Cellcog

CellCog - Any-to-Any for Agents

The Power of Any-to-Any

CellCog is the only AI that truly handles any input → any output in a single request. No tool chaining. No orchestration complexity. One call, multiple deliverables.

Send Multiple Files, Any Format

Provide as many documents as you need—all at once:

prompt = """
Analyze all of these together:
<SHOW_FILE>/data/q4_earnings.pdf</SHOW_FILE>
<SHOW_FILE>/data/competitor_analysis.pdf</SHOW_FILE>
<SHOW_FILE>/data/market_research.xlsx</SHOW_FILE>
<SHOW_FILE>/recordings/customer_interview.mp3</SHOW_FILE>
<SHOW_FILE>/designs/product_mockup.png</SHOW_FILE>

Give me a comprehensive market positioning analysis based on all these inputs.
"""

CellCog understands PDFs, spreadsheets, images, audio, video, code files, and more—simultaneously.

Request Multiple Outputs, Different Modalities

Ask for completely different output types in ONE request:

prompt = """
Based on this quarterly sales data:
<SHOW_FILE>/data/sales_q4_2025.csv</SHOW_FILE>

Create ALL of the following:
1. A PDF executive summary report with charts
2. An interactive HTML dashboard for the leadership team
3. A 60-second video presentation for the all-hands meeting
4. A PPTX slide deck for the board presentation
5. An Excel file with the underlying analysis and projections
"""

CellCog handles the entire workflow—analyzing, generating, and delivering all outputs with consistent insights across every format.

Why This Matters

This is your sub-agent for quality work. When you need depth, accuracy, or deliverables that require real work—research, videos, images, PDFs, dashboards, presentations, spreadsheets—use CellCog.

Quick Start

Installation

pip install cellcog==1.0.0

Important: This skill is tested with SDK v1.0.0. If you encounter issues:

1. Verify your SDK version matches: python3 -c "import cellcog; print(cellcog.__version__)"
2. Check for skill updates: clawhub update cellcog (may reference a different SDK version)

Authentication

Get your API key from: https://cellcog.ai/profile?tab=api-keys

from cellcog import CellCogClient

client = CellCogClient()
client.set_api_key("sk_...")  # SDK handles file storage automatically

Check configuration:

status = client.get_account_status()
print(status)  # {"configured": True, "email": "user@example.com", ...}

Fire-and-Forget Pattern (v1.0+)

No more blocking. No more spawning sub-sessions to babysit.

CellCog uses a fire-and-forget pattern:

1. Call create_chat() or send_message() - returns immediately
2. A background daemon monitors via WebSocket
3. Your session receives notification when complete

Why Fire-and-Forget?

Basic Usage

from cellcog import CellCogClient

client = CellCogClient()

# Fire-and-forget: returns immediately
result = client.create_chat(
    prompt="Research quantum computing advances in 2026",
    notify_session_key="agent:main:main",  # Your session key
    task_label="quantum-research"          # Label for notification
)

print(result["chat_id"])           # "abc123"
print(result["explanation"])       # Clear guidance on what happens next

# Continue with other work...
# You'll receive notification at your session when complete!

What the response looks like:

{
    "chat_id": "abc123",
    "status": "tracking",
    "daemon_listening": True,
    "listeners": 1,
    "explanation": "✓ Chat 'quantum-research' created (ID: abc123)\n✓ Daemon is monitoring via WebSocket\n✓ You'll receive notification at 'agent:main:main' when complete\n\nYou can continue with other work. Do NOT poll - the daemon will notify you automatically with the full response and any generated files."
}

What You Receive

When CellCog completes, your session receives a notification like:

✅ quantum-research completed!

Chat ID: abc123
Messages delivered: 5
Files downloaded:
  - /outputs/research_report.pdf
  - /outputs/data_analysis.xlsx

<MESSAGE FROM openclaw on Chat abc123 at 2026-02-04 14:00 UTC>
Research quantum computing advances in 2026
<MESSAGE END>

<MESSAGE FROM cellcog on Chat abc123 at 2026-02-04 14:30 UTC>
Research complete! I've analyzed 47 sources and compiled the findings...

Key Findings:
- Quantum supremacy achieved in error correction
- Major breakthrough in topological qubits
- Commercial quantum computers now available for $2M+

Generated deliverables:
<SHOW_FILE>/outputs/research_report.pdf</SHOW_FILE>
<SHOW_FILE>/outputs/data_analysis.xlsx</SHOW_FILE>
<MESSAGE END>

[CellCog stopped operating on Chat abc123 - waiting for response via send_message()]

Use `client.get_history("abc123")` to view full conversation.

Message Format Notes:

• Timestamps in UTC
• Files show as local paths (already downloaded by daemon)
• Full conversation history included
• Completion indicator after last CellCog message

Interim Updates (Long-Running Tasks)

For tasks taking more than 4 minutes, you'll automatically receive progress updates:

⏳ quantum-research - CellCog is still working

Your request is still being processed. The final response is not ready yet.

Here are interim updates from CellCog showing recent activity:
  • Searching for quantum computing research papers
  • Analyzing breakthrough in error correction
  • Generating comparison charts

Chat ID: abc123

We'll deliver the complete response when CellCog finishes processing.

These are NOT the final response - just progress indicators so you know CellCog is actively working. Continue with other tasks; the complete response arrives when processing finishes.

