TensorZero

Routes inference requests across multiple LLM providers (OpenAI, Anthropic, etc.) through a single abstraction layer, handling provider-specific API differences, authentication, and request/response normalization. Implements a provider registry pattern that abstracts away protocol differences and enables dynamic provider selection based on cost, latency, or capability constraints without application code changes.

Captures detailed telemetry from every LLM inference including latency, token counts, costs, provider, model, and custom metadata through a structured logging pipeline. Integrates with observability backends (likely Datadog, New Relic, or similar) to enable real-time dashboards, alerting, and debugging of LLM application behavior in production without requiring manual instrumentation.

Caches LLM responses based on exact request matching or semantic similarity, returning cached results for duplicate or similar requests without re-invoking the model. Implements cache invalidation strategies and provides cache hit/miss metrics to measure effectiveness and cost savings.

Orchestrates multi-step LLM reasoning workflows where outputs from one step feed into subsequent steps, with automatic prompt chaining, context passing, and error handling. Supports branching logic, conditional execution, and result aggregation across parallel branches, enabling complex reasoning tasks without manual orchestration code.

Applies safety filters to both inputs and outputs using a combination of built-in rules (PII detection, toxicity filtering, jailbreak detection) and custom user-defined rules. Implements a rule engine that can block, redact, or flag content based on configurable criteria, with audit logging of all filtering decisions.

Automatically selects the best model for a given task based on required capabilities (vision, function calling, JSON mode, etc.) and constraints (cost, latency, quality). Maintains a capability matrix of all supported models and uses it to route requests to models that meet requirements without manual provider/model selection.

Provides built-in infrastructure for running controlled experiments on LLM applications by splitting traffic between variants (different prompts, models, providers, parameters) and measuring outcomes against defined metrics. Implements statistical significance testing and variant selection logic to automatically route traffic toward better-performing configurations without manual intervention.

Evaluates LLM outputs against user-defined success criteria using a combination of automated metrics (BLEU, ROUGE, semantic similarity) and custom evaluation functions (LLM-as-judge, regex matching, structured validation). Runs evaluations on inference batches or in real-time to measure quality, cost, and latency tradeoffs across model/prompt variants.

Stores and versions prompts, system messages, and inference parameters as first-class artifacts with git-like history, enabling rollback to previous versions and comparison between variants. Integrates with the gateway so prompt changes can be deployed without application code changes, and tracks which prompt version was used for each inference in observability data.

Analyzes inference costs across providers and models based on token counts and pricing, then automatically selects the cheapest option that meets latency and quality constraints. Uses historical cost and performance data to make routing decisions, and provides dashboards showing cost breakdown by provider, model, and feature.

Validates LLM outputs against user-defined schemas (JSON Schema, Pydantic models, regex patterns) and automatically re-prompts or falls back if outputs don't conform. Integrates with providers that support constrained generation (like Anthropic's JSON mode) to enforce schemas at generation time, reducing invalid outputs and retry overhead.

Manages conversation history and context windows by automatically truncating, summarizing, or prioritizing messages to fit within model token limits. Implements strategies like sliding windows, importance-based pruning, and hierarchical summarization to preserve relevant context while staying within budget, and tracks token usage to prevent overages.

Provides a schema-based function registry that maps tool definitions to callable functions, handles provider-specific function calling APIs (OpenAI, Anthropic, etc.), and automatically executes selected tools with proper error handling and result formatting. Supports both synchronous and asynchronous tool execution, and integrates with the gateway to route tool calls transparently.

Processes large volumes of inferences in batches using provider-native batch APIs (where available) to reduce costs, or groups requests to maximize throughput and minimize latency. Handles batching logic transparently, tracks batch status, and provides progress monitoring and result aggregation.