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# UniversalAI **The Requests library for AI** — one unified SDK for every LLM provider. Write once, run anywhere. pip install universal-ai from universal_ai import AI ai = AI(provider="openai", model="gpt-4o") response = await ai.chat("What is quantum computing?") print(response.content) ## Why UniversalAI? Building AI applications today means juggling multiple provider SDKs, each with different APIs, error handling, and quirks. UniversalAI gives you **one clean interface** that works across all major providers: * **Same code** works with OpenAI, Anthropic, Gemini, Ollama, Groq, Mistral, OpenRouter, HuggingFace, and Azure OpenAI * **Switch providers** by changing one string — no code rewrite * **Built-in resilience** with retry, caching, rate limiting, and circuit breaker middleware * **Tool calling** works identically across all providers that support it ## Features Feature | Description ---|--- **9 Providers** | OpenAI, Anthropic, Gemini, Ollama, Groq, Mistral, OpenRouter, HuggingFace, Azure OpenAI **Async-first** | Full `async`/`await` with synchronous wrappers for scripts and notebooks **Streaming** | Real-time token streaming from any provider **Tool Calling** | `@tool` decorator with automatic execution loop **Middleware** | Retry, cache, rate limit, circuit breaker, cost tracking, logging **Routing** | Fallback, round-robin, lowest latency, lowest cost strategies **Vision** | Image-aware chat with OpenAI, Anthropic, Gemini **Audio** | Transcription (Whisper) and TTS with OpenAI **Image Generation** | DALL-E 3 support **Embeddings** | OpenAI, Gemini, Mistral, HuggingFace, Azure, OpenRouter **RAG** | Built-in retrieval-augmented generation with chunking and vector store **Agents** | Multi-agent orchestration with coordinator pattern **Context Safety** | Automatic validation and optional truncation **Cost Tracking** | Per-request and cumulative cost estimation **CLI** | Full-featured `uai` command-line tool ## Quick Start ### Installation # Core SDK (auto-detects available providers) pip install universal-ai # With specific provider support pip install universal-ai[openai] pip install universal-ai[anthropic] pip install universal-ai[gemini] pip install universal-ai[ollama] # Everything pip install universal-ai[all] ### Basic Usage import asyncio from universal_ai import AI async def main(): # Auto-detect provider from environment ai = AI() # Chat response = await ai.chat("Explain quantum computing in one sentence") print(response.content) # Streaming async for chunk in ai.stream("Write a haiku about programming"): print(chunk.delta, end="", flush=True) # Embeddings embed_response = await ai.embed("Hello, world!") print(f"Embedding dimensions: {len(embed_response.vector)}") asyncio.run(main()) ### With Specific Provider from universal_ai import AI # OpenAI ai = AI(provider="openai", model="gpt-4o") response = await ai.chat("Hello!") # Anthropic ai = AI(provider="anthropic", model="claude-sonnet-4-20250514") response = await ai.chat("Hello!") # Local Ollama ai = AI(provider="ollama", model="llama3") response = await ai.chat("Hello!") ### Synchronous Usage from universal_ai import AI ai = AI(provider="openai", model="gpt-4o") # Synchronous wrappers for scripts/notebooks response = ai.chat_sync("Hello!") print(response.content) # Sync streaming (returns full text) text = ai.stream_sync("Tell me a joke") print(text) ## Tool Calling Define tools with the `@tool` decorator and let the AI use them: from universal_ai import AI, tool @tool def get_weather(city: str, unit: str = "celsius") -> str: """Get current weather for a city.""" # In a real app, call a weather API return f"Weather in {city}: 22°{unit[0].upper()}, sunny" @tool