context-foundry 2.5.4

The AI That Builds Itself: Recursive Claude Spawning via Meta-MCP

Context Foundry

What if you could describe what you want to build, walk away, and come back to working code?

Context Foundry is an autonomous build system that does the tedious parts for you. Give it a task, and it researches, designs, builds, tests, and deploys—without you babysitting every step.

Quick Start | Documentation | Desktop App

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Context Foundry Desktop - Monitor builds, view artifacts, track progress

AI Sidekick - Chat with your builds, ask questions, get insights

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What is Context Foundry?

Most AI coding assistants need you to hold their hand—approving every file change, fixing their mistakes, keeping them on track. Context Foundry works differently.

You describe what you want. It figures out the rest.

You: "Build an interactive math game for kids learning multiplication"

[Go grab coffee]

Result: Working React app with 4 difficulty levels, progress tracking,
        animated feedback, and tests—all pushed to GitHub.

Behind the scenes, it spawns specialized AI agents for each phase: one to research, one to design the architecture, one to write code, one to test. Each agent has fresh context and a focused job.

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How It Works

Each build runs through phases, and each phase gets a fresh AI agent with its own context window:

1. Scout — Researches the problem, existing patterns, and constraints
2. Architect — Designs the solution structure, makes technical decisions, and writes Gherkin acceptance criteria
3. Builder — Writes the actual code against the acceptance criteria
4. Test — Validates each Gherkin scenario, catches failures, triggers fixes if needed

If tests fail, it loops back and fixes itself. No manual intervention required.

The key insight: instead of one AI that runs out of context, you get specialized agents that each do one thing well and pass artifacts to the next.

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Key Features

Feature	What it does
Self-Healing Builds	Tests fail? It automatically fixes and retries.
Pattern Learning	Remembers solutions that worked, avoids mistakes it's made before.
Human-in-the-Loop	Review Gherkin acceptance criteria before Builder starts. Clear sign-off gates.
Desktop App	Visual dashboard to watch builds, browse artifacts, chat with the AI.
Daemon Service	Runs in the background, manages job queues, handles resource limits.

Extensions

Want to build Roblox games? Flowise workflows? Something domain-specific?

Extensions let you teach Context Foundry your domain. Add patterns, examples, and constraints to extensions/<your-domain>/ and it'll reference them during builds.

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Architecture

sequenceDiagram
    participant U as User
    participant O as Orchestrator
    participant S as Scout Agent
    participant A as Architect Agent
    participant B as Builder Agent
    participant T as Test Agent
    participant FS as .context-foundry/

    U->>O: Task Description

    rect rgb(225, 245, 254)
        Note over O,S: Scout Phase
        O->>S: spawn claude --system-prompt phase_scout.txt
        S->>FS: Write scout_report.json
        S->>O: Return scout-report.md
        O->>O: Parse MD -> SCOUT_JSON
    end

    rect rgb(232, 245, 233)
        Note over O,A: Architect Phase
        O->>A: spawn claude --system-prompt phase_architect.txt<br/>+ SCOUT_JSON injected
        A->>FS: Write architecture.json
        A->>O: Return architecture.md
        O->>O: Parse MD -> ARCHITECTURE_JSON
    end

    rect rgb(255, 243, 224)
        Note over O,B: Builder Phase
        O->>B: spawn claude --system-prompt phase_builder.txt<br/>+ ARCHITECTURE_JSON injected
        B->>FS: Write build-tasks.json
        B->>FS: Write source code files
        B->>O: Return completion status
    end

    rect rgb(252, 228, 236)
        Note over O,T: Test Phase
        O->>T: spawn claude --system-prompt phase_test.txt<br/>+ ARCHITECTURE_JSON.test_plan
        T->>FS: Write test-report-N.md
        T->>O: Return PASSED/FAILED
    end

    alt Tests PASSED
        O->>U: Build Complete
    else Tests FAILED
        O->>A: Re-run with failure context
        Note over A,T: Loop: Architect Fix -> Builder Fix -> Test
    end

Each phase spawns a fresh Claude instance with isolated context, preventing token bloat and ensuring consistent quality across long builds.

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Understanding Phases and Agents

A common question: Are Scout, Architect, Builder, etc. "agents" or "phases"?

Answer: Both. They are phases from an orchestration perspective, and ephemeral agent instances from an execution perspective.

