You opened this page because Mem0 is not quite fitting — maybe the graph features are locked behind the $249/month Pro tier, maybe you need per-document permissions, or maybe you realised that remembering what a user said last Tuesday is a different problem from knowing what your company promised a customer last quarter.
Both problems are real. They just need different tools.
Key takeaways
- Mem0 is well-suited for agent personalization: user preferences, conversation history, and entity tracking across sessions. It has ~48,000 GitHub stars for good reason.
- The gap appears when teams need company-wide knowledge that is permissioned by role, traced to a source document, and kept current from live business apps.
- Agent memory and a business context layer solve different problems. Evaluating them as substitutes leads to picking the wrong one.
- For production agent memory (speed, accuracy, governance), Zep and Letta are the strongest Mem0 alternatives.
- For teams that need AI to understand their actual business — deals in Salesforce, decisions in Slack, documents in Notion — a company context layer like Gyld is the right category.
- The two layers are not mutually exclusive. Many teams will eventually run both.
What Mem0 actually does well
Mem0 is a managed memory layer for AI agents. It extracts facts from conversations, stores them as vector embeddings (and, on the Pro tier, as a knowledge graph), and retrieves relevant memories at inference time. On the LoCoMo benchmark, it scores 91.6% accuracy on long-context conversational memory questions — a strong result.
The use cases it handles cleanly: remembering that a user prefers metric units, tracking that a customer previously complained about billing, surfacing a preference a user stated three sessions ago. For consumer-facing AI assistants and personalized agents, it is a reasonable default.
Who should stay on Mem0: teams building user-facing agents where the memory is per-user, the data originates from conversations, and graph retrieval is not yet needed. The Standard tier at $19/month covers vector search. If that fits, there is no reason to switch.
Why teams look for Mem0 alternatives
According to Vectorize's 2026 survey, the two most common reasons are pricing architecture and capability depth.
On pricing: Mem0's graph features — entity relationships, multi-hop queries, structured knowledge — are gated behind the Pro tier at $249/month. The jump from $19 to $249 is steep when you have not yet validated that graph retrieval adds value in your specific pipeline.
On capability: as the Graphlit 2026 survey of agent memory frameworks argues, the interesting question in 2026 is no longer just "what did the user say before?" It is: what should the agent know right now, where did that knowledge come from, is it still true, and who is allowed to see it? Mem0's architecture was designed for the first question. The others require a different approach.
A third reason, less often stated: some teams conflate agent memory with company context. They want their AI to know what is in their CRM, their Slack channels, their finance system — and they reach for a memory framework because it sounds adjacent. It is not the same layer.
How to choose: route by your actual problem
Before the list, a routing question. Answer it honestly.
Is your data conversational or institutional?
Conversational data originates from interactions: what users say, what agents respond, what preferences emerge over sessions. Institutional data lives in your business systems: contracts in Google Drive, pipeline in Salesforce, decisions in Slack, invoices in QuickBooks.
If conversational: you need an agent memory framework. Compare Zep, Letta, Cognee, and Supermemory below.
If institutional: you need a business context layer for AI — a system that ingests your existing apps, permissions knowledge by role, and makes it available to the AI tools your team already uses. That is a different category, and Gyld is built for it.
If both: run both layers. They compose rather than compete.
Selection criteria used in this comparison:
- Retrieval accuracy and speed on published benchmarks (where available)
- Permission and governance controls
- Source attribution: does the system tell you where a fact came from?
- Data origin: conversations vs. business app integrations
- Pricing transparency
- Production readiness
The alternatives
1. Gyld — best for teams that need permissioned, source-cited company context inside AI agents
Best for: Founders, operators, and engineering teams who want Claude, ChatGPT, Cursor, or any MCP-compatible agent to understand their actual business — not just remember conversations.
What it is: Gyld is a business context layer for AI, sometimes called a company brain. It ingests data from the apps a company already uses — Slack, Gmail, Outlook, Notion, Google Drive, HubSpot, Salesforce, QuickBooks, and more — into a per-company knowledge base, then exposes that knowledge as MCP servers (Model Context Protocol). Any MCP-compatible agent plugs in and gets real company context: current, permissioned, and source-cited.
This is architecturally different from agent memory. Mem0 learns from conversations. Gyld indexes your business systems directly — no conversation required, no fine-tuning, no hand-built RAG pipeline to maintain.
Why it fits:
- Knowledge is permissioned at the item level: private, team-wide, or company-wide. An agent sees only what the user is allowed to see.
- Every answer carries a source citation — the Slack message, the document, the CRM record it came from. You can verify it.
- Stays current: as your apps update, the knowledge base updates. There is no stale snapshot problem.
- Works with agents you already use. Because Gyld exposes context as MCP servers, Claude Code, ChatGPT, Codex, and Cursor can all plug in without custom integration work. For more on what this unlocks, see what a context layer gives AI agents that bigger models cannot.
