LibreChat/api/server/controllers/agents/responses.js
Danny Avila 596f806f60 🛡️ fix: Strict Opt-In Skills Activation per Agent (#12823)
* 🛡️ fix: Strict opt-in skills activation per agent

Skills were activating on every agent run that had the capability +
RBAC enabled, regardless of whether the user (ephemeral) or author
(persisted) had opted in. `scopeSkillIds(undefined)` fell through to
"full accessible catalog" whenever `agent.skills` was unset, which is
the default state for any agent created before skills existed and for
every ephemeral agent.

Activation now requires an explicit signal:
- Ephemeral agent → per-conversation skills badge toggle.
- Persisted agent → new `skills_enabled` master switch on the agent
  doc, surfaced as a toggle in the Agent Builder skills section.
  Enabled + empty/undefined allowlist = full accessible catalog;
  enabled + non-empty allowlist = narrow to those ids; disabled (or
  undefined) = no skills available, even if an allowlist is set.

Centralised the predicate in `resolveAgentScopedSkillIds` so the
primary-agent path, handoff/discovery, the subagent loop, and both
OpenAI controllers all share one source of truth. Frontend `$`
popover scope mirrors the same logic so the UI never offers skills
the backend would refuse to activate.

* test: mock resolveAgentScopedSkillIds in agent controller specs

* refactor: address review findings on skills opt-in PR

- AgentConfig: associate skills label with toggle via htmlFor for
  click/keyboard affordance; simplify Switch handler to Boolean(value).
- skills: mark scopeSkillIds as @internal so runtime callers continue
  to route through resolveAgentScopedSkillIds and inherit the activation
  predicate (ephemeral toggle, persisted skills_enabled).

* fix(agents): include skills_enabled in agent list projection

Without this field, agents loaded via the list endpoint hydrate into the
client agentsMap with skills_enabled === undefined, causing the `$`
skill popover to hide every skill on a fresh page load even when the
agent was saved with skills_enabled: true.

* fix(skills): fail closed for persisted agents during agentsMap hydration

Returning undefined while the agents map loads let the popover render the
full catalog for a persisted agent before we could read its
skills_enabled flag, so the user could pick a skill the backend would
then refuse for the turn. Match the strict opt-in contract by returning
[] until the map is authoritative.

* refactor(skills): extract skillsHintKey for readability

Replaces the nested ternary in the skills section JSX with a
pre-computed constant so the activation -> hint key mapping reads
top-down.

* refactor(skills): unflatten skillsHintKey to remove nested ternary
2026-04-25 04:02:01 -04:00

