Add support for custom behavior (system message) when prompting an LLM without agent.

Author: alaa-eddine

examples/01-agent-code-skill/04.1-chat-planner-coder.ts

@@ -265,7 +265,10 @@ async function handleUserInput(input: string, rl: readline.Interface, chat: Chat
             parser.flush();
             console.log('\n');
             displayTasksList(currentTasks);
-            rl.prompt();
+            //wait for the parser to flush
+            parser.once('buffer-released', () => {
+                rl.prompt();
+            });
         });
 
         streamChat.on(TLLMEvent.Error, (error) => {
@@ -276,7 +279,6 @@ async function handleUserInput(input: string, rl: readline.Interface, chat: Chat
         const toolCalls = {};
 
         streamChat.on(TLLMEvent.ToolCall, (toolCall) => {
-            displayTasksList(currentTasks);
             if (toolCall?.tool?.name.startsWith('_sre_')) {
                 return;
             }
@@ -290,6 +292,8 @@ async function handleUserInput(input: string, rl: readline.Interface, chat: Chat
                 console.log(chalk.gray('\n[Calling Tool]'), chalk.gray(toolCall?.tool?.name), chalk.gray(args));
                 toolCalls[toolCall?.tool?.id] = { startTime: Date.now() };
             });
+
+            displayTasksList(currentTasks);
         });
 
         streamChat.on(TLLMEvent.ToolResult, (toolResult) => {

packages/sdk/package.json

@@ -1,6 +1,6 @@
 {
     "name": "@smythos/sdk",
-    "version": "1.1.0",
+    "version": "1.1.1",
     "description": "SRE SDK",
     "keywords": [
         "smythos",

packages/sdk/src/Core/SDKObject.class.ts

@@ -27,11 +27,14 @@ export class SDKObject {
         this._readyPromise = new ControlledPromise<any>(this.init.bind(this));
     }
 
-    protected async init() {
+    protected async init(revolveReadyPromise: boolean = true) {
         //if the SRE instance is not initializing, initialize it with default settings
         if (!SRE.initializing) SRE.init({});
         await SRE.ready();
 
+        if (revolveReadyPromise) this._readyPromise.resolve(true);
+    }
+    protected async initSignal() {
         this._readyPromise.resolve(true);
     }
 

packages/sdk/src/LLM/LLMInstance.class.ts

@@ -20,8 +20,20 @@ import { ChatOptions } from '../types/SDKTypes';
 import { findClosestModelInfo } from './Model';
 import * as fs from 'fs';
 
+export type TLLMCommandOptions = {
+    /**
+     * Used to describe the LLM behavior, this will be typically used as LLM system message.
+     */
+    behavior?: string;
+
+    /**
+     * List of files paths or urls to be processed by the LLM.
+     */
+    files?: string[];
+};
+
 class LLMCommand {
-    constructor(private _llm: LLMInstance, private _params: any, private _options?: any) {}
+    constructor(private _llm: LLMInstance, private _params: any, private _options?: TLLMCommandOptions) {}
 
     /**
      * Run the command and return the result as a promise.
@@ -141,6 +153,9 @@ export type TLLMInstanceParams = {
     /** the maximum output tokens that the model should generate */
     outputTokens?: number;
 
+    /** The behavior of the model, this will be typically used as LLM system message. */
+    behavior?: string;
+
     [key: string]: any;
 };
 
@@ -185,7 +200,9 @@ export class LLMInstance extends SDKObject {
     }
 
     protected async init() {
-        await super.init();
+        //false means we don't resolve the ready promise
+        //we will resolve it later in the init method
+        await super.init(false);
         // const llmConnector = ConnectorService.getLLMConnector(this._providerId);
         // this._candidate = this._candidate || AccessCandidate.team(DEFAULT_TEAM_ID);
         // this._llmRequester = llmConnector.user(this._candidate);
@@ -205,6 +222,8 @@ export class LLMInstance extends SDKObject {
         const llmConnector = ConnectorService.getLLMConnector(this._providerId);
         this._llmRequester = llmConnector.user(this._candidate);
         this._modelSettings = adaptModelParams(this._modelSettings, this._providerId, builtInModelInfo);
+
+        this.initSignal(); //resolve the ready promise
     }
 
     /**
@@ -225,8 +244,14 @@ export class LLMInstance extends SDKObject {
      * stream.on('data', chunk => process.stdout.write(chunk));
      * ```
      */
-    public prompt(prompt: string, options?: any): LLMCommand {
-        return new LLMCommand(this, { ...this._modelSettings, messages: [{ role: 'user', content: prompt }] }, options);
+    public prompt(prompt: string, options?: TLLMCommandOptions): LLMCommand {
+        const messages = [];
+        const behavior = options?.behavior || this._modelSettings?.behavior;
+        if (behavior) {
+            messages.push({ role: 'system', content: behavior });
+        }
+        messages.push({ role: 'user', content: prompt });
+        return new LLMCommand(this, { ...this._modelSettings, messages }, options);
     }
 
     public chat(options?: ChatOptions | string) {

packages/sdk/tests/agent.test.ts

@@ -14,7 +14,7 @@ import { expect, describe, it } from 'vitest';
 describe('SDK Agent Tests', () => {
     it('imported agent', async () => {
         const agent = Agent.import('./packages/sdk/tests/data/AgentData/crypto-info-agent.smyth', {
-            model: Model.OpenAI('gpt-4o-mini', { maxTokens: 10 }),
+            model: Model.OpenAI('gpt-4o-mini', { maxTokens: 100 }),
         });
 
         //const result = await agent.prompt('Hello, Who are you ?');

packages/sdk/tests/llm.test.ts

@@ -45,6 +45,29 @@ describe('SDK LLM Tests', () => {
         expect(result).toBeDefined();
         expect(result).toContain('Paris');
     });
+    it('LLM - Prompt with LLM behavior', async () => {
+        const llm = LLM.OpenAI('gpt-4o-mini', {
+            behavior: 'You start every answer with this prompt $> ',
+        });
+
+        const result = await llm.prompt('What is the capital of France?');
+
+        expect(result).toBeDefined();
+        expect(result).toContain('$>');
+    });
+
+    it('LLM - Prompt with LLM behavior override', async () => {
+        const llm = LLM.OpenAI('gpt-4o-mini', {
+            behavior: 'You start every answer with this prompt $> ',
+        });
+
+        const result = await llm.prompt('What is the capital of France?', {
+            behavior: 'You start every answer with this prompt [AGENT]> ',
+        });
+
+        expect(result).toBeDefined();
+        expect(result).toContain('[AGENT]>');
+    });
 
     it('LLM - Prompt with attachments', async () => {
         const llm = LLM.OpenAI('gpt-4o-mini', {