Merge branch 'dev' into feat/context-window-ui

api/models/Agent.js

@@ -1,8 +1,18 @@
 const mongoose = require('mongoose');
 const crypto = require('node:crypto');
 const { logger } = require('@librechat/data-schemas');
-const { ResourceType, SystemRoles, Tools, actionDelimiter } = require('librechat-data-provider');
-const { GLOBAL_PROJECT_NAME, EPHEMERAL_AGENT_ID, mcp_all, mcp_delimiter } =
+const { getCustomEndpointConfig } = require('@librechat/api');
+const {
+  Tools,
+  SystemRoles,
+  ResourceType,
+  actionDelimiter,
+  isAgentsEndpoint,
+  getResponseSender,
+  isEphemeralAgentId,
+  encodeEphemeralAgentId,
+} = require('librechat-data-provider');
+const { GLOBAL_PROJECT_NAME, mcp_all, mcp_delimiter } =
   require('librechat-data-provider').Constants;
 const {
   removeAgentFromAllProjects,
@@ -92,7 +102,7 @@ const getAgents = async (searchParameter) => await Agent.find(searchParameter).l
  * @param {import('@librechat/agents').ClientOptions} [params.model_parameters]
  * @returns {Promise<Agent|null>} The agent document as a plain object, or null if not found.
  */
-const loadEphemeralAgent = async ({ req, spec, agent_id, endpoint, model_parameters: _m }) => {
+const loadEphemeralAgent = async ({ req, spec, endpoint, model_parameters: _m }) => {
   const { model, ...model_parameters } = _m;
   const modelSpecs = req.config?.modelSpecs?.list;
   /** @type {TModelSpec | null} */
@@ -139,8 +149,28 @@ const loadEphemeralAgent = async ({ req, spec, agent_id, endpoint, model_paramet
   }
 
   const instructions = req.body.promptPrefix;
+
+  // Compute display name using getResponseSender (same logic used for addedConvo agents)
+  const appConfig = req.config;
+  let endpointConfig = appConfig?.endpoints?.[endpoint];
+  if (!isAgentsEndpoint(endpoint) && !endpointConfig) {
+    try {
+      endpointConfig = getCustomEndpointConfig({ endpoint, appConfig });
+    } catch (err) {
+      logger.error('[loadEphemeralAgent] Error getting custom endpoint config', err);
+    }
+  }
+
+  const sender = getResponseSender({
+    modelLabel: model_parameters?.modelLabel,
+    modelDisplayLabel: endpointConfig?.modelDisplayLabel,
+  });
+
+  // Encode ephemeral agent ID with endpoint, model, and computed sender for display
+  const ephemeralId = encodeEphemeralAgentId({ endpoint, model, sender });
+
   const result = {
-    id: agent_id,
+    id: ephemeralId,
     instructions,
     provider: endpoint,
     model_parameters,
@@ -169,8 +199,8 @@ const loadAgent = async ({ req, spec, agent_id, endpoint, model_parameters }) =>
   if (!agent_id) {
     return null;
   }
-  if (agent_id === EPHEMERAL_AGENT_ID) {
-    return await loadEphemeralAgent({ req, spec, agent_id, endpoint, model_parameters });
+  if (isEphemeralAgentId(agent_id)) {
+    return await loadEphemeralAgent({ req, spec, endpoint, model_parameters });
   }
   const agent = await getAgent({
     id: agent_id,

api/models/Agent.spec.js

@@ -1960,7 +1960,8 @@ describe('models/Agent', () => {
       });
 
       if (result) {
-        expect(result.id).toBe(EPHEMERAL_AGENT_ID);
+        // Ephemeral agent ID is encoded with endpoint and model
+        expect(result.id).toBe('openai__gpt-4');
         expect(result.instructions).toBe('Test instructions');
         expect(result.provider).toBe('openai');
         expect(result.model).toBe('gpt-4');
@@ -1978,7 +1979,7 @@ describe('models/Agent', () => {
       const mockReq = { user: { id: 'user123' } };
       const result = await loadAgent({
         req: mockReq,
-        agent_id: 'non_existent_agent',
+        agent_id: 'agent_non_existent',
         endpoint: 'openai',
         model_parameters: { model: 'gpt-4' },
       });
@@ -2105,7 +2106,7 @@ describe('models/Agent', () => {
       test('should handle loadAgent with malformed req object', async () => {
         const result = await loadAgent({
           req: null,
-          agent_id: 'test',
+          agent_id: 'agent_test',
           endpoint: 'openai',
           model_parameters: { model: 'gpt-4' },
         });

