POST /api/v1/tasks

Endpoint

POST http://localhost:8080/api/v1/tasks

Description

Submits a new task to Shannon for execution. The task is queued immediately and processed asynchronously by the Temporal workflow engine.

Authentication

Required: Yes Include API key in header:

X-API-Key: sk_test_123456

Request

Headers

Header	Required	Description	Example
`X-API-Key`	Yes	Authentication key	`sk_test_123456`
`Content-Type`	Yes	Must be `application/json`	`application/json`
`Idempotency-Key`	No	Unique key for idempotency	`550e8400-e29b-41d4-a716-446655440000`
`traceparent`	No	W3C trace context	`00-4bf92f...`

Body Parameters

Parameter	Type	Required	Description
`query`	string	Yes	Natural language task description
`session_id`	string	No	Session identifier for multi-turn conversations
`context`	object	No	Additional context data as key-value pairs
`mode`	string	No	Workflow routing: `simple`, `standard`, `complex`, or `supervisor`
`model_tier`	string	No	Preferred tier: `small`, `medium`, or `large`
`model_override`	string	No	Specific model name (canonical; e.g., `gpt-5`, `claude-sonnet-4-5-20250929`)
`provider_override`	string	No	Force provider (e.g., `openai`, `anthropic`, `google`)
`skill`	string	No	Markdown-defined skill to drive single-agent execution (Skills System)
`research_strategy`	string	No	Research strategy preset: `quick`, `standard`, `deep`, or `academic`
`max_iterations`	integer	No	Maximum number of iterations for the task
`max_concurrent_agents`	integer	No	Maximum number of concurrent agents
`enable_verification`	boolean	No	Enable verification step after task completion

Request Body Schema

Example 1: General AI-powered execution

{
  "query": "Analyze August website traffic trends",              // REQUIRED: Task to execute
  "session_id": "analytics-session-123",                        // OPTIONAL: Session ID for multi-turn conversations (auto-generated if omitted)
  "mode": "supervisor",                                         // OPTIONAL: Workflow routing - "simple", "standard", "complex", or "supervisor" (default: auto-detect)
  "model_tier": "large",                                        // OPTIONAL: Model size - "small", "medium", or "large" (default: "small")
  "model_override": "gpt-5",                                   // OPTIONAL: Specific model (canonical id)
  "provider_override": "openai",                                // OPTIONAL: Force specific provider
  "context": {                                                  // OPTIONAL: Execution context object
    "role": "data_analytics",                                   // OPTIONAL: Role preset name (e.g., "analysis", "research", "writer")
    "system_prompt": "You are a data analyst specializing in website analytics.",              // OPTIONAL: Custom system prompt (overrides role preset)
    "prompt_params": {                                         // OPTIONAL: Arbitrary key-value pairs for prompts/tools/adapters
      "profile_id": "49598h6e",                                // EXAMPLE: Custom parameter (passed to tools/adapters)
      "aid": "fcb1cd29-9104-47b1-b914-31db6ba30c1a",          // EXAMPLE: Custom parameter (application ID)
      "current_date": "2025-10-31"                             // EXAMPLE: Custom parameter (current date)
    },
    "history_window_size": 75,                                 // OPTIONAL: Max conversation history messages (default: 50)
    "primers_count": 3,                                        // OPTIONAL: Number of early messages to keep (default: 5)
    "recents_count": 20,                                       // OPTIONAL: Number of recent messages to keep (default: 15)
    "compression_trigger_ratio": 0.75,                         // OPTIONAL: Trigger compression at 75% of window (default: 0.8)
    "compression_target_ratio": 0.375                          // OPTIONAL: Compress to 37.5% of window (default: 0.5)
  }
}

Example 2: Template-only execution (no AI)

{
  "query": "Generate weekly research briefing",                 // REQUIRED: Task description
  "session_id": "research-session-456",                        // OPTIONAL: Session ID
  "context": {                                                  // OPTIONAL: Context object
    "template": "research_summary",                            // OPTIONAL: Template name to use
    "template_version": "1.0.0",                               // OPTIONAL: Template version (default: latest)
    "disable_ai": true,                                        // OPTIONAL: Template-only mode, no AI fallback (default: false)
    "prompt_params": {                                         // OPTIONAL: Parameters for template rendering
      "week": "2025-W44"                                       // EXAMPLE: Custom parameter for template
    }
  }
}

