mirror of
https://github.com/danny-avila/LibreChat.git
synced 2026-05-14 08:27:49 +00:00
1b79e0b785
* 🔧 chore: Update dependencies in package-lock.json and package.json - Bump version of @librechat/agents to 3.1.75-dev.0 in multiple package.json files. - Upgrade various AWS SDK and Smithy dependencies to their latest versions in package-lock.json for improved stability and performance. * 🔧 chore: Update AWS SDK and Smithy dependencies in package-lock.json - Bump version of @aws-sdk/client-bedrock-runtime to 3.1041.0 and update related dependencies for improved performance and stability. - Upgrade various AWS SDK and Smithy packages to their latest versions, ensuring compatibility and enhanced functionality. * chore: Align LibreChat with agents LangChain upgrade - Route LangChain imports through @librechat/agents facade exports - Update @librechat/agents to 3.1.75-dev.1 and remove direct LangChain deps - Normalize nullable agent model params and API key override typing - Update Google thinking config typing for newer LangChain packages - Refresh targeted audit-related dependency overrides * chore: Add Jest types for API specs * test: Fix LangChain upgrade CI specs * test: Exercise agents env facade * fix: Clean up TS preview diagnostics * fix: Address Codex review feedback
1412 lines
48 KiB
JavaScript
1412 lines
48 KiB
JavaScript
require('events').EventEmitter.defaultMaxListeners = 100;
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const { logger } = require('@librechat/data-schemas');
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const { getBufferString, HumanMessage } = require('@librechat/agents/langchain/messages');
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const {
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createRun,
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isEnabled,
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checkAccess,
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buildToolSet,
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logToolError,
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sanitizeTitle,
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payloadParser,
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resolveHeaders,
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createSafeUser,
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initializeAgent,
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getBalanceConfig,
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omitTitleOptions,
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getProviderConfig,
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memoryInstructions,
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createTokenCounter,
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applyContextToAgent,
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isMemoryAgentEnabled,
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recordCollectedUsage,
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GenerationJobManager,
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getTransactionsConfig,
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resolveRecursionLimit,
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createMemoryProcessor,
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loadAgent: loadAgentFn,
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createMultiAgentMapper,
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filterMalformedContentParts,
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countFormattedMessageTokens,
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hydrateMissingIndexTokenCounts,
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injectSkillPrimes,
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isSkillPrimeMessage,
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buildSkillPrimeContentParts,
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buildInitialToolSessions,
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} = require('@librechat/api');
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const {
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Callback,
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Providers,
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TitleMethod,
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formatMessage,
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formatAgentMessages,
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createMetadataAggregator,
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} = require('@librechat/agents');
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const {
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Constants,
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Permissions,
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VisionModes,
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ContentTypes,
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EModelEndpoint,
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PermissionTypes,
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AgentCapabilities,
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isAgentsEndpoint,
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isEphemeralAgentId,
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removeNullishValues,
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} = require('librechat-data-provider');
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const { filterFilesByAgentAccess } = require('~/server/services/Files/permissions');
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const { encodeAndFormat } = require('~/server/services/Files/images/encode');
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const { createContextHandlers } = require('~/app/clients/prompts');
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const { resolveConfigServers } = require('~/server/services/MCP');
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const { getMCPServerTools } = require('~/server/services/Config');
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const BaseClient = require('~/app/clients/BaseClient');
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const { getMCPManager } = require('~/config');
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const db = require('~/models');
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const loadAgent = (params) => loadAgentFn(params, { getAgent: db.getAgent, getMCPServerTools });
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class AgentClient extends BaseClient {
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constructor(options = {}) {
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super(null, options);
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/** The current client class
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* @type {string} */
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this.clientName = EModelEndpoint.agents;
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/** @deprecated @type {true} - Is a Chat Completion Request */
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this.isChatCompletion = true;
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/** @type {AgentRun} */
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this.run;
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const {
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agentConfigs,
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contentParts,
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collectedUsage,
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artifactPromises,
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maxContextTokens,
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subagentAggregatorsByToolCallId,
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...clientOptions
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} = options;
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this.agentConfigs = agentConfigs;
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this.maxContextTokens = maxContextTokens;
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/** @type {MessageContentComplex[]} */
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this.contentParts = contentParts;
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/** @type {Array<UsageMetadata>} */
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this.collectedUsage = collectedUsage;
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/** @type {ArtifactPromises} */
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this.artifactPromises = artifactPromises;
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/** Per-request map of `createContentAggregator` instances keyed by
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* the parent's `tool_call_id`. `ON_SUBAGENT_UPDATE` events stream
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* into each aggregator as they arrive; `finalizeSubagentContent`
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* harvests `contentParts` onto the matching `subagent` tool_call
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* so the child's full activity survives a page refresh. */
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this.subagentAggregatorsByToolCallId = subagentAggregatorsByToolCallId ?? new Map();
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/** @type {AgentClientOptions} */
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this.options = Object.assign({ endpoint: options.endpoint }, clientOptions);
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/** @type {string} */
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this.model = this.options.agent.model_parameters.model;
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/** The key for the usage object's input tokens
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* @type {string} */
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this.inputTokensKey = 'input_tokens';
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/** The key for the usage object's output tokens
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* @type {string} */
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this.outputTokensKey = 'output_tokens';
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/** @type {UsageMetadata} */
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this.usage;
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/** @type {Record<string, number>} */
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this.indexTokenCountMap = {};
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/** @type {(messages: BaseMessage[]) => Promise<void>} */
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this.processMemory;
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}
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/**
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* Returns the aggregated content parts for the current run.
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* @returns {MessageContentComplex[]} */
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getContentParts() {
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return this.contentParts;
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}
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/**
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* Harvest the `contentParts` from each per-subagent `createContentAggregator`
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* instance and attach them onto the matching parent `subagent` tool_call
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* as `subagent_content`. Runs once per message save (from
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* `sendCompletion`'s `finally`) so the child's full reasoning / tool
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* calls / final text survive a page refresh — the client-side Recoil
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* atom is session-only. Aggregators keyed by a tool_call_id that never
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* appeared in `contentParts` are discarded (no home to attach to).
