container lifecycle management
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306
gateway/src/harness/agent-harness.ts
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306
gateway/src/harness/agent-harness.ts
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import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import type { BaseMessage } from '@langchain/core/messages';
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import { HumanMessage, AIMessage, SystemMessage } from '@langchain/core/messages';
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import type { FastifyBaseLogger } from 'fastify';
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import type { UserLicense } from '../types/user.js';
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import type { InboundMessage, OutboundMessage } from '../types/messages.js';
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import { MCPClientConnector } from './mcp-client.js';
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import { CONTEXT_URIS, type ResourceContent } from '../types/resources.js';
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import { LLMProviderFactory, type ProviderConfig } from '../llm/provider.js';
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import { ModelRouter, RoutingStrategy } from '../llm/router.js';
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export interface AgentHarnessConfig {
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userId: string;
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sessionId: string;
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license: UserLicense;
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providerConfig: ProviderConfig;
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logger: FastifyBaseLogger;
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}
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/**
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* Agent harness orchestrates between LLM and user's MCP server.
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*
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* This is a STATELESS orchestrator - all conversation history, RAG, and context
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* lives in the user's MCP server container. The harness only:
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* 1. Fetches context from user's MCP resources
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* 2. Routes to appropriate LLM model
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* 3. Calls LLM with embedded context
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* 4. Routes tool calls to user's MCP or platform tools
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* 5. Saves messages back to user's MCP
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*/
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export class AgentHarness {
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private config: AgentHarnessConfig;
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private modelFactory: LLMProviderFactory;
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private modelRouter: ModelRouter;
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private mcpClient: MCPClientConnector;
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constructor(config: AgentHarnessConfig) {
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this.config = config;
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this.modelFactory = new LLMProviderFactory(config.providerConfig, config.logger);
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this.modelRouter = new ModelRouter(this.modelFactory, config.logger);
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this.mcpClient = new MCPClientConnector({
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userId: config.userId,
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mcpServerUrl: config.license.mcpServerUrl,
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logger: config.logger,
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});
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}
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/**
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* Initialize harness and connect to user's MCP server
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*/
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async initialize(): Promise<void> {
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this.config.logger.info(
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{ userId: this.config.userId, sessionId: this.config.sessionId },
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'Initializing agent harness'
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);
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try {
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await this.mcpClient.connect();
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this.config.logger.info('Agent harness initialized');
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} catch (error) {
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this.config.logger.error({ error }, 'Failed to initialize agent harness');
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throw error;
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}
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}
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/**
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* Handle incoming message from user
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*/
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async handleMessage(message: InboundMessage): Promise<OutboundMessage> {
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this.config.logger.info(
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{ messageId: message.messageId, userId: message.userId },
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'Processing user message'
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);
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try {
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// 1. Fetch context resources from user's MCP server
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this.config.logger.debug('Fetching context resources from MCP');
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const contextResources = await this.fetchContextResources();
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// 2. Build system prompt from resources
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const systemPrompt = this.buildSystemPrompt(contextResources);
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// 3. Build messages with conversation context from MCP
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const messages = this.buildMessages(message, contextResources);
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// 4. Route to appropriate model
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const model = await this.modelRouter.route(
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message.content,
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this.config.license,
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RoutingStrategy.COMPLEXITY
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);
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// 5. Build LangChain messages
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const langchainMessages = this.buildLangChainMessages(systemPrompt, messages);
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// 6. Call LLM with streaming
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this.config.logger.debug('Invoking LLM');
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const response = await model.invoke(langchainMessages);
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// 7. Extract text response (tool handling TODO)
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const assistantMessage = response.content as string;
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// 8. Save messages to user's MCP server
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this.config.logger.debug('Saving messages to MCP');
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await this.mcpClient.callTool('save_message', {
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role: 'user',
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content: message.content,
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timestamp: message.timestamp.toISOString(),
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});
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await this.mcpClient.callTool('save_message', {
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role: 'assistant',
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content: assistantMessage,
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timestamp: new Date().toISOString(),
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});
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return {
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messageId: `msg_${Date.now()}`,
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sessionId: message.sessionId,
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content: assistantMessage,
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timestamp: new Date(),
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};
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} catch (error) {
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this.config.logger.error({ error }, 'Error processing message');
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throw error;
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}
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}
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/**
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* Stream response from LLM
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*/
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async *streamMessage(message: InboundMessage): AsyncGenerator<string> {
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try {
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// Fetch context
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const contextResources = await this.fetchContextResources();
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const systemPrompt = this.buildSystemPrompt(contextResources);
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const messages = this.buildMessages(message, contextResources);
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// Route to model
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const model = await this.modelRouter.route(
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message.content,
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this.config.license,
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RoutingStrategy.COMPLEXITY
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);
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// Build messages
