Search Results for "you-get"

Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, Azure OpenAI etc.] liteLLM supports streaming the model response back, pass stream=True to get a streaming iterator in response. Streaming is supported for OpenAI, Azure, Anthropic, and Huggingface models.

...It presents itself as a complete connectivity solution that adds drop-in security, observability, and governance to agent-to-agent and agent-to-tool communication without requiring you to rebuild your agent stack. The project supports interoperable protocols designed for this ecosystem, including Agent2Agent (A2A) and Model Context Protocol (MCP), which helps standardize how tools and agents interoperate. It is designed for performance and scale, implemented in Rust and engineered to handle large throughput and multi-tenant deployments. ...

Bifrost is an LLM gateway designed to provide a unified OpenAI-compatible API front for many different model providers. It abstracts away the complexity of working directly with multiple backend providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, etc.), enabling you to plug in providers and switch between them without touching your client code. It is built to be high performance: in benchmark tests at 5,000 requests per second, it reportedly adds only microseconds of overhead and achieves perfect success rates with no failed requests. Bifrost supports features such as automatic fallback (failover between providers), load balancing across API keys/providers, and semantic caching to reduce latency and cost. ...

...It underpins the MCP Toolkit experience in Docker Desktop, but it can also be used independently as a general-purpose MCP operational layer. The core idea is to treat MCP servers like containerized services, giving each server controlled privileges and a lifecycle you can inspect, enable/disable, and reset as needed. Instead of having each AI client manage its own MCP server configuration, the gateway provides a unified interface so multiple clients can connect consistently to the same configured tool surface. The project emphasizes security and operational hygiene by supporting secrets management (to avoid leaking credentials via plain environment variables) and providing built-in OAuth flows for MCP servers that require authenticated service access.

...It offers a “single source of truth” registry where developers can register MCP servers and the tools they provide, and MCP clients (such as AI agents) discover and consume those tools through one gateway endpoint. This greatly simplifies the architecture when you have many MCP servers; agents only need to connect to one gateway rather than multiple endpoints. The platform supports enterprise-grade workflows; centralized tool management, access control, self-hosting so that internal servers and tools remain under your organization’s control, and registry metadata to track what tools exist and who can use them. ...

MetaMCP is an all-in-one MCP aggregator, orchestrator, and middleware that merges many MCP servers into a single, policy-aware server you can point any MCP client at. It’s built to centralize discovery, apply middlewares (e.g., auth, rate limits, transforms), and present a unified catalog of tools and resources from diverse backends. The repository and site describe it as “the one MCP to manage all your MCPs,” with diagrams and examples showing how to compose fleets behind a single endpoint. ...

Portkey AI Gateway aims to offer a blazing fast, secure, and flexible gateway for interacting with a wide variety of models and enforcing guardrails. It presents a single, friendly API through which you can route to 200+ LLMs, while applying configurable input/output guardrails to enforce policies or restrict certain content. It supports automatic retries, fallbacks, load balancing across providers or keys, and request timeouts to avoid latency spikes. The gateway is multimodal: it can handle text, vision, audio, and image models under a common interface. ...