords |
Auto-close issues, update status, link PRs |
| Archon → RAG |
in-process |
Python SDK |
Before each SPARC phase |
Feed codebase context, docs, graph relationships to agents |
| Archon → Coding Agents |
spawn |
CLI / API |
SPARC Refinement phase |
Launch Claude Code / Roo Code with task prompt + context |
| Agents → GitHub |
push |
git + gh CLI |
Code written |
Commit to branch, open/update PR with SPARC summary |
Where Things Run infrastructure
Fly.io / VPS
Archon — orchestrator process
Hermes + Cognee — RAG engine (co-located)
Honcho — sidecar memory service
Local SQLite FTS5 — conversation cache
Cloudflare
D1 — Cognee KB (FTS5 relational)
R2 — LanceDB vector files
Workers — webhook receivers, D1 API adapter
Separate Host
FalkorDB — graph DB (FalkorDB Cloud / Docker on Fly)
Postgres — Honcho user store (Neon / Supabase free)
Local Dev Machine
Claude Code CLI — interactive coding
Roo Code / Cursor — IDE agents
falkordblite — embedded graph for dev