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*/
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finalizeSubagentContent() {
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const buffer = this.subagentAggregatorsByToolCallId;
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if (!buffer || buffer.size === 0 || !Array.isArray(this.contentParts)) {
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return;
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}
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for (const part of this.contentParts) {
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if (part?.type !== ContentTypes.TOOL_CALL) continue;
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const toolCall = part[ContentTypes.TOOL_CALL];
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if (!toolCall || toolCall.name !== Constants.SUBAGENT || !toolCall.id) continue;
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const aggregator = buffer.get(toolCall.id);
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if (!aggregator) continue;
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try {
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/** `createContentAggregator` returns a sparse array (undefined
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* slots for indices that never received content). Strip those
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* so the persisted shape is a clean `TMessageContentParts[]`. */
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const parts = Array.isArray(aggregator.contentParts)
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? aggregator.contentParts.filter((p) => p != null)
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: [];
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if (parts.length > 0) {
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toolCall.subagent_content = parts;
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}
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} catch (err) {
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logger.warn(
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`[AgentClient] Failed to attach subagent content for tool_call ${toolCall.id}: ${err?.message ?? err}`,
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);
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}
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}
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buffer.clear();
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}
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setOptions(_options) {}
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/**
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* `AgentClient` is not opinionated about vision requests, so we don't do anything here
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* @param {MongoFile[]} attachments
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*/
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checkVisionRequest() {}
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getSaveOptions() {
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let runOptions = {};
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try {
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runOptions = payloadParser(this.options) ?? {};
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} catch (error) {
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logger.error(
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'[api/server/controllers/agents/client.js #getSaveOptions] Error parsing options',
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error,
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);
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}
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return removeNullishValues(
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Object.assign(
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{
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spec: this.options.spec,
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iconURL: this.options.iconURL,
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endpoint: this.options.endpoint,
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agent_id: this.options.agent.id,
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modelLabel: this.options.modelLabel,
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resendFiles: this.options.resendFiles,
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imageDetail: this.options.imageDetail,
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maxContextTokens: this.maxContextTokens,
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},
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// TODO: PARSE OPTIONS BY PROVIDER, MAY CONTAIN SENSITIVE DATA
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runOptions,
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),
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);
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}
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/**
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* Returns build message options. For AgentClient, agent-specific instructions
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* are retrieved directly from agent objects in buildMessages, so this returns empty.
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* @returns {Object} Empty options object
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*/
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getBuildMessagesOptions() {
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return {};
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}
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/**
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*
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* @param {TMessage} message
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* @param {Array<MongoFile>} attachments
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* @returns {Promise<Array<Partial<MongoFile>>>}
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*/
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async addImageURLs(message, attachments) {
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const { files, image_urls } = await encodeAndFormat(
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this.options.req,
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attachments,
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{
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provider: this.options.agent.provider,
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endpoint: this.options.endpoint,
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},
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VisionModes.agents,
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);
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message.image_urls = image_urls.length ? image_urls : undefined;
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return files;
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}
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async buildMessages(messages, parentMessageId, _buildOptions, opts) {
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/** Always pass mapMethod; getMessagesForConversation applies it only to messages with addedConvo flag */
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const orderedMessages = this.constructor.getMessagesForConversation({
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messages,
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parentMessageId,
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summary: this.shouldSummarize,
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mapMethod: createMultiAgentMapper(this.options.agent, this.agentConfigs),
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mapCondition: (message) => message.addedConvo === true,
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});
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let payload;
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/** @type {number | undefined} */
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let promptTokens;
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/** Normalize instruction fields before applying per-run context. */
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const normalizeInstructions = (agent) => {
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agent.instructions = agent.instructions?.trim() || undefined;
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agent.additional_instructions = agent.additional_instructions?.trim() || undefined;
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return agent;
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};
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/** Collect all agents for unified processing while preserving stable/dynamic instruction fields. */
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const allAgents = [
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{ agent: normalizeInstructions(this.options.agent), agentId: this.options.agent.id },
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...(this.agentConfigs?.size > 0
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? Array.from(this.agentConfigs.entries()).map(([agentId, agent]) => ({
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agent: normalizeInstructions(agent),
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agentId,
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}))
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: []),
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];
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if (this.options.attachments) {
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const attachments = await this.options.attachments;
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const latestMessage = orderedMessages[orderedMessages.length - 1];
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if (this.message_file_map) {
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this.message_file_map[latestMessage.messageId] = attachments;
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} else {
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this.message_file_map = {
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[latestMessage.messageId]: attachments,
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};
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}
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await this.addFileContextToMessage(latestMessage, attachments);
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const files = await this.processAttachments(latestMessage, attachments);
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this.options.attachments = files;
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}
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/** Note: Bedrock uses legacy RAG API handling */
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if (this.message_file_map && !isAgentsEndpoint(this.options.endpoint)) {
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this.contextHandlers = createContextHandlers(
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this.options.req,
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orderedMessages[orderedMessages.length - 1].text,
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);
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}
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/** @type {Record<number, number>} */
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const canonicalTokenCountMap = {};
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/** @type {Record<string, number>} */
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const tokenCountMap = {};
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let promptTokenTotal = 0;
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const formattedMessages = orderedMessages.map((message, i) => {
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const formattedMessage = formatMessage({
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message,
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userName: this.options?.name,
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assistantName: this.options?.modelLabel,
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});
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/** For non-latest messages, prepend file context directly to message content */
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if (message.fileContext && i !== orderedMessages.length - 1) {
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if (typeof formattedMessage.content === 'string') {
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formattedMessage.content = message.fileContext + '\n' + formattedMessage.content;
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} else {
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const textPart = formattedMessage.content.find((part) => part.type === 'text');
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textPart
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? (textPart.text = message.fileContext + '\n' + textPart.text)
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: formattedMessage.content.unshift({ type: 'text', text: message.fileContext });
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}
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}
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const dbTokenCount = orderedMessages[i].tokenCount;
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const needsTokenCount = !dbTokenCount || message.fileContext;
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if (needsTokenCount || (this.isVisionModel && (message.image_urls || message.files))) {
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orderedMessages[i].tokenCount = countFormattedMessageTokens(
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formattedMessage,
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this.getEncoding(),
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);
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}
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/* If message has files, calculate image token cost */
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if (this.message_file_map && this.message_file_map[message.messageId]) {
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const attachments = this.message_file_map[message.messageId];
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for (const file of attachments) {
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if (file.embedded) {
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this.contextHandlers?.processFile(file);
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continue;
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}
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if (file.metadata?.fileIdentifier) {
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continue;
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}
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}
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}
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const tokenCount = Number(orderedMessages[i].tokenCount);
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const normalizedTokenCount = Number.isFinite(tokenCount) && tokenCount > 0 ? tokenCount : 0;
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canonicalTokenCountMap[i] = normalizedTokenCount;
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promptTokenTotal += normalizedTokenCount;
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if (message.messageId) {
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tokenCountMap[message.messageId] = normalizedTokenCount;
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}
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if (isEnabled(process.env.AGENT_DEBUG_LOGGING)) {
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const role = message.isCreatedByUser ? 'user' : 'assistant';
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const hasSummary =
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Array.isArray(message.content) && message.content.some((p) => p && p.type === 'summary');
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const suffix = hasSummary ? '[S]' : '';
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const id = (message.messageId ?? message.id ?? '').slice(-8);
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const recalced = needsTokenCount ? orderedMessages[i].tokenCount : null;
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logger.debug(
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`[AgentClient] msg[${i}] ${role}${suffix} id=…${id} db=${dbTokenCount} needsRecount=${needsTokenCount} recalced=${recalced} tokens=${normalizedTokenCount}`,
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);
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}
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return formattedMessage;
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});
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payload = formattedMessages;
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messages = orderedMessages;
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promptTokens = promptTokenTotal;
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/**
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* Build shared run context - applies to ALL agents in the run.
