jugglerr 0.2.4

🤹 Juggle multiple AI providers like a pro - Chat, embeddings, vision, and more

🤹 Jugglerr

Juggle multiple LLM providers like a pro. Smart routing, multi-key rotation, and automatic fallbacks across Cerebras, Groq, NVIDIA, Mistral, and Cohere.

Why Jugglerr?

Access powerful LLMs across multiple providers with automatic fallbacks, smart routing, and response tracking. Jugglerr maximizes free tier usage while ensuring reliability.

Key Features

• 🤹 Multi-Provider Routing: Cerebras, Groq, NVIDIA, Mistral, Cohere
• 🆓 Free-Tier Optimized: Prioritizes providers with generous free tiers
• 🔄 Multi-Key Rotation: Multiple API keys per provider for maximum throughput
• 🎯 Capability-Based Routing: Chat, vision, embeddings, reranking, TTS, STT
• ⚡ Smart Rate Limiting: Parses headers from Cerebras/Groq to avoid 429s
• 🔀 Automatic Fallbacks: Complete fallback chains for all request types
• � *Response Tracking: Track which models/providers were used for each request
• � *Real-Time Streaming: Token-by-token streaming for chat
• 🎤 Specialized Models: Embeddings, reranking, transcription, text-to-speech
• 📦 Zero Config: Auto-loads from .env file

Installation

pip install jugglerr

Quick Start

from jugglerr import Jugglerr

# Auto-loads from .env file
jugglerr = Jugglerr()

# Simple chat request
response = jugglerr.chat("Hello, world!")
print(response)  # "Hello! How can I help you today?"

# Access tracking info
print(response.models_used)  # [{'provider': 'cerebras', 'model': 'llama3.1-8b', ...}]

Setup with .env

Create a .env file in your project root:

# Free tier providers (tried first)
CEREBRAS_API_KEYS=csk_key1,csk_key2,csk_key3
GROQ_API_KEYS=gsk_key1,gsk_key2
NVIDIA_API_KEYS=nvapi_key1

# Additional providers with free tiers (fallback)
MISTRAL_API_KEYS=mistral_key1
COHERE_API_KEYS=cohere_key1

# Keys loaded automatically
jugglerr = Jugglerr()

# Or pass keys directly
jugglerr = Jugglerr(
    cerebras_keys=["csk_..."],
    groq_keys=["gsk_..."],
    nvidia_keys=["nvapi_..."]
)

Core Features

1. Chat with Automatic Fallbacks

from jugglerr import Jugglerr

jugglerr = Jugglerr()

# Simple chat - automatically selects best model
response = jugglerr.chat("Explain quantum computing")
print(response)

# With conversation history
response = jugglerr.chat([
    {"role": "user", "content": "What's 2+2?"},
    {"role": "assistant", "content": "4"},
    {"role": "user", "content": "What about 2+3?"}
])

# Track which models were used
print(response.models_used)
# [{'provider': 'cerebras', 'model': 'llama3.1-8b', 'success': True, ...}]

2. Streaming Responses

# Stream tokens in real-time
for chunk in jugglerr.chat_stream("Tell me a story"):
    print(chunk, end='', flush=True)

# With parameters
for chunk in jugglerr.chat_stream(
    "Explain AI",
    power="super",
    temperature=0.7
):
    print(chunk, end='', flush=True)

3. Embeddings with Fallback Chain

# Generate embeddings (NVIDIA → Cohere → Mistral fallback)
embedding = jugglerr.embed("Hello, world!")
print(embedding)  # [0.123, -0.456, ...]
print(embedding.models_used)  # Track which provider was used
print(embedding.dimensions)  # 1024

# Batch embeddings
embeddings = jugglerr.embed([
    "First document",
    "Second document",
    "Third document"
])

4. Reranking for RAG

# Rerank search results (NVIDIA → Cohere fallback)
results = jugglerr.rerank(
    query="What is machine learning?",
    documents=[
        "ML is a subset of AI...",
        "Python is a programming language...",
        "Neural networks are..."
    ]
)

# Results sorted by relevance
for doc in results:
    print(f"Score: {doc['relevance_score']:.3f} - {doc['text'][:50]}")

print(results.models_used)  # Track provider
print(results.scores)  # [0.95, 0.12, 0.87]

