As we head deeper into the agent era, one truth is becoming obvious: your agent is only as useful as what it can remember. Context is king, and agents that forget everything after each prompt won't cut it in 2025.
Developers building long-term, context-aware, autonomous agents are now faced with a new challenge: What memory layer should I choose? The stakes are high — the right decision unlocks powerful reasoning and user personalization, while the wrong one leads to frustrating hallucinations and shallow interactions.
In this article, we take an in-depth look at two of the leading platforms for developer-accessible agent memory: Supermemory and Graphlit. We break down their strengths, gaps, and design philosophies — and explain why we believe Graphlit is the best foundation for teams building real-world agents.
Whether you're building an internal team assistant, a vertical SaaS product, or an intelligent workspace, this guide will help you choose the right platform.
TL;DR — Quick Feature Comparison
Understanding the Platforms
What is Supermemory?
Supermemory presents itself as a plug-and-play solution for developers who need memory-backed retrieval for LLM applications. You send it documents, URLs, or files. It stores them, chunks them, and makes them available via semantic search APIs.
The core use case is RAG: Retrieval-Augmented Generation. Supermemory excels when you're working with well-structured content like PDFs, Notion pages, or websites — and need to pull chunks of text into an LLM prompt.
What is Graphlit?
Graphlit is a semantic content infrastructure designed for agents. It's multimodal and graph-native: you can ingest not just documents, but audio, video, images, conversations, and more. Every piece of content is parsed, enriched, and linked to a shared graph of people, places, and topics.
Where Supermemory stores knowledge as text chunks, Graphlit models it as structured meaning. This enables graph-aware search, cross-format retrieval, and a more contextual foundation for intelligent agents.
If Supermemory is a smart filing cabinet, Graphlit is a living knowledge network.
Ingestion Capabilities
Supermemory allows ingestion via:
- File upload (PDF, images, docs, etc.)
- URL ingestion
- Native integrations (Notion, Google Drive)
Supermemory claims to support multimodal content, but testing reveals limitations. While images and PDFs with embedded text work reliably, standalone audio and some video formats may fail.
Graphlit offers:
- Text, file, and structured chat ingestion
- Native audio (MP3, WAV) with transcription and speaker diarization
- Image ingestion with OCR and semantic tagging
- Full document suite (PDF, DOCX, HTML)
- Web content (URLs, feeds, YouTube, RSS)
Graphlit was designed to be natively multimodal, so agents can reason across formats without developers needing to preprocess everything.
Search and Retrieval Differences
Supermemory:
- Vector-based search (embeddings)
- Filters on tags, metadata, users
- Chunk-based retrieval with optional feedback tagging
Graphlit:
- Hybrid search (semantic + keyword + metadata)
- Filter by time, user, entity, content type
- Knowledge graph-aware expansion
- Entity-based filters ("Find content where Alice discussed roadmap")
Graphlit's retrieval is more semantically expressive. It's not just "find similar chunks," it's "find things related to this idea, person, or concept." That's especially powerful in multi-turn agents that need to synthesize information.
Data Modeling Philosophy
The biggest difference isn't in endpoints — it's in worldview:
Supermemory: Memory is a list of documents and their chunks.
Graphlit: Memory is a web of entities, facts, and timelines.
This manifests in how the systems behave:
- Supermemory retrieves individual text chunks from isolated sources
- Graphlit retrieves contextual graphs that may span multiple documents, formats, and users
If you're building a document-based chatbot, Supermemory works fine. If you're building an agent that lives across Slack, meetings, dashboards, and code — Graphlit gives you the abstraction you need.
Developer Experience
Both platforms provide modern APIs and SDKs. Here's how they stack up:
Graphlit is closing the gap on administrative APIs, and already offers a more reactive and event-driven model via webhooks. This matters if your agents need to trigger actions when content is added, updated, or enriched.
Use Cases: When to Choose What
Use Supermemory if:
- You're building a document chatbot
- You need quick RAG with PDFs and Notion files
- You prefer simplicity over customization
Use Graphlit if:
- You're building multi-turn agents with memory
- You work with audio, images, video, and raw text
- You need search that goes beyond keywords
- You're planning for long-term user memory, not one-shot lookup
- You want your agents to connect ideas over time
Final Verdict: Building Smarter Memory for Smarter Agents
Supermemory is a solid choice for teams that need fast document search. It lowers the barrier to entry for RAG and is easy to integrate.
But as agents mature — becoming autonomous, cross-modal, and long-lived — teams will need something more powerful, more flexible, and more future-proof.
That's why we built Graphlit.
It's not just about storing context — it's about modeling meaning. With support for multimodal content, entity extraction, graph-based reasoning, and hybrid search, Graphlit provides the infrastructure today's agents need to operate tomorrow.
We're continuing to expand the platform with features like feedback APIs, expiration control, and dynamic project provisioning — while staying true to our vision: giving developers memory that thinks.
Explore more:
Your agent deserves more than storage. It deserves a memory that understands.