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* This includes file context from the latest message and augmented prompt (RAG).
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* Memory context is handled separately and applied per-agent based on config.
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*/
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const sharedRunContextParts = [];
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/** File context from the latest message (attachments) */
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const latestMessage = orderedMessages[orderedMessages.length - 1];
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if (latestMessage?.fileContext) {
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sharedRunContextParts.push(latestMessage.fileContext);
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}
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/** Augmented prompt from RAG/context handlers */
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if (this.contextHandlers) {
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this.augmentedPrompt = await this.contextHandlers.createContext();
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if (this.augmentedPrompt) {
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sharedRunContextParts.push(this.augmentedPrompt);
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}
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}
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/** Memory context (user preferences/memories) */
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const withoutKeys = await this.useMemory();
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const memoryContext = withoutKeys
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? `${memoryInstructions}\n\n# Existing memory about the user:\n${withoutKeys}`
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: undefined;
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const sharedRunContext = sharedRunContextParts.join('\n\n');
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const memoryAgentEnabled = isMemoryAgentEnabled(this.options.req.config?.memory);
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/** Preserve canonical pre-format token counts for all history entering graph formatting */
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this.indexTokenCountMap = canonicalTokenCountMap;
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/** Extract contextMeta from the parent response (second-to-last in ordered chain;
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* last is the current user message). Seeds the pruner's calibration EMA for this run. */
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const parentResponse =
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orderedMessages.length >= 2 ? orderedMessages[orderedMessages.length - 2] : undefined;
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if (parentResponse?.contextMeta && !parentResponse.isCreatedByUser) {
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this.contextMeta = parentResponse.contextMeta;
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}
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const result = {
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prompt: payload,
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tokenCountMap,
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promptTokens,
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messages,
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};
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if (promptTokens >= 0 && typeof opts?.getReqData === 'function') {
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opts.getReqData({ promptTokens });
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}
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/**
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* Apply context to all agents.
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* Stable agent/MCP instructions stay on `instructions`; shared runtime context
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* is appended to `additional_instructions` as the dynamic system tail.
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*
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* NOTE: This intentionally mutates agent objects in place. The agentConfigs Map
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* holds references to config objects that will be passed to the graph runtime.
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*/
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const ephemeralAgent = this.options.req.body.ephemeralAgent;
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const mcpManager = getMCPManager();
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const configServers = await resolveConfigServers(this.options.req);
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await Promise.all(
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allAgents.map(({ agent, agentId }) => {
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const agentRunContext =
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memoryContext && (agentId === this.options.agent.id || memoryAgentEnabled)
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? [sharedRunContext, memoryContext].filter(Boolean).join('\n\n')
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: sharedRunContext;
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return applyContextToAgent({
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agent,
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agentId,
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logger,
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mcpManager,
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configServers,
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sharedRunContext: agentRunContext,
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|
ephemeralAgent: agentId === this.options.agent.id ? ephemeralAgent : undefined,
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});
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}),
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);
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|
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return result;
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}
|
|
|
|
/**
|
|
* Creates a promise that resolves with the memory promise result or undefined after a timeout
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|
* @param {Promise<(TAttachment | null)[] | undefined>} memoryPromise - The memory promise to await
|
|
* @param {number} timeoutMs - Timeout in milliseconds (default: 3000)
|
|
* @returns {Promise<(TAttachment | null)[] | undefined>}
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*/
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|
async awaitMemoryWithTimeout(memoryPromise, timeoutMs = 3000) {
|
|
if (!memoryPromise) {
|
|
return;
|
|
}
|
|
|
|
try {
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|
const timeoutPromise = new Promise((_, reject) =>
|
|
setTimeout(() => reject(new Error('Memory processing timeout')), timeoutMs),
|
|
);
|
|
|
|
const attachments = await Promise.race([memoryPromise, timeoutPromise]);
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|
return attachments;
|
|
} catch (error) {
|
|
if (error.message === 'Memory processing timeout') {
|
|
logger.warn('[AgentClient] Memory processing timed out after 3 seconds');
|
|
} else {
|
|
logger.error('[AgentClient] Error processing memory:', error);
|
|
}
|
|
return;
|
|
}
|
|
}
|
|
|
|
/**
|
|
* @returns {Promise<string | undefined>}
|
|
*/
|
|
async useMemory() {
|
|
const user = this.options.req.user;
|
|
if (user.personalization?.memories === false) {
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|
return;
|
|
}
|
|
const hasAccess = await checkAccess({
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|
user,
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|
permissionType: PermissionTypes.MEMORIES,
|
|
permissions: [Permissions.USE],
|
|
getRoleByName: db.getRoleByName,
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|
});
|
|
|
|
if (!hasAccess) {
|
|
logger.debug(