1153 lines
37 KiB
JavaScript

const { nanoid } = require('nanoid');
const { v4: uuidv4 } = require('uuid');
const { logger } = require('@librechat/data-schemas');
const { Callback, ToolEndHandler, formatAgentMessages } = require('@librechat/agents');
const {
EModelEndpoint,
ResourceType,
PermissionBits,
hasPermissions,
AgentCapabilities,
} = require('librechat-data-provider');
const {
createRun,
buildToolSet,
loadSkillStates,
resolveAgentScopedSkillIds,
createSafeUser,
initializeAgent,
getBalanceConfig,
recordCollectedUsage,
getTransactionsConfig,
extractManualSkills,
injectSkillPrimes,
createToolExecuteHandler,
discoverConnectedAgents,
getRemoteAgentPermissions,
// Responses API
writeDone,
buildResponse,
generateResponseId,
isValidationFailure,
emitResponseCreated,
createResponseContext,
createResponseTracker,
setupStreamingResponse,
emitResponseInProgress,
convertInputToMessages,
validateResponseRequest,
buildAggregatedResponse,
createResponseAggregator,
sendResponsesErrorResponse,
createResponsesEventHandlers,
createAggregatorEventHandlers,
} = require('@librechat/api');
const {
createResponsesToolEndCallback,
buildSummarizationHandlers,
markSummarizationUsage,
createToolEndCallback,
agentLogHandlerObj,
} = require('~/server/controllers/agents/callbacks');
const { loadAgentTools, loadToolsForExecution } = require('~/server/services/ToolService');
const {
findAccessibleResources,
getEffectivePermissions,
} = require('~/server/services/PermissionService');
const {
getSkillToolDeps,
enrichWithSkillConfigurable,
buildSkillPrimedIdsByName,
} = require('~/server/services/Endpoints/agents/skillDeps');
const { getModelsConfig } = require('~/server/controllers/ModelController');
const { logViolation } = require('~/cache');
const db = require('~/models');
/**
* Creates a tool loader function for the agent.
* @param {AbortSignal} signal - The abort signal
* @param {boolean} [definitionsOnly=true] - When true, returns only serializable
* tool definitions without creating full tool instances (for event-driven mode)
*/
function createToolLoader(signal, definitionsOnly = true) {
return async function loadTools({
req,
res,
tools,
model,
agentId,
provider,
tool_options,
tool_resources,
}) {
const agent = { id: agentId, tools, provider, model, tool_options };
try {
return await loadAgentTools({
req,
res,
agent,
signal,
tool_resources,
definitionsOnly,
streamId: null,
});
} catch (error) {
logger.error('Error loading tools for agent ' + agentId, error);
}
};
}
/**
* Convert Open Responses input items to internal messages
* @param {import('@librechat/api').InputItem[]} input
* @returns {Array} Internal messages
*/
function convertToInternalMessages(input) {
return convertInputToMessages(input);
}
/**
* Load messages from a previous response/conversation
* @param {string} conversationId - The conversation/response ID
* @param {string} userId - The user ID
* @returns {Promise<Array>} Messages from the conversation
*/
async function loadPreviousMessages(conversationId, userId) {
try {
const messages = await db.getMessages({ conversationId, user: userId });
if (!messages || messages.length === 0) {
return [];
}
// Convert stored messages to internal format
return messages.map((msg) => {
const internalMsg = {
role: msg.isCreatedByUser ? 'user' : 'assistant',
content: '',
messageId: msg.messageId,
};
// Handle content - could be string or array
if (typeof msg.text === 'string') {
internalMsg.content = msg.text;
} else if (Array.isArray(msg.content)) {
// Handle content parts
internalMsg.content = msg.content;
} else if (msg.text) {
internalMsg.content = String(msg.text);
}
return internalMsg;
});
} catch (error) {
logger.error('[Responses API] Error loading previous messages:', error);
return [];
}
}
/**
* Save input messages to database
* @param {import('express').Request} req
* @param {string} conversationId
* @param {Array} inputMessages - Internal format messages
* @param {string} agentId
* @returns {Promise<void>}
*/
async function saveInputMessages(req, conversationId, inputMessages, agentId) {
for (const msg of inputMessages) {
if (msg.role === 'user') {
await db.saveMessage(
req,
{
messageId: msg.messageId || nanoid(),
conversationId,
parentMessageId: null,
isCreatedByUser: true,
text: typeof msg.content === 'string' ? msg.content : JSON.stringify(msg.content),
