Platform vs Open Source

Compare Mem0 Platform and Open Source to choose the right solution for managed hosting or self-hosted deployment.

Which Mem0 is right for you?

Mem0 offers two ways to add memory to your AI applications. Both run the same core extraction and retrieval logic; the Platform adds hosting, a small set of v3-only capabilities, and management surfaces that OSS does not have.

Mem0 Platform

Managed, hassle-free

Get started in 5 minutes with our hosted solution. No vector store, LLM, or embedder to configure.

Open Source

Self-hosted, full control

Deploy on your infrastructure. Choose your vector DB, LLM, and configure everything.

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What’s the same

The core memory loop is identical on both: add, search, get, get_all, update, delete, delete_all, and per-memory history all exist on the self-hosted Memory/AsyncMemory classes and on the hosted MemoryClient/AsyncMemoryClient. Both support:

• Entity scoping by user_id, agent_id, and run_id
• Filter grouping: both accept AND/OR/NOT wrappers, both implicitly AND a flat multi-key filter like {"user_id": "alice", "agent_id": "a1"}, and both accept * as a wildcard value. Which fields you may filter on, and which operators each field accepts, differ (see below)
• Entity-aware ranking: both extract entities from memory text and use shared entities to boost related results at search time
• Multimodal input, memory expiration (expiration_date), reranking, procedural memory (Python), and custom extraction instructions (custom_instructions)
• Python and JavaScript SDKs, plus a REST API (self-hosted via server/, or hosted)

What’s actually different

Hosting & infrastructure

Feature	Platform	Open Source
Vector store, LLM, embedder	Run and tuned by Mem0	You provision, configure, and pay for each
Scaling & availability	Managed	Your responsibility
Web dashboard	app.mem0.ai	Not included
Time to first memory	Minutes (API key only)	Depends on your vector DB / LLM setup

Entity scoping & workspace structure

Feature	Platform	Open Source
Scoping identifiers	user_id, agent_id, run_id, plus app_id for app/tenant separation	user_id, agent_id, run_id only, no app_id
Organizations & projects	Multi-org, multi-project, with member roles (API reference)	No org/project concept; a single local config
Project-wide event feed	GET /v1/events/ lists recent add/search/delete events per org and project, usable for dashboards, alerting, or audit trails	Only per-memory history(memory_id), no project-wide event log

See Entity-Scoped Memory for the full app_id model.

Search-time ranking (v3-only)

Feature	Platform	Open Source
Graph Memory	Native, always-on graph over extracted entities; connections feed directly into the ranking score (details)	Removed. OSS previously connected external graph stores (Neo4j, Memgraph, Kuzu, Apache AGE); that integration was dropped when the v3 pipeline landed. OSS still extracts entities and uses them to boost ranking, but there is no queryable graph and no relations field
Memory Decay	Opt-in per project; reinforces recently-used memories and gently dampens stale ones at search time (details)	Not supported. Passing decay raises "The decay parameter is not supported by the OSS Memory SDK."
Temporal Reasoning	Boosts memories whose event dates match the time expressed in a query (timestamp / reference_date) (details)	Not supported. Both parameters raise a “not supported by the OSS Memory SDK” error
Dream (background consolidation)	Continuously synthesizes patterns, supersedes outdated facts, and merges duplicates per user (details)	Not available

Configuration & data operations

Feature	Platform	Open Source
Custom categories	Set per project or per add call (details)	Not supported. Memory.add() has no custom_categories parameter, and OSS project updates are rejected outright
Webhooks	Project-scoped HTTP callbacks on memory and ingest events (details)	Not available
Memory Export	Schema-driven structured export jobs over filtered memories (details)	Not available
Batch operations	batch_update and batch_delete apply up to 1000 memories per call	Not available. Loop over the single-memory update/delete calls
Feedback	feedback(memory_id, ...) records POSITIVE, NEGATIVE, or VERY_NEGATIVE signals against a retrieved memory (details)	Not available
Summaries	get_summary(filters) returns a generated summary over the matching memories	Not available
Filterable fields	Top-level filter keys come from a fixed allowlist: user_id, agent_id, app_id, run_id, created_at, updated_at, timestamp, expiration_date, categories, metadata, keywords, memory_ids, plus the AND/OR/NOT operators and a few endpoint-specific extras. Any other key is rejected with a 400 (details)	Any metadata key is filterable directly, with no allowlist
Comparison operators	Depends on the field: metadata accepts eq, ne, contains; categories accepts contains, in; the date fields accept the range operators	The filter layer accepts the full set on any key: eq, ne, gt, gte, lt, lte, in, nin, contains, icontains. What actually runs depends on the vector store you configured, so confirm operator coverage for yours (details)

Support

Channel	Platform	Open Source
Community & maintainers	Discord, GitHub Discussions, direct calls with the founders	Same: Discord, GitHub Discussions, direct calls with the founders

Support channels are currently the same for both. If you need something contractual (a support SLA, for example), ask before assuming it exists: it is not documented as a Platform benefit today.

User Profiles are not listed above. The feature is still being finalized internally, so this page does not present it as an available Platform benefit.

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Decision Guide

Choose Platform if you want:

• Zero infrastructure: no vector store, LLM, or embedder to provision or tune.
• The v3-only ranking features: Graph Memory, Memory Decay, Temporal Reasoning, and Dream.
• App-level and org/project-level scoping, plus webhooks, memory export, and custom categories.

Choose Open Source if you need:

• Full data control: host everything on your own infrastructure.
• Custom configuration: your own vector DB, LLM provider, and embedder (25 vector stores, 18 LLM providers, 11 embedders at the time of writing).
• Extensibility: modify the codebase, add custom providers, and contribute back.
• Cost optimization: local LLMs (Ollama), self-hosted vector DBs, no usage-based billing.

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