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Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns
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
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*AI Agent Builder Skill - Part of Claude Office Skills*don't have the plugin yet? install it then click "run inline in claude" again.