Mem0 Migration

How to migrate from Mem0 to Zep

Zep delivers agent memory at enterprise scale, unifying chat and business data into a dynamic temporal Context Graph for each user. It tracks entities, relationships, and facts as they evolve, enabling you to build prompts with only the most relevant information—reducing hallucinations, improving recall, and lowering LLM costs.

Zep provides high-level APIs like thread.get_user_context and deep search with graph.search, supports custom entity/edge types, hybrid search, and granular graph updates. Mem0, by comparison, offers basic add/get/search APIs and an optional graph, but lacks built-in data unification, ontology customization, temporal fact management, and fine-grained graph control.

Got lots of data to migrate? Contact us for a discount and increased API limits.

Zep’s context model in one minute

Unified customer record

Messages sent via thread.add_messages go straight into the user’s knowledge graph; business objects (JSON, docs, e-mails, CRM rows) flow in through graph.add. Zep automatically deduplicates entities and keeps every fact’s valid and invalid dates so you always see the latest truth.

Domain-depth ontology

You can define Pydantic-style custom entity and edge classes so the graph speaks your business language (Accounts, Policies, Devices, etc.).

Temporal facts

Every edge stores when a fact was created, became valid, was invalidated, and (optionally) expired.

Hybrid & granular search

graph.search supports hybrid BM25 + semantic queries, graph search, with pluggable rerankers (RRF, MMR, cross-encoder) and can target nodes, edges, episodes, or everything at once.

How Zep differs from Mem0

Capability	Zep	Mem0
Business-data ingestion	Native via `graph.add` (JSON or text); business facts merge with user graph	No direct ingestion API; business data must be rewritten as “memories” or loaded into external graph store
Knowledge-graph storage	Built-in temporal graph; zero infra for developers	Optional “Graph Memory” layer that requires Neo4j/Memgraph and extra config
Custom ontology	First-class entity/edge type system	Not exposed; relies on generic nodes/relationships
Fact life-cycle (valid/invalid)	Automatic and queryable	Not documented / not supported
User summary customization	User summary instructions customize entity summaries per user	Not available
Search	Hybrid vector + graph search with multiple rerankers	Vector search with filters; basic Cypher queries if graph layer enabled
Graph CRUD	Full node/edge CRUD & bulk episode ingest	Add/Delete memories; no low-level edge ops
Context block	Auto-generated, temporal, prompt-ready	You assemble snippets manually from `search` output
LLM integration	Returns ready-made context; easily integrates with agentic tools	Returns raw strings you must format

SDK support

Zep offers Python, TypeScript, and Go SDKs. See Installation Instructions for more details.

Migrating your code

Basic flows

What you do in Mem0	Do this in Zep
`client.add(messages, user_id=ID)` → stores conversation snippets	`zep.thread.add_messages(thread_id, messages=[...])` – keeps chat sequence and updates graph
`client.add("json...", user_id=ID)` (not really supported)	`zep.graph.add(data=<JSON>, type="json", user_id=user_id)` – drop raw business records right in
`client.search(query, user_id=ID)` – vector+filter search	Easy path: `zep.thread.get_user_context(thread_id)` returns the `user_context.context` + recent messages Deep path: `zep.graph.search(query="...", user_id=user_id, reranker="rrf")`
`client.get_all(user_id=ID)` – list memories	`zep.graph.node.get_by_user_id(user_id)` to get all nodes, or `zep.graph.edge.get_by_user_id(user_id)` to get all edges
`client.update(memory_id, ...)` / `delete`	`zep.graph.edge.delete(uuid_="edge_uuid")` or `zep.graph.episode.delete(uuid_="episode_uuid")` for granular edits. Facts may not be updated directly; new data automatically invalidates old.

Practical tips

Thread mapping: Map Mem0’s user_id → Zep user_id, and create thread_id per conversation thread.
Business objects: Convert external records to JSON or text and feed them through graph.add; Zep will handle entity linking automatically.
Prompting: Replace your custom “summary builder” with the context block; it already embeds temporal ranges and entity summaries.
Summary customization: Use user summary instructions to guide how Zep generates entity summaries for each user, tailoring the context to your specific use case.
Search tuning: Start with the default rrf reranker; switch to mmr, node_distance, cross_encoder, or episode_mentions when you need speed or precision tweaks.

Side-by-side SDK cheat-sheet

Operation	Mem0 Method (Python)	Zep Method (Python)	Notes
Add chat messages	`m.add(messages, user_id=...)`	`zep.thread.add_messages(thread_id, messages)`	Zep expects ordered AI + user msgs per turn
Add business record	n/a (work-around)	`zep.graph.add(data, type, user_id=user_id)`	Direct ingestion of JSON/text
Retrieve context	`m.search(query,... )`	`zep.thread.get_user_context(thread_id)`	Zep auto-selects facts; no prompt assembly
Semantic / hybrid search	`m.search(query, ...)`	`zep.graph.search(query="...", user_id=user_id, reranker=...)`	Multiple rerankers, node/edge scopes
List memories	`m.get_all(user_id)`	`zep.graph.node.get_by_user_id(user_id)` or `zep.graph.edge.get_by_user_id(user_id)`	Get all nodes or edges for a user
Update fact	`m.update(id, ...)`	Not directly supported - add new data to supersede	Facts are temporal; new data invalidates old
Delete fact	`m.delete(id)`	`zep.graph.edge.delete(uuid_="edge_uuid")`	Episode deletion removes associated edges
Customize user summaries	not supported	`zep.user.add_user_summary_instructions(instructions=[...], user_ids=[user_id])`	Up to 5 custom instructions per user

Where to dig deeper

Quickstart
Graph Search guide
Entity / Edge customization
User summary instructions
Graph CRUD: Reading from the Graph | Adding to the Graph | Deleting from the Graph

For any questions, ping the Zep Discord or contact your account manager. Happy migrating!

