🐇 FallbackRabbit

Auto-generate, test, and optimize LLM fallback chains.

When your primary LLM goes down, FallbackRabbit makes sure you fail gracefully — not catastrophically. Build routing chains, simulate outages, measure latency, and export configs for your production stack.

Features

• 🔗 Smart Fallback Chains — Define priority-ordered provider chains with automatic failover
• 🧪 Simulation Engine — Run prompts through chains with simulated outages, rate limits, and timeouts
• ⚡ Real Provider Calls — Test against OpenAI, Anthropic, Azure, Ollama, or custom endpoints
• 📦 Multi-Format Export — Export to LiteLLM, OpenRouter, LangChain, Haystack, or custom Jinja2 templates
• 🌐 REST API — 15 endpoints for chain CRUD, testing, export, and import
• 📡 WebSocket — Live test progress and chain lifecycle events
• 📊 Web Dashboard — Dark-themed SPA at /dashboard — create chains, run tests, export configs
• 🔑 API Key Auth — Static keys with labeled key names, Bearer token, and query param support
• ⏱️ Rate Limiting — Token bucket, per-IP + global limits with burst control
• 💾 Persistent Storage — In-memory or SQLite backends
• 🖥️ Rich CLI — Tables, panels, progress bars via Rich

Quick Start

Install

pip install fallbackrabbit
# or
uv add fallbackrabbit

CLI Usage

# Create a starter chain config
fallbackrabbit init my-chain.yaml
# Validate a chain
fallbackrabbit validate my-chain.yaml
# Test a chain with simulated prompts
fallbackrabbit test my-chain.yaml --prompts 10
# Export to LiteLLM config
fallbackrabbit export my-chain.yaml --format litellm --output litellm.yaml
# Start the REST API server
fallbackrabbit serve --port 8000

Python SDK

importasynciofromfallbackrabbit.modelsimportChain, Provider, FallbackRule, ErrorType, FallbackActionfromfallbackrabbit.simulatorimportSimulator, generate_test_promptsasyncdefmain():
chain=Chain(
name="production-chain",
providers=[
Provider(name="GPT-4", model_id="gpt-4", api_base="https://api.openai.com/v1", priority=0),
Provider(name="Claude", model_id="claude-3-sonnet", api_base="https://api.anthropic.com", priority=1),
Provider(name="Llama3", model_id="llama3", api_base="http://localhost:11434", priority=2),
],
fallback_rules=[
FallbackRule(condition=ErrorType.RATE_LIMIT, action=FallbackAction.RETRY, max_retries=2, wait_seconds=1.0),
FallbackRule(condition=ErrorType.TIMEOUT, action=FallbackAction.FAILOVER),
],
)
sim=Simulator(chain=chain)
prompts=generate_test_prompts(10)
report=awaitsim.run_batch(prompts)
print(f"Success rate: {report.success_rate:.1%}")
print(f"Average latency: {report.avg_latency_ms:.0f}ms")
asyncio.run(main())

REST API

# Start the server
fallbackrabbit serve --port 8000
# Create a chain
curl -X POST http://localhost:8000/chains \
-H "Content-Type: application/json" \
-d '{"name": "my-chain", "providers": [{"name": "gpt-4", "model_id": "gpt-4", "api_base": "https://api.openai.com/v1", "priority": 1}]}'# Test it
curl -X POST http://localhost:8000/chains/{id}/test \
-H "Content-Type: application/json" \
-d '{"prompts": ["Hello"], "outages": [{"provider": "gpt-4", "error_type": "timeout"}]}'# Export to LiteLLM config
curl -X POST http://localhost:8000/chains/{id}/export \
-H "Content-Type: application/json" \
-d '{"format": "litellm"}'

Docker

# Build and run
docker compose up -d
# Or build manually
docker build -t fallbackrabbit .
docker run -p 8000:8000 -v ./data:/app/data fallbackrabbit

The API will be available at http://localhost:8000 and the dashboard at http://localhost:8000/dashboard.

API Endpoints

Method	Path	Description
GET	/health	Health check
POST	/chains	Create a new chain
GET	/chains	List all chains
GET	/chains/{id}	Get chain details
PATCH	/chains/{id}	Update a chain
DELETE	/chains/{id}	Delete a chain
GET	/chains/{id}/routing	Get routing table
GET	/chains/{id}/summary	Get chain summary
GET	/chains/{id}/validate	Validate chain
POST	/chains/{id}/optimize	Optimize provider order
POST	/chains/{id}/apply-rules	Apply fallback rules
POST	/chains/{id}/test	Run test simulation
GET	/chains/{id}/test/single	Test single prompt
POST	/chains/{id}/export	Export chain config
POST	/chains/{id}/export/template	Template-based export
GET	/chains/import	Import chain from file
GET	/ws	WebSocket (all events)
GET	/ws/chain/{id}	WebSocket (per-chain)
GET	/dashboard	Web dashboard

CLI Reference

Command	Description
init	Create a starter chain YAML
validate	Validate a chain config
test	Run a test simulation
optimize	Optimize chain order by latency
export	Export chain (litellm/langchain/haystack/template)
serve	Start the REST API server

Configuration

Environment Variable	Default	Description
FALLBACKRABBIT_API_KEYS	unset	Comma-separated API keys for auth
FALLBACKRABBIT_RATE_LIMIT_RPM	unset	Requests per minute limit
FALLBACKRABBIT_RATE_LIMIT_BURST	unset	Burst limit for rate limiter
FALLBACKRABBIT_STORAGE_URL	memory	Storage URL (sqlite:///path.db)
OPENAI_API_KEY	unset	OpenAI API key for real calls
ANTHROPIC_API_KEY	unset	Anthropic API key for real calls

Examples

Check the examples/ directory:

• basic_chain.py — Create and test a simple chain
• outage_simulation.py — Simulate provider outages
• export_chain.py — Export to multiple formats
• api_usage.py — Use the REST API from Python
• load_from_yaml.py — Load chains from YAML

Documentation

Full documentation is available at fallbackrabbit.melabuilt.ai.

Architecture

Provider → Chain → FallbackRule → Simulator → ChainReport
↓
Real Provider Calls (optional)
↓
Config Export (5 formats + templates)

Development

# Clone
git clone https://github.com/MelaBuilt-AI/FallbackRabbit.git
cd FallbackRabbit
# Install dev dependencies
uv sync
# Run tests
uv run pytest tests/ -v
# Lint
uv run ruff check fallbackrabbit/ tests/
# Build
uv run python -m build

See CONTRIBUTING.md for detailed development guide.

Tech Stack

• FastAPI — REST API framework
• Pydantic — Data validation
• Click — CLI framework
• httpx — Async HTTP client for real provider calls
• Rich — Terminal output
• Jinja2 — Template-based export
• PyYAML — YAML chain config support

License

MIT — see LICENSE.

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Built by MelaBuilt AI 🐺