5. Speech-to-Text (Transcription)

# Transcribe audio file
result = jugglerr.transcribe("audio.mp3")
print(result)  # "Hello, this is a test..."
print(result.language)  # "en"
print(result.duration)  # 5.2
print(result.models_used)

# With language hint
result = jugglerr.transcribe("spanish.mp3", language="es")

6. Text-to-Speech

# Generate speech
audio = jugglerr.speak("Hello, world!")
audio.write_to_file("output.mp3")
print(audio.models_used)

# With voice selection
audio = jugglerr.speak(
    "This is a test",
    voice="alloy",
    speed=1.2
)

7. Power Levels & Capabilities

# Use smaller, faster models (7B-32B)
response = jugglerr.chat(
    "Quick question",
    power="regular"
)

# Use larger, more capable models (70B+)
response = jugglerr.chat(
    "Complex analysis needed",
    power="super"
)

# Request specific capabilities
response = jugglerr.chat(
    "Analyze this image",
    capabilities=["vision"]
)

# Large context window
response = jugglerr.chat(
    "Analyze this long document...",
    context_window="large"
)

8. Response Tracking

All responses include models_used attribute:

response = jugglerr.chat("Hello")

# See which models were tried
for attempt in response.models_used:
    print(f"{attempt['provider']}/{attempt['model']}: {attempt['success']}")
    if not attempt['success']:
        print(f"  Error: {attempt['error']}")

# Example output:
# cerebras/llama3.1-8b: True

Fallback Chains

Jugglerr automatically falls back to alternative providers if one fails:

Request Type	Fallback Chain
Chat	Cerebras → Groq → NVIDIA → Mistral → Cohere
Embeddings	NVIDIA → Cohere → Mistral
Reranking	NVIDIA → Cohere
Transcription	Groq
Text-to-Speech	Groq

See FALLBACK_CHAINS.md for complete details.

Supported Providers & Models

Chat Models

Provider	Models	Notes
Cerebras	Llama 3.1/3.3, Qwen3, GLM 4.6, GPT-OSS	Ultra-fast inference (~1000-1700 tok/s)
Groq	Llama 3.1/3.3/4, Qwen3, GPT-OSS, Kimi K2	Vision support (Llama 4)
NVIDIA	Llama 3.1/3.3, Mistral, Qwen, Gemma, GPT-OSS	Hosted on NVIDIA NIM
Mistral	Large, Medium, Pixtral, Magistral, Codestral	Vision & reasoning models
Cohere	Command R/R+	Enterprise-grade

Specialized Models

Type	Providers	Models
Embeddings	NVIDIA, Cohere, Mistral	NV-Embed-v2, embed-english-v3.0, mistral-embed
Reranking	NVIDIA, Cohere	nv-rerankqa-mistral-4b-v3, rerank-english-v3.0
Transcription	Groq	whisper-large-v3, distil-whisper-large-v3
Text-to-Speech	Groq	whisper-large-v3-turbo

See MODEL_LIST.md for complete model catalog.

How It Works

1. Smart Provider Selection: Providers with generous free tiers (Cerebras, Groq, NVIDIA) tried first, others (Mistral, Cohere) as fallback
2. Model Matching: Finds best model based on your requirements (power, capabilities, context)
3. Key Rotation: Cycles through multiple API keys per provider to maximize throughput
4. Rate Limit Awareness: Parses headers from Cerebras/Groq to avoid hitting limits
5. Automatic Fallback: If one provider fails, tries the next automatically
6. Response Tracking: Every response includes which models/providers were attempted

Comparison with Alternatives

Feature	LiteLLM	OpenRouter	Jugglerr
Open source	✅	❌	✅
Multi-key rotation	❌	❌	✅
Rate limit parsing	❌	❌	✅
Response tracking	❌	❌	✅
Embeddings/Reranking	✅	❌	✅
Free-tier optimized	❌	❌	✅