sender: 'User',
endpoint: EModelEndpoint.agents,
model: agentId,
},
{ context: 'Responses API - save user input' },
);
}
}
}
/**
* Save response output to database
* @param {import('express').Request} req
* @param {string} conversationId
* @param {string} responseId
* @param {import('@librechat/api').Response} response
* @param {string} agentId
* @returns {Promise<void>}
*/
async function saveResponseOutput(req, conversationId, responseId, response, agentId) {
// Extract text content from output items
let responseText = '';
for (const item of response.output) {
if (item.type === 'message' && item.content) {
for (const part of item.content) {
if (part.type === 'output_text' && part.text) {
responseText += part.text;
}
}
}
}
// Save the assistant message
await db.saveMessage(
req,
{
messageId: responseId,
conversationId,
parentMessageId: null,
isCreatedByUser: false,
text: responseText,
sender: 'Agent',
endpoint: EModelEndpoint.agents,
model: agentId,
finish_reason: response.status === 'completed' ? 'stop' : response.status,
tokenCount: response.usage?.output_tokens,
},
{ context: 'Responses API - save assistant response' },
);
}
/**
* Save or update conversation
* @param {import('express').Request} req
* @param {string} conversationId
* @param {string} agentId
* @param {object} agent
* @returns {Promise<void>}
*/
async function saveConversation(req, conversationId, agentId, agent) {
await db.saveConvo(
{
userId: req?.user?.id,
isTemporary: req?.body?.isTemporary,
interfaceConfig: req?.config?.interfaceConfig,
},
{
conversationId,
endpoint: EModelEndpoint.agents,
agentId,
title: agent?.name || 'Open Responses Conversation',
model: agent?.model,
},
{ context: 'Responses API - save conversation' },
);
}
/**
* Convert stored messages to Open Responses output format
* @param {Array} messages - Stored messages
* @returns {Array} Output items
*/
function convertMessagesToOutputItems(messages) {
const output = [];
for (const msg of messages) {
if (!msg.isCreatedByUser) {
output.push({
type: 'message',
id: msg.messageId,
role: 'assistant',
status: 'completed',
content: [
{
type: 'output_text',
text: msg.text || '',
annotations: [],
},
],
});
}
}
return output;
}
/**
* Create Response - POST /v1/responses
*
* Creates a model response following the Open Responses API specification.
* Supports both streaming and non-streaming responses.
*
* @param {import('express').Request} req
* @param {import('express').Response} res
*/
const createResponse = async (req, res) => {
const appConfig = req.config;
const requestStartTime = Date.now();
// Validate request
const validation = validateResponseRequest(req.body);
if (isValidationFailure(validation)) {
return sendResponsesErrorResponse(res, 400, validation.error);
}
const request = validation.request;
const agentId = request.model;
const isStreaming = request.stream === true;
const summarizationConfig = appConfig?.summarization;
// Look up the agent
const agent = await db.getAgent({ id: agentId });
if (!agent) {
return sendResponsesErrorResponse(
res,
404,
`Agent not found: ${agentId}`,
'not_found',
'model_not_found',
);
}
// Generate IDs
const responseId = generateResponseId();
const context = createResponseContext(request, responseId);
logger.debug(
`[Responses API] Request ${responseId} started for agent ${agentId}, stream: ${isStreaming}`,
);
// Set up abort controller
const abortController = new AbortController();
// Handle client disconnect
req.on('close', () => {
if (!abortController.signal.aborted) {
abortController.abort();
logger.debug('[Responses API] Client disconnected, aborting');
}
});
try {
if (request.previous_response_id != null) {
if (typeof request.previous_response_id !== 'string') {
return sendResponsesErrorResponse(
res,
400,
'previous_response_id must be a string',
'invalid_request',
);
}
if (!(await db.getConvo(req.user?.id, request.previous_response_id))) {
return sendResponsesErrorResponse(res, 404, 'Conversation not found', 'not_found');
}
}
const conversationId = request.previous_response_id ?? uuidv4();
const parentMessageId = null;
// Build allowed providers set
const allowedProviders = new Set(
appConfig?.endpoints?.[EModelEndpoint.agents]?.allowedProviders,
);
// Create tool loader
const loadTools = createToolLoader(abortController.signal);
