mirror of
https://github.com/danny-avila/LibreChat.git
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* refactor: json schema tools with lazy loading - Added LocalToolExecutor class for lazy loading and caching of tools during execution. - Introduced ToolExecutionContext and ToolExecutor interfaces for better type management. - Created utility functions to generate tool proxies with JSON schema support. - Added ExtendedJsonSchema type for enhanced schema definitions. - Updated existing toolkits to utilize the new schema and executor functionalities. - Introduced a comprehensive tool definitions registry for managing various tool schemas. chore: update @librechat/agents to version 3.1.2 refactor: enhance tool loading optimization and classification - Improved the loadAgentToolsOptimized function to utilize a proxy pattern for all tools, enabling deferred execution and reducing overhead. - Introduced caching for tool instances and refined tool classification logic to streamline tool management. - Updated the handling of MCP tools to improve logging and error reporting for missing tools in the cache. - Enhanced the structure of tool definitions to support better classification and integration with existing tools. refactor: modularize tool loading and enhance optimization - Moved the loadAgentToolsOptimized function to a new service file for better organization and maintainability. - Updated the ToolService to utilize the new service for optimized tool loading, improving code clarity. - Removed legacy tool loading methods and streamlined the tool loading process to enhance performance and reduce complexity. - Introduced feature flag handling for optimized tool loading, allowing for easier toggling of this functionality. refactor: replace loadAgentToolsWithFlag with loadAgentTools in tool loader refactor: enhance MCP tool loading with proxy creation and classification refactor: optimize MCP tool loading by grouping tools by server - Introduced a Map to group cached tools by server name, improving the organization of tool data. - Updated the createMCPProxyTool function to accept server name directly, enhancing clarity. - Refactored the logic for handling MCP tools, streamlining the process of creating proxy tools for classification. refactor: enhance MCP tool loading and proxy creation - Added functionality to retrieve MCP server tools and reinitialize servers if necessary, improving tool availability. - Updated the tool loading logic to utilize a Map for organizing tools by server, enhancing clarity and performance. - Refactored the createToolProxy function to ensure a default response format, streamlining tool creation. refactor: update createToolProxy to ensure consistent response format - Modified the createToolProxy function to await the executor's execution and validate the result format. - Ensured that the function returns a default response structure when the result is not an array of two elements, enhancing reliability in tool proxy creation. refactor: ToolExecutionContext with toolCall property - Added toolCall property to ToolExecutionContext interface for improved context handling during tool execution. - Updated LocalToolExecutor to include toolCall in the runnable configuration, allowing for more flexible tool invocation. - Modified createToolProxy to pass toolCall from the configuration, ensuring consistent context across tool executions. refactor: enhance event-driven tool execution and logging - Introduced ToolExecuteOptions for improved handling of event-driven tool execution, allowing for parallel execution of tool calls. - Updated getDefaultHandlers to include support for ON_TOOL_EXECUTE events, enhancing the flexibility of tool invocation. - Added detailed logging in LocalToolExecutor to track tool loading and execution metrics, improving observability and debugging capabilities. - Refactored initializeClient to integrate event-driven tool loading, ensuring compatibility with the new execution model. chore: update @librechat/agents to version 3.1.21 refactor: remove legacy tool loading and executor components - Eliminated the loadAgentToolsWithFlag function, simplifying the tool loading process by directly using loadAgentTools. - Removed the LocalToolExecutor and related executor components to streamline the tool execution architecture. - Updated ToolService and related files to reflect the removal of deprecated features, enhancing code clarity and maintainability. refactor: enhance tool classification and definitions handling - Updated the loadAgentTools function to return toolDefinitions alongside toolRegistry, improving the structure of tool data returned to clients. - Removed the convertRegistryToDefinitions function from the initialize.js file, simplifying the initialization process. - Adjusted the buildToolClassification function to ensure toolDefinitions are built and returned simultaneously with the toolRegistry, enhancing efficiency in tool management. - Updated type definitions in initialize.ts to include toolDefinitions, ensuring consistency across the codebase. refactor: implement event-driven tool execution handler - Introduced createToolExecuteHandler function to streamline the handling of ON_TOOL_EXECUTE events, allowing for parallel execution of tool calls. - Updated getDefaultHandlers to utilize the new handler, simplifying the event-driven architecture. - Added handlers.ts file to encapsulate tool execution logic, improving code organization and maintainability. - Enhanced OpenAI handlers to integrate the new tool execution capabilities, ensuring consistent event handling across the application. refactor: integrate event-driven tool execution options - Added toolExecuteOptions to support event-driven tool execution in OpenAI and responses controllers, enhancing flexibility in tool handling. - Updated handlers to utilize createToolExecuteHandler, allowing for streamlined execution of tools during agent interactions. - Refactored service dependencies to include toolExecuteOptions, ensuring consistent integration across the application. refactor: enhance tool loading with definitionsOnly parameter - Updated createToolLoader and loadAgentTools functions to include a definitionsOnly parameter, allowing for the retrieval of only serializable tool definitions in event-driven mode. - Adjusted related interfaces and documentation to reflect the new parameter, improving clarity and flexibility in tool management. - Ensured compatibility across various components by integrating the definitionsOnly option in the initialization process. refactor: improve agent tool presence check in initialization - Added a check for tool presence using a new hasAgentTools variable, which evaluates both structuredTools and toolDefinitions. - Updated the conditional logic in the agent initialization process to utilize the hasAgentTools variable, enhancing clarity and maintainability in tool management. refactor: enhance agent tool extraction to support tool definitions - Updated