def calculate(expression: str) -> str: """Evaluate a mathematical expression.""" return str(eval(expression)) ai = AI(provider="openai", model="gpt-4o") # The AI will automatically call your tools response = await ai.chat( "What's the weather in Paris? Also calculate 15 * 23.", tools=[get_weather, calculate] ) print(response.content) ### Manual Tool Execution from universal_ai import AI, tool @tool def search(query: str) -> str: """Search the web.""" return f"Results for: {query}" ai = AI(provider="openai", model="gpt-4o") ai.register_tool(search) # Tools are auto-executed in the tool loop response = await ai.chat("Search for Python tutorials") ## Conversations Multi-turn conversations with automatic history management: from universal_ai import AI ai = AI(provider="openai", model="gpt-4o") # Create a conversation conv = ai.conversation( system_prompt="You are a helpful cooking assistant.", max_turns=20 ) # Send messages response = await conv.send(message="What should I cook for dinner?") print(response.content) response = await conv.send(message="Can you give me a recipe?") print(response.content) # Access history print(f"Turn count: {conv.turn_count}") print(f"Messages: {len(conv.history)}") # Reset conv.reset() ## Configuration ### Environment Variables # Provider selection export UNIVERSALAI_PROVIDER=openai export UNIVERSALAI_MODEL=gpt-4o # API keys (provider-specific) export OPENAI_API_KEY=sk-... export ANTHROPIC_API_KEY=sk-ant-... export GEMINI_API_KEY=... export GROQ_API_KEY=gsk_... export MISTRAL_API_KEY=... export OPENROUTER_API_KEY=sk-or-... export HF_API_KEY=hf_... # Azure OpenAI export AZURE_OPENAI_API_KEY=... export AZURE_OPENAI_API_BASE=https://your-resource.openai.azure.com export AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o # Ollama (local) export OLLAMA_HOST=http://localhost:11434 ### Config File # ~/.config/universalai/config.yaml provider: openai model: gpt-4o temperature: 0.7 max_tokens: 4096 timeout: 30 max_retries: 3 auto_truncate: true fallback_providers: - anthropic - gemini provider_api_keys: openai: sk-... anthropic: sk-ant-... ### Programmatic Configuration from universal_ai import AI, Config config = Config( provider="openai", model="gpt-4o", temperature=0.7, max_tokens=4096, timeout=30, max_retries=3, auto_truncate=True, provider_api_keys={ "openai": "sk-...", "anthropic": "sk-ant-...", } ) ai = AI(config=config) ## Middleware Add resilience and observability to your requests: from universal_ai import AI from universal_ai.middleware import ( RetryMiddleware, CacheMiddleware, RateLimitMiddleware, CircuitBreakerMiddleware, CostTrackingMiddleware, LoggingMiddleware, ) ai = AI(provider="openai", model="gpt-4o") # Add middleware in order (executed top to bottom) ai.add_middleware(LoggingMiddleware()) ai.add_middleware(CostTrackingMiddleware()) ai.add_middleware(RetryMiddleware(max_retries=3, base_delay=1.0)) ai.add_middleware(CacheMiddleware(ttl=300)) ai.add_middleware(RateLimitMiddleware(requests_per_minute=60)) ai.add_middleware(CircuitBreakerMiddleware(failure_threshold=5)) # All requests now go through the middleware pipeline response = await ai.chat("Hello!") ### Middleware Reference Middleware | Purpose | Key Options ---|---|--- `RetryMiddleware` | Retry failed requests | `max_retries`, `base_delay`, `max_delay`, `jitter` `CacheMiddleware` | Cache responses | `ttl`, `backend` (memory/sqlite/redis) `RateLimitMiddleware` | Limit request rate | `requests_per_minute`, `burst` `CircuitBreakerMiddleware` | Stop cascading failures | `failure_threshold`, `recovery_timeout` `CostTrackingMiddleware` | Track API costs | — `LoggingMiddleware` | Log requests/responses | `log_level` ## Routing Strategies Automatically select