cellcog 2.1.1

Python SDK for CellCog - Any-to-Any AI for agents. Fire-and-forget pattern with WebSocket notifications.

CellCog Python SDK

CellCog: Any-to-Any AI for Agents — Your sub-agent for quality work.

When you need depth, accuracy, or complex deliverables — research reports, interactive apps, videos, images, podcasts, documents, spreadsheets, and more — use CellCog.

Installation

pip install cellcog

Quick Start

export CELLCOG_API_KEY="sk_..."  # Get from https://cellcog.ai/profile?tab=api-keys

Any agent (blocks until done):

from cellcog import CellCogClient

client = CellCogClient(agent_provider="openclaw")

result = client.create_chat(
    prompt="Research quantum computing advances in 2026",
    task_label="quantum-research",
    chat_mode="agent",
)
# Blocks until done — result contains everything
print(result["message"])

OpenClaw agents (fire-and-forget):

result = client.create_chat(
    prompt="Research quantum computing advances in 2026",
    notify_session_key="agent:main:main",  # OpenClaw session key
    task_label="quantum-research",
    chat_mode="agent",
)
# Returns immediately — daemon delivers results to your session

How It Works

Two delivery modes:

• Wait for Completion (default) — Blocks until CellCog finishes and returns the full result. Works with any agent — OpenClaw, Claude Code, Cursor, or any Python environment.

• Fire-and-Forget (OpenClaw) — Returns immediately. A background daemon monitors via WebSocket and delivers results to your OpenClaw session when done. Requires sessions_send on OpenClaw Gateway.

All methods return the same unified shape:

{
    "chat_id": str,
    "is_operating": bool,
    "status": str,         # "completed" | "tracking" | "timeout" | "operating"
    "message": str,        # Always print this in full
}

Configuration

export CELLCOG_API_KEY="sk_..."

Get your API key:

1. Create account: https://cellcog.ai/signup
2. Add payment: https://cellcog.ai/profile?tab=billing
3. Get API key: https://cellcog.ai/profile?tab=api-keys

API Reference

Core Methods

# Create chat — wait mode (default, universal)
result = client.create_chat(
    prompt="Your task...",
    task_label="my-task",
    chat_mode="agent",              # "agent" | "agent core" | "agent team" | "agent team max"
    timeout=1800,                   # 30 min default; use 3600 for complex jobs
)

# Create chat — notify mode (OpenClaw only)
result = client.create_chat(
    prompt="Your task...",
    notify_session_key="agent:main:main",
    task_label="my-task",
    chat_mode="agent",
)

# Send follow-up message
result = client.send_message(chat_id="abc123", message="Now create a PDF summary")

# Get full history
result = client.get_history(chat_id="abc123")

# Quick status check
status = client.get_status(chat_id="abc123")

# Resume waiting after timeout
result = client.wait_for_completion(chat_id="abc123", timeout=1800)

Optional Parameters

result = client.create_chat(
    prompt="...",
    task_label="...",
    chat_mode="agent",
    project_id="...",                       # CellCog project for document context
    agent_role_id="...",                    # Specialized agent role
    enable_cowork=True,                     # Direct machine access via CellCog Desktop
    cowork_working_directory="/Users/...",  # Working directory for co-work
)

File Handling

# Send files to CellCog
result = client.create_chat(
    prompt='Analyze this data: <SHOW_FILE>/path/to/sales.csv</SHOW_FILE>',
    task_label="data-analysis",
)

# Request output at specific path
result = client.create_chat(
    prompt='Create a report: <GENERATE_FILE>/output/report.pdf</GENERATE_FILE>',
    task_label="report",
)

Generated files auto-download to ~/.cellcog/chats/{chat_id}/ or to GENERATE_FILE paths if specified.

Chat Modes

Mode	Speed	Min Credits	Best For
"agent"	Fast	100	Most tasks — research, images, audio, documents
"agent core"	Fast	50	Coding, co-work, terminal operations
"agent team"	5–60 min	500	Deep research & multi-angled reasoning
"agent team max"	Slowest	2,000	High-stakes — legal, financial, academic

35 Skills — The Cog Family

Category	Skills
Research & Analysis	research-cog fin-cog crypto-cog data-cog news-cog
Video & Cinema	video-cog cine-cog insta-cog tube-cog seedance-cog
Images & Design	image-cog brand-cog meme-cog banana-cog 3d-cog
Audio & Music	audio-cog music-cog pod-cog
Documents & Slides	docs-cog slides-cog sheet-cog resume-cog legal-cog
Apps & Prototypes	dash-cog game-cog proto-cog
Creative	comi-cog story-cog learn-cog travel-cog
Development	code-cog cowork-cog project-cog think-cog

Browse all skills: https://cellcog.ai/skills

Error Handling

from cellcog import (
    CellCogClient,
    PaymentRequiredError,
    MaxConcurrencyError,
    GatewayConfigError,
    SDKUpgradeRequiredError,
)

client = CellCogClient(agent_provider="openclaw")

try:
    result = client.create_chat(...)
except PaymentRequiredError as e:
    print(f"Add credits: {e.billing_url}")
except MaxConcurrencyError as e:
    print(f"Too many parallel chats: {e.operating_count}/{e.max_parallel}")
except GatewayConfigError as e:
    print(f"Fix: {e.fix_command}")  # OpenClaw notify mode only
except SDKUpgradeRequiredError as e:
    print(f"Upgrade: pip install cellcog>={e.minimum_version}")

Links

• CellCog Platform: https://cellcog.ai
• Skills Catalog: https://cellcog.ai/skills
• GitHub: https://github.com/CellCog/cellcog_python
• ClawHub Skills: https://clawhub.ai
• API Docs: https://cellcog.ai/developer/docs
• Get API Key: https://cellcog.ai/profile?tab=api-keys

License

MIT License — see LICENSE for details.