Implement forceFinalAnswer Logic and Update Test Cases (#22)

### PR Description:
This PR introduces the `forceFinalAnswer` logic, which triggers a final
answer from the AI agent when approaching the maximum iteration limit.
This feature ensures that the agent utilizes all gathered information to
provide a decisive response before the session expires. Additionally,
the PR includes several updates and fixes:

- **Fix in Self-Question Logic**: Revised the handling of
`self_question` actions to ensure they are passed back correctly to the
LLM for a coherent follow-up.
- **Gemini Test Cases**: Added new test cases to the `sports_news` team
scenarios, enhancing our test coverage.
- **Snapshot Updates**: Updated snapshots to reflect the new
`forceFinalAnswer` logic, ensuring our tests remain accurate and
reliable.

playground/react/src/stories/TripPlanningTeam.stories.js

@@ -1,5 +1,6 @@
 import AgentsBoardDebugger from '../AgentsBoardDebugger';
 import teamOpenAI from '../teams/trip_planning/openai';
+import teamGemini from '../teams/trip_planning/gemini';
 
 
 // More on how to set up stories at: https://storybook.js.org/docs/writing-stories#default-export
@@ -15,3 +16,10 @@ export const withOpenAI = {
     title: 'With OpenAI Model'
   },
 };
+
+export const withGemini = {
+  args: {
+    team: teamGemini,
+    title: 'With Gemini Model'
+  },
+};

playground/react/src/teams/sport_news/gemini.js

@@ -64,7 +64,8 @@ const team = new Team({
         ANTHROPIC_API_KEY: import.meta.env.VITE_ANTHROPIC_API_KEY, 
         MISTRAL_API_KEY: import.meta.env.VITE_MISTRAL_API_KEY,
         GOOGLE_API_KEY: import.meta.env.VITE_GOOGLE_API_KEY
-    }    
+    },
+    logLevel: 'debug'    
     // Results of the latest UEFA Champions League match.
 });
 

playground/react/src/teams/trip_planning/gemini.js

@@ -0,0 +1,106 @@
+import { Agent, Task, Team } from 'agenticjs';
+
+import { TavilySearchResults } from '@langchain/community/tools/tavily_search';
+
+// Define tools
+const searchInternet = new TavilySearchResults({
+    maxResults: 3,
+    // apiKey: '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',
+    apiKey: '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',
+});
+
+// Define agents with exact roles, goals, and backgrounds from Python example
+const citySelectorAgent = new Agent({
+    name: 'Peter Atlas',
+    role: 'City Selection Expert',
+    goal: 'Select the best city based on weather, season, and prices',
+    background: 'An expert in analyzing travel data to pick ideal destinations',
+    type: 'ReactChampionAgent',
+    tools: [searchInternet],
+    maxIterations: 20,
+    llmConfig: {
+        provider: "google",
+        model: "gemini-1.5-pro",
+    }
+});
+
+const localExpertAgent = new Agent({
+    name: 'Sophia Lore',
+    role: 'Local Expert at this city',
+    goal: 'Provide the BEST insights about the selected city',
+    background: `A knowledgeable local guide with extensive information about the city, it's attractions and customs`,
+    type: 'ReactChampionAgent',
+    tools: [searchInternet],
+    maxIterations: 5,
+    llmConfig: {
+        provider: "google",
+        model: "gemini-1.5-pro",
+    }  
+});
+
+const travelConciergeAgent = new Agent({
+    name: 'Maxwell Journey',
+    role: 'Amazing Travel Concierge',
+    goal: `Create the most amazing travel itineraries with budget and packing suggestions for the city`,
+    background: `Specialist in travel planning and logistics with decades of experience`,
+    type: 'ReactChampionAgent',
+    tools: [searchInternet],
+    maxIterations: 5,
+    llmConfig: {
+        provider: "google",
+        model: "gemini-1.5-pro",
+    }  
+});
+
+// Define tasks with dynamic input placeholders
+const identifyTask = new Task({
+    description: `Analyze and select the best city for the trip based on 
+    specific criteria such as weather patterns, seasonal events,
+    and travel costs. ... 
+    Origin: {origin}, City Options: {cities}, 
+    Trip Date: {range}, 
+    Traveler Interests: {interests}`,
+    expectedOutput: `Detailed report on the chosen city,
+     including flight costs, 
+     weather forecast and attractions`,
+    agent: citySelectorAgent
+});
+
+const gatherTask = new Task({
+    description: `Compile an in-depth guide for the selected city, 
+    considering key attractions, local customs, and special events.
+     ... Trip Date: {range}, Origin: {origin}, Interests: {interests}`,
+    expectedOutput: `A comprehensive city guide,
+     rich in cultural insights and practical tips`,
+    agent: localExpertAgent
+});
+
+const planTask = new Task({
+    description: `Develop a full 7-day travel itinerary 
+    with detailed daily plans, including places to eat, 
+    packing suggestions, and a budget breakdown. ... 
+    Trip Date: {range}, Origin: {origin}, Interests: {interests}`,
+    expectedOutput: 'A complete expanded travel plan formatted as markdown',
+    agent: travelConciergeAgent
+});
+
+// Team to coordinate the agents, with dynamic inputs
+const tripPlanningTeam = new Team({
+    name: 'Trip Planning Team',
+    agents: [citySelectorAgent, localExpertAgent, travelConciergeAgent],
+    tasks: [identifyTask, gatherTask, planTask],
+    logLevel: 'info',
+    inputs: {
+        origin: 'New York',
+        cities: ['Tokyo', 'Paris', 'Berlin'],
+        interests: 'Art and Culture',
+        range: '2024-12-01 to 2024-12-15'
+    },  // Actual dynamic inputs
+    env: {
+        OPENAI_API_KEY: import.meta.env.VITE_OPENAI_API_KEY, 
+        ANTHROPIC_API_KEY: import.meta.env.VITE_ANTHROPIC_API_KEY,
+        GOOGLE_API_KEY: import.meta.env.VITE_GOOGLE_API_KEY
+    }
+});
+
+export default tripPlanningTeam;

