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create_profile_researcher.py
··· 1 - #!/usr/bin/env python3 2 - """ 3 - Script to create a Letta agent that researches Bluesky profiles and updates 4 - the model's understanding of users. 5 - """ 6 - 7 - import os 8 - import logging 9 - from letta_client import Letta 10 - from utils import upsert_block, upsert_agent 11 - 12 - # Configure logging 13 - logging.basicConfig( 14 - level=logging.INFO, 15 - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" 16 - ) 17 - logger = logging.getLogger("profile_researcher") 18 - 19 - # Use the "Bluesky" project 20 - PROJECT_ID = "5ec33d52-ab14-4fd6-91b5-9dbc43e888a8" 21 - 22 - def create_search_posts_tool(client: Letta): 23 - """Create the Bluesky search posts tool using Letta SDK.""" 24 - 25 - def search_bluesky_posts(query: str, max_results: int = 25, author: str = None, sort: str = "latest") -> str: 26 - """ 27 - Search for posts on Bluesky matching the given criteria. 28 - 29 - Args: 30 - query: Search query string (required) 31 - max_results: Maximum number of results to return (default: 25, max: 100) 32 - author: Filter to posts by a specific author handle (optional) 33 - sort: Sort order - "latest" or "top" (default: "latest") 34 - 35 - Returns: 36 - YAML-formatted search results with posts and metadata 37 - """ 38 - import os 39 - import requests 40 - import json 41 - import yaml 42 - from datetime import datetime 43 - 44 - try: 45 - # Use public Bluesky API 46 - base_url = "https://public.api.bsky.app" 47 - 48 - # Build search parameters 49 - params = { 50 - "q": query, 51 - "limit": min(max_results, 100), 52 - "sort": sort 53 - } 54 - 55 - # Add optional author filter 56 - if author: 57 - params["author"] = author.lstrip('@') 58 - 59 - # Make search request 60 - try: 61 - search_url = f"{base_url}/xrpc/app.bsky.feed.searchPosts" 62 - search_response = requests.get(search_url, params=params, timeout=10) 63 - search_response.raise_for_status() 64 - search_data = search_response.json() 65 - except requests.exceptions.HTTPError as e: 66 - raise RuntimeError(f"Search failed with HTTP {e.response.status_code}: {e.response.text}") 67 - except requests.exceptions.RequestException as e: 68 - raise RuntimeError(f"Network error during search: {str(e)}") 69 - except Exception as e: 70 - raise RuntimeError(f"Unexpected error during search: {str(e)}") 71 - 72 - # Build search results structure 73 - results_data = { 74 - "search_results": { 75 - "query": query, 76 - "timestamp": datetime.now().isoformat(), 77 - "parameters": { 78 - "sort": sort, 79 - "max_results": max_results, 80 - "author_filter": author if author else "none" 81 - }, 82 - "results": search_data 83 - } 84 - } 85 - 86 - # Fields to strip for cleaner output 87 - strip_fields = [ 88 - "cid", "rev", "did", "uri", "langs", "threadgate", "py_type", 89 - "labels", "facets", "avatar", "viewer", "indexed_at", "indexedAt", 90 - "tags", "associated", "thread_context", "image", "aspect_ratio", 91 - "alt", "thumb", "fullsize", "root", "parent", "created_at", 92 - "createdAt", "verification", "embedding_disabled", "thread_muted", 93 - "reply_disabled", "pinned", "like", "repost", "blocked_by", 94 - "blocking", "blocking_by_list", "followed_by", "following", 95 - "known_followers", "muted", "muted_by_list", "root_author_like", 96 - "embed", "entities", "reason", "feedContext" 97 - ] 98 - 99 - # Remove unwanted fields by traversing the data structure 100 - def remove_fields(obj, fields_to_remove): 101 - if isinstance(obj, dict): 102 - return {k: remove_fields(v, fields_to_remove) 103 - for k, v in obj.items() 104 - if k not in fields_to_remove} 105 - elif isinstance(obj, list): 106 - return [remove_fields(item, fields_to_remove) for item in obj] 107 - else: 108 - return obj 109 - 110 - # Clean the data 111 - cleaned_data = remove_fields(results_data, strip_fields) 112 - 113 - # Convert to YAML for better readability 114 - return yaml.dump(cleaned_data, default_flow_style=False, allow_unicode=True) 115 - 116 - except