the extractMCPServers function to handle both tool instances and serializable tool definitions, improving flexibility in agent tool management. - Added a new property toolDefinitions to the AgentWithTools type for better integration of event-driven mode. - Enhanced documentation to clarify the function's capabilities in extracting unique MCP server names from both tools and tool definitions. refactor: enhance tool classification and registry building - Added serverName property to ToolDefinition for improved tool identification. - Introduced buildToolRegistry function to streamline the creation of tool registries based on MCP tool definitions and agent options. - Updated buildToolClassification to utilize the new registry building logic, ensuring basic definitions are returned even when advanced classification features are not allowed. - Enhanced documentation and logging for clarity in tool classification processes. refactor: update @librechat/agents dependency to version 3.1.22 fix: expose loadTools function in ToolService - Added loadTools function to the exported module in ToolService.js, enhancing the accessibility of tool loading functionality. chore: remove configurable options from tool execute options in OpenAI controller refactor: enhance tool loading mechanism to utilize agent-specific context chore: update @librechat/agents dependency to version 3.1.23 fix: simplify result handling in createToolExecuteHandler * refactor: loadToolDefinitions for efficient tool loading in event-driven mode * refactor: replace legacy tool loading with loadToolsForExecution in OpenAI and responses controllers - Updated OpenAIChatCompletionController and createResponse functions to utilize loadToolsForExecution for improved tool loading. - Removed deprecated loadToolsLegacy references, streamlining the tool execution process. - Enhanced tool loading options to include agent-specific context and configurations. * refactor: enhance tool loading and execution handling - Introduced loadActionToolsForExecution function to streamline loading of action tools, improving organization and maintainability. - Updated loadToolsForExecution to handle both regular and action tools, optimizing the tool loading process. - Added detailed logging for missing tools in createToolExecuteHandler, enhancing error visibility. - Refactored tool definitions to normalize action tool names, improving consistency in tool management. * refactor: enhance built-in tool definitions loading - Updated loadToolDefinitions to include descriptions and parameters from the tool registry for built-in tools, improving the clarity and usability of tool definitions. - Integrated getToolDefinition to streamline the retrieval of tool metadata, enhancing the overall tool management process. * feat: add action tool definitions loading to tool service - Introduced getActionToolDefinitions function to load action tool definitions based on agent ID and tool names, enhancing the tool loading process. - Updated loadToolDefinitions to integrate action tool definitions, allowing for better management and retrieval of action-specific tools. - Added comprehensive tests for action tool definitions to ensure correct loading and parameter handling, improving overall reliability and functionality. * chore: update @librechat/agents dependency to version 3.1.26 * refactor: add toolEndCallback to handle tool execution results * fix: tool definitions and execution handling - Introduced native tools (execute_code, file_search, web_search) to the tool service, allowing for better integration and management of these tools. - Updated isBuiltInTool function to include native tools in the built-in check, improving tool recognition. - Added comprehensive tests for loading parameters of native tools, ensuring correct functionality and parameter handling. - Enhanced tool definitions registry to include new agent tool definitions, streamlining tool retrieval and management. * refactor: enhance tool loading and execution context - Added toolRegistry to the context for OpenAIChatCompletionController and createResponse functions, improving tool management. - Updated loadToolsForExecution to utilize toolRegistry for better integration of programmatic tools and tool search functionalities. - Enhanced the initialization process to include toolRegistry in agent context, streamlining tool access and configuration. - Refactored tool classification logic to support event-driven execution, ensuring compatibility with new tool definitions. * chore: add request duration logging to OpenAI and Responses controllers - Introduced logging for request start and completion times in OpenAIChatCompletionController and createResponse functions. - Calculated and logged the duration of each request, enhancing observability and performance tracking. - Improved debugging capabilities by providing detailed logs for both streaming and non-streaming responses. * chore: update @librechat/agents dependency to version 3.1.27 * refactor: implement buildToolSet function for tool management - Introduced buildToolSet function to streamline the creation of tool sets from agent configurations, enhancing tool management across various controllers. - Updated AgentClient, OpenAIChatCompletionController, and createResponse functions to utilize buildToolSet, improving consistency in tool handling. - Added comprehensive tests for buildToolSet to ensure correct functionality and edge case handling, enhancing overall reliability. * refactor: update import paths for ToolExecuteOptions and createToolExecuteHandler * fix: update GoogleSearch.js description for maximum search results - Changed the default maximum number of search results from 10 to 5 in the Google Search JSON schema description, ensuring accurate documentation of the expected behavior. * chore: remove deprecated Browser tool and associated assets - Deleted the Browser tool definition from manifest.json, which included its name, plugin key, description, and authentication configuration. - Removed the web-browser.svg asset as it is no longer needed following the removal of the Browser tool. * fix: ensure tool definitions are valid before processing - Added a check to verify the existence of tool definitions in the registry before accessing their properties, preventing potential runtime errors. - Updated the loading logic for built-in tool definitions to ensure that only valid definitions are pushed to the built-in tool definitions array. * fix: extend ExtendedJsonSchema to support 'null' type and nullable enums - Updated the ExtendedJsonSchema type to include 'null' as a valid type option. - Modified the enum property to accept an array of values that can include strings, numbers, booleans, and null, enhancing schema flexibility. * test: add comprehensive tests for tool definitions loading and registry behavior - Implemented tests to verify the handling of built-in tools without registry definitions, ensuring they are skipped correctly. - Added tests to confirm