ai-agent-builder

name ai-agent-builder description Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns version 1.0.0 author claude-office-skills license MIT category ai tags ["ai-agent","chatgpt","openai","langchain","automation"] department Engineering models {"recommended":["claude-opus-4","claude-sonnet-4"]} capabilities ["agent_design","tool_integration","memory_management","multi_step_reasoning","conversation_flow"] languages ["en","zh"] related_skills ["deep-research","n8n-workflow","slack-workflows"] AI Agent Builder Design and build AI agents with tools, memory, and multi-step reasoning capabilities. Covers ChatGPT, Claude, Gemini integration patterns based on n8n's 5,000+ AI workflow templates. Overview This skill covers: AI agent architecture design Tool/function calling patterns Memory and context management Multi-step reasoning workflows Platform integrations (Slack, Telegram, Web) AI Agent Architecture Core Components ┌─────────────────────────────────────────────────────────────────┐ │ AI AGENT ARCHITECTURE │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Input │────▶│ Agent │────▶│ Output │ │ │ │ (Query) │ │ (LLM) │ │ (Response) │ │ │ └─────────────┘ └──────┬──────┘ └─────────────┘ │ │ │ │ │ ┌───────────────────┼───────────────────┐ │ │ │ │ │ │ │ ▼ ▼ ▼ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Tools │ │ Memory │ │ Knowledge │ │ │ │ (Functions) │ │ (Context) │ │ (RAG) │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────┘ Agent Types agent_types: reactive_agent: description: "Single-turn response, no memory" use_case: simple_qa, classification complexity: low conversational_agent: description: "Multi-turn with conversation memory" use_case: chatbots, support complexity: medium tool_using_agent: description: "Can call external tools/APIs" use_case: data_lookup, actions complexity: medium reasoning_agent: description: "Multi-step planning and execution" use_case: complex_tasks, research complexity: high multi_agent: description: "Multiple specialized agents collaborating" use_case: complex_workflows complexity: very_high Tool Calling Pattern Tool Definition tool_definition: name: "get_weather" description: "Get current weather for a location" parameters: type: object properties: location: type: string description: "City name or coordinates" units: type: string enum: [ "celsius" , "fahrenheit" ] default: "celsius" required: [ "location" ] implementation: type: api_call endpoint: "https://api.weather.com/v1/current" method: GET params: q: "{location}" units: "{units}" Common Tool Categories tool_categories: data_retrieval: - web_search: search the internet - database_query: query SQL/NoSQL - api_lookup: call external APIs - file_read: read documents actions: - send_email: send emails - create_calendar: schedule events - update_crm: modify CRM records - post_slack: send Slack messages computation: - calculator: math operations - code_interpreter: run Python - data_analysis: analyze datasets generation: - image_generation: create images - document_creation: generate docs - chart_creation: create visualizations n8n Tool Integration n8n_agent_workflow: nodes: - trigger: type: webhook path: "/ai-agent" - ai_agent: type: "@n8n/n8n-nodes-langchain.agent" model: openai_gpt4 system_prompt: | You are a helpful assistant that can: 1. Search the web for information 2. Query our customer database 3. Send emails on behalf of the user tools: - web_search - database_query - send_email - respond: type: respond_to_webhook data: " {{ $json.output }} " Memory Patterns Memory Types memory_types: buffer_memory: description: "Store last N messages" implementation: | messages = [] def add_message(role, content): messages.append({"role": role, "content": content}) if len(messages) > MAX_MESSAGES: messages.pop(0) use_case: simple_chatbots summary_memory: description: "Summarize conversation periodically" implementation: | When messages > threshold: summary = llm.summarize(messages[:-5]) messages = [summary_message] + messages[-5:] use_case: long_conversations vector_memory: description: "Store in vector DB for semantic retrieval" implementation: | # Store embedding = embed(message) vector_db.insert(embedding, message) # Retrieve relevant = vector_db.search(query_embedding, k=5) use_case: knowledge_retrieval entity_memory: description: "Track entities mentioned in conversation" implementation: | entities = {} def update_entities(message): extracted = llm.extract_entities(message) entities.update(extracted) use_case: personalized_assistants Context Window Management context_management: strategies: sliding_window: keep: last_n_messages n: 10 relevance_based: method: embed_and_rank keep: top_k_relevant k: 5 hierarchical: levels: - immediate: last_3_messages - recent: summary_of_last_10 - long_term: