Open Source: Migrating to the New Memory Algorithm

Breaking changes ahead. This release includes renamed parameters, removed parameters, changed defaults, and a fundamentally different extraction model. Read this guide before upgrading.

Overview

The new Mem0 release redesigns both extraction and retrieval, and cleans up the SDK surface across Python and TypeScript:

Extraction: Single-pass ADD-only (one LLM call, no UPDATE/DELETE)
Retrieval: Multi-signal hybrid search (semantic + BM25 keyword + entity matching)
Entity linking: Automatic entity extraction and cross-memory linking
SDK cleanup: Deprecated parameters removed, naming conventions standardized
API surface aligned with Platform: Entity IDs now follow the same convention across OSS and Platform: top-level kwargs for add() / delete_all(), inside filters for search() / get_all()

These changes produce a +20 point improvement on LoCoMo (71.4 → 91.6) and +26 point improvement on LongMemEval (67.8 → 93.4), while cutting extraction latency roughly in half.

Breaking Changes

Python Open Source

Change	Old	New	Migration
`search()` / `get_all()` entity IDs	Top-level kwargs (`user_id="..."`)	Inside `filters` dict	`m.search("q", filters={"user_id": "..."})`: top-level kwargs now raise `ValueError`
`top_k` default	`100`	`20`	Pass `top_k=100` explicitly to restore
`threshold` default	`None` (no filtering)	`0.1` (filters low-relevance)	Pass `threshold=0.0` for old behavior
`threshold` validation	Any float	Must be in `[0, 1]`	Out-of-range values now raise `ValueError`
`rerank` default	`True`	`False`	Pass `rerank=True` to restore
Entity ID validation	Accepted any string	Trimmed; empty / whitespace-only rejected (`ValueError`)	Pass a non-empty identifier without internal spaces
`messages` in `add()`	Could be `None`	Must be `str` / `dict` / `list[dict]`: other types raise `Mem0ValidationError` (code `VALIDATION_003`)	Always pass a string, dict, or list of messages
`add()` events	Returns `ADD`, `UPDATE`, `DELETE`	Returns `ADD` only	Update code expecting UPDATE/DELETE
Custom extraction prompt	`custom_fact_extraction_prompt`	`custom_instructions`	Rename in config
Custom update prompt	`custom_update_memory_prompt`	Deprecated	Use `custom_instructions` instead
Graph memory	`enable_graph` + `graph_store` in config	Removed	Graph store support has been removed entirely
Qdrant client	`>=1.9.1`	`>=1.12.0`	Update dependency
Upstash client	`>=0.1.0`	`>=0.6.0`	Update dependency

TypeScript Open Source

Change	Old	New	Migration
Search parameter	`search(query, { limit: 10 })`	`search(query, { topK: 10 })`	Rename `limit` → `topK`
`topK` default	`100`	`20`	Pass `topK: 100` explicitly to restore
`search()` / `getAll()` entity IDs	Top-level options (`userId: "..."`)	Inside `filters` object	`m.search("q", { filters: { userId: "..." } })`
`threshold` validation	Any number	Must be in `[0, 1]`	Out-of-range values now throw
Entity ID validation	Any string	Trimmed; empty / whitespace-only rejected	Pass non-empty identifiers without internal spaces
`messages` in `add()`	Could be `null` / `undefined`	Required: throws on null/undefined	Always pass a string or array
Payload key for lemmatized text	`text_lemmatized` (snake_case)	`textLemmatized` (camelCase)	TS-only internal field. If you share a vector store collection between Python and TS SDKs, lemma-based BM25 will not resolve across languages: keep collections language-scoped.
Custom prompt	`customPrompt`	`customInstructions`	Rename in config
Graph memory	`enableGraph` + `graphStore` in config	Removed	Graph store support has been removed entirely
Default graph config	Neo4j default config applied	No default graph config	Graph store config is no longer used

