# Documents In. Intelligence Out. Project Cog Brings Context Trees to Every Agent > Project Cog gives OpenClaw agents access to CellCog's Context Trees: AI memory structures that turn document collections into structured, navigable knowledge. - Author: Nitish Garg, Founder & CEO, CellCog - Published: 2026-03-26 - Canonical (HTML): https://cellcog.ai/blog/project-cog-context-trees/ - Section: Product Updates / Changelog - Publisher: CellCog (https://cellcog.ai), the AI employee platform. Blog index for agents: https://cellcog.ai/blog/llms.txt ## Key points - Project Cog launched March 26, 2026: a ClawHub skill giving OpenClaw agents full access to CellCog's Context Trees. - Context Trees are AI-optimized memory structures built in-house to manage large document collections for CellCog's own agents. - Upload documents in any format - PDFs, spreadsheets, code, images, audio - and a lightweight AI agent organizes them into a hierarchical tree with structured summaries. - Agents read the tree to understand what's available, then fetch signed URLs for specific files by path - no downloading everything. - Works standalone as a knowledge-management system, or passes project_id into create_chat() so CellCog agents see all project documents. - SDK v1.12.0 ships full project, document, and context tree management. ## At a glance - **What is Project Cog?** A ClawHub skill that gives OpenClaw agents access to CellCog Projects and their Context Trees - upload documents, read structured summaries, fetch files by path. - **What is a Context Tree?** An AI-optimized hierarchical index of a document collection, with per-file summaries - agents read the tree to know what exists, then fetch only what they need. - **How do agents use it?** Standalone as a knowledge base for any OpenClaw workflow, or with CellCog: pass a project_id to create_chat() and the agents automatically see every project document. - **What formats are supported?** Any format - PDFs, spreadsheets, code, images, audio. A lightweight AI agent processes each upload into the tree. **CellCog Projects are now a first-class citizen in the OpenClaw ecosystem.** Today we're shipping `project-cog` - a new ClawHub skill that gives your OpenClaw agents full access to CellCog's proprietary **Context Trees**: AI-optimized memory structures we built in-house to manage large document collections for our own agents. ## The Problem: Documents Don't Fit Every serious workflow accumulates documents - specs, reports, spreadsheets, research, code. And every agent hits the same wall: the collection is bigger than any context window, so the agent either downloads everything (slow, expensive, mostly irrelevant) or works blind (fast, wrong). The answer our own agents use is a map. A **Context Tree** is a hierarchical index of the collection with structured summaries per file: the agent reads the tree to understand what exists, then fetches only the files the task needs. It's the difference between [memory as architecture and memory as a pile](https://cellcog.ai/blog/ai-agent-memory-types/). ## How It Works - **Upload documents** - PDFs, spreadsheets, code, images, audio, any format - to a CellCog Project. - A **lightweight AI agent** processes each document and organizes it into a hierarchical Context Tree with structured summaries. - Your agents **read the tree markdown** to understand what's available - no need to download every file. - When they need a specific document, they get **signed URLs by file path** and pass them wherever needed. ## Two Ways to Use It **Standalone.** Use Project Cog as your agent's knowledge management system: upload docs, retrieve structured summaries, fetch signed URLs. No CellCog chat required - it works independently with any OpenClaw workflow. **With CellCog.** Pass a `project_id` to `create_chat()` and CellCog agents automatically have access to all your project documents. Works with every cog in the family - research, finance, documents, and the rest of the 30+ skills. ## What Your Agents Can Do - `list_projects()` / `create_project()` - manage knowledge workspaces - `upload_document()` - add documents with AI processing - `get_context_tree_markdown()` - read the structured document hierarchy - `get_document_signed_urls_by_path()` - fetch download URLs using paths from the tree - `list_agent_roles()` - discover specialized agent configurations SDK v1.12.0 ships full project, document, and context tree management. The same memory structures that power CellCog's internal agents - now available for yours. ## The Honest Caveats A tree is only as useful as what you upload: stale documents produce confidently stale answers, so treat the project as a living collection. Processing happens per upload, which takes moments per document rather than being instant. And summaries are navigation aids, not the truth itself - agents doing high-stakes work should read the underlying file, which is exactly what the signed-URL path is for. ## FAQ **What problem do Context Trees solve?** Document collections outgrow context windows fast. A tree gives agents a map instead: a hierarchical index with structured summaries per file, so an agent knows what exists and fetches only the files a task actually needs. **How does a document become part of the tree?** Upload it to a CellCog Project. A lightweight AI agent processes it - whatever the format - and files it into the hierarchy with a structured summary. **How do agents retrieve specific files?** By path: the tree markdown carries full file paths, and agents request signed URLs for the paths they need, then pass those URLs wherever the work happens. **Can I use it without CellCog chats?** Yes - standalone mode is a first-class use: upload docs, read summaries, fetch URLs, entirely within your own OpenClaw workflow. **What changes when I pass a project_id to create_chat()?** The CellCog agents working your task automatically see the project's Context Tree - so research, documents, and dashboards get produced with your knowledge base in scope, across every cog in the family. **Is this the same system CellCog's own agents use?** Yes - Context Trees were built in-house for CellCog's internal agents first. Project Cog opens the same structures to yours. ## Related - [AI Agent Memory Types: Working, Episodic, Semantic, and Procedural](https://cellcog.ai/blog/ai-agent-memory-types/index.md) - [Shared vs Role-Specific AI Memory](https://cellcog.ai/blog/shared-vs-role-specific-ai-memory/index.md) ## The AI employee for this read [AI Head of Growth](https://cellcog.ai/ai-employees/ai-head-of-growth): I built this page, checked every quote against its source and drew the charts. I can do the same for your company. --- Markdown alternate of https://cellcog.ai/blog/project-cog-context-trees/. Try CellCog free, no credit card needed: https://cellcog.ai/signup