Open Source Ecosystem

One Protocol,
Every AI Provider.

AI-Lib is an open-source ecosystem that standardizes AI model interactions. V2 protocol with MCP, Computer Use, and multimodal capabilities — 37 providers, three runtimes, zero hardcoded logic.

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AI Providers

Projects

Hardcoded Logic

Runtimes

Five Projects, One Ecosystem

A specification layer defines the rules. Three runtime implementations (Rust, Python, TypeScript) bring them to life.

v0.7.6

AI-Protocol

The provider-agnostic specification. V2 three-ring manifests with MCP, Computer Use, and multimodal schemas — no hardcoded logic, ever.

37 provider manifests (6 V2 + 36 V1)
V2 three-layer pyramid architecture
STT / TTS / Rerank in manifests (Jina, OpenAI, Cohere)
MCP / Computer Use / Multimodal schemas
ProviderContract + pricing schema
CLI tool for validation & inspection
JSON Schema validated
Hot-reloadable configuration

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v0.8.5

ai-lib-rust

High-performance Rust runtime. ProviderDriver abstraction, MCP tool bridge, Computer Use safety, extended multimodal, and operator-based streaming pipeline.

ProviderDriver (OpenAI/Anthropic/Gemini)
MCP tool bridge with namespace isolation
Computer Use + SafetyPolicy
Extended multimodal validation
Capability Registry (feature-gated)
Operator-based streaming pipeline
185+ tests, published on Crates.io

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v0.7.4

ai-lib-python

Developer-friendly Python runtime. ProviderDriver abstraction, MCP tool bridge, Computer Use safety, extended multimodal, STT/TTS/Rerank extras.

ProviderDriver (OpenAI/Anthropic/Gemini)
MCP tool bridge with namespace isolation
Computer Use + SafetyPolicy
STT / TTS / Rerank extras
Capability Registry (pip extras)
Pydantic v2 + full async/await
75+ V2 tests, published on PyPI

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v0.4.0

ai-lib-ts

TypeScript/Node.js runtime for the npm ecosystem. Protocol-driven, streaming-first, with Resilience, Routing, MCP, and Multimodal support.

V2 manifest parsing + standard error codes
Resilience (Retry, CircuitBreaker, RateLimiter, Backpressure)
ModelManager + CostBasedSelector + FallbackChain
SttClient, TtsClient, RerankerClient
McpToolBridge, EmbeddingClient, Plugins
BatchExecutor + PreflightChecker
Native fetch, published on npm

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Dev Tools

v0.1.7

ai-protocol-mock

Unified mock server for AI-Protocol runtimes. HTTP provider mock (OpenAI + Anthropic), STT/TTS/Rerank endpoints, and MCP JSON-RPC mock for integration testing.

Manifest-driven HTTP mock
OpenAI & Anthropic + STT/TTS/Rerank
MCP tools/list, tools/call, capabilities
Docker support
Third-party integration (ZeroClaw, CI)
Sync manifests from ai-protocol

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Showcase Projects

Reference applications built on the AI-Lib ecosystem — see the protocol and runtimes in action.

v0.3.1

AI Debate

Multi-model AI debate arena. Pro vs Con across four rounds, then a Judge delivers the verdict. Built on ai-lib-rust and ai-protocol.

4-round debate flow (Opening → Rebuttal → Defense → Closing → Judgement)
Web search tool calling via Tavily (optional)
Multi-provider: DeepSeek, Zhipu, Groq, Mistral, OpenAI, Anthropic
Auto fallback, real-time SSE streaming
Axum + SQLite, modern dark UI

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v0.2.0

ZeroSpider

Protocol-driven autonomous AI agent runtime. Intelligent model selection, multi-model negotiation, and hardware integration.

Protocol-driven providers via ai-lib-rust + ai-protocol
Smart routing: cost, speed, quality, reliability
Multi-model negotiation and parallel task execution
Channels: Telegram, Discord, Matrix
Remote deployment, hardware (GPIO, STM32)

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Protocol-Driven Design

"All logic is operators, all configuration is protocol." Every provider behavior is declared in YAML — runtimes contain zero hardcoded provider logic.

Declarative Configuration

Provider endpoints, auth, parameter mappings, streaming decoders, and error handling — all declared in YAML manifests, validated by JSON Schema.

Operator-Based Pipeline

Streaming responses flow through composable operators: Decoder, Selector, Accumulator, FanOut, EventMapper. Each operator is protocol-driven.

Hot-Reload Ready

Update provider configurations without restarting. Protocol changes propagate automatically to runtimes. Add new providers through configuration, not code.

Ecosystem Architecture

Three layers working together — specification defines the rules, runtimes execute them, applications consume unified interfaces.

How It Works

From user request to unified streaming events — every step is protocol-driven.

Choose Your Runtime

Same protocol, different strengths. Pick the runtime that fits your stack.

Capability	AI-Protocol	Rust SDK	Python SDK	TypeScript SDK
Type System	JSON Schema	Compile-time (Rust)	Runtime (Pydantic v2)	Compile-time (TypeScript)
Streaming	SSE/NDJSON spec	tokio async streams	async generators	AsyncIterator + fetch
Resilience	Retry policy spec	Circuit breaker, rate limiter, backpressure	ResilientExecutor with all patterns	RetryPolicy, CircuitBreaker, RateLimiter
V2 Driver	ProviderContract spec	Box<dyn ProviderDriver>	ProviderDriver ABC	ManifestV2 + HttpTransport
MCP	mcp.json schema	McpToolBridge	McpToolBridge	McpToolBridge
Computer Use	computer-use.json schema	ComputerAction + SafetyPolicy	ComputerAction + SafetyPolicy	—
Multimodal	multimodal.json schema	MultimodalCapabilities	MultimodalCapabilities	SttClient, TtsClient, RerankerClient
Embeddings	—	Vector operations, similarity	Vector operations, similarity	EmbeddingClient
Distribution	GitHub / npm	Crates.io	PyPI	npm
Best For	Specification & standards	Systems, performance-critical	ML, data science, prototyping	Node.js, npm ecosystem, full-stack