git:20260710.2925510 to git:20260710.62afd0f
19 added, 15 removed. Audit B to A.
---
- title: "Add graph-backed memory and context retrieval to agent workflows"
+ name: "Add graph-backed memory and context retrieval to agent workflows"
+ slug: "add-graph-backed-memory-and-context-retrieval-to-agent-workflows"
description: "Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows."
+ github_stars: 17584
verification: "security_reviewed"
source: "https://github.com/topoteretes/cognee"
author: "topoteretes"
publisher_type: "organization"
- category:
- - "Templates & Workflows"
- framework:
- - "Multi-Framework"
+ category: "Templates & Workflows"
+ framework: "Multi-Framework"
tool_ecosystem:
github_repo: "topoteretes/cognee"
github_stars: 17584
---
# Add graph-backed memory and context retrieval to agent workflows
Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.
## Prerequisites
Python, Cognee, LLM provider credentials, optional graph/vector database backend
## Installation
- Choose whichever fits your setup:
+ Use the upstream install or setup path that matches your environment:
+ - uv pip install cognee
+ - pip install cognee
+ - git clone https://github.com/topoteretes/cognee-integrations.git
- 1. Copy this skill folder into your local skills directory.
- 2. Clone the repo and symlink or copy the skill into your agent workspace.
- 3. Add the repo as a git submodule if you manage shared skills centrally.
- 4. Install it through your internal provisioning or packaging workflow.
- 5. Download the folder directly from GitHub and place it in your skills collection.
+ Requirements and caveats from upstream:
+ - Python 3.10 to 3.14
+ - You can install Cognee with **pip**, **poetry**, **uv**, or your preferred Python package manager.
+ - python
- Install command or upstream instructions:
+ Basic usage or getting-started notes:
+ - ## Basic Usage & Feature Guide
+ - Let’s try Cognee in just a few lines of code.
+ - ### Step 1: Install Cognee
- ```
- Install with `pip install cognee` or `uv pip install cognee`, configure the required LLM provider settings, then ingest data and query Cognee from the documented Python, plugin, or agent integration path.
- ```
+ - Source: https://github.com/topoteretes/cognee
+ - Extracted from upstream docs: https://raw.githubusercontent.com/topoteretes/cognee/HEAD/README.md
## Documentation
- https://docs.cognee.ai
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/add-graph-backed-memory-and-context-retrieval-to-agent-workflows/)