"I have more ideas than time to try them out" — The problem we're solving
Caution
This project is a research demonstrator. It is in early development and may change significantly. Using permissive AI tools in your repository requires careful attention to security considerations and careful human supervision, and even then things can still go wrong. Use it with caution, and at your own risk.
Amplifier is a complete development environment that takes AI coding assistants and supercharges them with discovered patterns, specialized expertise, and powerful automation — turning a helpful assistant into a force multiplier that can deliver complex solutions with minimal hand-holding.
We've taken our learnings about what works in AI-assisted development and packaged them into a ready-to-use environment. Instead of starting from scratch every session, you get immediate access to proven patterns, specialized agents for different tasks, and workflows that actually work.
Amplifier provides powerful tools and systems:
- 20+ Specialized Agents: Each expert in specific tasks (architecture, debugging, security, etc.)
- Pre-loaded Context: Proven patterns and philosophies built into the environment
- Parallel Worktree System: Build and test multiple solutions simultaneously
- Knowledge Extraction System: Transform your documentation into queryable, connected knowledge
- Conversation Transcripts: Never lose context - automatic export before compaction, instant restoration
- Automation Tools: Quality checks and patterns enforced automatically
Before starting, you'll need:
- Python 3.11+ - Download Python
- Node.js - Download Node.js
- VS Code (recommended) - Download VS Code
- Git - Download Git
Platform Note: Development and testing has primarily been done in Windows WSL2. macOS and Linux should work but have received less testing. Your mileage may vary.
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Clone the repository:
git clone https://github.com/microsoft/amplifier.git cd amplifier -
Run the installer:
make install
This installs Python dependencies, the Claude CLI, and sets up your environment.
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Configure your data directories (Recommended but optional):
Why configure this? By default, Amplifier stores data in
.data/(git-ignored). But centralizing your data externally gives you:- Shared knowledge across all worktrees - Every parallel experiment accesses the same knowledge base
- Cross-device synchronization - Work from any machine with the same accumulated knowledge
- Automatic cloud backup - Never lose your extracted insights
- Reusable across projects - Apply learned patterns to new codebases
Set up external directories:
cp .env.example .env # Edit .env to point to your preferred locationsExample configuration using cloud storage:
# Centralized knowledge base - shared across all worktrees and devices # Using OneDrive/Dropbox/iCloud enables automatic backup! AMPLIFIER_DATA_DIR=~/OneDrive/amplifier/data # Your source materials (documentation, specs, design docs, notes) # Can point to multiple folders where you keep content AMPLIFIER_CONTENT_DIRS=.data/content,~/OneDrive/amplifier/content,~/Documents/notes
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Activate the environment (if not already active):
source .venv/bin/activate # Linux/Mac/WSL .venv\Scripts\activate # Windows
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Install global access (Optional but recommended):
make install-global
This installs the
amplifiercommand globally, letting you use Amplifier on any project from anywhere:cd ~/my-other-project amplifier # Starts Claude with Amplifier agents for this project
Start Claude in the Amplifier directory to get all enhancements automatically:
cd amplifier
claude # Everything is pre-configured and readyThe power of Amplifier is no longer confined to the Amplifier directory. Use all 20+ specialized agents, knowledge extraction, and automation tools on any codebase, anywhere on your system.
After running make install-global, use Amplifier from any directory:
# Work on any project
cd ~/my-web-app
amplifier
# Or specify a different project
amplifier ~/dev/my-python-api
# Pass Claude options
amplifier ~/my-project --model sonnet
amplifier ~/my-app --print "Fix the authentication bug"If you prefer not to install globally:
cd ~/dev/amplifier
./amplifier-anywhere.sh ~/path/to/your/project
# Or with Claude options
./amplifier-anywhere.sh ~/my-app --model sonnetFor maximum control:
cd ~/dev/amplifier
source .venv/bin/activate
claude --add-dir /path/to/your/projectImportant: When Claude starts, always begin with this message template:
I'm working in [YOUR_PROJECT_PATH] which doesn't have Amplifier files.
Please cd to that directory and work there.
Do NOT update any issues or PRs in the Amplifier repo.
Use [AGENT_NAME] to [TASK_DESCRIPTION].
Examples:
"Use zen-architect to design my application's caching layer""Deploy bug-hunter to find why my login system is failing""Have security-guardian review my API implementation for vulnerabilities""Use modular-builder to implement the user profile feature"
✅ All 20+ specialized agents work on your projects
✅ Shared knowledge base - insights from one project help others
✅ Same powerful automation - quality checks, parallel development
✅ Project isolation - changes only affect your target project
✅ Works anywhere - no need to copy files or modify your projects
Command not found: amplifier
# Check if ~/bin is in PATH
echo $PATH | grep $HOME/bin
# Add to PATH if missing
echo 'export PATH="$HOME/bin:$PATH"' >> ~/.zshrc # or ~/.bashrc
source ~/.zshrcCannot find Amplifier installation
# The global command looks for Amplifier in these locations:
# - ~/dev/amplifier (most common)
# - ~/amplifier
# - ~/repos/amplifier
# - ~/code/amplifier
# Create a symlink if needed
ln -s /path/to/your/amplifier ~/dev/amplifierGet help anytime
amplifier --help # Show usage help
amplifier --version # Show version infoWhy use this? Stop wondering "what if" — build multiple solutions simultaneously and pick the winner.
# Try different approaches in parallel
make worktree feature-jwt # JWT authentication approach
make worktree feature-oauth # OAuth approach in parallel
# Compare and choose
make worktree-list # See all experiments
make worktree-rm feature-jwt # Remove the one you don't wantEach worktree is completely isolated with its own branch, environment, and context.