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const langchainMessages = this.buildLangChainMessages(systemPrompt, messages);
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// Stream response
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const stream = await model.stream(langchainMessages);
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let fullResponse = '';
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for await (const chunk of stream) {
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const content = chunk.content as string;
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fullResponse += content;
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yield content;
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}
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// Save after streaming completes
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await this.mcpClient.callTool('save_message', {
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role: 'user',
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content: message.content,
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timestamp: message.timestamp.toISOString(),
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});
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await this.mcpClient.callTool('save_message', {
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role: 'assistant',
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content: fullResponse,
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timestamp: new Date().toISOString(),
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});
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} catch (error) {
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this.config.logger.error({ error }, 'Error streaming message');
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throw error;
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}
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}
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/**
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* Fetch context resources from user's MCP server
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*/
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private async fetchContextResources(): Promise<ResourceContent[]> {
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const contextUris = [
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CONTEXT_URIS.USER_PROFILE,
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CONTEXT_URIS.CONVERSATION_SUMMARY,
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CONTEXT_URIS.WORKSPACE_STATE,
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CONTEXT_URIS.SYSTEM_PROMPT,
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];
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const resources = await Promise.all(
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contextUris.map(async (uri) => {
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try {
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return await this.mcpClient.readResource(uri);
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} catch (error) {
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this.config.logger.warn({ error, uri }, 'Failed to fetch resource, using empty');
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return { uri, text: '' };
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}
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})
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);
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return resources;
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}
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/**
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* Build messages array with context from resources
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*/
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private buildMessages(
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currentMessage: InboundMessage,
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contextResources: ResourceContent[]
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): Array<{ role: string; content: string }> {
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const conversationSummary = contextResources.find(
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(r) => r.uri === CONTEXT_URIS.CONVERSATION_SUMMARY
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);
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const messages: Array<{ role: string; content: string }> = [];
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// Add conversation context as a system-like user message
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if (conversationSummary?.text) {
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messages.push({
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role: 'user',
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content: `[Previous Conversation Context]\n${conversationSummary.text}`,
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});
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messages.push({
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role: 'assistant',
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content: 'I understand the context from our previous conversations.',
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});
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}
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// Add current user message
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messages.push({
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role: 'user',
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content: currentMessage.content,
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});
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return messages;
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}
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/**
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* Convert to LangChain message format
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*/
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private buildLangChainMessages(
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systemPrompt: string,
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messages: Array<{ role: string; content: string }>
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): BaseMessage[] {
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const langchainMessages: BaseMessage[] = [new SystemMessage(systemPrompt)];
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for (const msg of messages) {
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if (msg.role === 'user') {
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langchainMessages.push(new HumanMessage(msg.content));
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} else if (msg.role === 'assistant') {
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langchainMessages.push(new AIMessage(msg.content));
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}
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}
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return langchainMessages;
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}
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/**
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* Build system prompt from platform base + user resources
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*/
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private buildSystemPrompt(contextResources: ResourceContent[]): string {
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const userProfile = contextResources.find((r) => r.uri === CONTEXT_URIS.USER_PROFILE);
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const customPrompt = contextResources.find((r) => r.uri === CONTEXT_URIS.SYSTEM_PROMPT);
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const workspaceState = contextResources.find((r) => r.uri === CONTEXT_URIS.WORKSPACE_STATE);
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// Base platform prompt
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let prompt = `You are a helpful AI assistant for Dexorder, an AI-first trading platform.
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You help users research markets, develop indicators and strategies, and analyze trading data.
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User license: ${this.config.license.licenseType}
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Available features: ${JSON.stringify(this.config.license.features, null, 2)}`;
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// Add user profile context
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if (userProfile?.text) {
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prompt += `\n\n# User Profile\n${userProfile.text}`;
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}
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// Add workspace context
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if (workspaceState?.text) {
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prompt += `\n\n# Current Workspace\n${workspaceState.text}`;
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}
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// Add user's custom instructions (highest priority)
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if (customPrompt?.text) {
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prompt += `\n\n# User Instructions\n${customPrompt.text}`;
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}
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return prompt;
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}
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/**
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* Get platform tools (non-user-specific tools)
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*/
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private getPlatformTools(): Array<{ name: string; description?: string }> {
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// Platform tools that don't need user's MCP
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return [
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// TODO: Add platform tools like market data queries, chart rendering, etc.
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];
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}
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/**
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* Cleanup resources
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*/
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async cleanup(): Promise<void> {
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this.config.logger.info('Cleaning up agent harness');
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await this.mcpClient.disconnect();
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}
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}
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