Got lots of data to migrate? Contact us for a discount and increased API limits.

Zep’s context model in one minute

Unified customer record

Messages sent via thread.add_messages go straight into the user’s knowledge graph; business objects (JSON, docs, e-mails, CRM rows) flow in through graph.add. Zep automatically deduplicates entities and keeps every fact’s valid and invalid dates so you always see the latest truth.

Domain-depth ontology

You can define Pydantic-style custom entity and edge classes so the graph speaks your business language (Accounts, Policies, Devices, etc.).

Temporal facts

Every edge stores when a fact was created, became valid, was invalidated, and (optionally) expired.

Hybrid & granular search

graph.search supports hybrid BM25 + semantic queries, graph search, with pluggable rerankers (RRF, MMR, cross-encoder) and can target nodes, edges, episodes, or everything at once.

How Zep differs from Mem0

Capability	Zep	Mem0
Business-data ingestion	Native via `graph.add` (JSON or text); business facts merge with user graph	No direct ingestion API; business data must be rewritten as “memories” or loaded into external graph store
Knowledge-graph storage	Built-in temporal graph; zero infra for developers	Optional “Graph Memory” layer that requires Neo4j/Memgraph and extra config
Custom ontology	First-class entity/edge type system	Not exposed; relies on generic nodes/relationships
Fact life-cycle (valid/invalid)	Automatic and queryable	Not documented / not supported
User summary customization	User summary instructions customize entity summaries per user	Not available
Search	Hybrid vector + graph search with multiple rerankers	Vector search with filters; basic Cypher queries if graph layer enabled
Graph CRUD	Full node/edge CRUD & bulk episode ingest	Add/Delete memories; no low-level edge ops
Context block	Auto-generated, temporal, prompt-ready	You assemble snippets manually from `search` output
LLM integration	Returns ready-made context; easily integrates with agentic tools	Returns raw strings you must format

SDK support

Zep offers Python, TypeScript, and Go SDKs. See Installation Instructions for more details.

Migrating your code

Basic flows

What you do in Mem0	Do this in Zep
`client.add(messages, user_id=ID)` → stores conversation snippets	`zep.thread.add_messages(thread_id, messages=[...])` – keeps chat sequence and updates graph
`client.add("json...", user_id=ID)` (not really supported)	`zep.graph.add(data=<JSON>, type="json", user_id=user_id)` – drop raw business records right in
`client.search(query, user_id=ID)` – vector+filter search	Easy path: `zep.thread.get_user_context(thread_id)` returns the `user_context.context` + recent messages Deep path: `zep.graph.search(query="...", user_id=user_id, reranker="rrf")`
`client.get_all(user_id=ID)` – list memories	`zep.graph.node.get_by_user_id(user_id)` to get all nodes, or `zep.graph.edge.get_by_user_id(user_id)` to get all edges
`client.update(memory_id, ...)` / `delete`	`zep.graph.edge.delete(uuid_="edge_uuid")` or `zep.graph.episode.delete(uuid_="episode_uuid")` for granular edits. Facts may not be updated directly; new data automatically invalidates old.

Practical tips

Thread mapping: Map Mem0’s user_id → Zep user_id, and create thread_id per conversation thread.
Business objects: Convert external records to JSON or text and feed them through graph.add; Zep will handle entity linking automatically.
Prompting: Replace your custom “summary builder” with the context block; it already embeds temporal ranges and entity summaries.
Summary customization: Use user summary instructions to guide how Zep generates entity summaries for each user, tailoring the context to your specific use case.
Search tuning: Start with the default rrf reranker; switch to mmr, node_distance, cross_encoder, or episode_mentions when you need speed or precision tweaks.

Side-by-side SDK cheat-sheet

Operation	Mem0 Method (Python)	Zep Method (Python)	Notes
Add chat messages	`m.add(messages, user_id=...)`	`zep.thread.add_messages(thread_id, messages)`	Zep expects ordered AI + user msgs per turn
Add business record	n/a (work-around)	`zep.graph.add(data, type, user_id=user_id)`	Direct ingestion of JSON/text
Retrieve context	`m.search(query,... )`	`zep.thread.get_user_context(thread_id)`	Zep auto-selects facts; no prompt assembly
Semantic / hybrid search	`m.search(query, ...)`	`zep.graph.search(query="...", user_id=user_id, reranker=...)`	Multiple rerankers, node/edge scopes
List memories	`m.get_all(user_id)`	`zep.graph.node.get_by_user_id(user_id)` or `zep.graph.edge.get_by_user_id(user_id)`	Get all nodes or edges for a user
Update fact	`m.update(id, ...)`	Not directly supported - add new data to supersede	Facts are temporal; new data invalidates old
Delete fact	`m.delete(id)`	`zep.graph.edge.delete(uuid_="edge_uuid")`	Episode deletion removes associated edges
Customize user summaries	not supported	`zep.user.add_user_summary_instructions(instructions=[...], user_ids=[user_id])`	Up to 5 custom instructions per user

Where to dig deeper

Quickstart
Graph Search guide
Entity / Edge customization
User summary instructions
Graph CRUD: Reading from the Graph | Adding to the Graph | Deleting from the Graph

For any questions, ping the Zep Discord or contact your account manager. Happy migrating!