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|
`[api/server/controllers/agents/client.js #useMemory] User ${user.id} does not have USE permission for memories`,
|
|
);
|
|
return;
|
|
}
|
|
const appConfig = this.options.req.config;
|
|
const memoryConfig = appConfig.memory;
|
|
if (!memoryConfig || memoryConfig.disabled === true) {
|
|
return;
|
|
}
|
|
|
|
const userId = this.options.req.user.id + '';
|
|
this.processMemory = undefined;
|
|
|
|
if (!isMemoryAgentEnabled(memoryConfig)) {
|
|
try {
|
|
const { withoutKeys } = await db.getFormattedMemories({ userId });
|
|
return withoutKeys;
|
|
} catch (error) {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #useMemory] Error loading memories',
|
|
error,
|
|
);
|
|
return;
|
|
}
|
|
}
|
|
|
|
/** @type {Agent} */
|
|
let prelimAgent;
|
|
const allowedProviders = new Set(
|
|
appConfig?.endpoints?.[EModelEndpoint.agents]?.allowedProviders,
|
|
);
|
|
try {
|
|
if (memoryConfig.agent?.id != null && memoryConfig.agent.id !== this.options.agent.id) {
|
|
prelimAgent = await loadAgent({
|
|
req: this.options.req,
|
|
agent_id: memoryConfig.agent.id,
|
|
endpoint: EModelEndpoint.agents,
|
|
});
|
|
} else if (memoryConfig.agent?.id != null) {
|
|
prelimAgent = this.options.agent;
|
|
} else if (
|
|
memoryConfig.agent?.id == null &&
|
|
memoryConfig.agent?.model != null &&
|
|
memoryConfig.agent?.provider != null
|
|
) {
|
|
prelimAgent = { id: Constants.EPHEMERAL_AGENT_ID, ...memoryConfig.agent };
|
|
}
|
|
} catch (error) {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #useMemory] Error loading agent for memory',
|
|
error,
|
|
);
|
|
}
|
|
|
|
if (!prelimAgent) {
|
|
return;
|
|
}
|
|
|
|
/** Forward the same `execute_code` capability gate the chat flow uses —
|
|
* memory agents are unlikely to list `execute_code`, but if one does,
|
|
* Phase 8 relies on this flag to expand the string into
|
|
* `bash_tool` + `read_file` (pre-Phase 8 the legacy `execute_code`
|
|
* tool registered unconditionally; without this passthrough the
|
|
* memory path would silently lose code-execution tooling). */
|
|
const memoryCapabilities = new Set(appConfig?.endpoints?.[EModelEndpoint.agents]?.capabilities);
|
|
const agent = await initializeAgent(
|
|
{
|
|
req: this.options.req,
|
|
res: this.options.res,
|
|
agent: prelimAgent,
|
|
allowedProviders,
|
|
endpointOption: {
|
|
endpoint: !isEphemeralAgentId(prelimAgent.id)
|
|
? EModelEndpoint.agents
|
|
: memoryConfig.agent?.provider,
|
|
},
|
|
codeEnvAvailable: memoryCapabilities.has(AgentCapabilities.execute_code),
|
|
},
|
|
{
|
|
getFiles: db.getFiles,
|
|
getUserKey: db.getUserKey,
|
|
getConvoFiles: db.getConvoFiles,
|
|
updateFilesUsage: db.updateFilesUsage,
|
|
getUserKeyValues: db.getUserKeyValues,
|
|
getToolFilesByIds: db.getToolFilesByIds,
|
|
getCodeGeneratedFiles: db.getCodeGeneratedFiles,
|
|
filterFilesByAgentAccess,
|
|
},
|
|
);
|
|
|
|
if (!agent) {
|
|
logger.warn(
|
|
'[api/server/controllers/agents/client.js #useMemory] No agent found for memory',
|
|
memoryConfig,
|
|
);
|
|
return;
|
|
}
|
|
|
|
const llmConfig = Object.assign(
|
|
{
|
|
provider: agent.provider,
|
|
model: agent.model,
|
|
},
|
|
agent.model_parameters,
|
|
);
|
|
|
|
/** @type {import('@librechat/api').MemoryConfig} */
|
|
const config = {
|
|
validKeys: memoryConfig.validKeys,
|
|
instructions: agent.instructions,
|
|
llmConfig,
|
|
tokenLimit: memoryConfig.tokenLimit,
|
|
};
|
|
|
|
const messageId = this.responseMessageId + '';
|
|
const conversationId = this.conversationId + '';
|
|
const streamId = this.options.req?._resumableStreamId || null;
|
|
const [withoutKeys, processMemory] = await createMemoryProcessor({
|
|
userId,
|
|
config,
|
|
messageId,
|
|
streamId,
|
|
conversationId,
|
|
memoryMethods: {
|
|
setMemory: db.setMemory,
|
|
deleteMemory: db.deleteMemory,
|
|
getFormattedMemories: db.getFormattedMemories,
|
|
},
|
|
res: this.options.res,
|
|
user: createSafeUser(this.options.req.user),
|
|
});
|
|
|
|
this.processMemory = processMemory;
|
|
return withoutKeys;
|
|
}
|
|
|
|
/**
|
|
* Filters out image URLs from message content
|
|
* @param {BaseMessage} message - The message to filter
|
|
* @returns {BaseMessage} - A new message with image URLs removed
|
|
*/
|
|
filterImageUrls(message) {
|
|
if (!message.content || typeof message.content === 'string') {
|
|
return message;
|
|
}
|
|
|
|
if (Array.isArray(message.content)) {
|
|
const filteredContent = message.content.filter(
|
|
(part) => part.type !== ContentTypes.IMAGE_URL,
|
|
);
|
|
|
|
if (filteredContent.length === 1 && filteredContent[0].type === ContentTypes.TEXT) {
|
|
const MessageClass = message.constructor;
|
|
return new MessageClass({
|
|
content: filteredContent[0].text,
|
|
additional_kwargs: message.additional_kwargs,
|
|
});
|
|
}
|
|
|
|
const MessageClass = message.constructor;
|
|
return new MessageClass({
|
|
content: filteredContent,
|
|
additional_kwargs: message.additional_kwargs,
|
|
});
|
|
}
|
|
|
|
return message;
|
|
}
|
|
|
|
/**
|
|
* @param {BaseMessage[]} messages
|
|
* @returns {Promise<void | (TAttachment | null)[]>}
|
|
*/
|
|
async runMemory(messages) {
|
|
try {
|
|
if (this.processMemory == null) {
|
|
return;
|
|
}
|
|
const appConfig = this.options.req.config;
|
|
const memoryConfig = appConfig.memory;
|
|
const messageWindowSize = memoryConfig?.messageWindowSize ?? 5;
|
|
|
|
/**
|
|
* Strip skill-primed meta messages before memory extraction. The primes
|
|
* sit next to the latest user message and carry large SKILL.md bodies,
|
|
* so letting them into the window would crowd out real chat turns and
|
|
* pollute extracted memories with synthetic instruction content the
|
|
* user never typed.
|
|
*/
|
|
const chatMessages = messages.filter((m) => !isSkillPrimeMessage(m));
|
|
|
|
let messagesToProcess = [...chatMessages];
|
|
if (chatMessages.length > messageWindowSize) {
|
|
for (let i = chatMessages.length - messageWindowSize; i >= 0; i--) {
|
|
const potentialWindow = chatMessages.slice(i, i + messageWindowSize);
|
|
if (potentialWindow[0]?.role === 'user') {
|
|
messagesToProcess = [...potentialWindow];
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (messagesToProcess.length === chatMessages.length) {
|
|
messagesToProcess = [...chatMessages.slice(-messageWindowSize)];
|
|
}
|
|
}
|
|
|
|
const filteredMessages = messagesToProcess.map((msg) => this.filterImageUrls(msg));
|
|
const bufferString = getBufferString(filteredMessages);
|
|
const bufferMessage = new HumanMessage(`# Current Chat:\n\n${bufferString}`);
|
|
return await this.processMemory([bufferMessage]);
|
|
} catch (error) {
|
|
logger.error('Memory Agent failed to process memory', error);
|
|
}
|
|
}
|
|
|
|
/** @type {sendCompletion} */
|
|
async sendCompletion(payload, opts = {}) {
|
|
await this.chatCompletion({
|
|
payload,
|
|
onProgress: opts.onProgress,
|
|
userMCPAuthMap: opts.userMCPAuthMap,
|
|
abortController: opts.abortController,
|
|
});
|
|
|
|
const completion = filterMalformedContentParts(this.contentParts);
|
|
return { completion };
|
|
}
|
|
|
|
/**
|
|
* @param {Object} params
|
|
* @param {string} [params.model]
|
|
* @param {string} [params.context='message']
|
|
* @param {AppConfig['balance']} [params.balance]
|
|
* @param {AppConfig['transactions']} [params.transactions]
|
|
* @param {UsageMetadata[]} [params.collectedUsage=this.collectedUsage]
|
|
*/
|
|
async recordCollectedUsage({
|
|
model,
|
|
balance,
|
|
transactions,
|
|
context = 'message',
|
|
collectedUsage = this.collectedUsage,
|
|
}) {
|
|
const result = await recordCollectedUsage(
|
|
{
|
|
spendTokens: db.spendTokens,
|
|
spendStructuredTokens: db.spendStructuredTokens,
|
|
pricing: { getMultiplier: db.getMultiplier, getCacheMultiplier: db.getCacheMultiplier },
|
|
bulkWriteOps: { insertMany: db.bulkInsertTransactions, updateBalance: db.updateBalance },
|
|
},
|
|
{
|
|
user: this.user ?? this.options.req.user?.id,
|
|
conversationId: this.conversationId,
|
|
collectedUsage,
|
|
model: model ?? this.model ?? this.options.agent.model_parameters.model,
|
|
context,
|
|
messageId: this.responseMessageId,
|
|
balance,
|
|
transactions,
|
|
endpointTokenConfig: this.options.endpointTokenConfig,
|
|
},
|
|
);
|
|
|
|
if (result) {
|
|
this.usage = result;
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Get stream usage as returned by this client's API response.