API Reference

Initialization

from jugglerr import Jugglerr

# Auto-load from .env
jugglerr = Jugglerr()

# Or pass keys directly
jugglerr = Jugglerr(
    cerebras_keys=["key1", "key2"],
    groq_keys=["key1", "key2"],
    nvidia_keys=["key1"],
    mistral_keys=["key1"],
    cohere_keys=["key1"]
)

Chat Methods

# chat(messages, power="regular", capabilities=None, context_window=None, 
#      temperature=0.7, max_tokens=None, preferred_provider=None, 
#      preferred_model=None, **kwargs) -> ChatResponse

response = jugglerr.chat(
    "Hello",                    # String or list of message dicts
    power="regular",            # "regular" or "super"
    capabilities=["vision"],    # List of required capabilities
    context_window="large",     # "small", "medium", or "large"
    temperature=0.7,            # 0.0 to 2.0
    max_tokens=2000,            # Max tokens to generate
    preferred_provider="groq"   # Try this provider first
)

# chat_stream(...) -> Generator[str]
for chunk in jugglerr.chat_stream("Hello"):
    print(chunk, end='')

Specialized Methods

# Embeddings
embedding = jugglerr.embed("text")  # Returns EmbeddingResponse (list subclass)
embeddings = jugglerr.embed(["text1", "text2"])

# Reranking
results = jugglerr.rerank(
    query="search query",
    documents=["doc1", "doc2", "doc3"],
    top_n=3  # Optional
)  # Returns RerankResponse (list subclass)

# Transcription
text = jugglerr.transcribe(
    "audio.mp3",
    language="en"  # Optional
)  # Returns TranscriptionResponse (str subclass)

# Text-to-Speech
audio = jugglerr.speak(
    "Hello, world!",
    voice="alloy",  # Optional
    speed=1.0       # Optional
)  # Returns SpeechResponse
audio.write_to_file("output.mp3")

Response Objects

All responses include models_used attribute:

response = jugglerr.chat("Hello")
print(response.models_used)
# [{'provider': 'cerebras', 'model': 'llama3.1-8b', 'success': True, 
#   'timestamp': '2025-12-07T...', 'latency_ms': 234}]

# Response types behave like their base types
isinstance(response, str)  # True for ChatResponse
isinstance(embedding, list)  # True for EmbeddingResponse
isinstance(results, list)  # True for RerankResponse

Examples

See the examples/ directory for complete examples:

• naive_user_style.py - Simple, user-friendly examples
• test_tracking.py - Response tracking demonstration
• models_used_tracking.py - Detailed tracking examples
• test_new_api.py - Complete API test suite

What's Implemented

✅ Core Features

• Multi-provider routing (Cerebras, Groq, NVIDIA, Mistral, Cohere)
• Capability-based model selection
• Rate limit parsing (Cerebras, Groq)
• Multi-key rotation per provider
• Automatic fallback chains
• Response tracking (models_used)

✅ Request Types

• Chat (with streaming)
• Embeddings (with fallback chain)
• Reranking (with fallback chain)
• Transcription (speech-to-text)
• Text-to-speech

✅ Developer Experience

• Auto-load from .env file
• Simple API (chat(), embed(), rerank(), etc.)
• Response objects behave like native types (str, list)
• Comprehensive documentation

Roadmap

🔮 Future Enhancements

• Async support
• Cost tracking per request
• Caching layer
• More providers (Anthropic, DeepSeek)
• Vision model support
• Tool calling examples

Documentation

📚 Complete Documentation - All guides, references, and implementation details

Quick Links:

• 📖 User Guide - Complete usage guide
• 🔄 Fallback Chains - Provider fallback mapping
• 📊 Response Tracking - Track model usage
• 🗂️ Model List - All available models

Contributing

Contributions welcome! Please read CONTRIBUTING.md first.

License

MIT License - see LICENSE file for details.

Support & Sponsorship

Get Help

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

Support This Project

If Jugglerr helps you save time and money, consider supporting its development:

☕ Buy Me a Coffee - One-time support

Consulting & Enterprise Support

Need help integrating Jugglerr into your production systems? I offer:

• Custom integration support
• Enterprise deployment assistance
• Performance optimization
• Custom feature development
• Training for your team

📧 Contact: [e-mail]

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Built with ❤️ by Spandan Bhol

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Keep all your LLMs in the air! 🤹