Parameter Conflicts to Avoid:

Don’t use both template and template_name (they’re aliases - use template only)
Don’t combine disable_ai: true with model controls - Gateway returns 400 error when conflicts detected:
- disable_ai: true + model_tier → 400
- disable_ai: true + model_override → 400
- disable_ai: true + provider_override → 400
Top-level parameters override context equivalents:
- Top-level model_tier overrides context.model_tier
- Top-level model_override overrides context.model_override
- Top-level provider_override overrides context.provider_override
- Top-level skill overrides context.skill
- Top-level research_strategy overrides context.research_strategy

Context Parameters (`context.*`)

Recognized keys:

role — role preset (e.g., analysis, research, writer, ads_research, financial_news, browser_use)
system_prompt — overrides role prompt; supports ${var} from prompt_params
prompt_params — arbitrary parameters for prompts/tools/adapters
model_tier — fallback when top‑level not provided
model_override — specific model name (canonical; e.g., gpt-5, claude-sonnet-4-5-20250929)
provider_override — force provider (e.g., openai, anthropic, google)
research_strategy — (deprecated: use top-level research_strategy instead; context value is ignored when top-level is set)
skill — (deprecated: use top-level skill instead; context value is ignored when top-level is set)
template — template name (alias: template_name)
template_version — template version
disable_ai — template-only mode (no AI fallback) - cannot be combined with model controls
Window controls: history_window_size, use_case_preset, primers_count, recents_count, compression_trigger_ratio, compression_target_ratio
Deep Research 2.0 controls (when force_research: true):
- iterative_research_enabled — Enable/disable iterative coverage loop (default: true)
- iterative_max_iterations — Max iterations 1-5 (strategy presets seed defaults; otherwise falls back to 3)
- enable_fact_extraction — Extract structured facts into metadata (default: false)
Ads Research Platform Toggles (when role: "ads_research"):
- platforms.google — Enable/disable Google Shopping Ads (default: true)
- platforms.yahoo_jp — Enable/disable Yahoo Japan Ads (default: true)
- platforms.meta — Enable/disable Meta Ad Library (default: true)
- platforms.meta_platform — Meta platform filter: facebook, instagram, messenger, whatsapp, or all (default: all)

Rules:

Top-level parameters override context equivalents: model_tier, model_override, provider_override, skill, research_strategy
mode supports: simple|standard|complex|supervisor (default: auto-detect)
model_tier supports: small|medium|large
Conflict validation: disable_ai: true cannot be combined with model_tier, model_override, or provider_override (returns 400)

Role Presets

Role presets provide specialized system prompts and tool allowlists for different task types. Set via context.role:

Role	Description	Available Tools	Use Case
`generalist`	General-purpose assistant (default)	All tools	Simple queries, general chat
`analysis`	Analytical assistant with structured reasoning	`web_search`, `file_read`	Data analysis, structured reasoning
`research`	Research assistant for gathering and synthesizing information	`web_search`, `web_fetch`, `web_subpage_fetch`, `web_crawl`	Information gathering, fact-finding
`writer`	Technical writer for clear, organized prose	`file_read`	Documentation, content creation
`critic`	Critical reviewer for identifying flaws and risks	`file_read`	Code review, quality assurance
`developer`	Developer assistant with filesystem access	`file_read`, `file_write`, `file_list`, `bash`, `python_executor`	Coding, debugging, file operations
`browser_use`	Browser automation specialist	`browser_*` tools, `web_search`	Web scraping, UI automation, screenshot capture
`ads_research` (Shannon Cloud Only)	Multi-platform ads competitor analysis	`ads_serp_extract`, `ads_transparency_search`, `meta_ad_library`, `yahoo_jp_ads`	Marketing research, competitor ads analysis
`financial_news` (Shannon Cloud Only)	Financial news and sentiment analysis	`news_aggregator`, `alpaca_news`, `sec_filings`, `twitter_sentiment`	Stock news, market sentiment analysis
`data_analytics`	Data analytics with vendor integrations	Vendor-specific analytics tools	Business intelligence, analytics reporting

Shannon Cloud Only: Roles marked as “Shannon Cloud Only” are enterprise features and require a Shannon Cloud deployment with vendor adapter configuration.

