Octolib: Self-Sufficient AI Provider Library

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🚀 Overview

Octolib is a comprehensive, self-sufficient AI provider library that provides a unified, type-safe interface for interacting with multiple AI services. It offers intelligent model selection, robust error handling, and advanced features like cross-provider tool calling and vision support.

✨ Key Features

• 🔌 Multi-Provider Support: OpenAI, Anthropic, xAI, OpenRouter, Cerebras, NVIDIA NIM, Groq, BytePlus, Alibaba Model Studio, Ollama, Together, Featherless, Fireworks, Hetzner, OpenCode Zen/Go, Google Vertex, Google Studio, Amazon, Cloudflare, DeepSeek, MiniMax, Moonshot AI (Kimi), Z.ai, OctoHub, Local, CLI proxies
• 🛡️ Unified Interface: Consistent API across different providers
• 🔍 Intelligent Model Validation: Strict provider:model format parsing with case-insensitive model support
• 📋 Structured Output: JSON and JSON Schema support for OpenAI, xAI, OpenRouter, DeepSeek, Together, and Z.ai
• 💰 Cost Tracking: Automatic token usage and cost calculation
• 🖼️ Vision Support: Image and video attachment handling for vision-capable models
• 🧰 Tool Calling: Cross-provider tool call standardization
• 🧩 CLI Provider: Use cli:<backend>/<model> (e.g. cli:codex/gpt-5.2-codex). Proxy-only: tools/MCP are not used or controllable.
• ⏱️ Retry Management: Configurable exponential backoff
• 🔒 Secure Design: Environment-based API key management
• ⚙️ Configuration Migration: Reusable, comment-preserving TOML upgrades with locking, versioned backups, and atomic writes
• 🎯 Embedding Support: Multi-provider embedding generation with Jina, Voyage, Google, OpenAI, Together, OctoHub, Local (Ollama, llama.cpp, LM Studio, vLLM), FastEmbed, and HuggingFace
• 🔄 Reranking: Document relevance scoring with cross-encoder models (Voyage AI, Cohere, Jina AI, Mixedbread, Local (llama.cpp, vLLM, TEI), HuggingFace)

📦 Quick Installation

# From crates.io (recommended) — use the latest version from the badge above
octolib = "<latest>"

# Or latest from git
octolib = { git = "https://github.com/muvon/octolib" }

Local embedding/reranking (fastembed, huggingface) is enabled by default. Hardware acceleration is opt-in via features: metal, cuda, cudnn, mkl, accelerate.

🚀 Quick Start

use octolib::{ProviderFactory, ChatCompletionParams, Message};

async fn example() -> anyhow::Result<()> {
    // Parse model and get provider
    let (provider, model) = ProviderFactory::get_provider_for_model("openai:gpt-4o")?;

    // Create messages
    let messages = vec![
        Message::user("Hello, how are you?"),
    ];

    // Create completion parameters
    let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000);

    // Get completion (requires OPENAI_API_KEY environment variable)
    let response = provider.chat_completion(params).await?;
    println!("Response: {}", response.content);

    Ok(())
}

📋 Structured Output

Get structured JSON responses with schema validation:

use octolib::{ProviderFactory, ChatCompletionParams, Message, StructuredOutputRequest};
use serde::{Deserialize, Serialize};

#[derive(Serialize, Deserialize, Debug)]
struct PersonInfo {
    name: String,
    age: u32,
    skills: Vec<String>,
}

async fn structured_example() -> anyhow::Result<()> {
    let (provider, model) = ProviderFactory::get_provider_for_model("openai:gpt-4o")?;

    // Check if provider supports structured output
    if !provider.supports_structured_output(&model) {
        return Err(anyhow::anyhow!("Provider does not support structured output"));
    }

    let messages = vec![
        Message::user("Tell me about a software engineer in JSON format"),
    ];

    // Request structured JSON output
    let structured_request = StructuredOutputRequest::json();
    let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000)
        .with_structured_output(structured_request);

    let response = provider.chat_completion(params).await?;

    if let Some(structured) = response.structured_output {
        let person: PersonInfo = serde_json::from_value(structured)?;
        println!("Person: {:?}", person);
    }