// Initialize the agent first to check for disableStreaming
const endpointOption = {
endpoint: agent.provider,
model_parameters: agent.model_parameters ?? {},
};
// `filterFilesByAgentAccess` is intentionally omitted: it calls
// `checkPermission` with `resourceType: AGENT`, but this route
// authorizes callers through `REMOTE_AGENT` (via
// `getRemoteAgentPermissions`), so including it would silently drop
// owner-attached context files for any remote user who has
// `REMOTE_AGENT_VIEWER` but not direct `AGENT_VIEW`.
const dbMethods = {
getConvoFiles: db.getConvoFiles,
getFiles: db.getFiles,
getUserKey: db.getUserKey,
getMessages: db.getMessages,
updateFilesUsage: db.updateFilesUsage,
getUserKeyValues: db.getUserKeyValues,
getUserCodeFiles: db.getUserCodeFiles,
getToolFilesByIds: db.getToolFilesByIds,
getCodeGeneratedFiles: db.getCodeGeneratedFiles,
listSkillsByAccess: db.listSkillsByAccess,
listAlwaysApplySkills: db.listAlwaysApplySkills,
getSkillByName: db.getSkillByName,
};
const enabledCapabilities = new Set(
appConfig?.endpoints?.[EModelEndpoint.agents]?.capabilities,
);
const skillsCapabilityEnabled = enabledCapabilities.has(AgentCapabilities.skills);
const ephemeralSkillsToggle = req.body?.ephemeralAgent?.skills === true;
const accessibleSkillIds = skillsCapabilityEnabled
? await findAccessibleResources({
userId: req.user.id,
role: req.user.role,
resourceType: ResourceType.SKILL,
requiredPermissions: PermissionBits.VIEW,
})
: [];
const { skillStates, defaultActiveOnShare } = await loadSkillStates({
userId: req.user.id,
appConfig,
getUserById: db.getUserById,
accessibleSkillIds,
});
const manualSkills = extractManualSkills(req.body);
const primaryConfig = await initializeAgent(
{
req,
res,
loadTools,
requestFiles: [],
conversationId,
parentMessageId,
agent,
endpointOption,
allowedProviders,
isInitialAgent: true,
accessibleSkillIds: resolveAgentScopedSkillIds({
agent,
accessibleSkillIds,
skillsCapabilityEnabled,
ephemeralSkillsToggle,
}),
codeEnvAvailable: enabledCapabilities.has(AgentCapabilities.execute_code),
skillStates,
defaultActiveOnShare,
manualSkills,
},
dbMethods,
);
/**
* Per-agent tool-execution context map, keyed by agentId. Ensures the
* ON_TOOL_EXECUTE callback routes each sub-agent's tool calls to the
* correct toolRegistry / userMCPAuthMap / tool_resources.
* @type {Map<string, {
* agent: object,
* toolRegistry?: import('@librechat/agents').LCToolRegistry,
* userMCPAuthMap?: Record<string, Record<string, string>>,
* tool_resources?: object,
* actionsEnabled?: boolean,
* }>}
*/
const agentToolContexts = new Map();
agentToolContexts.set(primaryConfig.id, {
agent,
toolRegistry: primaryConfig.toolRegistry,
userMCPAuthMap: primaryConfig.userMCPAuthMap,
tool_resources: primaryConfig.tool_resources,
actionsEnabled: primaryConfig.actionsEnabled,
codeEnvAvailable: primaryConfig.codeEnvAvailable,
});
// Only run BFS discovery (and pay `getModelsConfig` upfront) when the
// primary has edges to follow — the common API case is single-agent.
let handoffAgentConfigs = new Map();
let discoveredEdges = [];
let discoveredMCPAuthMap;
if (primaryConfig.edges?.length) {
const modelsConfig = await getModelsConfig(req);
({
agentConfigs: handoffAgentConfigs,
edges: discoveredEdges,
userMCPAuthMap: discoveredMCPAuthMap,
} = await discoverConnectedAgents(
{
req,
res,
primaryConfig,
endpointOption,
allowedProviders,
modelsConfig,
loadTools,
requestFiles: [],
conversationId,
parentMessageId,
// The route enforces REMOTE_AGENT on the primary; every discovered
// sub-agent must clear the same sharing boundary, not the looser
// in-app AGENT one.
resourceType: ResourceType.REMOTE_AGENT,
/** @see DiscoverConnectedAgentsParams.codeEnvAvailable */
codeEnvAvailable: enabledCapabilities.has(AgentCapabilities.execute_code),
},
{
getAgent: db.getAgent,
// Use `getRemoteAgentPermissions` so sub-agent authorization
// matches what the route's `createCheckRemoteAgentAccess`
// middleware does for the primary: AGENT owners with the SHARE
// bit are treated as remotely authorized even without an
// explicit REMOTE_AGENT grant.
checkPermission: async ({ userId, role, resourceId, requiredPermission }) => {
const permissions = await getRemoteAgentPermissions(
{ getEffectivePermissions },
userId,
role,
resourceId,
);
return hasPermissions(permissions, requiredPermission);
},