api/models/loadAddedAgent.js

@@ -0,0 +1,218 @@
+const { logger } = require('@librechat/data-schemas');
+const { getCustomEndpointConfig } = require('@librechat/api');
+const {
+  Tools,
+  Constants,
+  isAgentsEndpoint,
+  getResponseSender,
+  isEphemeralAgentId,
+  appendAgentIdSuffix,
+  encodeEphemeralAgentId,
+} = require('librechat-data-provider');
+const { getMCPServerTools } = require('~/server/services/Config');
+
+const { mcp_all, mcp_delimiter } = Constants;
+
+/**
+ * Constant for added conversation agent ID
+ */
+const ADDED_AGENT_ID = 'added_agent';
+
+/**
+ * Get an agent document based on the provided ID.
+ * @param {Object} searchParameter - The search parameters to find the agent.
+ * @param {string} searchParameter.id - The ID of the agent.
+ * @returns {Promise<import('librechat-data-provider').Agent|null>}
+ */
+let getAgent;
+
+/**
+ * Set the getAgent function (dependency injection to avoid circular imports)
+ * @param {Function} fn
+ */
+const setGetAgent = (fn) => {
+  getAgent = fn;
+};
+
+/**
+ * Load an agent from an added conversation (TConversation).
+ * Used for multi-convo parallel agent execution.
+ *
+ * @param {Object} params
+ * @param {import('express').Request} params.req
+ * @param {import('librechat-data-provider').TConversation} params.conversation - The added conversation
+ * @param {import('librechat-data-provider').Agent} [params.primaryAgent] - The primary agent (used to duplicate tools when both are ephemeral)
+ * @returns {Promise<import('librechat-data-provider').Agent|null>} The agent config as a plain object, or null if invalid.
+ */
+const loadAddedAgent = async ({ req, conversation, primaryAgent }) => {
+  if (!conversation) {
+    return null;
+  }
+
+  // If there's an agent_id, load the existing agent
+  if (conversation.agent_id && !isEphemeralAgentId(conversation.agent_id)) {
+    if (!getAgent) {
+      throw new Error('getAgent not initialized - call setGetAgent first');
+    }
+    const agent = await getAgent({
+      id: conversation.agent_id,
+    });
+
+    if (!agent) {
+      logger.warn(`[loadAddedAgent] Agent ${conversation.agent_id} not found`);
+      return null;
+    }
+
+    agent.version = agent.versions ? agent.versions.length : 0;
+    // Append suffix to distinguish from primary agent (matches ephemeral format)
+    // This is needed when both agents have the same ID or for consistent parallel content attribution
+    agent.id = appendAgentIdSuffix(agent.id, 1);
+    return agent;
+  }
+
+  // Otherwise, create an ephemeral agent config from the conversation
+  const { model, endpoint, promptPrefix, spec, ...rest } = conversation;
+
+  if (!endpoint || !model) {
+    logger.warn('[loadAddedAgent] Missing required endpoint or model for ephemeral agent');
+    return null;
+  }
+
+  // If both primary and added agents are ephemeral, duplicate tools from primary agent
+  const primaryIsEphemeral = primaryAgent && isEphemeralAgentId(primaryAgent.id);
+  if (primaryIsEphemeral && Array.isArray(primaryAgent.tools)) {
+    // Get display name using getResponseSender
+    const appConfig = req.config;
+    let endpointConfig = appConfig?.endpoints?.[endpoint];
+    if (!isAgentsEndpoint(endpoint) && !endpointConfig) {
+      try {
+        endpointConfig = getCustomEndpointConfig({ endpoint, appConfig });
+      } catch (err) {
+        logger.error('[loadAddedAgent] Error getting custom endpoint config', err);
+      }
+    }
+
+    const sender = getResponseSender({
