Add graph-backed memory and context retrieval to agent workflows git:20260710.2925510B

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---
title: "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."
verification: "security_reviewed"
source: "https://github.com/topoteretes/cognee"
author: "topoteretes"
publisher_type: "organization"
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:

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.

Install command or upstream instructions:

```
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.
```

## Documentation

- https://docs.cognee.ai

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/add-graph-backed-memory-and-context-retrieval-to-agent-workflows/)