logViolation,
db: dbMethods,
onAgentInitialized: (agentId, handoffAgent, config) => {
agentToolContexts.set(agentId, {
agent: handoffAgent,
toolRegistry: config.toolRegistry,
userMCPAuthMap: config.userMCPAuthMap,
tool_resources: config.tool_resources,
actionsEnabled: config.actionsEnabled,
codeEnvAvailable: config.codeEnvAvailable,
});
},
initializeAgent,
},
));
}
primaryConfig.edges = discoveredEdges;
const runAgents = [primaryConfig, ...handoffAgentConfigs.values()];
const mergedMCPAuthMap = discoveredMCPAuthMap ?? primaryConfig.userMCPAuthMap;
// Determine if streaming is enabled (check both request and agent config)
const streamingDisabled = !!primaryConfig.model_parameters?.disableStreaming;
const actuallyStreaming = isStreaming && !streamingDisabled;
// Load previous messages if previous_response_id is provided
let previousMessages = [];
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 formatted = formatAgentMessages(allMessages, {}, toolSet);
const formattedMessages = formatted.messages;
const initialSummary = formatted.summary;
let indexTokenCountMap = formatted.indexTokenCountMap;
/**
* Inject manual + always-apply skill primes so the model sees SKILL.md
* bodies for this turn — parity with AgentClient's chat path. The
* Responses API uses its own response-builder shape, so LibreChat-
* style card SSE events don't apply; only the message-context part
* carries over.
*/
const manualSkillPrimes = primaryConfig.manualSkillPrimes;
const alwaysApplySkillPrimes = primaryConfig.alwaysApplySkillPrimes;
if (
(manualSkillPrimes && manualSkillPrimes.length > 0) ||
(alwaysApplySkillPrimes && alwaysApplySkillPrimes.length > 0)
) {
const primeResult = injectSkillPrimes({
initialMessages: formattedMessages,
indexTokenCountMap,
manualSkillPrimes,
alwaysApplySkillPrimes,
});
indexTokenCountMap = primeResult.indexTokenCountMap;
/* Surface the cap-driven always-apply truncation at the controller
layer too — `injectSkillPrimes` already logs internally, but the
controller-level warn includes endpoint context so operators can
tell at a glance which path hit the cap. Mirrors AgentClient's
warn in `client.js`. */
if (primeResult.alwaysApplyDropped > 0) {
logger.warn(
`[Responses API] Dropped ${primeResult.alwaysApplyDropped} always-apply prime(s) to stay within MAX_PRIMED_SKILLS_PER_TURN.`,
);
}
}
/* Stable for the turn: the prime lists are fixed once
`initializeAgent` resolves. Hoisted here so both the streaming
and non-streaming `loadTools` closures below reuse it without
recomputing per tool execution. `codeEnvAvailable` is read
per-agent from the stored tool context (admin cap AND that
agent's `tools` list includes `execute_code`) — a skills-only
agent never gains sandbox access even if the admin enabled the
capability globally. */
const skillPrimedIdsByName = buildSkillPrimedIdsByName(
manualSkillPrimes,
alwaysApplySkillPrimes,
);
// 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);
// Collect usage for balance tracking
const collectedUsage = [];
// 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, agentId) => {
const ctx =
agentToolContexts.get(agentId) ?? agentToolContexts.get(primaryConfig.id) ?? {};
const result = await loadToolsForExecution({
req,
res,
toolNames,
agent: ctx.agent ?? agent,
signal: abortController.signal,
toolRegistry: ctx.toolRegistry,
userMCPAuthMap: ctx.userMCPAuthMap,
tool_resources: ctx.tool_resources,
actionsEnabled: ctx.actionsEnabled,
});
return enrichWithSkillConfigurable(
result,
req,
primaryConfig.accessibleSkillIds,
ctx.codeEnvAvailable === true,
skillPrimedIdsByName,
);
},
toolEndCallback,
...getSkillToolDeps(),
};
// Combine handlers
const handlers = {
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: {
handle: (event, data, metadata) => {
responsesHandlers.on_chat_model_end.handle(event, data);
const usage = data?.output?.usage_metadata;
if (usage) {
const taggedUsage = markSummarizationUsage(usage, metadata);
collectedUsage.push(taggedUsage);
}
},
},
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),
on_agent_log: agentLogHandlerObj,
...(summarizationConfig?.enabled !== false
? buildSummarizationHandlers({ isStreaming: actuallyStreaming, res })
: {}),
};
// Create and run the agent
const userId = req.user?.id ?? 'api-user';
const userMCPAuthMap = mergedMCPAuthMap;
const run = await createRun({