+      modelLabel: rest.modelLabel,
+      modelDisplayLabel: endpointConfig?.modelDisplayLabel,
+    });
+
+    const ephemeralId = encodeEphemeralAgentId({ endpoint, model, sender, index: 1 });
+
+    return {
+      id: ephemeralId,
+      instructions: promptPrefix || '',
+      provider: endpoint,
+      model_parameters: {},
+      model,
+      tools: [...primaryAgent.tools],
+    };
+  }
+
+  // Extract ephemeral agent options from conversation if present
+  const ephemeralAgent = rest.ephemeralAgent;
+  const mcpServers = new Set(ephemeralAgent?.mcp);
+  const userId = req.user?.id;
+
+  // Check model spec for MCP servers
+  const modelSpecs = req.config?.modelSpecs?.list;
+  let modelSpec = null;
+  if (spec != null && spec !== '') {
+    modelSpec = modelSpecs?.find((s) => s.name === spec) || null;
+  }
+  if (modelSpec?.mcpServers) {
+    for (const mcpServer of modelSpec.mcpServers) {
+      mcpServers.add(mcpServer);
+    }
+  }
+
+  /** @type {string[]} */
+  const tools = [];
+  if (ephemeralAgent?.execute_code === true || modelSpec?.executeCode === true) {
+    tools.push(Tools.execute_code);
+  }
+  if (ephemeralAgent?.file_search === true || modelSpec?.fileSearch === true) {
+    tools.push(Tools.file_search);
+  }
+  if (ephemeralAgent?.web_search === true || modelSpec?.webSearch === true) {
+    tools.push(Tools.web_search);
+  }
+
+  const addedServers = new Set();
+  if (mcpServers.size > 0) {
+    for (const mcpServer of mcpServers) {
+      if (addedServers.has(mcpServer)) {
+        continue;
+      }
+      const serverTools = await getMCPServerTools(userId, mcpServer);
+      if (!serverTools) {
+        tools.push(`${mcp_all}${mcp_delimiter}${mcpServer}`);
+        addedServers.add(mcpServer);
+        continue;
+      }
+      tools.push(...Object.keys(serverTools));
+      addedServers.add(mcpServer);
+    }
+  }
+
+  // Build model_parameters from conversation fields
+  const model_parameters = {};
+  const paramKeys = [
+    'temperature',
+    'top_p',
+    'topP',
+    'topK',
+    'presence_penalty',
+    'frequency_penalty',
+    'maxOutputTokens',
+    'maxTokens',
+    'max_tokens',
+  ];
+
+  for (const key of paramKeys) {
+    if (rest[key] != null) {
+      model_parameters[key] = rest[key];
+    }
+  }
+
+  // Get endpoint config for modelDisplayLabel (same pattern as initialize.js)
+  const appConfig = req.config;
+  let endpointConfig = appConfig?.endpoints?.[endpoint];
+  if (!isAgentsEndpoint(endpoint) && !endpointConfig) {
+    try {
+      endpointConfig = getCustomEndpointConfig({ endpoint, appConfig });
+    } catch (err) {
+      logger.error('[loadAddedAgent] Error getting custom endpoint config', err);
+    }
+  }
+
+  // Compute display name using getResponseSender (same logic used for main agent)
+  const sender = getResponseSender({
+    modelLabel: rest.modelLabel,
+    modelDisplayLabel: endpointConfig?.modelDisplayLabel,
+  });
+
+  /** Encoded ephemeral agent ID with endpoint, model, sender, and index=1 to distinguish from primary */
+  const ephemeralId = encodeEphemeralAgentId({ endpoint, model, sender, index: 1 });
+
+  const result = {
+    id: ephemeralId,
+    instructions: promptPrefix || '',
+    provider: endpoint,
+    model_parameters,
+    model,
+    tools,
+  };
+
+  if (ephemeralAgent?.artifacts != null && ephemeralAgent.artifacts) {
+    result.artifacts = ephemeralAgent.artifacts;
+  }
+
+  return result;
+};
+
+module.exports = {
+  ADDED_AGENT_ID,
+  loadAddedAgent,
+  setGetAgent,
+};

