As the agent ecosystem matures, one question is coming up more often for developers and teams: what should I use for long-term memory? While many LLM tools focus on prompt engineering or chat history, serious agent builders know the real challenge is managing persistent, structured memory — memory that agents can search, reason over, and grow with.
If you're evaluating Mem0 or Graphlit, you're already thinking deeply about that problem. These platforms represent two different answers to the same question: how do you give your agent a brain?
In this post, we'll offer an honest, technical comparison between Mem0 and Graphlit — what they do well, where their APIs overlap, and where each platform takes a different approach. We'll also share why we built Graphlit the way we did, and why we think it's the better choice for anyone looking to build truly intelligent, future-ready agents.
Part 1: What Are Mem0 and Graphlit, Really?
At first glance, Mem0 and Graphlit both let you store and retrieve structured information through an API. But their mental models are different:
Mem0: Memory-as-a-Service
Mem0 is a focused platform that gives developers a simple REST API to create and retrieve "memories" — snippets of conversation or knowledge associated with a user. A memory might look like:
{
"user_id": "alice",
"memory": [
{"role": "user", "content": "What's my meeting schedule today?"},
{"role": "assistant", "content": "You have a 3pm with Marketing."}
]
}
Mem0 indexes the memory for semantic search and supports metadata tagging, filtering, and grouping by things like agent_id, run_id, or categories. It's compact, fast, and optimized for adding and retrieving short conversational context.
Graphlit: A Semantic Memory Engine
Graphlit approaches memory at a different level of abstraction. Instead of memories, we think in terms of content — any piece of information, regardless of its format or source. A document, a meeting transcript, a webpage, a Slack thread — they're all first-class citizens in Graphlit.
When you ingest content into Graphlit, we:
- Extract structured entities (people, orgs, places, etc.)
- Parse dates, events, and relationships
- Embed the content semantically for hybrid search
- Link it into a knowledge graph for context-rich retrieval
In short: Graphlit doesn't just store memory — it understands it.
Part 2: Comparing API Capabilities
Let's dig into how the two platforms compare across key developer needs.
1. Ingestion and Content Model
Our view: Mem0 makes it easy to store and tag short memories. Graphlit expands the idea of memory to any form of content, making it more adaptable to real-world agents that deal with multi-format, long-form, or external knowledge sources.
2. Retrieval and Search
Where Graphlit goes further: we extract and use structured entities (like "Sarah Chen, CTO") during search, so agents don't just match similar text — they retrieve conceptually linked ideas. Graph Memory in Mem0 offers a simpler version of this, but lacks the ability to model many-to-many relationships across formats or sources.
Part 3: Managing Memory at Scale
Mem0 offers a project/org structure with API support for managing users, projects, and exports. It's optimized for smaller or medium-scale apps with user-facing agents.
Graphlit also supports multi-user memory but approaches scalability differently:
- Content is linked to
userId, allowing isolated retrieval and ingestion - Workflow Actions allow real-time hooks when content is ingested or updated
- Bulk deletes and updates can be done via filters
- Project control plane APIs are under development for full lifecycle automation
If you're building SaaS products with agents per customer or tenant, Graphlit's multi-project architecture and per-user graphs give you more long-term flexibility.
Part 4: The Developer Experience
Mem0 is clean and easy to start with:
- One simple API key
- Minimal setup
- Small, fast API surface
Graphlit is deeper, but also more complex:
- Requires project + environment setup
- More powerful SDKs (TS, Python)
- More internal state: ingestion, processing, graph building
That said, we're investing heavily in documentation, examples, and self-serve tooling — and we're bringing more control plane features into the open. Our goal is to make that power accessible without adding cognitive load.
Part 5: What's Missing — and What's Coming
To be completely transparent:
Part 6: The Real Question — What Kind of Memory Do You Need?
If your agents are relatively narrow — say, customer support bots or small assistants managing short-term history — Mem0 is a solid tool. It's built for that purpose, and does it well.
But if your agents need to:
- Connect knowledge across multiple sources
- Build context over time
- Search semantically and relationally
- Learn from structured facts
- Scale across users, teams, and formats
Then you'll outgrow Mem0's model quickly. That's why we built Graphlit: to support agents with deep memory, rich context, and real-world adaptability.
Final Thoughts: Building for the Agent Era
In 2023, agents were simple wrappers over LLM prompts. In 2024, they started learning from documents. In 2025, they need semantic memory infrastructure: systems that grow with them, not just store chat logs.
We believe Graphlit is that infrastructure.
It gives you:
- Multimodal content support
- Structured knowledge extraction
- Graph-enhanced semantic search
- Workflow-based automation
- Per-user context graphs
And soon: feedback loops, TTL control, and full API-based lifecycle management.
If you're evaluating memory platforms for your next agent system, we hope this post helps you think clearly — and we'd love to show you what we're building.
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Thanks for reading. Agents need memory. We're building the brain.