See the Worktree Guide for advanced features, such as hiding worktrees from VSCode when not in use, adopting branches from other machines, and more.
See costs, model, and session info at a glance:
Example: ~/repos/amplifier (main → origin) Opus 4.1 💰$4.67 ⏱18m
Shows:
- Current directory and git branch/status
- Model name with cost-tier coloring (red=high, yellow=medium, blue=low)
- Running session cost and duration
Enable with:
/statusline use the script at .claude/tools/statusline-example.sh
Instead of one generalist AI, you get 20+ specialists:
Core Development:
zen-architect- Designs with ruthless simplicitymodular-builder- Builds following modular principlesbug-hunter- Systematic debuggingtest-coverage- Comprehensive testingapi-contract-designer- Clean API design
Analysis & Optimization:
security-guardian- Security analysisperformance-optimizer- Performance profilingdatabase-architect- Database design and optimizationintegration-specialist- External service integration
Knowledge & Insights:
insight-synthesizer- Finds hidden connectionsknowledge-archaeologist- Traces idea evolutionconcept-extractor- Extracts knowledge from documentsambiguity-guardian- Preserves productive contradictions
Meta & Support:
subagent-architect- Creates new specialized agentspost-task-cleanup- Maintains codebase hygienecontent-researcher- Researches from content collection
[See .claude/AGENTS_CATALOG.md for the complete list]
Why use this? Stop losing insights. Every document, specification, design decision, and lesson learned becomes part of your permanent knowledge that Claude can instantly access.
Note
Knowledge extraction is an evolving feature that continues to improve with each update.
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Add your content (any text-based files: documentation, specs, notes, decisions, etc.)
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Build your knowledge base:
make knowledge-update # Extracts concepts, relationships, patterns -
Query your accumulated wisdom:
make knowledge-query Q="authentication patterns" make knowledge-graph-viz # See how ideas connect
Never lose context again. Amplifier automatically exports your entire conversation before compaction, preserving all the details that would otherwise be lost. When Claude Code compacts your conversation to stay within token limits, you can instantly restore the full history.
Automatic Export: A PreCompact hook captures your conversation before any compaction event:
- Saves complete transcript with all content types (messages, tool usage, thinking blocks)
- Timestamps and organizes transcripts in
.data/transcripts/ - Works for both manual (
/compact) and auto-compact events
Easy Restoration: Use the /transcripts command in Claude Code to restore your full conversation:
/transcripts # Restores entire conversation history
The transcript system helps you:
- Continue complex work after compaction without losing details
- Review past decisions with full context
- Search through conversations to find specific discussions
- Export conversations for sharing or documentation
Transcript Commands (via Makefile):
make transcript-list # List available transcripts
make transcript-search TERM="auth" # Search past conversations
make transcript-restore # Restore full lineage (for CLI use)A one-command workflow to go from an idea to a module (Contract & Spec → Plan → Generate → Review) inside the Amplifier Claude Code environment.
- Run inside a Claude Code session:
/modular-build Build a module that reads markdown summaries, synthesizes net-new ideas with provenance, and expands them into plans. mode: auto level: moderate - Docs: see
docs/MODULAR_BUILDER_LITE.mdfor the detailed flow and guardrails. - Artifacts: planning goes to
ai_working/<module>/…(contract/spec/plan/review); code & tests toamplifier/<module>/…. - Isolation & discipline: workers read only this module’s contract/spec plus dependency contracts. The spec’s Output Files are the single source of truth for what gets written. Every contract Conformance Criterion maps to tests. 〔Authoring Guide〕
auto(default): runs autonomously if confidence ≥ 0.75; otherwise falls back toassist.assist: asks ≤ 5 crisp questions to resolve ambiguity, then proceeds.dry-run: plan/validate only (no code writes).
Re‑run /modular-build with a follow‑up ask; it resumes from ai_working/<module>/session.json.
make check # Format, lint, type-check
make test # Run tests
make ai-context-files # Rebuild AI context- Design: "Use zen-architect to design my notification system"
- Build: "Have modular-builder implement the notification module"
- Test: "Deploy test-coverage to add tests for the new notification feature"
- Investigate: "Use bug-hunter to find why my application's API calls are failing"
- Verify: "Have security-guardian review my authentication implementation"
- Extract:
make knowledge-update(processes your documentation) - Query:
make knowledge-query Q="error handling patterns" - Apply: "Implement error handling using patterns from our knowledge base"
[!IMPORTANT] > This is an experimental system. We break things frequently.
- Not accepting contributions yet (but we plan to!)
- No stability guarantees
- Pin commits if you need consistency
- This is a learning resource, not production software
- No support provided - See SUPPORT.md
We're building toward a future where:
- You describe, AI builds - Natural language to working systems
- Parallel exploration - Test 10 approaches simultaneously
- Knowledge compounds - Every project makes you more effective
- AI handles the tedious - You focus on creative decisions
The patterns, knowledge base, and workflows in Amplifier are designed to be portable and tool-agnostic, ready to evolve with the best available AI technologies.
See AMPLIFIER_VISION.md for details.
- Knowledge extraction works best in Claude environment
- Processing time: ~10-30 seconds per document
- Memory system still in development
"The best AI system isn't the smartest - it's the one that makes YOU most effective."
Note
This project is not currently accepting external contributions, but we're actively working toward opening this up. We value community input and look forward to collaborating in the future. For now, feel free to fork and experiment!
Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit Contributor License Agreements.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.