|
|
* @returns {UsageMetadata} The stream usage object.
|
|
*/
|
|
getStreamUsage() {
|
|
return this.usage;
|
|
}
|
|
|
|
/**
|
|
* @param {TMessage} responseMessage
|
|
* @returns {number}
|
|
*/
|
|
getTokenCountForResponse({ content }) {
|
|
return countFormattedMessageTokens({ role: 'assistant', content }, this.getEncoding());
|
|
}
|
|
|
|
/**
|
|
* @param {object} params
|
|
* @param {string | ChatCompletionMessageParam[]} params.payload
|
|
* @param {Record<string, Record<string, string>>} [params.userMCPAuthMap]
|
|
* @param {AbortController} [params.abortController]
|
|
*/
|
|
async chatCompletion({ payload, userMCPAuthMap, abortController = null }) {
|
|
/** @type {Partial<GraphRunnableConfig>} */
|
|
let config;
|
|
/** @type {ReturnType<createRun>} */
|
|
let run;
|
|
/** @type {Promise<(TAttachment | null)[] | undefined>} */
|
|
let memoryPromise;
|
|
const appConfig = this.options.req.config;
|
|
const balanceConfig = getBalanceConfig(appConfig);
|
|
const transactionsConfig = getTransactionsConfig(appConfig);
|
|
try {
|
|
if (!abortController) {
|
|
abortController = new AbortController();
|
|
}
|
|
|
|
/** @type {AppConfig['endpoints']['agents']} */
|
|
const agentsEConfig = appConfig.endpoints?.[EModelEndpoint.agents];
|
|
|
|
config = {
|
|
runName: 'AgentRun',
|
|
configurable: {
|
|
thread_id: this.conversationId,
|
|
last_agent_index: this.agentConfigs?.size ?? 0,
|
|
user_id: this.user ?? this.options.req.user?.id,
|
|
hide_sequential_outputs: this.options.agent.hide_sequential_outputs,
|
|
requestBody: {
|
|
messageId: this.responseMessageId,
|
|
conversationId: this.conversationId,
|
|
parentMessageId: this.parentMessageId,
|
|
},
|
|
user: createSafeUser(this.options.req.user),
|
|
},
|
|
recursionLimit: resolveRecursionLimit(agentsEConfig, this.options.agent),
|
|
signal: abortController.signal,
|
|
streamMode: 'values',
|
|
version: 'v2',
|
|
};
|
|
|
|
const toolSet = buildToolSet(this.options.agent);
|
|
const tokenCounter = createTokenCounter(this.getEncoding());
|
|
|
|
/** Pre-resolve invoked skill bodies + re-prime files before formatting messages */
|
|
const skillPrimeResult = this.options.primeInvokedSkills
|
|
? await this.options.primeInvokedSkills(payload)
|
|
: undefined;
|
|
|
|
/**
|
|
* Seed `Graph.sessions` with code-env files primed across every
|
|
* reachable agent (primary, handoff/addedConvo, and nested
|
|
* subagents) plus skill-priming output. The merge logic and its
|
|
* run-wide semantics live in `buildInitialToolSessions`; see that
|
|
* helper's doc for why this is intentionally NOT per-agent.
|
|
*/
|
|
const initialSessions = buildInitialToolSessions({
|
|
skillSessions: skillPrimeResult?.initialSessions,
|
|
agents: [this.options.agent, ...(this.agentConfigs ? this.agentConfigs.values() : [])],
|
|
});
|
|
|
|
let {
|
|
messages: initialMessages,
|
|
indexTokenCountMap,
|
|
summary: initialSummary,
|
|
boundaryTokenAdjustment,
|
|
} = formatAgentMessages(payload, this.indexTokenCountMap, toolSet, skillPrimeResult?.skills);
|
|
if (boundaryTokenAdjustment) {
|
|
logger.debug(
|
|
`[AgentClient] Boundary token adjustment: ${boundaryTokenAdjustment.original} → ${boundaryTokenAdjustment.adjusted} (${boundaryTokenAdjustment.remainingChars}/${boundaryTokenAdjustment.totalChars} chars)`,
|
|
);
|
|
}
|
|
|
|
/**
|
|
* Skill priming — both manual ($ popover) and always-apply (frontmatter).
|
|
*
|
|
* Splice + index-shift logic lives in `injectSkillPrimes`
|
|
* (packages/api/src/agents/skills.ts) so the delicate position math
|
|
* can be unit-tested in TS without standing up AgentClient. The
|
|
* resolver enforces a combined ceiling (manual-first, always-apply
|
|
* truncated first when over cap) before reaching here; the splice
|
|
* re-applies the cap as defense-in-depth. Runs for both single-
|
|
* agent and multi-agent runs; how primes interact with handoff /
|
|
* added-convo agents' per-agent state is an agents-SDK concern,
|
|
* not this layer's to gate.
