Merge main - resolve conflicts

auto-agents-framework/.gitignore

@@ -1 +1,3 @@
-character.ts
+characters/
+!characters/character.example.ts
+.cookies/

auto-agents-framework/README.md

@@ -14,39 +14,95 @@ Auto-Agents-Framework is an experimental framework for building AI agents that c
 ## Getting Started
 
 1. Install dependencies:
-   `yarn install`
+   `yarn install
+`
 
 2. Copy the environment file and configure your credentials:
    `cp .env.sample .env`
 
-3. Create your agent character by copying the example:
-   `cp src/agents/workflows/kol/characters/character.example.ts src/agents/workflows/kol/characters/character.ts`
+3. Configure your `.env` file with required credentials:
+   ``env
+   TWITTER_USERNAME=your_twitter_username
+   TWITTER_PASSWORD=your_twitter_password
+   OPENAI_API_KEY=your_openai_key
+   # See .env.sample for other configuration options
+   ``
 
-4. Configure your character:
+## Character System
 
-   ```
-   // character.ts
-   const name = 'Your Agent Name';
-   const username = 'twitter_handle';
-   const walletAddress = '0x...'; // Your Autonomys Network wallet
-   const description = 'Your agent's personality description...';
+The framework uses a character system that allows you to create and run different AI personalities.
 
+### Creating Characters
 
-    // Configure additional personality traits, expertise, rules, etc.
+1. Characters are stored in `src/agents/workflows/kol/characters/`
+2. Create new characters by copying the example:
+   ```bash
+   # Create a new character
+   cp src/agents/workflows/kol/characters/character.example.ts src/agents/workflows/kol/characters/my-character.ts
    ```
 
-## Character Configuration
+### Character Configuration
+
+Each character file should export a `character` object with the following properties:
+
+```typescript
+export const character = {
+  name: 'Agent Name',
+  username: 'twitter_handle',
+  description: 'Core personality description',
+  personality: 'Key behavioral traits',
+  expertise: 'Areas of knowledge',
+  rules: 'Operating guidelines',
+  trendFocus: 'Topics to monitor',
+  contentFocus: 'Content creation guidelines',
+  replyStyle: 'Engagement approach',
+  wordsToAvoid: ['list', 'of', 'words', 'to', 'avoid'],
+  engagementCriteria: 'Rules for when to engage with tweets',
+};
+```
+
+### Running with Different Characters
+
+Run the agent with a specific character:
+``bash
+
+# Use default character (character.ts)
+
+yarn dev
+
+# Use a specific character (omit .ts extension)
 
-The character configuration defines your agent's personality and behavior. Key sections include:
+```
+yarn dev argumint
+yarn dev techie
+```
 
-- `description`: Core personality and background
-- `personality`: Key behavioral traits
-- `expertise`: Areas of knowledge
-- `rules`: Operating guidelines
-- `trendFocus`: Topics to monitor
-- `contentFocus`: Content creation guidelines
-- `replyStyle`: Engagement approach
-- `wordsToAvoid`: Restricted vocabulary
+### Example Characters
+
+1. Default Rational Agent (`character.ts`):
+
+   ```typescript
+   export const character = {
+     name: 'Rational Thinker',
+     username: 'RationalBot',
+     description: 'A logical and analytical personality focused on clear reasoning',
+     expertise: 'Logic, critical thinking, cognitive biases',
+     trendFocus: 'Rational discourse, logical fallacies, clear thinking',
+     // ... other configuration
+   };
+   ```
+
+2. Tech Enthusiast (`techie.ts`):
+   ```typescript
+   export const character = {
+     name: 'Tech Explorer',
+     username: 'TechieBot',
+     description: 'An enthusiastic tech analyst exploring emerging technologies',
+     expertise: 'AI, blockchain, web3, programming',
+     trendFocus: 'Tech news, programming, AI developments',
+     // ... other configuration
+   };
+   ```
 
 ## Autonomys Network Integration
 
@@ -79,7 +135,14 @@ The KOL workflow enables agents to:
 ## Running the Agent
 
 Start the agent with:
-`yarn dev`
+
+```bash
+# Use default character
+yarn dev
+
+# Use a specific character (without .ts extension)
+yarn dev argumint
+```
 
 Monitor the agent's activity in the console and configured log files.
 