that built-in tools include descriptions and parameters in the registry. - Enhanced tests for action tools, checking for proper inclusion of metadata and handling of tools without parameters in the registry. * test: add tests for mixed-type and number enum schema handling - Introduced tests to validate the parsing of mixed-type enum values, including strings, numbers, booleans, and null. - Added tests for number enum schema values to ensure correct parsing of numeric inputs, enhancing schema validation coverage. * fix: update mock implementation for @librechat/agents - Changed the mock for @librechat/agents to spread the actual module's properties, ensuring that all necessary functionalities are preserved in tests. - This adjustment enhances the accuracy of the tests by reflecting the real structure of the module. * fix: change max_results type in GoogleSearch schema from number to integer - Updated the type of max_results in the Google Search JSON schema to 'integer' for better type accuracy and validation consistency. * fix: update max_results description and type in GoogleSearch schema - Changed the type of max_results from 'number' to 'integer' for improved type accuracy. - Updated the description to reflect the new default maximum number of search results, changing it from 10 to 5. * refactor: remove unused code and improve tool registry handling - Eliminated outdated comments and conditional logic related to event-driven mode in the ToolService. - Enhanced the handling of the tool registry by ensuring it is configurable for better integration during tool execution. * feat: add definitionsOnly option to buildToolClassification for event-driven mode - Introduced a new parameter, definitionsOnly, to the BuildToolClassificationParams interface to enable a mode that skips tool instance creation. - Updated the buildToolClassification function to conditionally add tool definitions without instantiating tools when definitionsOnly is true. - Modified the loadToolDefinitions function to pass definitionsOnly as true, ensuring compatibility with the new feature. * test: add unit tests for buildToolClassification with definitionsOnly option - Implemented tests to verify the behavior of buildToolClassification when definitionsOnly is set to true or false. - Ensured that tool instances are not created when definitionsOnly is true, while still adding necessary tool definitions. - Confirmed that loadAuthValues is called appropriately based on the definitionsOnly parameter, enhancing test coverage for this new feature.
841 lines
25 KiB
JavaScript
841 lines
25 KiB
JavaScript
const { nanoid } = require('nanoid');
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const { v4: uuidv4 } = require('uuid');
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const { logger } = require('@librechat/data-schemas');
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const { EModelEndpoint, ResourceType, PermissionBits } = require('librechat-data-provider');
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const {
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Callback,
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ToolEndHandler,
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formatAgentMessages,
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ChatModelStreamHandler,
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} = require('@librechat/agents');
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const {
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createRun,
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buildToolSet,
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createSafeUser,
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initializeAgent,
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createToolExecuteHandler,
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// Responses API
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writeDone,
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buildResponse,
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generateResponseId,
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isValidationFailure,
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emitResponseCreated,
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createResponseContext,
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createResponseTracker,
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setupStreamingResponse,
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emitResponseInProgress,
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convertInputToMessages,
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validateResponseRequest,
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buildAggregatedResponse,
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createResponseAggregator,
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sendResponsesErrorResponse,
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createResponsesEventHandlers,
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createAggregatorEventHandlers,
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} = require('@librechat/api');
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const {
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createResponsesToolEndCallback,
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createToolEndCallback,
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} = require('~/server/controllers/agents/callbacks');
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const { loadAgentTools, loadToolsForExecution } = require('~/server/services/ToolService');
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const { findAccessibleResources } = require('~/server/services/PermissionService');
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const { getConvoFiles, saveConvo, getConvo } = require('~/models/Conversation');
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const { getAgent, getAgents } = require('~/models/Agent');
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const db = require('~/models');
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/** @type {import('@librechat/api').AppConfig | null} */
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let appConfig = null;
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/**
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* Set the app config for the controller
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* @param {import('@librechat/api').AppConfig} config
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*/
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function setAppConfig(config) {
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appConfig = config;
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}
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/**
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* Creates a tool loader function for the agent.