the best provider: from universal_ai import AI from universal_ai.router import ( Router, FallbackStrategy, RoundRobinStrategy, LowestLatencyStrategy, LowestCostStrategy, ) # Configure fallback in config config = Config( provider="openai", fallback_providers=["anthropic", "gemini"] ) ai = AI(config=config) # Or use router directly router = Router( providers=["openai", "anthropic", "gemini"], strategy=FallbackStrategy() ) ### Strategy Options Strategy | Behavior ---|--- `FallbackStrategy` | Try first provider, failover to next on error `RoundRobinStrategy` | Distribute requests evenly across providers `LowestLatencyStrategy` | Always use the fastest responding provider `LowestCostStrategy` | Always use the cheapest provider ## RAG (Retrieval-Augmented Generation) Build knowledge-base powered chat: from universal_ai import AI from universal_ai.rag import RAG, TextLoader, DirectoryLoader # Initialize RAG rag = RAG(chunk_size=500, chunk_overlap=50, top_k=3) # Add content rag.add_text("Python is a high-level programming language...") rag.add_document(Document(content="...", source="docs.txt")) rag.add_folder("./knowledge_base") rag.add_url("https://example.com/article.txt") rag.add_github("owner/repo") # Search chunks = await rag.search("What is Python?") for chunk in chunks: print(f"Score: {chunk.content[:50]}...") # Use with AI ai = AI(provider="openai", model="gpt-4o") augmented_request = await rag.augment_request(chat_request) response = await ai.chat(augmented_request) ## Audio & Image ### Transcription (Whisper) ai = AI(provider="openai", model="gpt-4o") # Transcribe audio file text = await ai.transcribe("audio.mp3") print(text) # Transcribe from bytes text = await ai.transcribe(audio_bytes) ### Text-to-Speech # Generate speech audio_bytes = await ai.speak("Hello, world!", voice="alloy") with open("output.mp3", "wb") as f: f.write(audio_bytes) ### Image Generation # Generate image urls = await ai.image("A sunset over mountains", size="1024x1024") print(urls[0]) # URL to generated image ## CLI Usage UniversalAI includes a full-featured command-line tool: # Chat interactively uai chat # Chat with specific provider uai chat -p openai -m gpt-4o # Send a single message uai chat "What is machine learning?" # List available providers uai providers # Run diagnostics uai doctor # Manage configuration uai config show uai config set provider openai uai config set-api-key openai # Benchmark providers uai benchmark --iterations 10 # Start local API server uai serve --port 8000 ## Provider Details ### OpenAI ai = AI(provider="openai", model="gpt-4o") # Features: Chat, Streaming, Vision, Tools, Embeddings, Audio, Image Gen # Requires: OPENAI_API_KEY ### Anthropic ai = AI(provider="anthropic", model="claude-sonnet-4-20250514") # Features: Chat, Streaming, Vision, Tools # Requires: ANTHROPIC_API_KEY ### Gemini ai = AI(provider="gemini", model="gemini-2.0-flash") # Features: Chat, Streaming, Vision, Tools, Embeddings # Requires: GEMINI_API_KEY ### Ollama (Local) ai = AI(provider="ollama", model="llama3") # Features: Chat, Streaming, Embeddings # Requires: Ollama running locally # Install: https://ollama.ai ### Groq ai = AI(provider="groq", model="llama-3.1-70b-versatile") # Features: Chat, Streaming, Tools # Requires: GROQ_API_KEY ### Mistral ai = AI(provider="mistral", model="mistral-large-latest") # Features: Chat, Streaming, Tools, Embeddings # Requires: MISTRAL_API_KEY ### OpenRouter ai = AI(provider="openrouter", model="openai/gpt-4o") # Features: Chat, Streaming, Vision, Tools, Embeddings # Requires: OPENROUTER_API_KEY ### HuggingFace ai = AI(provider="huggingface", model="meta-llama/Llama-2-7b-chat-hf") # Features: Chat, Streaming, Embeddings # Requires: HF_API_KEY ### Azure