    Ok(())
}

🧩 CLI Provider (Proxy Mode)

Use local CLIs as a lightweight proxy. This mode is prompt-only; tool calling/MCP integration is not used or controllable.

let (provider, model) = ProviderFactory::get_provider_for_model("cli:codex/gpt-5.2-codex")?;
// or: "cli:claude/claude-sonnet-4-5"
// or: "cli:gemini/gemini-2.5-pro"
// or: "cli:cursor/auto"

Set a backend-specific command if it is not on PATH:

CLI_CODEX_COMMAND=/path/to/codex
CLI_CLAUDE_COMMAND=/path/to/claude
CLI_GEMINI_COMMAND=/path/to/gemini
CLI_CURSOR_COMMAND=/path/to/cursor-agent

🧰 Tool Calling

Use AI models to call functions with automatic parameter extraction:

use octolib::{ProviderFactory, ChatCompletionParams, Message, FunctionDefinition, ToolCall};
use serde_json::json;

async fn tool_calling_example() -> anyhow::Result<()> {
    let (provider, model) = ProviderFactory::get_provider_for_model("openai:gpt-4o")?;

    // Define available tools/functions
    let tools = vec![
        FunctionDefinition {
            name: "get_weather".to_string(),
            description: "Get the current weather for a location".to_string(),
            parameters: json!({
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }),
            cache_control: None,
        },
        FunctionDefinition {
            name: "calculate".to_string(),
            description: "Perform a mathematical calculation".to_string(),
            parameters: json!({
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "Mathematical expression to evaluate"
                    }
                },
                "required": ["expression"]
            }),
            cache_control: None,
        },
    ];

    let mut messages = vec![
        Message::user("What's the weather in Tokyo and calculate 15 * 23?"),
    ];

    // Initial request with tools
    let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000)
        .with_tools(tools.clone());

    let response = provider.chat_completion(params).await?;

    // Check if model wants to call tools
    if let Some(tool_calls) = response.tool_calls {
        println!("Model requested {} tool calls", tool_calls.len());

        // Add assistant's response with tool calls to conversation
        let mut assistant_msg = Message::assistant(&response.content);
        assistant_msg.tool_calls = Some(serde_json::to_value(&tool_calls)?);
        messages.push(assistant_msg);

        // Execute each tool call and add results
        for tool_call in tool_calls {
            println!("Calling tool: {} with args: {}", tool_call.name, tool_call.arguments);

            // Execute the tool (your implementation)
            let result = match tool_call.name.as_str() {
                "get_weather" => {
                    let location = tool_call.arguments["location"].as_str().unwrap_or("Unknown");
                    json!({
                        "location": location,
                        "temperature": 22,
                        "unit": "celsius",
                        "condition": "sunny"
                    })
                }
                "calculate" => {
                    let expr = tool_call.arguments["expression"].as_str().unwrap_or("0");
                    // Simple calculation (in real app, use proper eval)
                    json!({
                        "expression": expr,
                        "result": 345  // 15 * 23
                    })
                }
                _ => json!({"error": "Unknown tool"}),
            };

            // Add tool result to conversation
            messages.push(Message::tool(
                &serde_json::to_string(&result)?,
                &tool_call.id,
                &tool_call.name,
            ));
        }

        // Get final response with tool results
        let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000)
            .with_tools(tools);

        let final_response = provider.chat_completion(params).await?;
        println!("Final response: {}", final_response.content);
    } else {
        println!("Direct response: {}", response.content);
    }

    Ok(())
}

Tool Calling Features:

• ✅ Cross-provider support (OpenAI, Anthropic, xAI, Google Vertex, Google Studio, Amazon, OpenRouter, MiniMax, Moonshot, Z.ai, Alibaba, Hetzner, OpenCode, OctoHub, and other OpenAI-compatible providers)
• ✅ Automatic parameter validation via JSON Schema
• ✅ Multi-turn conversations with tool results
• ✅ Parallel tool execution support
• ✅ Standardized ToolCall and GenericToolCall formats across all providers
• ✅ Provider-specific metadata preservation (e.g., Gemini thought signatures)
• ✅ Clean conversion API with to_generic_tool_calls() method