key_facts_from_all token_budget: total: 8000 system_prompt: 1000 tools: 1000 memory: 4000 current_query: 1000 response: 1000 Multi-Step Reasoning ReAct Pattern Thought: I need to find information about X Action: web_search("X") Observation: [search results] Thought: Based on the results, I should also check Y Action: database_query("SELECT * FROM Y") Observation: [database results] Thought: Now I have enough information to answer Action: respond("Final answer based on X and Y") Planning Agent planning_workflow: step_1_plan: prompt: | Task: {user_request} Create a step-by-step plan to complete this task. Each step should be specific and actionable. output: numbered_steps step_2_execute: for_each: step actions: - execute_step - validate_result - adjust_if_needed step_3_synthesize: prompt: | Steps completed: {executed_steps} Results: {results} Synthesize a final response for the user. Platform Integrations Slack Bot Agent slack_agent: trigger: slack_message workflow: 1. receive_message: extract: [ user , channel , text , thread_ts ] 2. get_context: if: thread_ts action: fetch_thread_history 3. process_with_agent: model: gpt-4 system: "You are a helpful Slack assistant" tools: [ web_search , jira_lookup , calendar_check ] 4. respond: action: post_to_slack channel: "{channel}" thread_ts: "{thread_ts}" text: "{agent_response}" Telegram Bot Agent telegram_agent: trigger: telegram_message handlers: text_message: - extract_text - process_with_ai - send_response voice_message: - transcribe_with_whisper - process_with_ai - send_text_or_voice_response image: - analyze_with_vision - process_with_ai - send_response document: - extract_content - process_with_ai - send_response Web Chat Interface web_chat_agent: frontend: type: react_component features: - message_input - message_history - typing_indicator - file_upload backend: endpoint: "/api/chat" method: POST streaming: true session_management: method: jwt_token storage: redis ttl: 24_hours Agent Templates Customer Support Agent support_agent: name: "Customer Support AI" model: gpt-4 system_prompt: | You are a customer support agent for {company}. Guidelines: - Be helpful, professional, and empathetic - Use the knowledge base to answer questions - Escalate to human if you cannot help - Never make up information Available actions: - Search knowledge base - Look up customer account - Create support ticket - Escalate to human agent tools: - knowledge_search: description: "Search help articles" - customer_lookup: description: "Get customer account info" - create_ticket: description: "Create support ticket" - escalate: description: "Transfer to human agent" escalation_triggers: - sentiment: very_negative - keywords: [ "speak to human" , "manager" , "complaint" ] - failed_resolution: after_3_attempts Research Agent research_agent: name: "Research Assistant" model: claude-opus system_prompt: | You are a research assistant that helps users find and synthesize information on any topic. Process: 1 . Understand the research question 2 . Plan search strategy 3 . Execute searches across multiple sources 4 . Synthesize findings 5 . Present with citations tools: - web_search - academic_search - news_search - wikipedia_lookup output_format: - executive_summary - key_findings - detailed_analysis - sources_cited Output Example Request : "Design an AI agent for appointment scheduling" Output : # Appointment Scheduling Agent Design ## Architecture ```yaml agent: name: "Scheduling Assistant" model: gpt-4 capabilities: - Check calendar availability - Book appointments - Send confirmations - Handle rescheduling - Answer FAQs Tools Required calendar_check Input: date_range, duration Output: available_slots[] book_appointment Input: datetime, customer_info, service_type Output: confirmation_id send_notification Input: type, recipient, details Output: success/failure customer_lookup Input: email or phone Output: customer_record Conversation Flow User: "I'd like to book an appointment" Agent: [Thought: Need to know service type and preferred time] Agent: "I'd be happy to help! What service are you looking for, and do you have a preferred date/time?" User: "Haircut, next Tuesday afternoon" Agent: [Action: calendar_check(date="next Tuesday", time="afternoon")] Agent: "I have availability at 2pm, 3pm, and 4:30pm next Tuesday. Which works best for you?" User: "3pm please" Agent: [Action: book_appointment(datetime="Tuesday 3pm", service="haircut")] Agent: [Action: send_notification(type="confirmation", ...)] Agent: "You're all set for a haircut at 3pm next Tuesday. I've sent a confirmation to your email. See you then!" n8n Implementation workflow: - webhook_trigger: /schedule-chat - ai_agent: tools: [ calendar , booking , notification ] - respond_to_user --- *AI Agent Builder Skill - Part of Claude Office Skills*