|
|
*/
|
|
const manualSkillPrimes = this.options.agent?.manualSkillPrimes;
|
|
const alwaysApplySkillPrimes = this.options.agent?.alwaysApplySkillPrimes;
|
|
if (
|
|
(manualSkillPrimes && manualSkillPrimes.length > 0) ||
|
|
(alwaysApplySkillPrimes && alwaysApplySkillPrimes.length > 0)
|
|
) {
|
|
const primeResult = injectSkillPrimes({
|
|
initialMessages,
|
|
indexTokenCountMap,
|
|
manualSkillPrimes,
|
|
alwaysApplySkillPrimes,
|
|
});
|
|
indexTokenCountMap = primeResult.indexTokenCountMap;
|
|
if (primeResult.inserted > 0) {
|
|
const manualNames = (manualSkillPrimes ?? []).map((p) => p.name);
|
|
const alwaysApplyNames = (alwaysApplySkillPrimes ?? []).map((p) => p.name);
|
|
logger.debug(
|
|
`[AgentClient] Primed ${primeResult.inserted} skill(s) at message index ${primeResult.insertIdx} — manual: [${manualNames.join(', ')}], always-apply: [${alwaysApplyNames.join(', ')}]`,
|
|
);
|
|
}
|
|
if (primeResult.alwaysApplyDropped > 0) {
|
|
logger.warn(
|
|
`[AgentClient] Dropped ${primeResult.alwaysApplyDropped} always-apply prime(s) to stay within MAX_PRIMED_SKILLS_PER_TURN.`,
|
|
);
|
|
}
|
|
}
|
|
|
|
if (indexTokenCountMap && isEnabled(process.env.AGENT_DEBUG_LOGGING)) {
|
|
const entries = Object.entries(indexTokenCountMap);
|
|
const perMsg = entries.map(([idx, count]) => {
|
|
const msg = initialMessages[Number(idx)];
|
|
const type = msg ? msg._getType() : '?';
|
|
return `${idx}:${type}=${count}`;
|
|
});
|
|
logger.debug(
|
|
`[AgentClient] Token map after format: [${perMsg.join(', ')}] (payload=${payload.length}, formatted=${initialMessages.length})`,
|
|
);
|
|
}
|
|
indexTokenCountMap = hydrateMissingIndexTokenCounts({
|
|
messages: initialMessages,
|
|
indexTokenCountMap,
|
|
tokenCounter,
|
|
});
|
|
|
|
/**
|
|
* @param {BaseMessage[]} messages
|
|
*/
|
|
const runAgents = async (messages) => {
|
|
const agents = [this.options.agent];
|
|
// Include additional agents when:
|
|
// - agentConfigs has agents (from addedConvo parallel execution or agent handoffs)
|
|
// - Agents without incoming edges become start nodes and run in parallel automatically
|
|
if (this.agentConfigs && this.agentConfigs.size > 0) {
|
|
agents.push(...this.agentConfigs.values());
|
|
}
|
|
|
|
// TODO: needs to be added as part of AgentContext initialization
|
|
// const noSystemModelRegex = [/\b(o1-preview|o1-mini|amazon\.titan-text)\b/gi];
|
|
// const noSystemMessages = noSystemModelRegex.some((regex) =>
|
|
// agent.model_parameters.model.match(regex),
|
|
// );
|
|
// if (noSystemMessages === true && systemContent?.length) {
|
|
// const latestMessageContent = _messages.pop().content;
|
|
// if (typeof latestMessageContent !== 'string') {
|
|
// latestMessageContent[0].text = [systemContent, latestMessageContent[0].text].join('\n');
|
|
// _messages.push(new HumanMessage({ content: latestMessageContent }));
|
|
// } else {
|
|
// const text = [systemContent, latestMessageContent].join('\n');
|
|
// _messages.push(new HumanMessage(text));
|
|
// }
|
|
// }
|
|
// let messages = _messages;
|
|
// if (agent.useLegacyContent === true) {
|
|
// messages = formatContentStrings(messages);
|
|
// }
|
|
// if (
|
|
// agent.model_parameters?.clientOptions?.defaultHeaders?.['anthropic-beta']?.includes(
|
|
// 'prompt-caching',
|
|
// )
|
|
// ) {
|
|
// messages = addCacheControl(messages);
|
|
// }
|
|
|
|
if (this.processMemory) {
|
|
memoryPromise = this.runMemory(messages);
|
|
}
|
|
|
|
/** Seed calibration state from previous run if encoding matches */
|
|
const currentEncoding = this.getEncoding();
|
|
const prevMeta = this.contextMeta;
|
|
const encodingMatch = prevMeta?.encoding === currentEncoding;
|
|
const calibrationRatio =
|
|
encodingMatch && prevMeta?.calibrationRatio > 0 ? prevMeta.calibrationRatio : undefined;
|
|
|
|
if (prevMeta) {
|
|
logger.debug(
|
|
`[AgentClient] contextMeta from parent: ratio=${prevMeta.calibrationRatio}, encoding=${prevMeta.encoding}, current=${currentEncoding}, seeded=${calibrationRatio ?? 'none'}`,
|
|
);
|
|
}
|
|
|
|
run = await createRun({
|
|
agents,
|
|
messages,
|
|
indexTokenCountMap,
|
|
initialSummary,
|
|
initialSessions,
|
|
calibrationRatio,
|
|
runId: this.responseMessageId,
|
|
signal: abortController.signal,
|
|
customHandlers: this.options.eventHandlers,
|
|
requestBody: config.configurable.requestBody,
|
|
user: createSafeUser(this.options.req?.user),
|
|
summarizationConfig: appConfig?.summarization,
|
|
appConfig,
|
|
tokenCounter,
|
|
});
|
|
|
|
if (!run) {
|
|
throw new Error('Failed to create run');
|
|
}
|
|
|
|
this.run = run;
|
|
|
|
const streamId = this.options.req?._resumableStreamId;
|
|
if (streamId && run.Graph) {
|
|
GenerationJobManager.setGraph(streamId, run.Graph);
|
|
}
|
|
|
|
if (userMCPAuthMap != null) {
|
|
config.configurable.userMCPAuthMap = userMCPAuthMap;
|
|
}
|
|
|
|
/** @deprecated Agent Chain */
|
|
config.configurable.last_agent_id = agents[agents.length - 1].id;
|
|
await run.processStream({ messages }, config, {
|
|
callbacks: {
|
|
[Callback.TOOL_ERROR]: logToolError,
|
|
},
|
|
});
|
|
|
|
config.signal = null;
|
|
};
|
|
|
|
const hideSequentialOutputs = config.configurable.hide_sequential_outputs;
|
|
await runAgents(initialMessages);
|
|
|
|
/**
|
|
* Surface a completed `skill` tool_call content part per *manually*-
|
|
* primed skill so the existing `SkillCall` frontend renderer shows
|
|
* a "Skill X loaded" card on the assistant response. Applied after
|
|
* the graph finishes to avoid clashing with the aggregator's own
|
|
* per-step content indexing. Prepended (not appended) so cards sit
|
|
* above the model's output — priming ran before the turn, the
|
|
* reply follows.
|
|
*
|
|
* Always-apply primes intentionally do NOT emit assistant-side
|
|
* cards. `extractInvokedSkillsFromPayload` scans history for
|
|
* `skill` tool_calls and feeds `primeInvokedSkills`, which is
|
|
* Phase 3's sticky-re-prime path — that's the right behavior for
|
|
* manual (user picked `$skill` once; re-prime on every subsequent
|
|
* turn from history). For always-apply, `resolveAlwaysApplySkills`
|
|
* already re-primes every turn from fresh DB state, so persisting
|
|
* the card would cause the skill body to get primed twice per
|
|
* turn starting on turn 2. The user-facing acknowledgement for
|
|
* always-apply lives on the user bubble as the pinned
|
|
* `SkillPills` row (`message.alwaysAppliedSkills`), which
|
|
* is the durable signal the user wants: "this skill auto-primes".