agents: runAgents,
messages: formattedMessages,
indexTokenCountMap,
initialSummary,
runId: responseId,
summarizationConfig,
appConfig,
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),
requestBody: {
messageId: responseId,
conversationId,
},
...(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);
},
},
});
// Record token usage against balance
const balanceConfig = getBalanceConfig(appConfig);
const transactionsConfig = getTransactionsConfig(appConfig);
recordCollectedUsage(
{
spendTokens: db.spendTokens,
spendStructuredTokens: db.spendStructuredTokens,
pricing: { getMultiplier: db.getMultiplier, getCacheMultiplier: db.getCacheMultiplier },
bulkWriteOps: { insertMany: db.bulkInsertTransactions, updateBalance: db.updateBalance },
},
{
user: userId,
conversationId,
collectedUsage,
context: 'message',
messageId: responseId,
balance: balanceConfig,
transactions: transactionsConfig,
model: primaryConfig.model || agent.model_parameters?.model,
},
).catch((err) => {
logger.error('[Responses API] Error recording usage:', err);
});
// 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);
// Collect usage for balance tracking
const collectedUsage = [];
/** @type {Promise<import('librechat-data-provider').TAttachment | null>[]} */
const artifactPromises = [];
const toolEndCallback = createToolEndCallback({ req, res, artifactPromises, streamId: null });
const toolExecuteOptions = {
loadTools: async (toolNames, agentId) => {
const ctx =
agentToolContexts.get(agentId) ?? agentToolContexts.get(primaryConfig.id) ?? {};
const result = await loadToolsForExecution({
req,
res,
toolNames,
agent: ctx.agent ?? agent,
signal: abortController.signal,
toolRegistry: ctx.toolRegistry,
userMCPAuthMap: ctx.userMCPAuthMap,
tool_resources: ctx.tool_resources,
actionsEnabled: ctx.actionsEnabled,
});
return enrichWithSkillConfigurable(
result,
req,
primaryConfig.accessibleSkillIds,
ctx.codeEnvAvailable === true,
skillPrimedIdsByName,
);
},
toolEndCallback,
...getSkillToolDeps(),
};
const handlers = {
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: {
handle: (event, data, metadata) => {
aggregatorHandlers.on_chat_model_end.handle(event, data);
const usage = data?.output?.usage_metadata;
if (usage) {
const taggedUsage = markSummarizationUsage(usage, metadata);
collectedUsage.push(taggedUsage);
}
},
},
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),
on_agent_log: agentLogHandlerObj,
...(summarizationConfig?.enabled !== false
? buildSummarizationHandlers({ isStreaming: false, res })
: {}),
};
const userId = req.user?.id ?? 'api-user';
const userMCPAuthMap = mergedMCPAuthMap;
const run = await createRun({
agents: runAgents,
messages: formattedMessages,
indexTokenCountMap,
initialSummary,
runId: responseId,
summarizationConfig,
appConfig,
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),
requestBody: {
messageId: responseId,
conversationId,
},
...(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);
},
},
});
// Record token usage against balance
const balanceConfig = getBalanceConfig(appConfig);
const transactionsConfig = getTransactionsConfig(appConfig);
recordCollectedUsage(
{
spendTokens: db.spendTokens,
spendStructuredTokens: db.spendStructuredTokens,
pricing: { getMultiplier: db.getMultiplier, getCacheMultiplier: db.getCacheMultiplier },
bulkWriteOps: { insertMany: db.bulkInsertTransactions, updateBalance: db.updateBalance },
},
{
user: userId,
conversationId,
collectedUsage,
context: 'message',
messageId: responseId,
balance: balanceConfig,
transactions: transactionsConfig,
model: primaryConfig.model || agent.model_parameters?.model,
},
).catch((err) => {
logger.error('[Responses API] Error recording usage:', err);
});
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 {
// Forward upstream provider status codes (e.g., Anthropic 400s) instead of masking as 500
const statusCode =
typeof error?.status === 'number' && error.status >= 400 && error.status < 600
? error.status
: 500;
const errorType = statusCode >= 400 && statusCode < 500 ? 'invalid_request' : 'server_error';
sendResponsesErrorResponse(res, statusCode, errorMessage, errorType);
}
}
};
/**
* 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 db.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 db.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,
};