docs.graphlit.dev

# Graphlit Platform ## Graphlit Documentation - [What is Graphlit?](/readme.md) - [Why Graphlit?](/why-graphlit.md): Why developers choose Graphlit over DIY solutions and other memory platforms - TCO comparison, time savings, and competitive advantages - [Coding with AI Agents](/getting-started/coding-with-ai.md): Use AI coding agents to work with Graphlit's documentation and SDK - [Platform Overview](/getting-started/overview.md): Complete overview of Graphlit - what it is, why it exists, and what you can build with it - [Quickstart: Your First Agent](/getting-started/quickstart.md): Build an AI agent with semantic memory in 7 minutes - [Python](/sdk-setup/for-python-developers.md): Install the Python SDK and start building AI applications with semantic memory. - [TypeScript](/sdk-setup/for-typescript-developers.md): Install the TypeScript/Node.js SDK and start building AI applications with semantic memory. - [.NET](/sdk-setup/for-dotnet-developers.md): Install the .NET SDK and start building AI applications with semantic memory. - [AI Agents with Memory](/tutorials/ai-agents.md) - [Knowledge Graph](/tutorials/knowledge-graph.md) - [Context Engineering](/tutorials/context-engineering.md) - [Zine Case Study](/examples/zine-case-study.md) - [Deep Research](/examples/deep-research.md) - [Mastra (TypeScript)](/examples/deep-research/mastra.md): Build an autonomous AI research agent that performs multi-hop web research with entity extraction and intelligent filtering—like OpenAI's Deep Research - [Agno (Python)](/examples/deep-research/agno.md): Build an autonomous AI research agent in Python with Agno and Graphlit—5000x faster with simpler code - [Key Concepts](/platform/key-concepts.md): Core concepts for building AI agents with semantic memory using Graphlit - [Workflows](/platform/workflows.md): Complete reference for Graphlit workflows - memory formation pipeline configuration - [Specifications](/platform/specifications.md): Complete reference for Graphlit sp