Response

Success Response

Status: 200 OK Headers:

X-Workflow-ID: Temporal workflow identifier
X-Session-ID: Session identifier (auto-generated if not provided)

Body:

{
  "task_id": "string",
  "status": "string",
  "message": "string (optional)",
  "created_at": "timestamp"
}

Response Fields

Field	Type	Description
`task_id`	string	Unique task identifier (also workflow ID)
`status`	string	Submission status (e.g., `STATUS_CODE_OK`)
`message`	string	Optional status message
`created_at`	timestamp	Task creation time (ISO 8601)

Examples

Basic Task Submission

curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is the capital of France?"
  }'

Response:

{
  "task_id": "task_01HQZX3Y9K8M2P4N5S7T9W2V",
  "status": "STATUS_CODE_OK",
  "message": "Task submitted successfully",
  "created_at": "2025-10-22T10:30:00Z"
}

Task with Session ID (Multi-Turn)

# First turn
curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is Python?",
    "session_id": "user-123-chat"
  }'

# Second turn (references previous context)
curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What are its main advantages?",
    "session_id": "user-123-chat"
  }'

Task with Context

curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Summarize this user feedback",
    "context": {
      "user_id": "user_12345",
      "feedback_type": "bug_report",
      "severity": "high",
      "product": "mobile_app",
      "role": "analysis",
      "model_override": "gpt-5"
    }
  }'

Force Tier (Top‑Level)

curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Complex analysis",
    "model_tier": "large"
  }'

Template‑Only Execution

curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Weekly research briefing",
    "context": {"template": "research_summary", "template_version": "1.0.0", "disable_ai": true}
  }'

Supervisor Mode

curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Assess system reliability",
    "mode": "supervisor"
  }'

Ads Research (Shannon Cloud Only)

Multi-platform advertising competitor analysis with platform toggles.

# Analyze competitors across all platforms
curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Analyze competitor ads for organic skincare products",
    "context": {
      "role": "ads_research"
    }
  }'

# Google Shopping Ads only
curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Find competitor pricing strategies for wireless earbuds",
    "context": {
      "role": "ads_research",
      "platforms": {
        "google": true,
        "yahoo_jp": false,
        "meta": false
      }
    }
  }'

# Meta (Instagram only) + Yahoo Japan
curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Research fashion brand advertising on Instagram and Yahoo Japan",
    "context": {
      "role": "ads_research",
      "platforms": {
        "google": false,
        "yahoo_jp": true,
        "meta": true,
        "meta_platform": "instagram"
      }
    }
  }'

Platform Defaults: All platforms are enabled by default. Use platforms object to selectively disable platforms or filter Meta by platform (facebook, instagram, messenger, whatsapp, all).

Deep Research 2.0

Deep Research 2.0 provides iterative coverage improvement for comprehensive research tasks.

# Basic Deep Research (uses default settings)
curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "AI trends in 2025",
    "context": {
      "force_research": true
    }
  }'

# Custom iterations (faster, less thorough)
curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Compare major LLM providers",
    "context": {
      "force_research": true,
      "iterative_max_iterations": 2
    }
  }'

# Disable iterative mode (use legacy research)
curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Explain machine learning basics",
    "context": {
      "force_research": true,
      "iterative_research_enabled": false
    }
  }'

Deep Research 2.0 is enabled by default when force_research: true. It uses a multi-stage workflow with coverage evaluation to ensure comprehensive results. Use iterative_max_iterations to control depth (1-5, default: 3).

