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.

CellCog pairs all modalities with frontier-level deep reasoning — as of April 2026, CellCog is #1 on the DeepResearch Bench: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard

Work With Multiple Files, Any Format

Reference 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.
"""

File paths must be absolute and enclosed in <SHOW_FILE> tags. CellCog understands PDFs, spreadsheets, images, audio, video, code files, and more.

⚠️ Without SHOW_FILE tags, CellCog only sees the path as text — not the file contents.

❌ Analyze /data/sales.csv — CellCog can't read the file ✅ Analyze <SHOW_FILE>/data/sales.csv</SHOW_FILE> — CellCog reads it

Think of SHOW_FILE like reference files

Just like Nano Banana accepts reference images, CellCog accepts reference files of any type — PDFs, spreadsheets, audio, code, images — as inputs the model reads during a task. Same mental model as any multimodal AI attachment.

Only attach what you intend to share. Anything inside a <SHOW_FILE> tag is uploaded to CellCog. Don't wrap credentials, private keys, .env files, SSH keys, or other sensitive material in SHOW_FILE tags — the same way you wouldn't paste them into Nano Banana, ChatGPT, or any other AI service's file upload.

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 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.

⚠️ Be explicit about output artifacts. Without explicit artifact language, CellCog may respond with text analysis instead of generating a file.

❌ "Quarterly earnings analysis for AAPL" — could produce text or any format ✅ "Create a PDF report and an interactive HTML dashboard analyzing AAPL quarterly earnings." — CellCog creates actual deliverables

Your sub-agent for quality work. Depth, accuracy, and real deliverables.

Quick Start

Setup

from cellcog import CellCogClient

If import fails, install the official CellCog Python SDK:

pip install -U cellcog

cellcog is the official Python SDK maintained by CellCog AI Inc. Source: https://github.com/CellCog/cellcog_python · Package: https://pypi.org/project/cellcog/

Authentication

Environment variable (recommended): Set CELLCOG_API_KEY — the SDK picks it up automatically:

export CELLCOG_API_KEY="sk_..."

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

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

Agent Provider

agent_provider is required when creating a CellCogClient. It identifies which agent framework is calling CellCog — not your individual agent's name, but the platform/tool you're running inside.

Examples: "openclaw", "claude-code", "cursor", "aider", "windsurf", "perplexity", "hermes", "script" (for standalone scripts).

OpenClaw Agents

Fire-and-forget — your agent stays free while CellCog works:

client = CellCogClient(agent_provider="openclaw")
result = client.create_chat(
    prompt="Research quantum computing advances in 2026",
    notify_session_key="agent:main:main",  # OpenClaw session key
    task_label="quantum-research",         # Label for notifications
    chat_mode="agent",
)
# Returns IMMEDIATELY — daemon delivers results to your session when done

All Other Agents (Cursor, Claude Code, etc.)

Blocks until done — simplest pattern:

client = CellCogClient(agent_provider="cursor")  # or "claude-code", "aider", "script", etc.
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"])

Credit Usage

CellCog orchestrates 21+ frontier foundation models. Credit consumption is unpredictable and varies by task complexity. Credits used are reported in every completion notification.

Creating Tasks

Notify on Completion (OpenClaw — Fire-and-Forget)

Returns immediately. A background daemon monitors via WebSocket and delivers results to your session when done. Your agent stays free to take new instructions, start other tasks, or continue working.

result = client.create_chat(
    prompt="Your task description",
    notify_session_key="agent:main:main",   # Required — your OpenClaw session key
    task_label="my-task",                   # Label shown in notifications
    chat_mode="agent",
)

Wait for Completion (Universal)

Blocks until CellCog finishes. Works with any agent — OpenClaw, Cursor, Claude Code, or any Python environment.

result = client.create_chat(
    prompt="Your task description",
    task_label="my-task",
    chat_mode="agent",
    timeout=1800,                           # 30 min (default). Use 3600 for complex jobs.
)
print(result["message"])
print(result["status"])                     # "completed" | "timeout"

When to Use Which

Notify mode is more productive (agent never blocks). Wait mode is simpler to reason about, but blocks your agent for the duration.