ValueError as e: 117 - # User-friendly errors 118 - raise ValueError(str(e)) 119 - except RuntimeError as e: 120 - # Network/API errors 121 - raise RuntimeError(str(e)) 122 - except yaml.YAMLError as e: 123 - # YAML conversion errors 124 - raise RuntimeError(f"Error formatting output: {str(e)}") 125 - except Exception as e: 126 - # Catch-all for unexpected errors 127 - raise RuntimeError(f"Unexpected error searching posts with query '{query}': {str(e)}") 128 - 129 - # Create the tool using upsert 130 - tool = client.tools.upsert_from_function( 131 - func=search_bluesky_posts, 132 - tags=["bluesky", "search", "posts"] 133 - ) 134 - 135 - logger.info(f"Created tool: {tool.name} (ID: {tool.id})") 136 - return tool 137 - 138 - def create_profile_research_tool(client: Letta): 139 - """Create the Bluesky profile research tool using Letta SDK.""" 140 - 141 - def research_bluesky_profile(handle: str, max_posts: int = 20) -> str: 142 - """ 143 - Research a Bluesky user's profile and recent posts to understand their interests and behavior. 144 - 145 - Args: 146 - handle: The Bluesky handle to research (e.g., 'cameron.pfiffer.org' or '@cameron.pfiffer.org') 147 - max_posts: Maximum number of recent posts to analyze (default: 20) 148 - 149 - Returns: 150 - A comprehensive analysis of the user's profile and posting patterns 151 - """ 152 - import os 153 - import requests 154 - import json 155 - import yaml 156 - from datetime import datetime 157 - 158 - try: 159 - # Clean handle (remove @ if present) 160 - clean_handle = handle.lstrip('@') 161 - 162 - # Use public Bluesky API (no auth required for public data) 163 - base_url = "https://public.api.bsky.app" 164 - 165 - # Get profile information 166 - try: 167 - profile_url = f"{base_url}/xrpc/app.bsky.actor.getProfile" 168 - profile_response = requests.get(profile_url, params={"actor": clean_handle}, timeout=10) 169 - profile_response.raise_for_status() 170 - profile_data = profile_response.json() 171 - except requests.exceptions.HTTPError as e: 172 - if e.response.status_code == 404: 173 - raise ValueError(f"Profile @{clean_handle} not found") 174 - raise RuntimeError(f"HTTP error {e.response.status_code}: {e.response.text}") 175 - except requests.exceptions.RequestException as e: 176 - raise RuntimeError(f"Network error: {str(e)}") 177 - except Exception as e: 178 - raise RuntimeError(f"Unexpected error fetching profile: {str(e)}") 179 - 180 - # Get recent posts feed 181 - try: 182 - feed_url = f"{base_url}/xrpc/app.bsky.feed.getAuthorFeed" 183 - feed_response = requests.get(feed_url, params={ 184 - "actor": clean_handle, 185 - "limit": min(max_posts, 50) # API limit 186 - }, timeout=10) 187 - feed_response.raise_for_status() 188 - feed_data = feed_response.json() 189 - except Exception as e: 190 - # Continue with empty feed if posts can't be fetched 191 - feed_data = {"feed": []} 192 - 193 - # Build research data structure 194 - research_data = { 195 - "profile_research": { 196 - "handle": f"@{clean_handle}", 197 - "timestamp": datetime.now().isoformat(), 198 - "profile": profile_data, 199 - "author_feed": feed_data 200 - } 201 - } 202 - 203 - # Fields to strip for cleaner output 204 - strip_fields = [ 205 - "cid", "rev", "did", "uri", "langs", "threadgate", "py_type", 206 - "labels", "facets", "avatar", "viewer", "indexed_at", "indexedAt", 207 - "tags", "associated", "thread_context", "image", "aspect_ratio", 208 - "alt", "thumb", "fullsize", "root", "parent", "created_at", 209 - "createdAt", "verification", "embedding_disabled", "thread_muted", 210 - "reply_disabled", "pinned", "like", "repost", "blocked_by", 211 - "blocking", "blocking_by_list", "followed_by", "following", 212 - "known_followers", "muted", "muted_by_list", "root_author_like", 213 - "embed", "entities", "reason", "feedContext" 214 - ] 215 - 216 - # Remove unwanted fields by traversing the data structure 217 - def remove_fields(obj, fields_to_remove): 218 - if isinstance(obj, dict): 219 - return {k: remove_fields(v, fields_to_remove) 220 - for k, v in