- The customer controls what gets indexed. Nothing is ingested without explicit selection.
Trade-offs:
- Gyld is not an agent memory framework. It does not track per-user conversation history or extract preferences from chat sessions. If that is your primary need, look at Zep or Letta instead.
- MCP compatibility is required on the agent side. Agents that do not support MCP need a different integration path.
- Pricing is not publicly listed; contact Gyld directly.
Pricing: Not published. Contact Gyld at gyld.ai.
2. Zep — best for production agent memory that needs accuracy and speed
Best for: Engineering teams building customer-facing agents where retrieval latency and accuracy are production requirements.
What it is: Zep is a managed agent memory service with a graph-based temporal memory layer. It tracks entities, relationships, and how facts change over time across sessions.
Why it fits:
- On the LoCoMo benchmark (1,540 questions), Zep scores 94.7% accuracy vs. Mem0's 91.6%.
- Retrieval is 35x faster than Mem0 on the same benchmark: 87ms vs. 3,060ms per Zep's published figures.
- Context windows are 17% smaller (5,760 vs. 6,956 tokens), which reduces inference cost.
- Graph memory tracks entity relationships and temporal changes — no manual schema required.
- Production customers include Zscaler, Samsung, and HoneyBook, per Zep's own published customer list.
Trade-offs:
- Benchmark figures are self-reported by Zep. Independent replication would strengthen the case.
- Zep is still a conversational memory layer. It does not integrate with business apps like Salesforce or QuickBooks.
- Pricing details require contacting sales for enterprise tiers.
Pricing: Free tier available. Enterprise pricing not published.
3. Letta (formerly MemGPT) — best for agents that need to manage their own memory
Best for: Teams building autonomous, long-running agents that need to decide what to remember, what to forget, and what to retrieve — without human intervention.
What it is: Letta is an open-source agent framework built around the idea of self-editing memory. Agents running on Letta can read and write their own memory blocks, making explicit decisions about what context to retain. It emerged from the MemGPT research project at UC Berkeley.
Why it fits:
- Agents control their own memory state, which is useful for long-horizon tasks where the relevant context changes significantly over time.
- Open-source core means you can inspect and modify the memory architecture.
- Active community and research backing from the MemGPT paper lineage.
- Handles multi-agent coordination: agents can share memory blocks.
Trade-offs:
- Higher implementation complexity than Mem0 or Zep. You are building with primitives, not a managed service.
- The self-editing model adds latency — the agent must reason about memory before acting.
- Not designed for ingesting structured business app data.
Pricing: Open-source core is free. Letta Cloud pricing not publicly detailed.
4. Cognee — best for teams that want knowledge graphs from unstructured documents
Best for: Teams that have large bodies of unstructured documents — research, reports, internal wikis — and want agents to reason over entity relationships within that corpus.
What it is: Cognee is an open-source memory and knowledge graph framework. It ingests documents, extracts entities and relationships, and builds a graph that agents can query. It sits between a pure vector store and a full graph database.
Why it fits:
- Automates entity extraction and relationship modeling from text — no manual ontology required.
- Supports both vector and graph retrieval, so agents can do semantic search and relationship traversal in the same query.
- Open-source, so the graph structure is inspectable and modifiable.
- Listed among the top graph-based memory solutions in Mem0's own January 2026 comparison.
Trade-offs:
- Requires engineering effort to set up and maintain. There is no managed cloud equivalent of Zep's ease of use.
- Entity extraction quality depends heavily on document quality and model choice.
- No native integrations with live business apps — you feed it documents, not live Slack or CRM data.
- Permission controls are not a built-in feature; you manage access at the application layer.
Pricing: Open-source. Hosted pricing not published.
5. Supermemory — best for lightweight personal and team memory without infrastructure overhead
Best for: Smaller teams or individual developers who want a simple, hosted memory API without managing vector databases or graph infrastructure.
What it is: Supermemory is a hosted memory API that stores and retrieves information across sessions. It is designed for simplicity: send text in, retrieve relevant context out. The focus is on ease of integration rather than advanced graph features.
Why it fits:
- Minimal setup: a few API calls and you have persistent memory across sessions.
- Managed infrastructure — no vector database to operate.
- Works well for lightweight personalization use cases where Mem0's pricing is the blocker.
Trade-offs:
- Feature depth is limited compared to Zep or Letta. No graph memory, no temporal entity tracking.
- Not designed for enterprise governance requirements: permissions, audit logs, source attribution.
- Smaller community and less published benchmark data than Mem0 or Zep.
Pricing: Free tier available. Paid tiers not publicly detailed at time of writing.
6. Graphlit — best for teams building document-heavy AI pipelines
Best for: Developer teams building content pipelines where ingesting, chunking, and retrieving from large document sets is the core requirement.