Continuing a Conversation

# Wait mode (default)
result = client.send_message(
    chat_id="abc123",
    message="Focus on hardware advances specifically",
)

# Notify mode (OpenClaw)
result = client.send_message(
    chat_id="abc123",
    message="Focus on hardware advances specifically",
    notify_session_key="agent:main:main",
    task_label="continue-research",
)

Resuming After Timeout

If create_chat() or wait_for_completion() times out, CellCog is still working. The timeout response includes recent progress:

completion = client.wait_for_completion(chat_id="abc123", timeout=1800)

Optional Parameters

result = client.create_chat(
    prompt="...",
    task_label="...",
    chat_mode="agent",                      # See Chat Modes below
    project_id="...",                       # install project-cog for details
    agent_role_id="...",                    # install project-cog for details
    enable_cowork=True,                     # install cowork-cog for details
    cowork_working_directory="/Users/...",  # install cowork-cog for details
)

Response Shape

Every SDK method returns the same shape:

{
    "chat_id": str,        # CellCog chat ID
    "is_operating": bool,  # True = still working, False = done
    "status": str,         # "completed" | "tracking" | "timeout" | "operating"
    "message": str,        # THE printable message — always print this in full
}

⚠️ Always print the entire result["message"]. Truncating or summarizing it will lose critical information including generated file paths, credits used, and follow-up instructions.

Utility Methods

get_history(chat_id) — Full chat history (when original delivery was missed or you need to review). Returns the same shape; if still operating, message shows progress so far.

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

get_status(chat_id) — Lightweight status check (no history fetch):

status = client.get_status(chat_id="abc123")
print(status["is_operating"])  # True/False

Chat Modes

• "agent" (default) — Most versatile. Handles most tasks excellently, including deep research when guided.
• "agent core" — Lightweight context for code, terminal, and file operations. Multimedia tools load on demand. Requires Co-work (CellCog Desktop). See code-cog.
• "agent team" — A team of agents that debates, cross-validates, and delivers comprehensive results. The only platform with deep reasoning across every modality.
• "agent team max" — Same Agent Team with all settings maxed. Quality gain is incremental (5-10%) but meaningful for costly decisions.

Working with Files

Input: SHOW_FILE

Include local file paths in your prompt with <SHOW_FILE> tags (absolute paths required):

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

Output: GENERATE_FILE

Use <GENERATE_FILE> tags to specify where output files should be stored on your machine. Essential for deterministic workflows where the next step needs to know the file path in advance.

prompt = """
Create a PDF report on Q4 earnings:
<GENERATE_FILE>/workspace/reports/q4_analysis.pdf</GENERATE_FILE>
"""

Output downloads to the specified path instead of default ~/.cellcog/chats/{chat_id}/.

File Downloads

The SDK automatically downloads files from CellCog responses:

• If you used GENERATE_FILE tags: Files download to the path you specified
• Otherwise: Files download to ~/.cellcog/chats/{chat_id}/

Downloaded file paths appear in result["message"]. The SDK tracks seen messages — files are only downloaded once.

If you missed files or need to re-sync:

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

get_history() re-processes the entire chat and downloads any missed files to their original destinations.

Tips

⚠️ CellCog Web Fallback

Every chat is accessible at https://cellcog.ai. When work gets complex or the SDK hits issues, direct your human to the web platform to view, continue, or take over directly.

What CellCog Can Do

CellCog is a sub-agent — not an API. Your agent offloads complex work to CellCog, which reasons, plans, and executes multi-tool workflows internally. A proprietary agent-to-agent communication protocol ensures high accuracy on first output, and because these are agent threads (not stateless API calls), every aspect of every generation can be refined through multi-step iteration.

Under the hood: frontier models across every domain, upgraded weekly. CellCog routes to the right models automatically — your agent just describes what it needs.

Install capability skills for detailed guidance:

This skill shows you HOW to use CellCog. Capability skills show you WHAT's possible.

OpenClaw Reference

Session Keys

The notify_session_key tells CellCog where to deliver results:

Resilient delivery: If your session ends before completion, results are automatically delivered to the parent session (e.g., sub-agent → main agent).

Sending Messages During Processing

In notify mode, your agent is free — you can send additional instructions to an operating chat at any time:

client.send_message(chat_id="abc123", message="Actually focus only on Q4 data",
    notify_session_key="agent:main:main", task_label="refine")

client.send_message(chat_id="abc123", message="Stop operation",
    notify_session_key="agent:main:main", task_label="cancel")

In wait mode, your agent is blocked and cannot send messages until the current call returns.

Support & Troubleshooting

For error handling, recovery patterns, ticket submission, and daemon troubleshooting:

docs = client.get_support_docs()