Primary Methods

create_chat()

Create a new CellCog task and return immediately:

result = client.create_chat(
    prompt="Your task description",
    notify_session_key="agent:main:main",  # Who to notify
    task_label="my-task",                   # Human-readable label
    chat_mode="agent team",                 # See Chat Modes below
    project_id=None,                        # Optional CellCog project
    gateway_url=None                        # Auto-detected from OPENCLAW_GATEWAY_URL env
)

Returns:

{
    "chat_id": "abc123",
    "status": "tracking",
    "daemon_listening": True,
    "listeners": 1,
    "explanation": "✓ Chat created...\n✓ Daemon monitoring...\n✓ Do NOT poll..."
    # "uploaded_files": [...] - only if SHOW_FILE tags were in prompt
}

send_message()

Continue an existing conversation:

result = client.send_message(
    chat_id="abc123",
    message="Focus on hardware advances specifically",
    notify_session_key="agent:main:main",
    task_label="continue-research"  # Optional, defaults to "continue-{chat_id[:8]}"
)

Listener Behavior:

• One listener per session_key per chat
• Calling send_message() with same notify_session_key updates the existing listener (new task_label)
• Does NOT create duplicate listeners

get_history()

Get full chat history (for manual inspection):

result = client.get_history(chat_id="abc123")

print(result["is_operating"])      # True/False
print(result["formatted_output"])  # Full formatted messages
print(result["downloaded_files"])  # List of downloaded file paths
print(result["status_message"])    # Operating status

get_status()

Quick status check:

status = client.get_status(chat_id="abc123")
print(status["is_operating"])  # True/False
print(status["error_type"])    # None, "security_threat", or "out_of_memory"

Chat Modes

CellCog offers two powerful modes optimized for different scenarios:

Selection Guide:

• Default to "agent" - It's powerful, fast, and handles most tasks excellently
• Use "agent team" when you need:
  • Deep research with multi-source synthesis
  • Boardroom-quality investor decks
  • Marketing videos requiring creative direction
  • Work that benefits from multiple reasoning passes

The key question: Does this task require thinking from multiple angles and deep deliberation? If yes → "agent team". If it's execution-focused → "agent".

Clarifying Questions Behavior

Agent mode asks one round of clarifying questions in most cases (~99%) to ensure it delivers exactly what you need.

To skip clarifying questions, explicitly state in your prompt:

• "No clarifying questions needed"
• "Proceed directly without questions"
• "Skip clarifications and deliver"

This gives you control over the interaction style while still getting high-quality results.

Session Keys

The notify_session_key tells CellCog where to deliver the notification.

Common Patterns

Parent Fallback

If your session dies before completion, the SDK automatically notifies the parent session:

• agent:main:subagent:uuid123 → falls back to agent:main:main
• Nested subagents walk up the chain until delivery succeeds

File Handling

Sending Files to CellCog (SHOW_FILE)

Include local files in your prompts:

prompt = """
Analyze this sales data and create a report:
<SHOW_FILE>/path/to/sales.csv</SHOW_FILE>
"""

The SDK automatically:

1. Uploads the file to CellCog
2. Tracks the original path
3. Downloads output files back to your filesystem

Requesting File Output (GENERATE_FILE)

Tell CellCog where to save generated files:

prompt = """
Create a PDF report and save it here:
<GENERATE_FILE>/outputs/quarterly_report.pdf</GENERATE_FILE>
"""

CellCog will generate the file and the SDK will download it to your specified path.

Auto-Download

Files without GENERATE_FILE are automatically downloaded to:

~/.cellcog/chats/{chat_id}/downloads/

Background Daemon

The SDK runs a background daemon that:

• Monitors tracked chats via WebSocket
• Downloads files when complete
• Notifies your session with results
• Survives system restarts

Automatic Management

The daemon is automatically started when you call create_chat() or send_message(). You don't need to manage it manually.

State Persistence

Tracking state is stored in files:

~/.cellcog/
├── tracked_chats/     # One file per tracked chat
│   └── abc123.json
└── chats/             # Per-chat data
    └── abc123/
        └── .seen_indices/  # Per-session message tracking

This means:

• Survives daemon restarts
• Survives system reboots
• No messages lost

Error Handling

Payment Required

from cellcog.exceptions import PaymentRequiredError

try:
    result = client.create_chat(...)
except PaymentRequiredError as e:
    print(f"Add credits at: {e.subscription_url}")

Authentication Error

from cellcog.exceptions import AuthenticationError

try:
    result = client.create_chat(...)
except AuthenticationError:
    print("Invalid API key. Get a new one at: https://cellcog.ai/profile?tab=api-keys")

Quick Reference

Satellite Skills

Install capability-specific skills to explore what CellCog can do:

• clawhub install research-cog - Deep research, market analysis, competitive intelligence
• clawhub install video-cog - AI video generation, lipsync, marketing videos
• clawhub install image-cog - Image generation, consistent characters
• clawhub install audio-cog - Text-to-speech, music generation
• clawhub install dash-cog - Interactive dashboards, data visualization
• clawhub install slides-cog - Presentations, pitch decks
• clawhub install sheet-cog - Spreadsheets, financial models

This mothership skill shows you HOW to call CellCog. Satellite skills show you WHAT's possible.

All satellite skills work with SDK v1.0.0. Each satellite references this mothership for technical details.