With Idempotency

# Generate idempotency key (use UUID)
IDEMPOTENCY_KEY=$(uuidgen)

curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "Idempotency-Key: $IDEMPOTENCY_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Analyze sales data for Q4"
  }'

With Distributed Tracing

curl -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: sk_test_123456" \
  -H "traceparent: 00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-01" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Research latest AI trends"
  }'

Error Responses

400 Bad Request

Missing Query:

{
  "error": "Query is required"
}

Invalid JSON:

{
  "error": "Invalid request body: unexpected EOF"
}

401 Unauthorized

Missing API Key:

{
  "error": "Unauthorized"
}

Invalid API Key:

{
  "error": "Unauthorized"
}

429 Too Many Requests

{
  "error": "Rate limit exceeded"
}

Headers:

X-RateLimit-Limit: 100
X-RateLimit-Remaining: 0
X-RateLimit-Reset: 1609459200
Retry-After: 60

500 Internal Server Error

{
  "error": "Failed to submit task: database connection failed"
}

Code Examples

Python with httpx

import httpx

response = httpx.post(
    "http://localhost:8080/api/v1/tasks",
    headers={
        "X-API-Key": "sk_test_123456",
        "Content-Type": "application/json"
    },
    json={
        "query": "What is the capital of France?"
    }
)

if response.status_code == 200:
    data = response.json()
    print(f"Task ID: {data['task_id']}")
    print(f"Status: {data['status']}")
else:
    print(f"Error: {response.status_code}")
    print(response.json())

Python with requests

import requests

response = requests.post(
    "http://localhost:8080/api/v1/tasks",
    headers={
        "X-API-Key": "sk_test_123456"
    },
    json={
        "query": "Analyze customer sentiment",
        "context": {
            "source": "twitter",
            "date_range": "2025-10-01 to 2025-10-22"
        }
    }
)

task = response.json()
print(f"Task submitted: {task['task_id']}")

JavaScript/Node.js

const axios = require('axios');

async function submitTask(query) {
  try {
    const response = await axios.post(
      'http://localhost:8080/api/v1/tasks',
      {
        query: query
      },
      {
        headers: {
          'X-API-Key': 'sk_test_123456',
          'Content-Type': 'application/json'
        }
      }
    );

    console.log('Task ID:', response.data.task_id);
    console.log('Status:', response.data.status);

    return response.data;
  } catch (error) {
    console.error('Error:', error.response?.data || error.message);
    throw error;
  }
}

submitTask('What is quantum computing?');

cURL with Idempotency

#!/bin/bash

API_KEY="sk_test_123456"
IDEMPOTENCY_KEY=$(uuidgen)

# Submit task
RESPONSE=$(curl -s -X POST http://localhost:8080/api/v1/tasks \
  -H "X-API-Key: $API_KEY" \
  -H "Idempotency-Key: $IDEMPOTENCY_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Analyze Q4 revenue trends"
  }')

echo $RESPONSE | jq

TASK_ID=$(echo $RESPONSE | jq -r '.task_id')
echo "Track progress: http://localhost:8088/workflows/$TASK_ID"

Go

package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "net/http"
)

type TaskRequest struct {
    Query     string                 `json:"query"`
    SessionID string                 `json:"session_id,omitempty"`
    Context   map[string]interface{} `json:"context,omitempty"`
}

type TaskResponse struct {
    TaskID    string `json:"task_id"`
    Status    string `json:"status"`
    Message   string `json:"message,omitempty"`
}

func submitTask(query string) (*TaskResponse, error) {
    req := TaskRequest{
        Query: query,
    }

    body, _ := json.Marshal(req)

    httpReq, _ := http.NewRequest(
        "POST",
        "http://localhost:8080/api/v1/tasks",
        bytes.NewBuffer(body),
    )

    httpReq.Header.Set("X-API-Key", "sk_test_123456")
    httpReq.Header.Set("Content-Type", "application/json")

    client := &http.Client{}
    resp, err := client.Do(httpReq)
    if err != nil {
        return nil, err
    }
    defer resp.Body.Close()

    var taskResp TaskResponse
    json.NewDecoder(resp.Body).Decode(&taskResp)

    return &taskResp, nil
}

func main() {
    task, err := submitTask("What is machine learning?")
    if err != nil {
        fmt.Println("Error:", err)
        return
    }

    fmt.Printf("Task ID: %s\n", task.TaskID)
    fmt.Printf("Status: %s\n", task.Status)
}

