Exabase vs. Mem0

Last updated May 2026

Mem0 is the name most developers encounter first when evaluating agent memory. That is not an accident. Here is how the two systems compare, including where Mem0 still has the advantage.

Mem0 has 47,000 GitHub stars and a $24 million Series A. It is the default reference point for agent memory in 2025. If you are building an AI system that needs to remember things, you have probably already looked at them. You should. They built something real.

This page exists because we think the comparison is worth making carefully, with numbers and methodology on the table rather than marketing claims on both sides.

Comparison Table

What is Mem0?

Mem0 is an open-source memory layer for AI agents. It stores facts and preferences extracted from conversations, retrieves them on subsequent queries, and makes them available as context for your prompts. It has a large developer community, broad framework integrations, and a free tier that makes it easy to start with. Graph memory, which tracks relationships between stored facts, is available on the Pro plan at $249 per month. Mem0 is focused on memory specifically. It does not offer document storage, extraction, or workspaces.

What is Exabase?

Exabase is a managed memory API built for production AI agents. Its core engine, M-1, handles the full memory pipeline: chunking, enrichment, relationship tracking, contradiction resolution, temporal weighting, and hybrid retrieval. Rather than storing raw conversation logs and running similarity search over them, Exabase builds a dynamic knowledge graph that evolves as new information comes in. Contradictions get resolved. Stale facts get updated. The context returned to your agent is accurate, not just adjacent.

Alongside memory, Exabase offers search, extraction, and bases (cloud filesystems) as part of a unified data layer for agents. Graph memory is included across all plans.

Key Differences

Benchmark accuracy

On LongMemEval, the most rigorous public benchmark for conversational memory retrieval, M-1 scores 96.4%. Mem0 scores 94.8%. The gap is meaningful on its own. What makes it more significant is that M-1 achieved it using Gemini 3 Flash, while Mem0 used Gemini 3 Pro. A larger model can compensate for weaker retrieval by extracting correct answers from noisy context. M-1's higher score with a smaller model suggests the retrieval architecture is doing more of the work, which is the right place for it to happen. Read our research report.

Evaluation methodology

We built our evaluation by forking Mem0's own open-source benchmarking script. The script contained question-category-specific prompt templates and targeted heuristics designed to improve scores on particular question types. We removed all of them and used a single uniform prompt across all 500 questions. Our prompt and results are published and reproducible. A memory system evaluated with question-specific tuning is being tested on benchmark optimisation, not production retrieval quality.

Cost at scale

The difference between Gemini 3 Flash and Gemini 3 Pro is not subtle. At scale, the cost gap compounds across millions of queries. A memory system that requires a frontier model to perform well is not a production memory system. Higher accuracy and a cheaper model are not usually properties that go together. In this case they do.

Contradiction and temporal handling

When a user states something in January and contradicts it in March, a vector store will often surface both statements with similar confidence scores and leave the resolution to the model. Exabase tracks the relationship between those statements, identifies the contradiction, and resolves it in favour of the most recent version before anything reaches the model. Temporal reasoning is built into the retrieval pipeline, not delegated downstream.

Feature scope

Mem0 is a memory product. Exabase is a data layer for agents that includes memory alongside filesystems, extraction, and workspaces. If your agent needs to store, retrieve, and reason over documents as well as conversational memory, Exabase covers both without requiring a second vendor.

Open source

Mem0's codebase is public and self-hostable. It has 47,000 GitHub stars, a broad set of framework integrations, and a large developer community. These are meaningful properties for teams that weight them. Exabase is a managed API. The M-1 engine is proprietary, though the benchmark methodology, prompt, and results are fully published.

When to Use Each

Use Exabase if you are building production AI agents where retrieval accuracy and cost efficiency matter at scale. You need memory that handles contradictions and temporal updates without manual intervention. You want a unified data layer that covers memory, documents, and workspaces without assembling multiple tools. You are comfortable with a managed API and do not need self-hosting.

Use Mem0 if open source and self-hosting are hard requirements. You want to inspect and modify the codebase directly. You are early in development and want to start on a free tier with a large community behind it. You need a broad set of framework integrations today and are not yet at a scale where retrieval accuracy differences compound significantly.

Why Developers Move from Mem0 to Exabase

Retrieval quality degraded at scale. Mem0 works well at small knowledge base sizes. As the volume of stored memories grows, vector similarity starts returning things that are adjacent rather than relevant. The model has no visibility into this and responds with equal confidence regardless. This is the point where teams start looking for an alternative.

Contradictions were surfacing as facts. A user updates a preference, changes a decision, or corrects something they said earlier. The old version stays in the store, scores well against related queries, and gets passed to the model alongside the new version. Resolving that manually is not sustainable. Exabase handles it automatically.

The frontier model requirement became expensive. Getting reliable results from a memory system that needs a Pro-tier model to compensate for imprecise retrieval adds up quickly at production query volumes. The switch to Exabase reduced both model cost and hallucination rate.

They needed more than memory. Document storage, extraction, bases (cloud filesystems). Once an agent needs to reason over files as well as conversation history, a memory-only tool requires a second integration. Exabase covers the full data layer.

FAQs

How did you evaluate against Mem0?

We forked Mem0's open-source benchmarking script and replaced the storage and retrieval layer with M-1. We removed all question-category-specific prompt templates and heuristics present in the original script and used a single uniform prompt across all 500 questions. We used Gemini 3 Flash as both the answering and judging model. The full methodology, prompt, and results JSON are published at exabase.io/research.

Why does the model choice matter?

A larger model can compensate for weaker retrieval by extracting correct answers from noisy or partially relevant context. Systems reporting results with Gemini 3 Pro are, in part, measuring the model's reasoning capacity rather than the memory system's retrieval quality. M-1 achieves a higher score with a smaller, cheaper model, which reflects retrieval architecture rather than model power.

What is LongMemEval?

A public benchmark designed to evaluate long-term memory in conversational AI systems. It presents approximately 115,000 tokens of conversational history across multiple sessions and tests six distinct memory capabilities: single-session recall, preference tracking, assistant-provided information, multi-session reasoning, temporal reasoning, and knowledge update. It is the most widely used public benchmark for this class of problem.

Does Exabase support graph memory?

Yes. Graph memory is included across all Exabase plans. It is not a paid upgrade.

Is Exabase open source?

No. Exabase is a managed API. The M-1 retrieval engine is proprietary. The benchmark evaluation methodology, prompt, and results are published and reproducible at exabase.io/research.

Does Exabase work with my existing stack?

Exabase is model-agnostic and framework-agnostic. It works via REST API, Python and JavaScript SDKs, and MCP support for Claude, Cursor, Windsurf, and other compatible tools.

What is the difference between Exabase and a vector database?

A vector database stores embeddings and retrieves by similarity. Exabase Memory is a full memory engine: chunking, enrichment, relationship tracking, contradiction resolution, temporal weighting, and hybrid retrieval, managed for you. You call one endpoint and get back context that is accurate and ready for your prompt.

Can I migrate from Mem0 to Exabase?

Yes. Exabase provides an API that accepts memories in standard formats. If you are currently storing conversation history or extracted facts with Mem0, these can be ingested into Exabase. Contact us and we can walk through the migration path for your specific setup.