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* @param {AbortSignal} signal - The abort signal
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* @param {boolean} [definitionsOnly=true] - When true, returns only serializable
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* tool definitions without creating full tool instances (for event-driven mode)
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*/
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function createToolLoader(signal, definitionsOnly = true) {
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return async function loadTools({
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req,
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res,
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tools,
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model,
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agentId,
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provider,
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tool_options,
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tool_resources,
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}) {
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const agent = { id: agentId, tools, provider, model, tool_options };
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try {
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return await loadAgentTools({
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req,
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res,
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agent,
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signal,
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tool_resources,
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definitionsOnly,
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streamId: null,
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});
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} catch (error) {
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logger.error('Error loading tools for agent ' + agentId, error);
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}
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};
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}
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/**
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* Convert Open Responses input items to internal messages
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* @param {import('@librechat/api').InputItem[]} input
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* @returns {Array} Internal messages
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*/
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function convertToInternalMessages(input) {
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return convertInputToMessages(input);
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}
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/**
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* Load messages from a previous response/conversation
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* @param {string} conversationId - The conversation/response ID
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* @param {string} userId - The user ID
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* @returns {Promise<Array>} Messages from the conversation
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*/
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async function loadPreviousMessages(conversationId, userId) {
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try {
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const messages = await db.getMessages({ conversationId, user: userId });
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if (!messages || messages.length === 0) {
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return [];
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}
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// Convert stored messages to internal format
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return messages.map((msg) => {
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const internalMsg = {
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role: msg.isCreatedByUser ? 'user' : 'assistant',
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content: '',
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messageId: msg.messageId,
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};
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// Handle content - could be string or array
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if (typeof msg.text === 'string') {
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internalMsg.content = msg.text;
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} else if (Array.isArray(msg.content)) {
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// Handle content parts
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internalMsg.content = msg.content;
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} else if (msg.text) {
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internalMsg.content = String(msg.text);
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}
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return internalMsg;
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});
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} catch (error) {
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logger.error('[Responses API] Error loading previous messages:', error);
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return [];
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}
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}
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/**
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* Save input messages to database
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* @param {import('express').Request} req
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* @param {string} conversationId
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* @param {Array} inputMessages - Internal format messages
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* @param {string} agentId
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* @returns {Promise<void>}
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*/
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async function saveInputMessages(req, conversationId, inputMessages, agentId) {
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for (const msg of inputMessages) {
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if (msg.role === 'user') {
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await db.saveMessage(
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req,
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{
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messageId: msg.messageId || nanoid(),
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conversationId,
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parentMessageId: null,
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isCreatedByUser: true,
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text: typeof msg.content === 'string' ? msg.content : JSON.stringify(msg.content),
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sender: 'User',
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endpoint: EModelEndpoint.agents,
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model: agentId,
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},
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{ context: 'Responses API - save user input' },
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);
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}
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}
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}
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/**
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* Save response output to database
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* @param {import('express').Request} req
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* @param {string} conversationId
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* @param {string} responseId
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* @param {import('@librechat/api').Response} response
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* @param {string} agentId
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* @returns {Promise<void>}
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*/
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async function saveResponseOutput(req, conversationId, responseId, response, agentId) {
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// Extract text content from output items
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let responseText = '';
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for (const item of response.output) {
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if (item.type === 'message' && item.content) {
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for (const part of item.content) {
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if (part.type === 'output_text' && part.text) {
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responseText += part.text;
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}
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}
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}
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}
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// Save the assistant message
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await db.saveMessage(
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req,
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{
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messageId: responseId,
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conversationId,
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parentMessageId: null,
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isCreatedByUser: false,
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text: responseText,
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sender: 'Agent',
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endpoint: EModelEndpoint.agents,
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model: agentId,
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finish_reason: response.status === 'completed' ? 'stop' : response.status,
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tokenCount: response.usage?.output_tokens,
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},
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{ context: 'Responses API - save assistant response' },
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);
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}
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/**
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* Save or update conversation
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* @param {import('express').Request} req
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* @param {string} conversationId
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* @param {string} agentId
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* @param {object} agent
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* @returns {Promise<void>}
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*/
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async function saveConversation(req, conversationId, agentId, agent) {
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await saveConvo(
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req,
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{
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conversationId,
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endpoint: EModelEndpoint.agents,
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agentId,
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title: agent?.name || 'Open Responses Conversation',
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model: agent?.model,
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},
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{ context: 'Responses API - save conversation' },
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);
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}
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/**
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* Convert stored messages to Open Responses output format
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* @param {Array} messages - Stored messages
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* @returns {Array} Output items
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*/
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function convertMessagesToOutputItems(messages) {
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const output = [];
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for (const msg of messages) {
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if (!msg.isCreatedByUser) {
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output.push({
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type: 'message',
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id: msg.messageId,
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role: 'assistant',
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status: 'completed',
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content: [
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{
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type: 'output_text',
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text: msg.text || '',
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annotations: [],
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},
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],
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});
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}
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}
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return output;
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}
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/**
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* Create Response - POST /v1/responses
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*
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* Creates a model response following the Open Responses API specification.
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* Supports both streaming and non-streaming responses.