auto-agents-framework/src/agents/workflows/kol/nodes/analyzeTrendNode.ts

@@ -1,7 +1,7 @@
 import { WorkflowConfig } from '../types.js';
 import { createLogger } from '../../../../utils/logger.js';
 import { State } from '../workflow.js';
-import { trendParser, trendPrompt } from '../prompts.js';
+import { trendParser } from '../prompts.js';
 
 const logger = createLogger('analyze-timeline-trend-node');
 
@@ -15,7 +15,7 @@ export const createAnalyzeTrendNode =
     }));
     logger.info('Tweets:', { tweets: tweets.length });
 
-    const trendAnalysis = await trendPrompt
+    const trendAnalysis = await config.prompts.trendPrompt
       .pipe(config.llms.analyze)
       .pipe(trendParser)
       .invoke({

auto-agents-framework/src/agents/workflows/kol/nodes/engagementNode.ts

@@ -1,7 +1,7 @@
 import { WorkflowConfig } from '../types.js';
 import { createLogger } from '../../../../utils/logger.js';
 import { State } from '../workflow.js';
-import { engagementParser, engagementPrompt } from '../prompts.js';
+import { engagementParser } from '../prompts.js';
 import { Tweet } from '../../../../services/twitter/types.js';
 
 const logger = createLogger('engagement-node');
@@ -12,7 +12,7 @@ const getEngagementDecision = async (tweet: Tweet, config: WorkflowConfig) => {
       ? tweet.thread.map(t => ({ text: t.text, username: t.username }))
       : 'No thread';
 
-  const formattedPrompt = await engagementPrompt.format({
+  const formattedPrompt = await config.prompts.engagementPrompt.format({
     tweet: JSON.stringify({ text: tweet.text, username: tweet.username }),
     thread: thread,
   });

auto-agents-framework/src/agents/workflows/kol/nodes/generateTweetNode.ts

@@ -2,7 +2,7 @@ import { EngagementDecision, WorkflowConfig } from '../types.js';
 import { createLogger } from '../../../../utils/logger.js';
 import { State } from '../workflow.js';
 import { invokePostTweetTool } from '../../../tools/postTweetTool.js';
-import { tweetPrompt, trendTweetParser, responsePrompt, responseParser } from '../prompts.js';
+import { trendTweetParser, responseParser } from '../prompts.js';
 import { AIMessage } from '@langchain/core/messages';
 import { summarySchema } from '../schemas.js';
 import { z } from 'zod';
@@ -18,12 +18,15 @@ const postResponse = async (
       ? decision.tweet.thread.map(t => ({ text: t.text, username: t.username }))
       : 'No thread';
   const decisionInfo = { tweet: decision.tweet.text, reason: decision.decision.reason };
-  const response = await responsePrompt.pipe(config.llms.generation).pipe(responseParser).invoke({
-    decision: decisionInfo,
-    thread,
-    patterns: summary.patterns,
-    commonWords: summary.commonWords,
-  });
+  const response = await config.prompts.responsePrompt
+    .pipe(config.llms.generation)
+    .pipe(responseParser)
+    .invoke({
+      decision: decisionInfo,
+      thread,
+      patterns: summary.patterns,
+      commonWords: summary.commonWords,
+    });
   //TODO: After sending the tweet, we need to get the latest tweet, ensure it is the same as we sent and return it
   //This has not been working as expected, so we need to investigate this later
   const tweet = await invokePostTweetTool(config.toolNode, response.content, decision.tweet.id);
@@ -76,7 +79,7 @@ export const createGenerateTweetNode =
 
     // Generate a top level tweet
     //TODO: add a check to see if it has been long enough since the last tweet
-    const generatedTweet = await tweetPrompt
+    const generatedTweet = await config.prompts.tweetPrompt
       .pipe(config.llms.generation)
       .pipe(trendTweetParser)
       .invoke({

auto-agents-framework/src/agents/workflows/kol/nodes/summaryNode.ts

@@ -1,15 +1,15 @@
 import { WorkflowConfig } from '../types.js';
 import { createLogger } from '../../../../utils/logger.js';
 import { State } from '../workflow.js';
-import { summaryParser, summaryPrompt } from '../prompts.js';
+import { summaryParser } from '../prompts.js';
 
 const logger = createLogger('summary-node');
 
 export const createSummaryNode = (config: WorkflowConfig) => async (state: typeof State.State) => {
   logger.info('Summary Node - Summarizing previous replies');
   const myRecentReplies = Array.from(state.myRecentReplies.values()).map(reply => reply.text);
 
-  const summary = await summaryPrompt
+  const summary = await config.prompts.summaryPrompt
     .pipe(config.llms.analyze)
     .pipe(summaryParser)
     .invoke({