|
|
*
|
|
* Live streaming display of manual user-bubble pills is handled
|
|
* by `SkillPills` reading `message.manualSkills`. No
|
|
* separate SSE emit is needed here; trying to stream a mid-run
|
|
* tool_call at index 0 collided with the LLM's first text
|
|
* content, while emitting at a sparse offset pushed the card
|
|
* below the reply on finalize. Post-run unshift keeps the final
|
|
* responseMessage.content in the right order.
|
|
*/
|
|
const manualPrimed = this.options.agent?.manualSkillPrimes ?? [];
|
|
if (manualPrimed.length > 0) {
|
|
const runId = this.responseMessageId ?? 'skill-prime';
|
|
const manualParts = buildSkillPrimeContentParts(manualPrimed, { runId });
|
|
this.contentParts.unshift(...manualParts);
|
|
}
|
|
|
|
/** @deprecated Agent Chain */
|
|
if (hideSequentialOutputs) {
|
|
this.contentParts = this.contentParts.filter((part, index) => {
|
|
// Include parts that are either:
|
|
// 1. At or after the finalContentStart index
|
|
// 2. Of type tool_call
|
|
// 3. Have tool_call_ids property
|
|
return (
|
|
index >= this.contentParts.length - 1 ||
|
|
part.type === ContentTypes.TOOL_CALL ||
|
|
part.tool_call_ids
|
|
);
|
|
});
|
|
}
|
|
} catch (err) {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #sendCompletion] Operation aborted',
|
|
err,
|
|
);
|
|
if (!abortController.signal.aborted) {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #sendCompletion] Unhandled error type',
|
|
err,
|
|
);
|
|
this.contentParts.push({
|
|
type: ContentTypes.ERROR,
|
|
[ContentTypes.ERROR]: `An error occurred while processing the request${err?.message ? `: ${err.message}` : ''}`,
|
|
});
|
|
}
|
|
} finally {
|
|
/** Capture calibration state from the run for persistence on the response message.
|
|
* Runs in finally so values are captured even on abort. */
|
|
const ratio = this.run?.getCalibrationRatio() ?? 0;
|
|
if (ratio > 0 && ratio !== 1) {
|
|
this.contextMeta = {
|
|
calibrationRatio: Math.round(ratio * 1000) / 1000,
|
|
encoding: this.getEncoding(),
|
|
};
|
|
} else {
|
|
this.contextMeta = undefined;
|
|
}
|
|
|
|
this.finalizeSubagentContent();
|
|
|
|
try {
|
|
const attachments = await this.awaitMemoryWithTimeout(memoryPromise);
|
|
if (attachments && attachments.length > 0) {
|
|
this.artifactPromises.push(...attachments);
|
|
}
|
|
|
|
/** Skip token spending if aborted - the abort handler (abortMiddleware.js) handles it
|
|
This prevents double-spending when user aborts via `/api/agents/chat/abort` */
|
|
const wasAborted = abortController?.signal?.aborted;
|
|
if (!wasAborted) {
|
|
await this.recordCollectedUsage({
|
|
context: 'message',
|
|
balance: balanceConfig,
|
|
transactions: transactionsConfig,
|
|
});
|
|
} else {
|
|
logger.debug(
|
|
'[api/server/controllers/agents/client.js #chatCompletion] Skipping token spending - handled by abort middleware',
|
|
);
|
|
}
|
|
} catch (err) {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #chatCompletion] Error in cleanup phase',
|
|
err,
|
|
);
|
|
}
|
|
run = null;
|
|
config = null;
|
|
memoryPromise = null;
|
|
}
|
|
}
|
|
|
|
/**
|
|
*
|
|
* @param {Object} params
|
|
* @param {string} params.text
|
|
* @param {string} params.conversationId
|
|
*/
|
|
async titleConvo({ text, abortController }) {
|
|
if (!this.run) {
|
|
throw new Error('Run not initialized');
|
|
}
|
|
const { handleLLMEnd, collected: collectedMetadata } = createMetadataAggregator();
|
|
const { req, agent } = this.options;
|
|
|
|
if (req?.body?.isTemporary) {
|
|
logger.debug(
|
|
`[api/server/controllers/agents/client.js #titleConvo] Skipping title generation for temporary conversation`,
|
|
);
|
|
return;
|
|
}
|
|
|
|
const appConfig = req.config;
|
|
let endpoint = agent.endpoint;
|
|
|
|
/** @type {import('@librechat/agents').ClientOptions} */
|
|
let clientOptions = {
|
|
model: agent.model || agent.model_parameters.model,
|
|
};
|
|
|
|
let titleProviderConfig = getProviderConfig({ provider: endpoint, appConfig });
|
|
|
|
/** @type {TEndpoint | undefined} */
|
|
const endpointConfig =
|
|
appConfig.endpoints?.all ??
|
|
appConfig.endpoints?.[endpoint] ??
|
|
titleProviderConfig.customEndpointConfig;
|
|
if (!endpointConfig) {
|
|
logger.debug(
|
|
`[api/server/controllers/agents/client.js #titleConvo] No endpoint config for "${endpoint}"`,
|
|
);
|
|
}
|
|
|
|
if (endpointConfig?.titleConvo === false) {
|
|
logger.debug(
|
|
`[api/server/controllers/agents/client.js #titleConvo] Title generation disabled for endpoint "${endpoint}"`,
|
|
);
|
|
return;
|
|
}
|
|
|
|
if (endpointConfig?.titleEndpoint && endpointConfig.titleEndpoint !== endpoint) {
|
|
try {
|
|
titleProviderConfig = getProviderConfig({
|
|
provider: endpointConfig.titleEndpoint,
|
|
appConfig,
|
|
});
|
|
endpoint = endpointConfig.titleEndpoint;
|
|
} catch (error) {
|
|
logger.warn(
|
|
`[api/server/controllers/agents/client.js #titleConvo] Error getting title endpoint config for "${endpointConfig.titleEndpoint}", falling back to default`,
|
|
error,
|
|
);
|
|
// Fall back to original provider config
|
|
endpoint = agent.endpoint;
|
|
titleProviderConfig = getProviderConfig({ provider: endpoint, appConfig });
|
|
}
|
|
}
|
|
|
|
if (
|
|
endpointConfig &&
|
|
endpointConfig.titleModel &&
|
|
endpointConfig.titleModel !== Constants.CURRENT_MODEL
|
|
) {
|
|
clientOptions.model = endpointConfig.titleModel;
|
|
}
|
|
|
|
const options = await titleProviderConfig.getOptions({
|
|
req,
|
|
endpoint,
|
|
model_parameters: clientOptions,
|
|
db: {
|
|
getUserKey: db.getUserKey,
|
|
getUserKeyValues: db.getUserKeyValues,
|
|
},
|
|
});
|
|
|
|
let provider = options.provider ?? titleProviderConfig.overrideProvider ?? agent.provider;
|
|
if (
|
|
endpoint === EModelEndpoint.azureOpenAI &&
|
|
options.llmConfig?.azureOpenAIApiInstanceName == null
|
|
) {
|
|
provider = Providers.OPENAI;
|
|
} else if (
|
|
endpoint === EModelEndpoint.azureOpenAI &&
|
|
options.llmConfig?.azureOpenAIApiInstanceName != null &&
|
|
provider !== Providers.AZURE
|
|
) {
|
|
provider = Providers.AZURE;
|
|
}
|
|
|
|
/** @type {import('@librechat/agents').ClientOptions} */
|
|
clientOptions = { ...options.llmConfig };
|
|
if (options.configOptions) {
|
|
clientOptions.configuration = options.configOptions;
|
|
}
|
|
|
|
if (clientOptions.maxTokens != null) {
|
|
delete clientOptions.maxTokens;
|
|
}
|
|
if (clientOptions?.modelKwargs?.max_completion_tokens != null) {
|
|
delete clientOptions.modelKwargs.max_completion_tokens;
|
|
}
|
|
if (clientOptions?.modelKwargs?.max_output_tokens != null) {
|
|
delete clientOptions.modelKwargs.max_output_tokens;
|
|
}
|
|
|
|
clientOptions = Object.assign(
|
|
Object.fromEntries(
|
|
Object.entries(clientOptions).filter(([key]) => !omitTitleOptions.has(key)),
|
|
),
|
|
);
|
|
|
|
if (
|
|
provider === Providers.GOOGLE &&
|
|
(endpointConfig?.titleMethod === TitleMethod.FUNCTIONS ||
|
|
endpointConfig?.titleMethod === TitleMethod.STRUCTURED)
|
|
) {
|
|
clientOptions.json = true;
|
|
}
|
|
|
|
/** Resolve request-based headers for Custom Endpoints. Note: if this is added to
|
|
* non-custom endpoints, needs consideration of varying provider header configs.
