https://github.com/PiscesGameDev/UniAI.git

git clone https://github.com/PiscesGameDev/UniAI.git Assets/UniAI

https://github.com/Cysharp/UniTask.git?path=src/UniTask/Assets/Plugins/UniTask

using System.Collections.Generic;
using UniAI;

var client = AIClient.Create(new ChannelEntry
{
    Protocol = ProviderProtocol.OpenAI,
    ApiKey = "your-api-key",
    BaseUrl = "https://api.openai.com/v1",
    Models = new List<string> { "gpt-4o" }
});

using Cysharp.Threading.Tasks;
using UnityEngine;
using UniAI;

var client = AIClient.Create(UniAISettings.Instance.ToConfig());

var response = await client.SendAsync(new AIRequest
{
    Model = "gpt-4o",
    SystemPrompt = "你是一个 Unity 游戏开发助手。",
    Messages =
    {
        AIMessage.User("如何优化 Draw Call？")
    }
});

if (response.IsSuccess)
    Debug.Log(response.Text);

await foreach (var chunk in client.StreamAsync(request))
{
    if (!string.IsNullOrEmpty(chunk.DeltaText))
        Debug.Log(chunk.DeltaText);
}

var typed = await client.SendAsync<MyData>(new AIRequest
{
    Model = "gpt-4o",
    Messages = { AIMessage.User("返回 JSON 格式的角色属性") },
    ResponseFormat = AIResponseFormat.JsonSchema<MyData>()
});

var request = new AIRequest
{
    Model = "gpt-4o",
    Messages =
    {
        AIMessage.UserWithImage("分析这张截图", imageBytes, "image/png")
    }
};

var entry = ModelRegistry.Get("gpt-image-2");

bool canChat = ModelRegistry.HasCapability("gpt-4o", ModelCapability.Chat);
bool canGenerateImage = ModelRegistry.HasCapability("gpt-image-2", ModelCapability.ImageGen);
int contextWindow = ModelRegistry.GetContextWindow("deepseek-v4-pro");
ModelEndpoint endpoint = ModelRegistry.GetEndpoint("gpt-image-2");

[UniAITool(
    Name = "read_file",
    Group = ToolGroups.Core,
    Description = "Read file content. Actions: 'read'.",
    Actions = new[] { "read" })]
internal static class ReadFileTool
{
    public static async UniTask<object> HandleAsync(JObject args, CancellationToken ct)
    {
        var path = (string)args["path"];
        var text = await File.ReadAllTextAsync(path, ct);
        return ToolResponse.Success(new { text });
    }

    public class ReadArgs
    {
        [ToolParam(Description = "File path to read.")]
        public string Path;
    }
}

var runner = new AIAgentRunner(client, agentDefinition);

await foreach (var evt in runner.RunStreamAsync(messages))
{
    switch (evt.Type)
    {
        case AgentEventType.TextDelta:
            Debug.Log(evt.Text);
            break;
        case AgentEventType.ToolCallStart:
            Debug.Log($"Tool: {evt.ToolCall.Name}");
            break;
        case AgentEventType.ToolCallResult:
            Debug.Log(evt.ToolResult);
            break;
    }
}

var pipeline = new ContextPipeline(client);
pipeline.AddProvider(new MyContextProvider());

var processed = await pipeline.ProcessAsync(
    messages,
    systemPrompt,
    modelId,
    config.General.ContextWindow,
    session,
    ct);

var config = UniAISettings.Instance.ToConfig();
var model = "gpt-image-2";
var entry = ModelRegistry.Get(model);
var channels = config.FindChannelsForModel(model);

var route = GenerativeProviderRouter.Resolve(channels, entry, model, config.General);
if (route.Provider == null)
{
    Debug.LogError(route.Error);
    return;
}

var result = await route.Provider.GenerateAsync(new GenerateRequest
{
    Prompt = "A stylized Unity editor robot mascot",
    AssetType = GenerativeAssetType.Image,
    Size = "1024x1024",
    Quality = "auto",
    OutputFormat = "png",
    Count = 1
}, ct);

var orchestrator = new ChatOrchestrator();
orchestrator.Configure(new ChatOrchestratorDependencies
{
    ContextProvider = NullConversationContextProvider.Instance,
    Persistence = new ChatHistorySessionPersistence(history),
    TitlePolicy = new FirstUserMessageTitlePolicy()
});

orchestrator.EnsureRuntime(config, modelSelector, agentDefinition);

await orchestrator.StreamResponseAsync(new ChatStreamRequest
{
    Session = session,
    ContextSlots = 0,
    Config = config,
    ModelId = modelSelector.CurrentModelId
});

Runtime/
├── Core/
│   ├── AIClient / ChannelManager
│   ├── ChannelRouteSelector / ProviderCache / AIProviderFactoryRegistry
│   ├── Adapters/
│   │   ├── AdapterAttribute / AdapterCatalog
│   │   └── AdapterRegistry / AdapterDiscovery
│   ├── ChatOrchestrator
│   ├── ConversationRuntimeFactory / ConversationRuntime
│   ├── ConversationContextPreparer
│   ├── McpConversationInitializer
│   ├── AgentEventSessionApplier
│   └── ChatSessionPolicies
├── Models/
│   └── AIRequest / AIResponse / AIMessage / AIContent / AITool / AIStreamChunk
├── Providers/
│   ├── Claude/
│   ├── OpenAI/
│   │   └── Chat/
│   ├── IAIProvider / IAIProviderFactory
│   └── JsonSseProviderBase
├── Agent/
│   ├── AIAgentRunner / AgentLoop / AgentToolExecutor
│   └── UniAIToolRegistry / ToolSchemaGenerator / ToolResponse
├── Chat/
│   └── ChatSession / ChatHistoryManager / FileChatHistoryStorage
├── Context/
│   └── ContextPipeline / TokenEstimator / MessageSummarizer / IContextProvider
├── MCP/
│   └── McpClientManager / McpClient / Transports / McpResourceProvider
├── Generative/
│   ├── GenerativeProviderRouter / IGenerativeProviderFactory
│   └── Providers/OpenAI/Images/
└── Http/
    └── AIHttpClient / SSEDownloadHandler / SSEParser

Editor/
├── Setting/                 # Manager 窗口和配置 Tab
├── Chat/                    # Chat 窗口、StreamingController、ContextCollector
├── Agent/                   # Agent 资产扫描和 Inspector
├── MCP/                     # MCP Server 管理和 Inspector
└── Tools/                   # Editor 内置 [UniAITool]

public sealed class MyProvider : IAIProvider
{
    public string Name => "MyProvider";

    public UniTask<AIResponse> SendAsync(AIRequest request, CancellationToken ct)
    {
        throw new NotImplementedException();
    }

    public IUniTaskAsyncEnumerable<AIStreamChunk> StreamAsync(AIRequest request, CancellationToken ct)
    {
        throw new NotImplementedException();
    }
}

var client = new AIClient(new MyProvider());