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*
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* @param {import('express').Request} req
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* @param {import('express').Response} res
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*/
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const createResponse = async (req, res) => {
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const requestStartTime = Date.now();
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// Validate request
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const validation = validateResponseRequest(req.body);
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if (isValidationFailure(validation)) {
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return sendResponsesErrorResponse(res, 400, validation.error);
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}
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const request = validation.request;
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const agentId = request.model;
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const isStreaming = request.stream === true;
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// Look up the agent
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const agent = await getAgent({ id: agentId });
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if (!agent) {
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return sendResponsesErrorResponse(
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res,
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404,
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`Agent not found: ${agentId}`,
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'not_found',
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'model_not_found',
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);
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}
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// Generate IDs
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const responseId = generateResponseId();
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const conversationId = request.previous_response_id ?? uuidv4();
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const parentMessageId = null;
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// Create response context
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const context = createResponseContext(request, responseId);
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logger.debug(
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`[Responses API] Request ${responseId} started for agent ${agentId}, stream: ${isStreaming}`,
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);
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// Set up abort controller
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const abortController = new AbortController();
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// Handle client disconnect
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req.on('close', () => {
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if (!abortController.signal.aborted) {
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abortController.abort();
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logger.debug('[Responses API] Client disconnected, aborting');
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}
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});
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try {
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// Build allowed providers set
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const allowedProviders = new Set(
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appConfig?.endpoints?.[EModelEndpoint.agents]?.allowedProviders,
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);
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// Create tool loader
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const loadTools = createToolLoader(abortController.signal);
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// Initialize the agent first to check for disableStreaming
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const endpointOption = {
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endpoint: agent.provider,
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model_parameters: agent.model_parameters ?? {},
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};
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const primaryConfig = await initializeAgent(
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{
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req,
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res,
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loadTools,
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requestFiles: [],
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conversationId,
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parentMessageId,
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agent,
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endpointOption,
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allowedProviders,
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isInitialAgent: true,
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},
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{
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getConvoFiles,
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getFiles: db.getFiles,