🎯 Embedding Generation

Generate embeddings using multiple providers:

use octolib::embedding::{generate_embeddings, generate_embeddings_batch, InputType};

async fn embedding_example() -> anyhow::Result<()> {
    // Single embedding generation
    let embedding = generate_embeddings(
        "Hello, world!",
        "voyage",  // provider
        "voyage-3.5-lite"  // model
    ).await?;

    println!("Embedding dimension: {}", embedding.len());

    // Batch embedding generation
    let texts = vec![
        "First document".to_string(),
        "Second document".to_string(),
    ];

    let embeddings = generate_embeddings_batch(
        texts,
        "jina",  // provider
        "jina-embeddings-v4",  // model
        InputType::Document,  // input type for better embeddings
        16,  // batch size
        100_000,  // max tokens per batch
    ).await?;

    println!("Generated {} embeddings", embeddings.len());

    Ok(())
}

// Supported embedding providers:
// - Jina: jina-embeddings-v4, jina-clip-v2, etc.
// - Voyage: voyage-3.5, voyage-code-2, etc.
// - Google: gemini-embedding-001, text-embedding-005
// - OpenAI: text-embedding-3-small, text-embedding-3-large
// - FastEmbed: Local models (feature-gated)
// - HuggingFace: sentence-transformers models

🎯 Document Reranking

Improve search results by scoring document relevance with cross-encoder models:

use octolib::reranker::rerank;

async fn reranking_example() -> anyhow::Result<()> {
    let query = "What is machine learning?";
    let documents = vec![
        "Machine learning is a subset of AI.".to_string(),
        "Cooking recipes for beginners.".to_string(),
        "Deep learning uses neural networks.".to_string(),
    ];

    // Rerank documents by relevance to query
    let response = rerank(
        query,
        documents,
        "voyage",           // provider: voyage, cohere, jina, fastembed
        "rerank-2.5",       // model
        Some(2)             // top_k: return top 2 results
    ).await?;

    for (rank, result) in response.results.iter().enumerate() {
        println!("Rank {}: Score {:.4}", rank + 1, result.relevance_score);
        println!("  Document: {}", result.document);
    }

    println!("Total tokens used: {}", response.total_tokens);

    Ok(())
}

// Supported Providers:
//
// API-Based (require API keys):
// - Voyage AI (VOYAGE_API_KEY): rerank-2.5, rerank-2.5-lite, rerank-2, rerank-2-lite
// - Cohere (COHERE_API_KEY): rerank-english-v3.0, rerank-multilingual-v3.0
// - Jina AI (JINA_API_KEY): jina-reranker-v3, jina-reranker-v2-base-multilingual
//
// Local (no API keys, requires features):
// - FastEmbed (fastembed feature): bge-reranker-base, bge-reranker-large, jina-reranker-v1-turbo-en

🔐 OAuth Authentication

Octolib supports OAuth authentication for ChatGPT subscriptions and Anthropic:

OpenAI OAuth (ChatGPT Plus/Pro/Team/Enterprise):

export OPENAI_OAUTH_ACCESS_TOKEN="your_oauth_token"
export OPENAI_OAUTH_ACCOUNT_ID="your_account_id"

Anthropic OAuth:

export ANTHROPIC_OAUTH_TOKEN="your_bearer_token"

The library automatically detects OAuth credentials and prefers them over API keys. See examples/openai_oauth.rs and examples/anthropic_oauth.rs for full usage examples.

