LLM

Provides a unified Python and CLI interface that abstracts away provider-specific API differences (OpenAI, Anthropic, Ollama, local models, etc.). Uses a plugin-based model registry pattern where each provider implements a standardized interface, allowing users to swap providers without changing application code. Handles authentication, request formatting, and response parsing transparently across heterogeneous LLM backends.

Exposes LLM interactions as Unix-style CLI commands that accept stdin/stdout piping, enabling composition with standard shell tools (grep, sed, jq, etc.). Implements a thin command-line parser that maps arguments to model parameters (temperature, max_tokens, system prompt) and streams responses to stdout, making LLM calls scriptable and composable in bash/shell pipelines without Python code.

Provides templating syntax for prompts with variable substitution, conditional logic, and reusable prompt components. Supports Jinja2-style templates or simple string interpolation, allowing prompts to be parameterized and composed. Enables prompt versioning and reuse across multiple calls without hardcoding values.

Provides detailed logging of all LLM interactions (prompts, responses, parameters, latency, costs) with optional structured output for analysis. Implements execution tracing that captures the full context of each call (provider, model, tokens, timing) for debugging and auditing. Supports multiple log levels and output formats (JSON, human-readable, CSV).

Provides discovery, installation, and execution of local LLMs (via Ollama, llama.cpp, or other backends) without requiring cloud API calls. Maintains a local model registry, handles model downloading/caching, and manages inference parameters (context window, quantization level, GPU/CPU allocation). Abstracts the complexity of running local models behind the same unified interface as cloud providers.

Implements streaming LLM responses at the token level, allowing real-time output consumption and early termination without waiting for full completion. Uses provider-specific streaming APIs (OpenAI's Server-Sent Events, Anthropic's streaming protocol) and normalizes them into a unified token stream interface. Supports callbacks for each token, enabling progress tracking, live UI updates, or dynamic response filtering during generation.

Manages multi-turn conversation state by maintaining message history (user/assistant/system roles) and automatically formatting it for provider APIs. Handles context window limits by implementing sliding-window or summarization strategies to keep conversations within token budgets. Supports conversation persistence (save/load from files or databases) and context injection for maintaining state across CLI invocations.

Enables LLM responses to be constrained to a specific JSON schema, with automatic parsing and validation. Uses provider-native schema enforcement (OpenAI's JSON mode, Anthropic's structured output) when available, or implements client-side validation with retry logic for providers without native support. Automatically converts schema definitions (Pydantic models, JSON Schema) into provider-compatible formats.

Provides runtime discovery of available models across all configured providers, with capability metadata (context window, cost, supported features like vision or function calling). Queries provider APIs to fetch current model lists and caches results locally. Allows filtering and searching models by capability, cost, or performance characteristics without manual configuration.

Tracks token usage and estimated costs across LLM calls, with per-model pricing data and real-time cost calculation. Implements token counting using provider-specific tokenizers (tiktoken for OpenAI, manual estimation for others) and accumulates usage statistics. Supports cost budgeting with warnings or hard limits to prevent runaway spending.

Allows third-party developers to implement custom LLM providers by implementing a minimal plugin interface (model listing, completion, streaming). Uses Python entry points or direct registration to discover and load provider plugins at runtime without modifying core code. Enables integration of proprietary, self-hosted, or experimental LLM backends into the unified interface.

Supports batch processing of multiple prompts with concurrent execution, rate limiting, and error handling. Implements async/await patterns for non-blocking I/O, allowing efficient processing of large prompt batches without blocking the main thread. Handles provider rate limits automatically with exponential backoff and request queuing.