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getUserKey: db.getUserKey,
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getMessages: db.getMessages,
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updateFilesUsage: db.updateFilesUsage,
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getUserKeyValues: db.getUserKeyValues,
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getUserCodeFiles: db.getUserCodeFiles,
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getToolFilesByIds: db.getToolFilesByIds,
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getCodeGeneratedFiles: db.getCodeGeneratedFiles,
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},
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);
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// Determine if streaming is enabled (check both request and agent config)
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const streamingDisabled = !!primaryConfig.model_parameters?.disableStreaming;
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const actuallyStreaming = isStreaming && !streamingDisabled;
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// Load previous messages if previous_response_id is provided
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let previousMessages = [];
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if (request.previous_response_id) {
|
|
const userId = req.user?.id ?? 'api-user';
|
|
previousMessages = await loadPreviousMessages(request.previous_response_id, userId);
|
|
}
|
|
|
|
// Convert input to internal messages
|
|
const inputMessages = convertToInternalMessages(
|
|
typeof request.input === 'string' ? request.input : request.input,
|
|
);
|
|
|
|
// Merge previous messages with new input
|
|
const allMessages = [...previousMessages, ...inputMessages];
|
|
|
|
const toolSet = buildToolSet(primaryConfig);
|
|
const { messages: formattedMessages, indexTokenCountMap } = formatAgentMessages(
|
|
allMessages,
|
|
{},
|
|
toolSet,
|
|
);
|
|
|
|
// Create tracker for streaming or aggregator for non-streaming
|
|
const tracker = actuallyStreaming ? createResponseTracker() : null;
|
|
const aggregator = actuallyStreaming ? null : createResponseAggregator();
|
|
|
|
// Set up response for streaming
|
|
if (actuallyStreaming) {
|
|
setupStreamingResponse(res);
|
|
|
|
// Create handler config
|
|
const handlerConfig = {
|
|
res,
|
|
context,
|
|
tracker,
|
|
};
|
|
|
|
// Emit response.created then response.in_progress per Open Responses spec
|
|
emitResponseCreated(handlerConfig);
|
|
emitResponseInProgress(handlerConfig);
|
|
|
|
// Create event handlers
|
|
const { handlers: responsesHandlers, finalizeStream } =
|
|
createResponsesEventHandlers(handlerConfig);
|
|
|
|
// Built-in handler for processing raw model stream chunks
|
|
const chatModelStreamHandler = new ChatModelStreamHandler();
|
|
|
|
// Artifact promises for processing tool outputs
|
|
/** @type {Promise<import('librechat-data-provider').TAttachment | null>[]} */
|
|
const artifactPromises = [];
|
|
// Use Responses API-specific callback that emits librechat:attachment events
|
|
const toolEndCallback = createResponsesToolEndCallback({
|
|
req,
|
|
res,
|
|
tracker,
|
|
artifactPromises,
|
|
});
|
|
|
|
// Create tool execute options for event-driven tool execution
|
|
const toolExecuteOptions = {
|
|
loadTools: async (toolNames) => {
|
|
return loadToolsForExecution({
|
|
req,
|
|
res,
|
|
agent,
|
|
toolNames,
|
|
signal: abortController.signal,
|
|
toolRegistry: primaryConfig.toolRegistry,
|
|
userMCPAuthMap: primaryConfig.userMCPAuthMap,
|
|
tool_resources: primaryConfig.tool_resources,
|
|
});
|
|
},
|
|
toolEndCallback,
|
|
};
|
|
|
|
// Combine handlers
|
|
const handlers = {
|
|
on_chat_model_stream: {
|
|
handle: async (event, data, metadata, graph) => {
|
|
await chatModelStreamHandler.handle(event, data, metadata, graph);
|
|
},
|
|
},
|
|
on_message_delta: responsesHandlers.on_message_delta,
|
|
on_reasoning_delta: responsesHandlers.on_reasoning_delta,
|
|
on_run_step: responsesHandlers.on_run_step,
|
|
on_run_step_delta: responsesHandlers.on_run_step_delta,
|
|
on_chat_model_end: responsesHandlers.on_chat_model_end,
|
|
on_tool_end: new ToolEndHandler(toolEndCallback, logger),
|
|
on_run_step_completed: { handle: () => {} },
|
|
on_chain_stream: { handle: () => {} },
|
|
on_chain_end: { handle: () => {} },
|
|
on_agent_update: { handle: () => {} },
|
|
on_custom_event: { handle: () => {} },
|
|
on_tool_execute: createToolExecuteHandler(toolExecuteOptions),
|
|
};
|
|
|
|
// Create and run the agent
|
|
const userId = req.user?.id ?? 'api-user';
|
|
const userMCPAuthMap = primaryConfig.userMCPAuthMap;
|
|
|
|
const run = await createRun({
|
|
agents: [primaryConfig],
|
|
messages: formattedMessages,
|
|
indexTokenCountMap,
|
|
runId: responseId,
|
|
signal: abortController.signal,
|
|
customHandlers: handlers,
|
|
requestBody: {
|
|
messageId: responseId,
|
|
conversationId,
|
|
},
|
|
user: { id: userId },
|
|
});
|
|
|
|
if (!run) {
|
|
throw new Error('Failed to create agent run');
|
|
}
|
|
|
|
// Process the stream
|
|
const config = {
|
|
runName: 'AgentRun',
|
|
configurable: {
|
|
thread_id: conversationId,
|
|
user_id: userId,
|
|
user: createSafeUser(req.user),
|
|
...(userMCPAuthMap != null && { userMCPAuthMap }),
|
|
},
|
|
signal: abortController.signal,
|
|
streamMode: 'values',
|
|
version: 'v2',
|
|
};
|
|
|
|
await run.processStream({ messages: formattedMessages }, config, {
|
|
callbacks: {
|
|
[Callback.TOOL_ERROR]: (graph, error, toolId) => {
|
|
logger.error(`[Responses API] Tool Error "${toolId}"`, error);