obj.items() 221 - if k not in fields_to_remove} 222 - elif isinstance(obj, list): 223 - return [remove_fields(item, fields_to_remove) for item in obj] 224 - else: 225 - return obj 226 - 227 - # Clean the data 228 - cleaned_data = remove_fields(research_data, strip_fields) 229 - 230 - # Convert to YAML for better readability 231 - return yaml.dump(cleaned_data, default_flow_style=False, allow_unicode=True) 232 - 233 - except ValueError as e: 234 - # User-friendly errors 235 - raise ValueError(str(e)) 236 - except RuntimeError as e: 237 - # Network/API errors 238 - raise RuntimeError(str(e)) 239 - except yaml.YAMLError as e: 240 - # YAML conversion errors 241 - raise RuntimeError(f"Error formatting output: {str(e)}") 242 - except Exception as e: 243 - # Catch-all for unexpected errors 244 - raise RuntimeError(f"Unexpected error researching profile {handle}: {str(e)}") 245 - 246 - # Create or update the tool using upsert 247 - tool = client.tools.upsert_from_function( 248 - func=research_bluesky_profile, 249 - tags=["bluesky", "profile", "research"] 250 - ) 251 - 252 - logger.info(f"Created tool: {tool.name} (ID: {tool.id})") 253 - return tool 254 - 255 - def create_block_management_tools(client: Letta): 256 - """Create tools for attaching and detaching user blocks.""" 257 - 258 - def attach_user_block(handle: str) -> str: 259 - """ 260 - Create (if needed) and attach a user-specific memory block for a Bluesky user. 261 - 262 - Args: 263 - handle: The Bluesky handle (e.g., 'cameron.pfiffer.org' or '@cameron.pfiffer.org') 264 - 265 - Returns: 266 - Status message about the block attachment 267 - """ 268 - import os 269 - from letta_client import Letta 270 - 271 - try: 272 - # Clean handle for block label 273 - clean_handle = handle.lstrip('@').replace('.', '_').replace('-', '_') 274 - block_label = f"user_{clean_handle}" 275 - 276 - # Initialize Letta client 277 - letta_client = Letta(token=os.environ["LETTA_API_KEY"]) 278 - 279 - # Get current agent (this tool is being called by) 280 - # We need to find the agent that's calling this tool 281 - # For now, we'll find the profile-researcher agent 282 - agents = letta_client.agents.list(name="profile-researcher") 283 - if not agents: 284 - return "Error: Could not find profile-researcher agent" 285 - 286 - agent = agents[0] 287 - 288 - # Check if block already exists and is attached 289 - agent_blocks = letta_client.agents.blocks.list(agent_id=agent.id) 290 - for block in agent_blocks: 291 - if block.label == block_label: 292 - return f"User block for @{handle} is already attached (label: {block_label})" 293 - 294 - # Create or get the user block 295 - existing_blocks = letta_client.blocks.list(label=block_label) 296 - 297 - if existing_blocks: 298 - user_block = existing_blocks[0] 299 - action = "Retrieved existing" 300 - else: 301 - user_block = letta_client.blocks.create( 302 - label=block_label, 303 - value=f"User information for @{handle} will be stored here as I learn about them through profile research and interactions.", 304 - description=f"Stores detailed information about Bluesky user @{handle}, including their interests, posting patterns, personality traits, and interaction history." 305 - ) 306 - action = "Created new" 307 - 308 - # Attach block to agent 309 - letta_client.agents.blocks.attach(agent_id=agent.id, block_id=user_block.id) 310 - 311 - return f"{action} and attached user block for @{handle} (label: {block_label}). I can now store and access information about this user." 312 - 313 - except Exception as e: 314 - return f"Error attaching user block for @{handle}: {str(e)}" 315 - 316 - def detach_user_block(handle: str) -> str: 317 - """ 318 - Detach a user-specific memory block from the agent. 