How It Works

┌─────────────────────────────────────────────────────────────────┐
│                     DAEMON (Orchestrator)                        │
│  runner.py manages pipeline state, spawns agents sequentially   │
└─────────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        ▼                     ▼                     ▼
   ┌─────────┐          ┌─────────┐          ┌─────────┐
   │  Scout  │    →     │Architect│    →     │ Builder │  → ...
   │ Agent   │          │ Agent   │          │ Agent   │
   └─────────┘          └─────────┘          └─────────┘
   200K tokens          200K tokens          200K tokens
   (ephemeral)          (ephemeral)          (ephemeral)
        │                     │                     │
        ▼                     ▼                     ▼
   scout-prompt.json    architect-prompt.json  builder-prompt.json
   scout-report.md      architecture.md        (code files)

Key Characteristics

Aspect	Reality
Context Window	Each phase gets its own fresh 200K tokens
Lifecycle	Ephemeral - spawned, runs, exits, context gone
Communication	Via disk artifacts (not shared memory)
Implementation	Each is a claude CLI subprocess
State	Persisted in .context-foundry/ between phases

Why This Design?

1. Fresh context every phase — Most builds use only 30-50K tokens per phase, leaving plenty of headroom
2. Isolation — If Builder crashes, you don't lose Scout's analysis
3. Resumability — Can restart from any phase since state lives on disk
4. Focus — Each agent has one job and a tailored prompt for it

They're essentially stateless workers that read artifacts, do work, write artifacts, and disappear.

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Features

Feature	Description
Visual Dashboard	See all jobs at a glance with status, duration, and phase progress
Live Duration Counter	Real-time timer showing build progress in seconds
AI Sidekick Chat	Natural language interface to check status, trigger builds, and get help
Phase Timeline	Visual progress through Scout → Architect → Builder → Test phases
Conversation View	See what the AI is thinking during each phase
Artifact Browser	View generated code with syntax highlighting and line numbers
Dark Theme	Native dark mode with beautiful purple accents

Install Desktop App

# Download from releases (macOS)
curl -L https://github.com/your-org/context-foundry/releases/latest/download/ContextFoundry.dmg -o ContextFoundry.dmg
open ContextFoundry.dmg

# Or build from source
cd apps/context-foundry-desktop
npm install
npm run tauri:build

See Desktop App Documentation for full details.

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Quick Start

Option 1: Desktop App (Recommended)

1. Download and install the Desktop App (see above)
2. Launch Context Foundry Desktop
3. Use the Sidekick chat to start your first build:

   "Build a todo app with React"
   

Option 2: CLI

# Install via npm
npm install -g context-foundry

# Start the daemon
cfd start

# Build something
cf build "Create a weather dashboard with React"

This installs the package and automatically configures Claude Code. (Alternative: pip install context-foundry && cf setup)

Option 3: Claude Code Integration

In Claude Code, just ask in natural language:

"Use CF to build a weather dashboard with React"

Walk away. Come back to deployed code on GitHub.

See Quick Start Guide for detailed setup instructions.

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CLI Tools

cfd start             # Start the daemon
cfd status            # Check status
cfd logs <job-id>     # View build logs
cfd list              # List active builds
cfd stop              # Stop the daemon

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Documentation

Guide	Description
Quick Start	Get running in 5 minutes
Desktop App	Native macOS/Windows application
Dashboard	Web dashboard development guide
User Guide	Detailed usage instructions
Architecture	How it works under the hood
Features	Complete feature reference
Innovations	Technical breakthroughs explained
Phase Handoff Flow	Inter-phase data contracts
Troubleshooting	Common issues and fixes
FAQ	Frequently asked questions

Extension Development

Guide	Description
Extension Development	Create domain-specific extensions
Teaching New Languages	Teach Claude languages not in its training data

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File Structure

context-foundry/
├── apps/
│   └── context-foundry-desktop/  # Native macOS/Windows desktop app (Tauri)
├── tools/
│   ├── dashboard/             # React dashboard frontend
│   ├── mcp_server.py          # MCP server entry point
│   ├── mcp_utils/             # Build orchestration, delegation, patterns
│   ├── prompts/phases/        # Phase-specific system prompts
│   ├── evolution/             # Daemon, self-improvement, safety
│   ├── cli.py                 # Main CLI (cf command)
│   └── cfd                    # Daemon CLI script
├── context_foundry/
│   └── daemon/                # Python daemon (HTTP API, job management)
├── extensions/                # Domain-specific extensions
├── npm/                       # npm package wrapper
├── docs/                      # Documentation
└── CLAUDE.md                  # Instructions for AI agents

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Contributing

We welcome contributions. See Contributing Guide for details.

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License

MIT License. See LICENSE for details.