src/agents/baseAgent.js

@@ -3,7 +3,7 @@ import { AGENT_STATUS_enum } from '../utils/enums';
 
 
 class BaseAgent {
-    constructor({ name, role, goal, background, tools, llmConfig = {}, maxIterations = 10 }) {
+    constructor({ name, role, goal, background, tools, llmConfig = {}, maxIterations = 10, forceFinalAnswer = true }) {
         this.id = uuidv4();
         this.name = name;
         this.role = role;
@@ -21,6 +21,7 @@ class BaseAgent {
             ...llmConfig 
         };
         this.llmSystemMessage = null;
+        this.forceFinalAnswer = forceFinalAnswer;
     }
 
     setStore(store) {

src/agents/reactChampionAgent.js

@@ -39,6 +39,11 @@ class ReactChampionAgent extends BaseAgent {
             try {
                 _self.handleIterationStart({agent: _self, task, iterations, maxAgentIterations});
 
+                // Check if we need to force the final answer
+                if (_self.forceFinalAnswer && iterations === maxAgentIterations - 2) {
+                    feedbackMessage = "We don't have more time to keep looking for the answer. Please use all the information you have gathered until now and give the finalAnswer right away.";
+                }                
+
                 // pure function that returns the result of the agent thinking
                 const thinkingResult = await this.executeThinking(_self, task, ExecutableAgent, feedbackMessage);
                 // sometimes the LLM does not returns a valid JSON object so we try to sanitize the output here
@@ -323,13 +328,17 @@ class ReactChampionAgent extends BaseAgent {
 
     handleThought({agent, task, parsedLLMOutput}) {
         agent.store.getState().handleAgentThought({agent, task, output: parsedLLMOutput});
-        const feedbackMessage = "Your toughts are great, let's keep going.";
+        let feedbackMessage = "Your toughts are great, let's keep going.";
+        if(parsedLLMOutput.action === 'self_question' && parsedLLMOutput.actionInput) {
+            const actionAsString = typeof parsedLLMOutput.actionInput == 'object' ? JSON.stringify(parsedLLMOutput.actionInput) : parsedLLMOutput.actionInput;
+            feedbackMessage = "Awesome, please answer yourself the question: " + actionAsString;
+        }
         return feedbackMessage;
     }
 
     handleSelfQuestion({agent, task, parsedLLMOutput}) {
         agent.store.getState().handleAgentSelfQuestion({agent, task, output: parsedLLMOutput});
-        const feedbackMessage = "Awesome please answer yourself the question";
+        const feedbackMessage = "Awesome, please answer yourself the question";
         return feedbackMessage;
     }
 

src/index.js

@@ -86,6 +86,9 @@ class Agent {
     get llmSystemMessage() {
         return this.agentInstance.llmSystemMessage;
     }    
+    get forceFinalAnswer() {
+        return this.agentInstance.forceFinalAnswer;
+    }    
 }
 class Task {
     constructor({ title = '', description, expectedOutput, agent, isDeliverable = false }) {