Implementation Details

Workflow Creation

When you submit a task:

Gateway receives request → Validates authentication, rate limits
Generates session ID → If not provided, auto-generates UUID
Calls Orchestrator gRPC → SubmitTask(metadata, query, context)
Orchestrator starts Temporal workflow → Durable execution
Response returned → Task ID, initial status
Task executes asynchronously → Independent of HTTP connection

Idempotency Behavior

Idempotency keys allow safe retries of task submissions without creating duplicate tasks. How it works:

First request with an Idempotency-Key:
- Shannon creates the task
- Caches the response in Redis with 24-hour TTL
- Returns task ID and status
Duplicate requests (same Idempotency-Key):
- Shannon detects the cached response
- Returns the same task ID without creating a new task
- Response is identical to the first request
After 24 hours:
- Cache expires
- New request with same key creates a new task

Cache Details:

Storage: Redis
TTL: 24 hours (86400 seconds)
Key format: idempotency:<16-char-hash> (SHA-256 of the idempotency key plus user ID, path, and request body)
Scope: Per authenticated user (user ID is part of the hash; when auth is disabled the hash is based on the header, path, and body)
Cached responses: Only 2xx responses are stored; cached hits include X-Idempotency-Cached: true and X-Idempotency-Key: <your-key>

Body Behavior: If the request body changes, the cache key changes too, so the gateway treats it as a brand-new request. Duplicate detection only triggers when the header, user, path, and body all match. Best Practice: Generate a unique key per unique request body. Example:

import uuid
import httpx

# Generate unique key
idempotency_key = str(uuid.uuid4())

# First request - creates task
response1 = httpx.post(
    "http://localhost:8080/api/v1/tasks",
    headers={"X-API-Key": "sk_test_123456", "Idempotency-Key": idempotency_key},
    json={"query": "Analyze Q4 sales"}
)
task_id_1 = response1.json()["task_id"]

# Retry with same key - returns same task ID
response2 = httpx.post(
    "http://localhost:8080/api/v1/tasks",
    headers={"X-API-Key": "sk_test_123456", "Idempotency-Key": idempotency_key},
    json={"query": "Analyze Q4 sales"}
)
task_id_2 = response2.json()["task_id"]

assert task_id_1 == task_id_2  # Same task, no duplicate

When to use:

Network retry logic (avoid duplicate tasks on timeout)
Webhook deliveries (handle duplicate webhook calls)
Critical operations (payments, data writes)
Background job queues (prevent duplicate scheduling)

Session Management

No session_id: Auto-generates UUID, fresh context
With session_id: Loads previous conversation history from Redis
Session persistence: 30 days default TTL
Multi-turn conversations: All tasks with same session_id share context

Context Object

The context object is stored as metadata and passed to:

Agent execution environment
Tool invocations (can access via ctx.get("key"))
Session memory (for reference in future turns)

Example use cases:

User preferences: {"language": "spanish", "format": "markdown"}
Business context: {"company_id": "acme", "department": "sales"}
Constraints: {"max_length": 500, "tone": "formal"}

Best Practices

1. Always Use Idempotency Keys for Critical Tasks

import uuid

idempotency_key = str(uuid.uuid4())

response = httpx.post(
    "http://localhost:8080/api/v1/tasks",
    headers={
        "X-API-Key": "sk_test_123456",
        "Idempotency-Key": idempotency_key
    },
    json={"query": "Process payment for order #12345"}
)

2. Use Sessions for Conversations

session_id = "user-456-chat"

# Turn 1
httpx.post(..., json={
    "query": "Load sales data for Q4",
    "session_id": session_id
})

# Turn 2 (references Q4 data from Turn 1)
httpx.post(..., json={
    "query": "Compare it to Q3",
    "session_id": session_id
})