|
|
*/
|
|
if (clientOptions?.configuration?.defaultHeaders != null) {
|
|
clientOptions.configuration.defaultHeaders = resolveHeaders({
|
|
headers: clientOptions.configuration.defaultHeaders,
|
|
user: createSafeUser(this.options.req?.user),
|
|
body: {
|
|
messageId: this.responseMessageId,
|
|
conversationId: this.conversationId,
|
|
parentMessageId: this.parentMessageId,
|
|
},
|
|
});
|
|
}
|
|
|
|
try {
|
|
const titleResult = await this.run.generateTitle({
|
|
provider,
|
|
clientOptions,
|
|
inputText: text,
|
|
contentParts: this.contentParts,
|
|
titleMethod: endpointConfig?.titleMethod,
|
|
titlePrompt: endpointConfig?.titlePrompt,
|
|
titlePromptTemplate: endpointConfig?.titlePromptTemplate,
|
|
chainOptions: {
|
|
runName: 'TitleRun',
|
|
signal: abortController.signal,
|
|
callbacks: [
|
|
{
|
|
handleLLMEnd,
|
|
},
|
|
],
|
|
configurable: {
|
|
thread_id: this.conversationId,
|
|
user_id: this.user ?? this.options.req.user?.id,
|
|
},
|
|
},
|
|
});
|
|
|
|
const collectedUsage = collectedMetadata.map((item) => {
|
|
let input_tokens, output_tokens;
|
|
|
|
if (item.usage) {
|
|
input_tokens =
|
|
item.usage.prompt_tokens || item.usage.input_tokens || item.usage.inputTokens;
|
|
output_tokens =
|
|
item.usage.completion_tokens || item.usage.output_tokens || item.usage.outputTokens;
|
|
} else if (item.tokenUsage) {
|
|
input_tokens = item.tokenUsage.promptTokens;
|
|
output_tokens = item.tokenUsage.completionTokens;
|
|
} else if (item.usage_metadata) {
|
|
input_tokens = item.usage_metadata.input_tokens;
|
|
output_tokens = item.usage_metadata.output_tokens;
|
|
}
|
|
|
|
return {
|
|
input_tokens: input_tokens,
|
|
output_tokens: output_tokens,
|
|
};
|
|
});
|
|
|
|
const balanceConfig = getBalanceConfig(appConfig);
|
|
const transactionsConfig = getTransactionsConfig(appConfig);
|
|
await this.recordCollectedUsage({
|
|
collectedUsage,
|
|
context: 'title',
|
|
model: clientOptions.model,
|
|
balance: balanceConfig,
|
|
transactions: transactionsConfig,
|
|
messageId: this.responseMessageId,
|
|
}).catch((err) => {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #titleConvo] Error recording collected usage',
|
|
err,
|
|
);
|
|
});
|
|
|
|
return sanitizeTitle(titleResult.title);
|
|
} catch (err) {
|
|
logger.error('[api/server/controllers/agents/client.js #titleConvo] Error', err);
|
|
return;
|
|
}
|
|
}
|
|
|
|
/**
|
|
* @param {object} params
|
|
* @param {number} params.promptTokens
|
|
* @param {number} params.completionTokens
|
|
* @param {string} [params.model]
|
|
* @param {OpenAIUsageMetadata} [params.usage]
|
|
* @param {AppConfig['balance']} [params.balance]
|
|
* @param {string} [params.context='message']
|
|
* @returns {Promise<void>}
|
|
*/
|
|
async recordTokenUsage({
|
|
model,
|
|
usage,
|
|
balance,
|
|
promptTokens,
|
|
completionTokens,
|
|
context = 'message',
|
|
}) {
|
|
try {
|
|
await db.spendTokens(
|
|
{
|
|
model,
|
|
context,
|
|
balance,
|
|
messageId: this.responseMessageId,
|
|
conversationId: this.conversationId,
|
|
user: this.user ?? this.options.req.user?.id,
|
|
endpointTokenConfig: this.options.endpointTokenConfig,
|
|
},
|
|
{ promptTokens, completionTokens },
|
|
);
|
|
|
|
if (
|
|
usage &&
|
|
typeof usage === 'object' &&
|
|
'reasoning_tokens' in usage &&
|
|
typeof usage.reasoning_tokens === 'number'
|
|
) {
|
|
await db.spendTokens(
|
|
{
|
|
model,
|
|
balance,
|
|
context: 'reasoning',
|
|
messageId: this.responseMessageId,
|
|
conversationId: this.conversationId,
|
|
user: this.user ?? this.options.req.user?.id,
|
|
endpointTokenConfig: this.options.endpointTokenConfig,
|
|
},
|
|
{ completionTokens: usage.reasoning_tokens },
|
|
);
|
|
}
|
|
} catch (error) {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #recordTokenUsage] Error recording token usage',
|
|
error,
|
|
);
|
|
}
|
|
}
|
|
|
|
/** Anthropic Claude models use a distinct BPE tokenizer; all others default to o200k_base. */
|
|
getEncoding() {
|
|
if (this.model && this.model.toLowerCase().includes('claude')) {
|
|
return 'claude';
|
|
}
|
|
return 'o200k_base';
|
|
}
|
|
}
|
|
|
|
module.exports = AgentClient;
|