🎯 Provider Support Matrix

Provider	Structured Output	Vision	Tool Calls	Caching
OpenAI	✅ JSON + Schema	✅ Yes	✅ Yes	✅ Yes
xAI	✅ JSON + Schema	✅ Yes	✅ Yes	✅ Yes
OpenRouter	✅ JSON + Schema	✅ Yes	✅ Yes	✅ Yes
DeepSeek	✅ JSON Mode	❌ No	❌ No	✅ Yes
Moonshot AI (Kimi)	✅ JSON Mode	✅ kimi-k2.5	✅ Yes	✅ Yes
MiniMax	✅ JSON Mode	❌ No	✅ Yes	✅ Yes
Anthropic	❌ No	✅ Yes	✅ Yes	✅ Yes
Z.ai	✅ JSON Mode	❌ No	✅ Yes	✅ Yes
NVIDIA NIM	✅ JSON + Schema	Per-model	✅ Yes	❌ No
Groq	✅ JSON + Schema	Per-model	❌ No	✅ Select models
BytePlus	✅ JSON + Schema	Per-model	❌ No	✅ Yes
Alibaba Model Studio	❌ No	Per-model	✅ Yes	✅ Yes
Cerebras	✅ JSON + Schema	❌ No	❌ No	❌ No
Featherless	✅ JSON + Schema	❌ No	❌ No	❌ No
Hetzner	✅ JSON + Schema	Per-model	✅ Yes	❌ No
OpenCode Zen	Per-model	Per-model	✅ Yes	❌ No
OpenCode Go	✅ JSON + Schema	Per-model	✅ Yes	✅ Yes
Google Vertex	❌ No	✅ Yes	✅ Yes	❌ No
Google Studio	✅ JSON + Schema	✅ Yes	✅ Yes	✅ Yes
Amazon Bedrock	❌ No	✅ Yes	✅ Yes	❌ No
OctoHub	Per-model	Per-model	✅ Yes	✅ Yes
Together	Per-model	Per-model	✅ Yes	✅ Yes (auto)
Fireworks	✅ JSON + Schema	Per-model	✅ Yes	✅ Yes (auto)
Cloudflare	❌ No	❌ No	❌ No	❌ No
Local	Per-model	Per-model	Per-model	❌ No
Ollama	Per-model	Per-model	Per-model	❌ No

Structured Output Details

• JSON Mode: Basic JSON object output
• JSON Schema: Full schema validation with strict mode
• Provider Detection: Use provider.supports_structured_output(&model) to check capability

🧠 Thinking/Reasoning Support

Octolib provides first-class support for models that produce thinking/reasoning content. Thinking is stored separately from the main response content, similar to how tool_calls are separate from content.

use octolib::{ProviderFactory, ChatCompletionParams, Message, ThinkingBlock};

async fn thinking_example() -> anyhow::Result<()> {
    // Thinking-capable models: MiniMax, OpenAI o-series, Moonshot (kimi-k2-thinking*), Z.ai, xAI
    let (provider, model) = ProviderFactory::get_provider_for_model("minimax:MiniMax-M2")?;

    let messages = vec![
        Message::user("Solve this complex math problem step by step"),
    ];

    let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000);
    let response = provider.chat_completion(params).await?;

    // Access thinking content (separate from response.content)
    if let Some(ref thinking) = response.thinking {
        println!("=== MODEL THINKING ({}) ===", thinking.tokens);
        println!("{}", thinking.content);
        println!("==========================");
    }

    // Final response (clean, no thinking prefix)
    println!("Response: {}", response.content);
    // Token usage breakdown
    if let Some(usage) = &response.exchange.usage {
        println!("Input tokens: {}", usage.input_tokens);
        println!("Cache read tokens: {}", usage.cache_read_tokens);
        println!("Cache write tokens: {}", usage.cache_write_tokens);
        println!("Output tokens: {}", usage.output_tokens);
        println!("Reasoning tokens: {}", usage.reasoning_tokens);
    }
    Ok(())
}

Supported Providers

Provider	Thinking Format	Notes
MiniMax	Content blocks ({"type": "thinking"})	Full thinking block extraction
xAI	Responses API reasoning items	Summary extraction plus encrypted reasoning preservation across tool rounds
OpenAI o-series	reasoning_content field	o1, o3, o4 models
OpenRouter	reasoning_details	Gemini and other providers

