MindBridge

Provides a standardized API that abstracts away provider-specific differences (OpenAI, Anthropic, Ollama, etc.), allowing developers to write model-agnostic code once and switch providers at runtime without refactoring. Implements a provider registry pattern where each LLM backend implements a common interface contract, enabling dynamic provider selection based on task requirements or cost optimization.

Enables intelligent routing of requests to different LLM providers based on configurable criteria such as task type, required capabilities (vision, function-calling, reasoning), cost thresholds, or latency requirements. Uses a routing policy engine that evaluates request metadata against provider capability matrices to select the optimal provider at runtime without manual intervention.

Implements intelligent error handling that distinguishes between retryable errors (rate limits, transient failures) and non-retryable errors (authentication, invalid input). Applies exponential backoff with jitter for retries, and optionally falls back to alternative providers if the primary provider fails, with configurable retry policies per error type.

Enforces rate limits and quotas for each provider, tracking request counts and token usage against provider-specific limits. Implements a token bucket or sliding window algorithm to smooth request distribution, with queuing to defer requests that would exceed limits rather than failing them immediately.

Supports batch processing of multiple requests with optimized throughput, using async/await patterns to handle concurrent requests without blocking. Implements batching strategies like request grouping and token packing to maximize efficiency, with progress tracking and partial failure handling.

Handles concurrent streaming from multiple LLM providers simultaneously, aggregating token streams in real-time and exposing a unified streaming interface. Implements a multiplexing pattern that buffers and orders tokens from multiple sources, enabling use cases like ensemble voting or competitive streaming where the fastest/best response wins.

Leverages the Model Context Protocol (MCP) to allow new LLM providers to be registered as external MCP servers without modifying the core framework. Each provider implements the MCP interface for model invocation, capability advertisement, and streaming, enabling a plugin architecture where community members can contribute providers as standalone MCP servers.

Allows individual requests to override the default routing policy and explicitly specify which provider(s) to use, enabling per-request A/B testing and experimentation. Supports specifying primary and fallback providers at request time, with built-in instrumentation to track which provider was used and how it performed.

Tracks API costs for each request based on token usage and provider pricing, enforcing configurable budgets at request level and aggregate level. Implements a cost calculator that multiplies token counts by provider-specific rates, with hooks to reject requests exceeding budget or trigger alerts when approaching limits.

Manages conversation history and request context across multiple provider calls, maintaining a unified message format that abstracts away provider-specific message structures. Implements context windowing logic to trim or summarize old messages when approaching token limits, and provides hooks for custom context management strategies.

Provides a unified function calling interface that abstracts away provider-specific function calling schemas (OpenAI's format vs. Anthropic's vs. others). Implements a schema registry where tools are defined once in a provider-agnostic format, then automatically translated to each provider's function calling format at request time.

Extracts structured data from LLM responses using configurable parsing strategies (JSON parsing, regex, custom parsers). Implements a parser registry where different output formats can be defined, with automatic validation and error handling for malformed responses, including retry logic with alternative providers.

Automatically logs all requests and responses with configurable detail levels, including prompts, responses, provider selection decisions, costs, and latency. Integrates with observability platforms (OpenTelemetry, custom webhooks) to export metrics and traces, enabling debugging and performance monitoring across the entire request lifecycle.