What it is: Graphlit is a managed content ingestion and retrieval API. It handles document parsing, chunking, embedding, and retrieval — the infrastructure layer under a RAG pipeline — without requiring teams to assemble those components themselves.
As the Graphlit 2026 survey distinguishes, Graphlit sits in the context assembly category rather than the conversational memory category. It is about getting the right content into context for a task, not about remembering what a user said.
Why it fits:
- Strong document ingestion: PDFs, web pages, audio transcripts, and more.
- Managed chunking and embedding removes infrastructure decisions from the team.
- Free tier includes 100 credits/month with no credit card required, per Graphlit's own site.
- Good fit for teams that want RAG-style retrieval without building the pipeline.
Trade-offs:
- No native business app integrations (Slack, Salesforce, HubSpot). You push documents to it; it does not pull from live systems.
- No permission layer built in — access control lives in your application.
- Source citation depends on how you structure ingestion; it is not automatic.
Pricing: Free tier (100 credits/month). Paid tiers not published at time of writing.
How to choose
| Situation | Go with |
|---|---|
| Need faster, more accurate conversational memory in production | Zep |
| Building autonomous agents that manage their own context | Letta |
| Large unstructured document corpus, need graph reasoning | Cognee |
| Lightweight memory API, Mem0 pricing is the blocker | Supermemory |
| Document-heavy pipeline, want managed RAG infrastructure | Graphlit |
| Need AI to understand your business apps — Slack, Salesforce, Notion, Gmail — with permissions and source citations | Gyld |
| Need both conversational memory and company context | Zep or Letta + Gyld |
The clearest signal: if the data you want the agent to know originates from conversations, you need an agent memory framework. If it originates from your business systems, you need a company context layer. These are complementary layers, not competing ones.
For a deeper look at why context from business systems is distinct from what memory frameworks provide, see why AI agents give wrong answers about your business.
If your concern is specifically about what gets indexed and who can see it, MCP server security for enterprise covers how permissioned context works in practice.
Frequently asked questions
Is Mem0 good enough for most teams?
For user-facing agents where the memory is per-user and conversational, Mem0's Standard tier at $19/month handles the common cases well. The gap appears when teams need graph retrieval (gated at $249/month), production-grade latency, or company-wide institutional knowledge from business apps.
What is the difference between agent memory and a company context layer?
Agent memory tracks what happened in conversations: user preferences, past interactions, stated facts. A company context layer indexes what exists in your business systems: documents, CRM records, Slack decisions, financial data. The data origins are different, the permission models are different, and the retrieval patterns are different. Most production AI deployments will eventually need both.
Can I use Gyld alongside Mem0 or Zep?
Yes. Gyld exposes company knowledge as MCP servers; Mem0 or Zep handle per-user conversational memory. An agent can call both: Gyld for "what did we agree with this customer in the contract?" and Zep for "what did this user tell me last session?" They operate at different layers.
How does source citation work in a context layer?
In Gyld, every piece of knowledge is traced to the source record it came from — a specific Slack message, a Google Drive document, a HubSpot note. When an agent answers a question, the answer includes a reference to that source. This matters for verification: a team can check whether the answer is accurate without re-running the query. For more on why this matters operationally, see company brain examples: questions teams ask and sources that answer.
What benchmark should I use to compare agent memory systems?
The LoCoMo benchmark (1,540 questions on long-context conversational memory) is the most commonly cited in 2026. Zep publishes results showing 94.7% accuracy vs. Mem0's 91.6% and 35x faster retrieval, though these figures are self-reported by Zep. The arXiv paper "Memory in the Age of AI Agents" provides a broader framework for evaluating what memory systems need to handle.
Does Gyld require building a RAG pipeline?
No. Gyld handles ingestion, chunking, embedding, permissioning, and retrieval internally. Teams connect their apps, choose what to index, and the MCP server is ready. There is no pipeline to build or maintain. For a direct comparison of this approach to building RAG yourself, see how to ground AI in your company data.
What happens when my business data changes?
Gyld keeps the knowledge base current as connected apps update. There is no manual re-indexing step. This is the key operational difference from a one-time document upload: the context your agents work with reflects the current state of your business, not a snapshot from last month.
Related reading
- What a Context Layer Gives AI Agents That Bigger Models Cannot — why scaling the model does not solve the company knowledge problem.
- Company Brain vs Knowledge Base: Why a Wiki Nobody Updates Isn't Company Context for AI — the structural difference between a static wiki and live, indexed company context.
- How to Ground AI in Your Company Data (So Agents Stop Guessing) — practical steps for connecting business systems to AI without building a pipeline.
- MCP Server Security for Enterprise: Keep AI Informed, Not Exposed — how permissioned context works when the stakes are real.
If the problem you are solving is company context — getting AI to know what is in your Slack, your CRM, your documents, with permissions and source citations intact — start building your company brain with Gyld and connect the apps your team already uses.