Documentation Index
Fetch the complete documentation index at: https://docs.adenhq.com/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Nodes are the building blocks of Aden agents. Each node type serves a specific purpose in the agent graph.
| Node Type | Purpose | When to Use |
|---|
| LLM | Language model inference | Text generation, classification, extraction |
| Router | Control flow decisions | Branching logic, conditional paths |
| Function | Custom code execution | API calls, data transformation, business logic |
| Human | Human intervention | Approvals, complex decisions, quality checks |
LLM Node
Executes a language model call with structured inputs and outputs.
Configuration
{
"id": "analyze_sentiment",
"type": "llm",
"model": "claude-sonnet-4-5-20250929",
"prompt": "Analyze the sentiment of this text: {{text}}",
"system": "You are a sentiment analysis expert.",
"output_schema": {
"type": "object",
"properties": {
"sentiment": {"type": "string", "enum": ["positive", "negative", "neutral"]},
"confidence": {"type": "number", "minimum": 0, "maximum": 1}
},
"required": ["sentiment", "confidence"]
},
"temperature": 0.3,
"max_tokens": 500
}
Parameters
| Parameter | Type | Required | Description |
|---|
model | string | Yes | LLM model identifier |
prompt | string | Yes | Prompt template with {{variable}} placeholders |
system | string | No | System message for the LLM |
output_schema | object | No | JSON schema for structured output |
temperature | number | No | Sampling temperature (0-2) |
max_tokens | number | No | Maximum tokens to generate |
tools | array | No | Tools the LLM can call |
{
"id": "research_agent",
"type": "llm",
"model": "claude-sonnet-4-5-20250929",
"prompt": "Research this topic: {{topic}}",
"tools": [
{"name": "web_search", "description": "Search the web"},
{"name": "read_file", "description": "Read a file from disk"}
]
}
Router Node
Routes execution flow based on conditions or LLM decisions.
Conditional Routing
Route based on output values from previous nodes:
{
"id": "priority_router",
"type": "router",
"conditions": [
{"path": "urgent_handler", "when": "priority == 'high'"},
{"path": "normal_handler", "when": "priority == 'medium'"},
{"path": "low_priority_queue", "when": "priority == 'low'"},
{"path": "default_handler", "default": true}
]
}
LLM-Decided Routing
Let the LLM choose the path:
{
"id": "intent_router",
"type": "router",
"strategy": "llm",
"model": "claude-haiku-4-5-20251001",
"prompt": "Which handler should process this request? {{request}}",
"paths": [
{"name": "billing", "description": "Billing and payment questions"},
{"name": "technical", "description": "Technical support issues"},
{"name": "sales", "description": "Sales and pricing inquiries"}
]
}
Weighted Routing
Probabilistic routing for A/B testing:
{
"id": "ab_router",
"type": "router",
"strategy": "weighted",
"weights": [
{"path": "variant_a", "weight": 0.8},
{"path": "variant_b", "weight": 0.2}
]
}
Function Node
Executes custom Python code.
Basic Function
{
"id": "send_email",
"type": "function",
"handler": "tools.email.send",
"inputs": ["recipient", "subject", "body"],
"timeout": "30s"
}
Implementation
# tools/email.py
async def send(ctx, recipient: str, subject: str, body: str):
"""Send an email via the configured SMTP server."""
smtp = ctx.tools.get("smtp_client")
result = await smtp.send(
to=recipient,
subject=subject,
body=body
)
return {"sent": True, "message_id": result.id}
Parameters
| Parameter | Type | Required | Description |
|---|
handler | string | Yes | Python function path |
inputs | array | No | Required input fields |
timeout | string | No | Execution timeout |
retry | object | No | Retry configuration |
Human Node
Pauses execution for human input.
Basic Configuration
{
"id": "manager_approval",
"type": "human",
"prompt": "Approve refund of ${{amount}} for {{customer}}?",
"options": ["approve", "reject", "escalate"],
"timeout": "24h",
"escalation": "auto_reject"
}
Parameters
| Parameter | Type | Required | Description |
|---|
prompt | string | Yes | Question/instructions for the human |
options | array | No | Predefined response options |
timeout | string | No | Time limit for human response |
escalation | string | No | Action on timeout: auto_approve, auto_reject, escalate |
assignee | string | No | Specific user/role to notify |
Node Context
All nodes receive a context object:
class NodeContext:
# Input from previous node
input: dict
# Shared memory store
memory: MemoryStore
# LLM client
llm: LLMClient
# Tool registry
tools: ToolRegistry
# Run metadata
metadata: RunMetadata
Using Context in Functions
async def my_function(ctx):
# Read from memory
user_data = await ctx.memory.get("user_profile")
# Call LLM
response = await ctx.llm.complete(
model="claude-haiku-4-5-20251001",
prompt="Summarize: " + ctx.input["text"]
)
# Use tools
result = await ctx.tools.call("web_search", query="...")
# Write to memory
await ctx.memory.set("summary", response.text)
return {"summary": response.text}
Next Steps
Edge Configuration
Connect nodes with success, failure, and conditional edges
Human-in-the-Loop
Configure human intervention patterns