3. Provide Rich Context

httpx.post(..., json={
    "query": "Analyze this customer feedback",
    "context": {
        "customer_id": "cust_789",
        "subscription_tier": "enterprise",
        "account_age_days": 365,
        "previous_tickets": 3,
        "sentiment_history": ["positive", "neutral", "negative"]
    }
})

4. Handle Errors Gracefully

try:
    response = httpx.post(..., timeout=30.0)
    response.raise_for_status()
    task = response.json()
except httpx.TimeoutException:
    print("Request timed out")
except httpx.HTTPStatusError as e:
    if e.response.status_code == 429:
        retry_after = int(e.response.headers.get("Retry-After", 60))
        time.sleep(retry_after)
        # Retry...
    else:
        print(f"Error: {e.response.json()}")

5. Store Task IDs for Tracking

response = httpx.post(...)
task_id = response.json()["task_id"]
workflow_id = response.headers["X-Workflow-ID"]

# Save to database
db.tasks.insert({
    "task_id": task_id,
    "workflow_id": workflow_id,
    "user_id": "user_123",
    "query": "...",
    "created_at": datetime.now()
})

# Later: check status
status = httpx.get(f"http://localhost:8080/api/v1/tasks/{task_id}")

Submit + Stream in One Call

Need real-time updates? Use POST /api/v1/tasks/stream instead to submit a task and get a stream URL in one call. Perfect for frontend applications that need immediate progress updates.See Unified Submit + Stream for examples.

Submit + Stream

POST /api/v1/tasks/stream (recommended for UIs)

Get Task Status

GET /api/v1/tasks/

Stream Events

Real-time task events

List Tasks

GET /api/v1/tasks

Python SDK

Use the SDK instead

Getting Started

Tasks

Streaming

Agents

Sessions

Swarm

Skills

Schedules

HITL Review

Reference

​Endpoint

​Description

​Authentication

​Request

​Headers

​Body Parameters

​Request Body Schema

​Context Parameters (context.*)

​Role Presets

​Response

​Success Response

​Response Fields

​Examples

​Basic Task Submission

​Task with Session ID (Multi-Turn)

​Task with Context

​Force Tier (Top‑Level)

​Template‑Only Execution

​Supervisor Mode

​Ads Research (Shannon Cloud Only)

​Deep Research 2.0

​With Idempotency

​With Distributed Tracing

​Error Responses

​400 Bad Request

​401 Unauthorized

​429 Too Many Requests

​500 Internal Server Error

​Code Examples

​Python with httpx

​Python with requests

​JavaScript/Node.js

​cURL with Idempotency

​Go

​Implementation Details

​Workflow Creation

​Idempotency Behavior

​Session Management

​Context Object

​Best Practices

​1. Always Use Idempotency Keys for Critical Tasks

​2. Use Sessions for Conversations

​3. Provide Rich Context

​4. Handle Errors Gracefully

​5. Store Task IDs for Tracking

​Submit + Stream in One Call

​Related Endpoints

Submit + Stream

Get Task Status

Stream Events

List Tasks

Python SDK

Endpoint

Description

Authentication

Request

Headers

Body Parameters

Request Body Schema

Context Parameters (`context.*`)

Role Presets

Response

Success Response

Response Fields

Examples

Basic Task Submission

Task with Session ID (Multi-Turn)

Task with Context

Force Tier (Top‑Level)

Template‑Only Execution

Supervisor Mode

Ads Research (Shannon Cloud Only)

Deep Research 2.0

With Idempotency

With Distributed Tracing

Error Responses

400 Bad Request

401 Unauthorized

429 Too Many Requests

500 Internal Server Error

Code Examples

Python with httpx

Python with requests

JavaScript/Node.js

cURL with Idempotency

Go

Implementation Details

Workflow Creation

Idempotency Behavior

Session Management

Context Object

Best Practices

1. Always Use Idempotency Keys for Critical Tasks

2. Use Sessions for Conversations

3. Provide Rich Context

4. Handle Errors Gracefully

5. Store Task IDs for Tracking

Submit + Stream in One Call

Related Endpoints