api/models/tx.js

@@ -113,6 +113,8 @@ const tokenValues = Object.assign(
     'gpt-4o-2024-05-13': { prompt: 5, completion: 15 },
     'gpt-4o-mini': { prompt: 0.15, completion: 0.6 },
     'gpt-5': { prompt: 1.25, completion: 10 },
+    'gpt-5.1': { prompt: 1.25, completion: 10 },
+    'gpt-5.2': { prompt: 1.75, completion: 14 },
     'gpt-5-nano': { prompt: 0.05, completion: 0.4 },
     'gpt-5-mini': { prompt: 0.25, completion: 2 },
     'gpt-5-pro': { prompt: 15, completion: 120 },

api/models/tx.spec.js

@@ -35,6 +35,19 @@ describe('getValueKey', () => {
     expect(getValueKey('gpt-5-0130')).toBe('gpt-5');
   });
 
+  it('should return "gpt-5.1" for model name containing "gpt-5.1"', () => {
+    expect(getValueKey('gpt-5.1')).toBe('gpt-5.1');
+    expect(getValueKey('gpt-5.1-chat')).toBe('gpt-5.1');
+    expect(getValueKey('gpt-5.1-codex')).toBe('gpt-5.1');
+    expect(getValueKey('openai/gpt-5.1')).toBe('gpt-5.1');
+  });
+
+  it('should return "gpt-5.2" for model name containing "gpt-5.2"', () => {
+    expect(getValueKey('gpt-5.2')).toBe('gpt-5.2');
+    expect(getValueKey('gpt-5.2-chat')).toBe('gpt-5.2');
+    expect(getValueKey('openai/gpt-5.2')).toBe('gpt-5.2');
+  });
+
   it('should return "gpt-3.5-turbo-1106" for model name containing "gpt-3.5-turbo-1106"', () => {
     expect(getValueKey('gpt-3.5-turbo-1106-some-other-info')).toBe('gpt-3.5-turbo-1106');
     expect(getValueKey('openai/gpt-3.5-turbo-1106')).toBe('gpt-3.5-turbo-1106');
@@ -310,6 +323,34 @@ describe('getMultiplier', () => {
     );
   });
 
+  it('should return the correct multiplier for gpt-5.1', () => {
+    expect(getMultiplier({ model: 'gpt-5.1', tokenType: 'prompt' })).toBe(
+      tokenValues['gpt-5.1'].prompt,
+    );
+    expect(getMultiplier({ model: 'gpt-5.1', tokenType: 'completion' })).toBe(
+      tokenValues['gpt-5.1'].completion,
+    );
+    expect(getMultiplier({ model: 'openai/gpt-5.1', tokenType: 'prompt' })).toBe(
+      tokenValues['gpt-5.1'].prompt,
+    );
+    expect(tokenValues['gpt-5.1'].prompt).toBe(1.25);
+    expect(tokenValues['gpt-5.1'].completion).toBe(10);
+  });
+
+  it('should return the correct multiplier for gpt-5.2', () => {
+    expect(getMultiplier({ model: 'gpt-5.2', tokenType: 'prompt' })).toBe(
+      tokenValues['gpt-5.2'].prompt,
+    );
+    expect(getMultiplier({ model: 'gpt-5.2', tokenType: 'completion' })).toBe(
+      tokenValues['gpt-5.2'].completion,
+    );
+    expect(getMultiplier({ model: 'openai/gpt-5.2', tokenType: 'prompt' })).toBe(
+      tokenValues['gpt-5.2'].prompt,
+    );
+    expect(tokenValues['gpt-5.2'].prompt).toBe(1.75);
+    expect(tokenValues['gpt-5.2'].completion).toBe(14);
+  });
+
   it('should return the correct multiplier for gpt-4o', () => {
     const valueKey = getValueKey('gpt-4o-2024-08-06');
     expect(getMultiplier({ valueKey, tokenType: 'prompt' })).toBe(tokenValues['gpt-4o'].prompt);