Token Tracking

Thinking tokens are tracked separately in TokenUsage.reasoning_tokens:

if let Some(usage) = &response.exchange.usage {
    println!("Total tokens: {}", usage.total_tokens);
    println!("  - Input: {}", usage.input_tokens);
    println!("  - Cache Read: {}", usage.cache_read_tokens);
    println!("  - Cache Write: {}", usage.cache_write_tokens);
    println!("  - Output: {}", usage.output_tokens);
    println!("  - Reasoning: {}", usage.reasoning_tokens);
}

📚 Complete Documentation

📖 Quick Navigation

• Overview - Library introduction and core concepts
• Installation Guide - Setup and configuration
• Advanced Usage - Advanced features and customization
• Advanced Guide - Comprehensive usage patterns
• Embedding Guide - Embedding generation with multiple providers
• Reranking Guide - Document relevance scoring
• Tool Calling - Cross-provider tool calling
• Thinking/Reasoning - Reasoning model support
• Configuration Migration - Versioned TOML upgrades and safe file persistence

🌐 Supported Providers

Provider	Status	Capabilities
OpenAI	✅ Full Support	Chat, Vision, Tools, Structured Output, Caching
xAI	✅ Full Support	Grok 4.5/4.3/4.20/Build, Vision, Tools, Structured Output, Caching, Encrypted Reasoning
Anthropic	✅ Full Support	Claude Models, Vision, Tools, Caching
OpenRouter	✅ Full Support	Multi-Provider Proxy, Vision, Caching, Structured Output
Groq	✅ Full Support	Fast Inference, Structured Output, Caching
BytePlus	✅ Full Support	Seed Models, Structured Output, Caching
Alibaba Model Studio	✅ Full Support	Qwen Models + Resold DeepSeek/GLM, Vision, Video, Tools, Thinking, Caching
DeepSeek	✅ Full Support	Open-Source AI Models, Structured Output, Caching
Moonshot AI (Kimi)	✅ Full Support	Kimi K2 Series, Vision (kimi-k2.5), Tools, Structured Output, Caching, Thinking
MiniMax	✅ Full Support	Anthropic-Compatible API, Tools, Caching, Thinking, Structured Output
Z.ai	✅ Full Support	GLM Models, Caching, Structured Output
NVIDIA NIM	✅ Full Support	100+ Hosted Models, Tools, Structured Output, Reference Pricing
Together AI	✅ Full Support	Multi-Provider Proxy, Vision, Tools, Structured Output
Cerebras	✅ Full Support	Fast Inference, Structured Output
Featherless	✅ Full Support	Open-Weight Models (Qwen, Llama, Mistral, DeepSeek, RWKV), Subscription Billing
Hetzner	✅ Full Support	Open-Weight Models (DeepSeek, GLM, Kimi, Qwen), Free While Experimental
OpenCode Zen	✅ Full Support	Multi-Provider Proxy (Claude, GPT, Gemini, Grok, DeepSeek, Kimi…), Pay-As-You-Go
OpenCode Go	✅ Full Support	Multi-Provider Proxy (Kimi, GLM, DeepSeek, Qwen, MiniMax…), Subscription Billing
OctoHub	✅ Supported	Local AI Serving
Google Vertex AI	✅ Supported	Enterprise AI Integration
Google AI Studio	✅ Supported	Gemini API, API-Key Auth
Amazon Bedrock	✅ Supported	Cloud AI Services
Cloudflare Workers AI	✅ Supported	Edge AI Compute
Local LLM	✅ Supported	Ollama, LM Studio, LocalAI, Jan, vLLM
Ollama	✅ Supported	Local LLM Runner
CLI Proxy	✅ Supported	Codex, Claude, Gemini, Cursor

🔒 Privacy & Security

• 🏠 Local-first design
• 🔑 Secure API key management
• 📁 Respects .gitignore
• 🛡️ Comprehensive error handling

🤝 Support & Community

• 🐛 Issues: GitHub Issues
• 📧 Email: opensource@muvon.io
• 🏢 Company: Muvon Un Limited (Hong Kong)

⚖️ License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

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