add-graph-backed-memory-and-context-retrieval-to-agent-workflowslisted

# 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 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 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 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 - 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/)