TensorZero

Routes LLM requests across multiple providers (OpenAI, Anthropic, etc.) through a single abstraction layer, handling provider-specific API differences, request/response normalization, and fallback logic. Implements a gateway pattern that abstracts away provider-specific schemas and authentication, enabling seamless switching between models and providers without application code changes.

Captures detailed traces of LLM requests, including prompt inputs, model outputs, latency, token usage, and cost metrics across the entire chain execution. Implements automatic instrumentation of LLM calls and integrates with distributed tracing patterns to correlate requests across multiple providers and steps, enabling debugging and performance analysis of complex LLM workflows.

Provides a schema-based function calling system that validates LLM-generated function calls against defined schemas, with automatic retry and error handling for invalid calls. Supports multiple function calling formats (OpenAI, Anthropic, custom) with provider-agnostic schema definition, enabling reliable tool use across different LLM providers and models.

Enables safe prompt templating with variable injection, automatic escaping to prevent prompt injection attacks, and validation of injected values against type/format constraints. Supports conditional sections, loops, and filters within templates, with audit logging of all variable substitutions for security and debugging purposes.

Supports batch processing of LLM requests with automatic queuing, rate limiting, and cost optimization through batch APIs where available. Implements asynchronous request handling with callbacks or webhooks for result delivery, enabling efficient processing of large volumes of LLM requests without blocking application threads, with automatic retry and error handling.

Collects training data from production LLM interactions (prompts, outputs, user feedback) and prepares datasets for fine-tuning, with automatic filtering and quality checks. Supports fine-tuning workflows for both proprietary models (OpenAI) and open-source models, with integration to observability for tracking fine-tuned model performance and automatic rollback if quality degrades.

Analyzes production traces and metrics to automatically suggest and run A/B tests for prompt improvements, model selection, and parameter tuning. Uses observability data to identify underperforming LLM calls, then orchestrates controlled experiments comparing variants (different prompts, models, temperatures) against baseline metrics, with statistical significance testing to determine winners.

Provides a framework for defining and executing evaluations against LLM outputs using custom metrics (accuracy, relevance, safety, cost) and comparison baselines. Supports both automated metrics (regex matching, semantic similarity) and human-in-the-loop evaluation, with integration to observability data for tracking metric trends over time and correlating with code/prompt changes.

Enables definition of multi-step LLM workflows (chains, agents, RAG pipelines) using a declarative configuration format, with automatic orchestration of dependencies, error handling, and state management. Supports conditional branching, loops, and tool/function calling within workflows, with built-in integration to the gateway and observability layer for unified tracing and optimization.

Automatically selects LLM providers and models based on cost, latency, and quality constraints using observability data and configurable optimization policies. Implements dynamic routing that considers real-time provider performance, model pricing, and application SLAs to minimize cost while meeting latency and quality targets, with fallback strategies for provider outages.

Enables version control of LLM prompts, models, parameters, and workflows as first-class artifacts, with Git-like workflows for branching, merging, and rollback. Supports canary deployments, A/B testing across versions, and automatic rollback on quality metric regressions, with audit trails tracking who changed what and when.

Captures user feedback on LLM outputs (thumbs up/down, detailed ratings, corrections) and integrates feedback into observability and optimization pipelines. Enables feedback to be used as ground truth for evaluation metrics, training data for fine-tuning, and signals for automatic prompt/model optimization, with privacy-preserving aggregation across users.

Supports LLM requests with images, PDFs, and other document formats alongside text, with automatic preprocessing (OCR, image resizing, document parsing) and provider-specific format conversion. Handles vision-capable models (GPT-4V, Claude 3 Vision) and routes multi-modal requests appropriately, with cost optimization for vision tokens and fallback to text-only models when appropriate.

Automatically manages LLM context windows by implementing chunking, summarization, and retrieval strategies to fit long documents or conversations within provider limits. Supports dynamic context window sizing based on model capabilities, with intelligent selection of which information to include based on relevance and importance, enabling efficient use of long-context models (100K+ tokens).