Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.rotavision.com/llms.txt

Use this file to discover all available pages before exploring further.

​
Overview

Orchestrate enables building sophisticated AI workflows that coordinate multiple specialized agents. This guide covers workflow design patterns and best practices.

​
Core Concepts

​
Agents

Agents are specialized workers that perform specific tasks:
TypeUse Case
LLM AgentNatural language reasoning, generation
Code AgentExecute Python/JavaScript
Search AgentWeb, document, or database search
Tool AgentCall external APIs
Human AgentHuman-in-the-loop approval

​
Workflows

Workflows define how agents collaborate: 
workflow = {
    "agents": [...],  # Agent definitions
    "steps": [...],   # Execution sequence
    "config": {...}   # Settings
}

​
Building Your First Workflow

​
Research Assistant Example

from rotavision import Rotavision

client = Rotavision()

workflow = client.orchestrate.create_workflow(
    name="Research Assistant",
    agents=[
        {
            "id": "planner",
            "type": "llm",
            "model": "gpt-5-mini",
            "system_prompt": """You are a research planner. Given a topic,
            output a JSON list of 3-5 specific search queries to research it thoroughly."""
        },
        {
            "id": "searcher",
            "type": "search",
            "sources": ["web", "news", "academic"]
        },
        {
            "id": "synthesizer",
            "type": "llm",
            "model": "claude-4.5-sonnet",
            "system_prompt": """You are a research synthesizer. Given search results,
            create a comprehensive summary with citations."""
        }
    ],
    steps=[
        {
            "id": "plan",
            "agent": "planner",
            "action": "generate",
            "input": "Research topic: {{topic}}"
        },
        {
            "id": "search",
            "agent": "searcher",
            "action": "search",
            "input": "{{plan.queries}}"
        },
        {
            "id": "synthesize",
            "agent": "synthesizer",
            "action": "generate",
            "input": "Topic: {{topic}}\n\nSearch Results:\n{{search.results}}"
        }
    ]
)

# Run the workflow
execution = client.orchestrate.run_workflow(
    workflow_id=workflow.id,
    inputs={"topic": "Electric vehicle adoption in India 2026"}
)

# Wait for completion
result = client.orchestrate.wait_for_execution(execution.id)
print(result.outputs["synthesize"])

​
Workflow Patterns

​
Sequential Processing

Steps execute one after another: 
steps = [
    {"agent": "classifier", "action": "classify", "input": "{{text}}"},
    {"agent": "processor", "action": "process", "input": "{{classifier.output}}"},
    {"agent": "formatter", "action": "format", "input": "{{processor.output}}"}
]

​
Parallel Processing

Steps execute simultaneously: 
steps = [
    {
        "parallel": [
            {"agent": "web_search", "action": "search", "input": "{{query}}"},
            {"agent": "news_search", "action": "search", "input": "{{query}}"},
            {"agent": "academic_search", "action": "search", "input": "{{query}}"}
        ]
    },
    {
        "agent": "aggregator",
        "action": "aggregate",
        "input": "{{web_search.results}}\n{{news_search.results}}\n{{academic_search.results}}"
    }
]

​
Conditional Execution

Branch based on results: 
steps = [
    {"agent": "classifier", "action": "classify", "input": "{{ticket}}"},
    {
        "condition": "{{classifier.priority}} == 'high'",
        "then": [
            {"agent": "escalation", "action": "notify", "input": "High priority: {{ticket}}"}
        ],
        "else": [
            {"agent": "auto_responder", "action": "respond", "input": "{{ticket}}"}
        ]
    }
]

​
Loop/Iteration

Process items iteratively: 
steps = [
    {"agent": "splitter", "action": "split", "input": "{{document}}"},
    {
        "loop": {
            "items": "{{splitter.chunks}}",
            "as": "chunk",
            "steps": [
                {"agent": "processor", "action": "process", "input": "{{chunk}}"}
            ]
        }
    },
    {"agent": "combiner", "action": "combine", "input": "{{loop.results}}"}
]

​
Human-in-the-Loop

Add approval gates for sensitive operations: 
workflow = client.orchestrate.create_workflow(
    name="Content Moderation",
    agents=[
        {
            "id": "moderator",
            "type": "llm",
            "model": "gpt-5-mini",
            "system_prompt": "Analyze content for policy violations..."
        },
        {
            "id": "human_review",
            "type": "human",
            "assignees": ["moderation-team@company.com"],
            "timeout_hours": 24
        },
        {
            "id": "action_taker",
            "type": "tool",
            "tools": ["content_api"]
        }
    ],
    steps=[
        {"agent": "moderator", "action": "analyze", "input": "{{content}}"},
        {
            "condition": "{{moderator.requires_human_review}}",
            "then": [
                {
                    "agent": "human_review",
                    "action": "approve",
                    "input": "Review needed for: {{content}}\nAI Assessment: {{moderator.assessment}}"
                }
            ]
        },
        {
            "condition": "{{human_review.approved}} or not {{moderator.requires_human_review}}",
            "then": [
                {"agent": "action_taker", "action": "publish", "input": "{{content}}"}
            ],
            "else": [
                {"agent": "action_taker", "action": "reject", "input": "{{content}}"}
            ]
        }
    ]
)

​
Error Handling

​
Retry Configuration

workflow = client.orchestrate.create_workflow(
    name="Robust Workflow",
    config={
        "max_retries": 3,
        "retry_delay_ms": 1000,
        "on_error": "retry"  # or "stop", "continue"
    },
    steps=[...]
)

​
Per-Step Error Handling

steps = [
    {
        "agent": "api_caller",
        "action": "call",
        "input": "{{data}}",
        "on_error": {
            "retry": 3,
            "fallback": {
                "agent": "fallback_handler",
                "action": "handle",
                "input": "Error: {{error}}"
            }
        }
    }
]

​
Cost Controls

​
Budget Limits

workflow = client.orchestrate.create_workflow(
    name="Controlled Workflow",
    config={
        "budget_usd": 1.00,  # Max $1 per execution
        "on_budget_exceeded": "stop"
    },
    steps=[...]
)

​
Token Limits

agents = [
    {
        "id": "writer",
        "type": "llm",
        "model": "claude-4.5-sonnet",
        "max_tokens": 2000  # Limit per call
    }
]

​
Monitoring Executions

# Get execution status
execution = client.orchestrate.get_execution("exec_xyz789")

print(f"Status: {execution.status}")
print(f"Current step: {execution.current_step}")
print(f"Progress: {execution.progress.completed}/{execution.progress.total}")

# View step-by-step results
for step in execution.steps:
    print(f"\n{step.id}: {step.status}")
    if step.output:
        print(f"  Output: {step.output[:200]}...")
    if step.error:
        print(f"  Error: {step.error}")

​
Best Practices

Each agent should do one thing well. Instead of one mega-agent, use multiple specialized agents.
  • Fast/cheap models (GPT-5-mini) for classification, routing
  • Powerful models (Claude 4.5 Sonnet) for synthesis, writing
  • Code agents for deterministic operations
For anything that could cause harm or significant business impact, add human approval.
Always set budget limits to prevent runaway costs from infinite loops or unexpected usage.

​
Next Steps

Orchestrate API Reference

Full API documentation

Sankalp LLM Gateway

Route LLM requests efficiently