|
|
},
|
|
},
|
|
});
|
|
|
|
// Finalize the stream
|
|
finalizeStream();
|
|
res.end();
|
|
|
|
const duration = Date.now() - requestStartTime;
|
|
logger.debug(`[Responses API] Request ${responseId} completed in ${duration}ms (streaming)`);
|
|
|
|
// Save to database if store: true
|
|
if (request.store === true) {
|
|
try {
|
|
// Save conversation
|
|
await saveConversation(req, conversationId, agentId, agent);
|
|
|
|
// Save input messages
|
|
await saveInputMessages(req, conversationId, inputMessages, agentId);
|
|
|
|
// Build response for saving (use tracker with buildResponse for streaming)
|
|
const finalResponse = buildResponse(context, tracker, 'completed');
|
|
await saveResponseOutput(req, conversationId, responseId, finalResponse, agentId);
|
|
|
|
logger.debug(
|
|
`[Responses API] Stored response ${responseId} in conversation ${conversationId}`,
|
|
);
|
|
} catch (saveError) {
|
|
logger.error('[Responses API] Error saving response:', saveError);
|
|
// Don't fail the request if saving fails
|
|
}
|
|
}
|
|
|
|
// Wait for artifact processing after response ends (non-blocking)
|
|
if (artifactPromises.length > 0) {
|
|
Promise.all(artifactPromises).catch((artifactError) => {
|
|
logger.warn('[Responses API] Error processing artifacts:', artifactError);
|
|
});
|
|
}
|
|
} else {
|
|
const aggregatorHandlers = createAggregatorEventHandlers(aggregator);
|
|
|
|
const chatModelStreamHandler = new ChatModelStreamHandler();
|
|
|
|
/** @type {Promise<import('librechat-data-provider').TAttachment | null>[]} */
|
|
const artifactPromises = [];
|
|
const toolEndCallback = createToolEndCallback({ req, res, artifactPromises, streamId: null });
|
|
|
|
const toolExecuteOptions = {
|
|
loadTools: async (toolNames) => {
|
|
return loadToolsForExecution({
|
|
req,
|
|
res,
|
|
agent,
|
|
toolNames,
|
|
signal: abortController.signal,
|
|
toolRegistry: primaryConfig.toolRegistry,
|
|
userMCPAuthMap: primaryConfig.userMCPAuthMap,
|
|
tool_resources: primaryConfig.tool_resources,
|
|
});
|
|
},
|
|
toolEndCallback,
|
|
};
|
|
|
|
const handlers = {
|
|
on_chat_model_stream: {
|
|
handle: async (event, data, metadata, graph) => {
|
|
await chatModelStreamHandler.handle(event, data, metadata, graph);
|
|
},
|
|
},
|
|
on_message_delta: aggregatorHandlers.on_message_delta,
|
|
on_reasoning_delta: aggregatorHandlers.on_reasoning_delta,
|
|
on_run_step: aggregatorHandlers.on_run_step,
|
|
on_run_step_delta: aggregatorHandlers.on_run_step_delta,
|
|
on_chat_model_end: aggregatorHandlers.on_chat_model_end,
|
|
on_tool_end: new ToolEndHandler(toolEndCallback, logger),
|
|
on_run_step_completed: { handle: () => {} },
|
|
on_chain_stream: { handle: () => {} },
|
|
on_chain_end: { handle: () => {} },
|
|
on_agent_update: { handle: () => {} },
|
|
on_custom_event: { handle: () => {} },
|
|
on_tool_execute: createToolExecuteHandler(toolExecuteOptions),
|
|
};
|
|
|
|
const userId = req.user?.id ?? 'api-user';
|
|
const userMCPAuthMap = primaryConfig.userMCPAuthMap;
|
|
|
|
const run = await createRun({
|
|
agents: [primaryConfig],
|
|
messages: formattedMessages,
|
|
indexTokenCountMap,
|
|
runId: responseId,
|
|
signal: abortController.signal,
|
|
customHandlers: handlers,
|
|
requestBody: {
|
|
messageId: responseId,
|
|
conversationId,
|
|
},
|
|
user: { id: userId },
|
|
});
|
|
|
|
if (!run) {
|
|
throw new Error('Failed to create agent run');
|
|
}
|
|
|
|
const config = {
|
|
runName: 'AgentRun',
|
|
configurable: {
|
|
thread_id: conversationId,
|
|
user_id: userId,
|
|
user: createSafeUser(req.user),
|
|
...(userMCPAuthMap != null && { userMCPAuthMap }),
|
|
},
|
|
signal: abortController.signal,
|
|
streamMode: 'values',
|
|
version: 'v2',
|
|
};
|
|
|
|
await run.processStream({ messages: formattedMessages }, config, {
|
|
callbacks: {
|
|
[Callback.TOOL_ERROR]: (graph, error, toolId) => {
|
|
logger.error(`[Responses API] Tool Error "${toolId}"`, error);
|
|
},
|
|
},
|
|
});
|
|
|
|
if (artifactPromises.length > 0) {
|
|
try {
|
|
await Promise.all(artifactPromises);
|
|
} catch (artifactError) {
|
|
logger.warn('[Responses API] Error processing artifacts:', artifactError);
|
|
}
|
|
}
|
|
|
|
const response = buildAggregatedResponse(context, aggregator);
|
|
|
|
if (request.store === true) {
|
|
try {
|
|
await saveConversation(req, conversationId, agentId, agent);
|
|
|
|
await saveInputMessages(req, conversationId, inputMessages, agentId);
|
|
|
|
await saveResponseOutput(req, conversationId, responseId, response, agentId);
|
|
|
|
logger.debug(
|
|
`[Responses API] Stored response ${responseId} in conversation ${conversationId}`,
|
|
);
|
|
} catch (saveError) {
|
|
logger.error('[Responses API] Error saving response:', saveError);
|
|
// Don't fail the request if saving fails
|
|
}
|
|
}
|
|
|
|
res.json(response);
|
|
|
|
const duration = Date.now() - requestStartTime;
|
|
logger.debug(
|
|
`[Responses API] Request ${responseId} completed in ${duration}ms (non-streaming)`,
|
|
);
|
|
}
|
|
} catch (error) {
|
|
const errorMessage = error instanceof Error ? error.message : 'An error occurred';
|
|
logger.error('[Responses API] Error:', error);
|
|
|
|
// Check if we already started streaming (headers sent)
|
|
if (res.headersSent) {
|
|
// Headers already sent, write error event and close
|
|
writeDone(res);
|
|
res.end();
|
|
} else {
|
|
sendResponsesErrorResponse(res, 500, errorMessage, 'server_error');
|
|
}
|
|
}
|
|
};
|
|
|
|
/**
|
|
* List available agents as models - GET /v1/models (also works with /v1/responses/models)
|
|
*
|
|
* Returns a list of available agents the user has remote access to.