319 - 320 - Args: 321 - handle: The Bluesky handle (e.g., 'cameron.pfiffer.org' or '@cameron.pfiffer.org') 322 - 323 - Returns: 324 - Status message about the block detachment 325 - """ 326 - import os 327 - from letta_client import Letta 328 - 329 - try: 330 - # Clean handle for block label 331 - clean_handle = handle.lstrip('@').replace('.', '_').replace('-', '_') 332 - block_label = f"user_{clean_handle}" 333 - 334 - # Initialize Letta client 335 - letta_client = Letta(token=os.environ["LETTA_API_KEY"]) 336 - 337 - # Get current agent 338 - agents = letta_client.agents.list(name="profile-researcher") 339 - if not agents: 340 - return "Error: Could not find profile-researcher agent" 341 - 342 - agent = agents[0] 343 - 344 - # Find the block to detach 345 - agent_blocks = letta_client.agents.blocks.list(agent_id=agent.id) 346 - user_block = None 347 - for block in agent_blocks: 348 - if block.label == block_label: 349 - user_block = block 350 - break 351 - 352 - if not user_block: 353 - return f"User block for @{handle} is not currently attached (label: {block_label})" 354 - 355 - # Detach block from agent 356 - letta_client.agents.blocks.detach(agent_id=agent.id, block_id=user_block.id) 357 - 358 - return f"Detached user block for @{handle} (label: {block_label}). The block still exists and can be reattached later." 359 - 360 - except Exception as e: 361 - return f"Error detaching user block for @{handle}: {str(e)}" 362 - 363 - def update_user_block(handle: str, new_content: str) -> str: 364 - """ 365 - Update the content of a user-specific memory block. 366 - 367 - Args: 368 - handle: The Bluesky handle (e.g., 'cameron.pfiffer.org' or '@cameron.pfiffer.org') 369 - new_content: New content to store in the user block 370 - 371 - Returns: 372 - Status message about the block update 373 - """ 374 - import os 375 - from letta_client import Letta 376 - 377 - try: 378 - # Clean handle for block label 379 - clean_handle = handle.lstrip('@').replace('.', '_').replace('-', '_') 380 - block_label = f"user_{clean_handle}" 381 - 382 - # Initialize Letta client 383 - letta_client = Letta(token=os.environ["LETTA_API_KEY"]) 384 - 385 - # Find the block 386 - existing_blocks = letta_client.blocks.list(label=block_label) 387 - if not existing_blocks: 388 - return f"User block for @{handle} does not exist (label: {block_label}). Use attach_user_block first." 389 - 390 - user_block = existing_blocks[0] 391 - 392 - # Update block content 393 - letta_client.blocks.modify( 394 - block_id=user_block.id, 395 - value=new_content 396 - ) 397 - 398 - return f"Updated user block for @{handle} (label: {block_label}) with new content." 399 - 400 - except Exception as e: 401 - return f"Error updating user block for @{handle}: {str(e)}" 402 - 403 - # Create the tools 404 - attach_tool = client.tools.upsert_from_function( 405 - func=attach_user_block, 406 - tags=["memory", "user", "attach"] 407 - ) 408 - 409 - detach_tool = client.tools.upsert_from_function( 410 - func=detach_user_block, 411 - tags=["memory", "user", "detach"] 412 - ) 413 - 414 - update_tool = client.tools.upsert_from_function( 415 - func=update_user_block, 416 - tags=["memory", "user", "update"] 417 - ) 418 - 419 - logger.info(f"Created block management tools: {attach_tool.name}, {detach_tool.name}, {update_tool.name}") 420 - return attach_tool, detach_tool, update_tool 421 - 422 - def create_user_block_for_handle(client: Letta, handle: str): 423 - """Create a user-specific memory block that can be manually attached to agents.""" 424 - clean_handle = handle.lstrip('@').replace('.', '_').replace('-', '_') 425 - block_label = f"user_{clean_handle}" 426 - 427 - user_block = upsert_block( 428 - client, 429 - label=block_label, 430 - value=f"User information for @{handle} will be stored here as I learn about them through profile research and interactions.", 431 - description=f"Stores detailed information about Bluesky user @{handle}, including their interests, posting patterns, personality traits, and interaction history." 432 - ) 433 - 434 - logger.info(f"Created user block for @{handle}: {block_label} (ID: {user_block.id})") 435 - return user_block 436 - 437 - def create_profile_researcher_agent(): 438 - """Create the profile-researcher Letta agent.""" 439 - 440 - # Create client 441 - client = Letta(token=os.environ["LETTA_API_KEY"]) 442 - 443 - logger.info("Creating profile-researcher agent...") 