@gopluto_ai/llm-tools-orchestration/dist/index.esm.js

A scalable tools or functions orchestration SDK for LLM agents using OpenAI's /v1/responses API with memory, hooks, and tool planning support. Alternative of MCP for LLM tools orchestration.

const toolRegistry = [];
const hookProcessors = {};
const registerTool = (tool) => toolRegistry.push(tool);
const registerHookProcessor = (hookName, processor) => {
    hookProcessors[hookName] = processor;
};
const getToolSchemas = () => toolRegistry.map(({ type, name, description, parameters }) => ({
    type, name, description, parameters,
}));
const getTool = (name) => toolRegistry.find(t => t.name === name);
const runHooks = async (hookNames = [], memory) => {
    for (const hook of hookNames) {
        const processor = hookProcessors[hook];
        if (processor)
            memory = await processor(memory);
    }
    return memory;
};
const planTools = async (messages, getOpenAIResData, modal = "gpt-4o") => {
    const messagesData = {
        agentContext: messages.sysprompt,
        conversationHistory: messages.conversationHistory,
        agentMemory: messages.agentMemory || {},
        userPrompt: messages.userMessage,
        functions: getToolSchemas(),
    };
    console.log("🧠 Planning initiated with:", messagesData);
    const final = await getOpenAIResData(messagesData, modal);
    console.log("📩 Response from OpenAI:", final);
    const message = final.aiResponse;
    // No tools triggered — direct reply
    if (message && (!final.function_call || final.function_call.length === 0)) {
        return {
            tools: [],
            neededTools: [],
            args: {},
            directReply: message,
            convTopic: final.convTopic || "General",
            newMemory: final.newMemory || {},
            modal: final.modal || "gpt-4o",
            usage: final.usage,
        };
    }
    // Tool calls exist — parse them
    if (Array.isArray(final.function_call) && final.function_call.length > 0) {
        const args = {};
        const neededTools = [];
        final.function_call.forEach((tool) => {
            const parsedArgs = typeof tool.arguments === "string"
                ? JSON.parse(tool.arguments)
                : tool.arguments;
            args[tool.name] = {
                ...parsedArgs,
                call_id: tool.call_id,
            };
            neededTools.push(tool.name);
        });
        return {
            tools: final.function_call,
            neededTools,
            args,
            directReply: message || "",
            convTopic: final.convTopic || "General",
            newMemory: final.newMemory || {},
            modal: final.modal || "gpt-4o",
            usage: final.usage,
        };
    }
    throw new Error("❌ Invalid GPT planning response format");
};
const executeParallelTools = async (toolList, argsMap, memory) => {
    if (!Array.isArray(toolList) || toolList.length === 0) {
        throw new Error("No tools to execute");
    }
    const toolCalls = toolList.map(async (toolName) => {
        const tool = getTool(toolName);
        if (!tool)
            throw new Error(`Tool ${toolName} not found`);
        memory = await runHooks(tool.preHooks || [], memory);
        const result = await tool.handler(argsMap[toolName], memory);
        memory = await runHooks(tool.postHooks || [], memory);
        return {
            name: toolName,
            result,
            call_id: argsMap[toolName]?.call_id || null,
        };
    });
    const results = await Promise.all(toolCalls);
    return results;
};
const synthesizeFinalReply = async (userMessage, toolResults, messages, tools, getOpenAIResData, modal = "gpt-4o") => {
    const toolResultsWithNames = toolResults.map(({ name, result, call_id }) => ({
        recipient_name: name,
        call_id,
        data: result, // Ensure data is always an object
    }));
    const agentContext = `
system context: ${messages.sysprompt.content}
Additional Instruction:
Here is the data returned by tools you called:
Please generate a useful, human-like reply summarizing and give the data what’s most relevant data in json.`;
    const messagesData = {
        agentContext, // ✅ now a string
        function_output: toolResultsWithNames,
        function_called: tools, // ✅ array of function calls
        conversationHistory: messages.conversationHistory,
        agentMemory: messages.agentMemory || {},
        userPrompt: messages.userMessage,
    };
    console.log("Synthesizing final reply with messages:");
    const final = await getOpenAIResData(messagesData, modal);
    console.log("Final synthesized reply:");
    return final || { aiResponse: "No reply generated." };
};

export { executeParallelTools, getTool, getToolSchemas, planTools, registerHookProcessor, registerTool, synthesizeFinalReply };