OpenAI ai = AI(provider="azure", model="gpt-4o") # Features: Chat, Streaming, Vision, Tools, Embeddings # Requires: AZURE_OPENAI_API_KEY, AZURE_OPENAI_API_BASE ## Error Handling from universal_ai import AI from universal_ai.exceptions import ( AuthenticationError, RateLimitError, ContextWindowExceededError, ProviderError, TimeoutError, ) ai = AI(provider="openai", model="gpt-4o") try: response = await ai.chat("Hello!") except AuthenticationError as e: print(f"Invalid API key: {e}") except RateLimitError as e: print(f"Rate limited, retry after: {e.retry_after}s") except ContextWindowExceededError as e: print(f"Context too long: {e.estimated_tokens} > {e.context_window}") except ProviderError as e: print(f"Provider error: {e}") except TimeoutError: print("Request timed out") ## Context Window Safety UniversalAI validates that messages fit within the provider's context window: from universal_ai import AI, Config # Option 1: Raise error if too long (default) config = Config(auto_truncate=False) ai = AI(config=config) # Option 2: Auto-truncate to fit config = Config(auto_truncate=True) ai = AI(config=config) ## Cost Estimation ai = AI(provider="openai", model="gpt-4o") # Estimate cost before sending estimated_cost = ai.estimate_cost("Hello, world!") print(f"Estimated cost: ${estimated_cost:.6f}") # Track actual costs with middleware from universal_ai.middleware import CostTrackingMiddleware cost_middleware = CostTrackingMiddleware() ai.add_middleware(cost_middleware) response = await ai.chat("Hello!") print(f"Actual cost: ${response.usage.estimated_cost:.6f}") print(f"Total cost: ${cost_middleware.total_cost:.6f}") ## Sync Wrappers For scripts and notebooks where you can't use `async`: Async Method | Sync Wrapper ---|--- `await ai.chat(...)` | `ai.chat_sync(...)` `async for chunk in ai.stream(...)` | `ai.stream_sync(...)` `await ai.embed(...)` | `ai.embed_sync(...)` `await ai.chat_with_tools(...)` | `ai.chat_with_tools_sync(...)` `await ai.chat_json(...)` | `ai.chat_json_sync(...)` ## Examples See the examples/ directory for complete working examples: * `basic_chat.py` - Simple chat usage * `streaming.py` - Real-time streaming * `tool_calling.py` - Tool definition and execution * `middleware_demo.py` - Middleware configuration * `rag_demo.py` - RAG with document loading * `multi_provider.py` - Provider switching ## Contributing We welcome contributions! Please see CONTRIBUTING.md for guidelines. # Clone the repo git clone https://github.com/6t9xstar/universal-ai.git cd universal-ai # Install dev dependencies pip install -e ".[dev]" # Run tests pytest # Run linting ruff check . # Run type checking mypy . ## License MIT License - see LICENSE for details.

Lietuvoje, Latvijoje ir Estijoje šeimos automobilis dažnai yra ne tik įrankis kelionėms į darbą, mokyklą ar darželį, bet ir nedidelė logistikos sistema, apimanti viską – nuo vaikiško vežimėlio ir pirkinių maišų iki slidžių savaitgalio išvykai. Nors pardavimo salonuose ir gatvėse dabar dominuoja įvairūs SUV modeliai, racionaliausia reiklių šeimų lūkesčių išpildymo dalimi išlieka universalai, rašoma „Velocitos“ pranešime. „Vieno automobilio viskam“ titulą pelnę modeliai šį vardą vis dar pagrindžia ypatingu praktiškumu, tačiau ar ilgam gamintojams pakaks kantrybės juo rungtis su mados vėjais?

Padidinto pravažumo automobiliai išpopuliarėjo taip smarkiai, kad gamintojams teko nutraukti daugelio populiarių modelių su tradiciniais kėbulo tipais gamybą. Tačiau pramonei vis sparčiau elektrifikuojantis, į rinką po truputį grįžta ir žemesnių automobilių mada – hečbekai bei universalai, rašoma „Velocitos“ pranešime. Ar tokie automobiliai gali vėl iškopti į populiarumo viršūnes?

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