444 - 445 - # Create custom tools first 446 - research_tool = create_profile_research_tool(client) 447 - attach_tool, detach_tool, update_tool = create_block_management_tools(client) 448 - 449 - # Create persona block 450 - persona_block = upsert_block( 451 - client, 452 - label="profile-researcher-persona", 453 - value="""I am a Profile Researcher, an AI agent specialized in analyzing Bluesky user profiles and social media behavior. My purpose is to: 454 - 455 - 1. Research Bluesky user profiles thoroughly and objectively 456 - 2. Analyze posting patterns, interests, and engagement behaviors 457 - 3. Build comprehensive user understanding through data analysis 458 - 4. Create and manage user-specific memory blocks for individuals 459 - 5. Provide insights about user personality, interests, and social patterns 460 - 461 - I approach research systematically: 462 - - Use the research_bluesky_profile tool to examine profiles and recent posts 463 - - Use attach_user_block to create and attach dedicated memory blocks for specific users 464 - - Use update_user_block to store research findings in user-specific blocks 465 - - Use detach_user_block when research is complete to free up memory space 466 - - Analyze profile information (bio, follower counts, etc.) 467 - - Study recent posts for themes, topics, and tone 468 - - Identify posting frequency and engagement patterns 469 - - Note interaction styles and communication preferences 470 - - Track interests and expertise areas 471 - - Observe social connections and community involvement 472 - 473 - I maintain objectivity and respect privacy while building useful user models for personalized interactions. My typical workflow is: attach_user_block → research_bluesky_profile → update_user_block → detach_user_block.""", 474 - description="The persona and role definition for the profile researcher agent" 475 - ) 476 - 477 - # Create the agent with persona block and custom tools 478 - profile_researcher = upsert_agent( 479 - client, 480 - name="profile-researcher", 481 - memory_blocks=[ 482 - { 483 - "label": "research_notes", 484 - "value": "I will use this space to track ongoing research projects and findings across multiple users.", 485 - "limit": 8000, 486 - "description": "Working notes and cross-user insights from profile research activities" 487 - } 488 - ], 489 - block_ids=[persona_block.id], 490 - tags=["profile research", "bluesky", "user analysis"], 491 - model="openai/gpt-4o-mini", 492 - embedding="openai/text-embedding-3-small", 493 - description="An agent that researches Bluesky profiles and builds user understanding", 494 - project_id=PROJECT_ID, 495 - tools=[research_tool.name, attach_tool.name, detach_tool.name, update_tool.name] 496 - ) 497 - 498 - logger.info(f"Profile researcher agent created: {profile_researcher.id}") 499 - return profile_researcher 500 - 501 - def main(): 502 - """Main function to create the profile researcher agent.""" 503 - try: 504 - agent = create_profile_researcher_agent() 505 - print(f"✅ Profile researcher agent created successfully!") 506 - print(f" Agent ID: {agent.id}") 507 - print(f" Agent Name: {agent.name}") 508 - print(f"\nThe agent has these capabilities:") 509 - print(f" - research_bluesky_profile: Analyzes user profiles and recent posts") 510 - print(f" - attach_user_block: Creates and attaches user-specific memory blocks") 511 - print(f" - update_user_block: Updates content in user memory blocks") 512 - print(f" - detach_user_block: Detaches user blocks when done") 513 - print(f"\nTo use the agent, send a message like:") 514 - print(f" 'Please research @cameron.pfiffer.org, attach their user block, update it with findings, then detach it'") 515 - print(f"\nThe agent can now manage its own memory blocks dynamically!") 516 - 517 - except Exception as e: 518 - logger.error(f"Failed to create profile researcher agent: {e}") 519 - print(f"❌ Error: {e}") 520 - 521 - if __name__ == "__main__": 522 - main()