|
|
*
|
|
* @param {import('express').Request} req
|
|
* @param {import('express').Response} res
|
|
*/
|
|
const listModels = async (req, res) => {
|
|
try {
|
|
const userId = req.user?.id;
|
|
const userRole = req.user?.role;
|
|
|
|
if (!userId) {
|
|
return sendResponsesErrorResponse(res, 401, 'Authentication required', 'auth_error');
|
|
}
|
|
|
|
// Find agents the user has remote access to (VIEW permission on REMOTE_AGENT)
|
|
const accessibleAgentIds = await findAccessibleResources({
|
|
userId,
|
|
role: userRole,
|
|
resourceType: ResourceType.REMOTE_AGENT,
|
|
requiredPermissions: PermissionBits.VIEW,
|
|
});
|
|
|
|
// Get the accessible agents
|
|
let agents = [];
|
|
if (accessibleAgentIds.length > 0) {
|
|
agents = await getAgents({ _id: { $in: accessibleAgentIds } });
|
|
}
|
|
|
|
// Convert to models format
|
|
const models = agents.map((agent) => ({
|
|
id: agent.id,
|
|
object: 'model',
|
|
created: Math.floor(new Date(agent.createdAt).getTime() / 1000),
|
|
owned_by: agent.author ?? 'librechat',
|
|
// Additional metadata
|
|
name: agent.name,
|
|
description: agent.description,
|
|
provider: agent.provider,
|
|
}));
|
|
|
|
res.json({
|
|
object: 'list',
|
|
data: models,
|
|
});
|
|
} catch (error) {
|
|
logger.error('[Responses API] Error listing models:', error);
|
|
sendResponsesErrorResponse(
|
|
res,
|
|
500,
|
|
error instanceof Error ? error.message : 'Failed to list models',
|
|
'server_error',
|
|
);
|
|
}
|
|
};
|
|
|
|
/**
|
|
* Get Response - GET /v1/responses/:id
|
|
*
|
|
* Retrieves a stored response by its ID.
|
|
* The response ID maps to a conversationId in LibreChat's storage.
|
|
*
|
|
* @param {import('express').Request} req
|
|
* @param {import('express').Response} res
|
|
*/
|
|
const getResponse = async (req, res) => {
|
|
try {
|
|
const responseId = req.params.id;
|
|
const userId = req.user?.id;
|
|
|
|
if (!responseId) {
|
|
return sendResponsesErrorResponse(res, 400, 'Response ID is required');
|
|
}
|
|
|
|
// The responseId could be either the response ID or the conversation ID
|
|
// Try to find a conversation with this ID
|
|
const conversation = await getConvo(userId, responseId);
|
|
|
|
if (!conversation) {
|
|
return sendResponsesErrorResponse(
|
|
res,
|
|
404,
|
|
`Response not found: ${responseId}`,
|
|
'not_found',
|
|
'response_not_found',
|
|
);
|
|
}
|
|
|
|
// Load messages for this conversation
|
|
const messages = await db.getMessages({ conversationId: responseId, user: userId });
|
|
|
|
if (!messages || messages.length === 0) {
|
|
return sendResponsesErrorResponse(
|
|
res,
|
|
404,
|
|
`No messages found for response: ${responseId}`,
|
|
'not_found',
|
|
'response_not_found',
|
|
);
|
|
}
|
|
|
|
// Convert messages to Open Responses output format
|
|
const output = convertMessagesToOutputItems(messages);
|
|
|
|
// Find the last assistant message for usage info
|
|
const lastAssistantMessage = messages.filter((m) => !m.isCreatedByUser).pop();
|
|
|
|
// Build the response object
|
|
const response = {
|
|
id: responseId,
|
|
object: 'response',
|
|
created_at: Math.floor(new Date(conversation.createdAt || Date.now()).getTime() / 1000),
|
|
completed_at: Math.floor(new Date(conversation.updatedAt || Date.now()).getTime() / 1000),
|
|
status: 'completed',
|
|
incomplete_details: null,
|
|
model: conversation.agentId || conversation.model || 'unknown',
|
|
previous_response_id: null,
|
|
instructions: null,
|
|
output,
|
|
error: null,
|
|
tools: [],
|
|
tool_choice: 'auto',
|
|
truncation: 'disabled',
|
|
parallel_tool_calls: true,
|
|
text: { format: { type: 'text' } },
|
|
temperature: 1,
|
|
top_p: 1,
|
|
presence_penalty: 0,
|
|
frequency_penalty: 0,
|
|
top_logprobs: null,
|
|
reasoning: null,
|
|
user: userId,
|
|
usage: lastAssistantMessage?.tokenCount
|
|
? {
|
|
input_tokens: 0,
|
|
output_tokens: lastAssistantMessage.tokenCount,
|
|
total_tokens: lastAssistantMessage.tokenCount,
|
|
}
|
|
: null,
|
|
max_output_tokens: null,
|
|
max_tool_calls: null,
|
|
store: true,
|
|
background: false,
|
|
service_tier: 'default',
|
|
metadata: {},
|
|
safety_identifier: null,
|
|
prompt_cache_key: null,
|
|
};
|
|
|
|
res.json(response);
|
|
} catch (error) {
|
|
logger.error('[Responses API] Error getting response:', error);
|
|
sendResponsesErrorResponse(
|
|
res,
|
|
500,
|
|
error instanceof Error ? error.message : 'Failed to get response',
|
|
'server_error',
|
|
);
|
|
}
|
|
};
|
|
|
|
module.exports = {
|
|
createResponse,
|
|
getResponse,
|
|
listModels,
|
|
setAppConfig,
|
|
};
|