Python Client SDK

Change	Old	New	Migration
Constructor	`MemoryClient(api_key, org_id, project_id)`	`MemoryClient(api_key)`	Remove `org_id`, `project_id` from constructor
Method options	`client.add(messages, **kwargs)`	`client.add(messages, options=AddMemoryOptions(...))`	Use typed option classes (or `**kwargs` still works)
Removed params	`api_version`, `output_format`, `async_mode`, `filter_memories`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name`	N/A	Remove from all calls

TypeScript Client SDK

Change	Old	New	Migration
Constructor	`new MemoryClient({ apiKey, organizationId, projectId })`	`new MemoryClient({ apiKey })`	Remove `organizationId`, `projectId`, `organizationName`, `projectName`
All params	snake_case: `user_id`, `agent_id`, `top_k`	camelCase: `userId`, `agentId`, `topK`	Rename all params to camelCase
Removed params	`api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `includes`, `excludes`, `keyword_search`	N/A	Remove from all calls
Output format enum	`OutputFormat.V1`, `OutputFormat.V1_1`	Removed	v1.1 is now always used
API version enum	`API_VERSION.V1`, `API_VERSION.V2`	Removed	Handled internally

Step-by-Step Migration

1. Update Installation

Python
TypeScript

# Basic upgrade
pip install --upgrade mem0ai

# For hybrid search + entity extraction (recommended)
pip install --upgrade "mem0ai[nlp]"
python -m spacy download en_core_web_sm

# Qdrant users: also install fastembed to enable BM25 keyword search
pip install fastembed

Supported Python versions for [nlp] extras: 3.10 – 3.12. spaCy and its blis / thinc dependencies do not yet ship prebuilt wheels for Python 3.13, so installs on 3.13 will fail at build time. Use Python 3.12 (or older) for the [nlp] extras until upstream support lands. The base mem0ai package works on all supported Python versions; only the NLP extras are constrained.

npm install mem0ai@latest

The Python [nlp] extra installs spaCy for entity extraction and keyword lemmatization. Without it, Mem0 still works but falls back to semantic-only search (no entity linking, no BM25 lemmatization).

Qdrant users: install fastembed to enable BM25 keyword search. The Qdrant backend uses fastembed to encode sparse (BM25) vectors alongside dense vectors in the same collection. Without it, BM25 is silently disabled and search falls back to semantic-only. The first search call logs a warning that BM25 keyword search is disabled. Other vector stores use their native full-text capabilities and don’t need fastembed.

pip install fastembed

2. Update Configuration

Python OSS
TypeScript OSS

# Before
config = {
    "custom_fact_extraction_prompt": "Focus on user preferences",  # [REMOVED] Renamed
    "custom_update_memory_prompt": "Be concise when updating",      # [REMOVED] Deprecated
    "graph_store": {
        "provider": "neo4j",
        "config": { "url": "...", "username": "...", "password": "..." }
    },
    "enable_graph": True,  # [REMOVED] Removed
}

# After
config = {
    "custom_instructions": "Focus on user preferences",  # [OK] New name
    # custom_update_memory_prompt removed: use custom_instructions
    # enable_graph and graph_store removed: graph store support has been removed
}

// Before
const config = {
  customPrompt: "Focus on user preferences",  // [REMOVED] Renamed
  enableGraph: true,                            // [REMOVED] Removed
  graphStore: {
    provider: "neo4j",
    config: { url: "...", username: "...", password: "..." }
  }
};

// After
const config = {
  customInstructions: "Focus on user preferences",  // [OK] New name
  // enableGraph and graphStore removed: graph store support has been removed
};

3. Update Search Calls

Python OSS
TypeScript OSS
Python Client SDK
TypeScript Client SDK

# Before: entity IDs as top-level kwargs
results = m.search(
    "what meetings did I attend?",
    user_id="alice",
    top_k=20
)
for r in results:
    print(r["score"])  # Was raw cosine similarity

# After: entity IDs go inside `filters` (matches Platform API)
results = m.search(
    "what meetings did I attend?",
    filters={"user_id": "alice"},   # [REMOVED top-level kwarg, use filters]
    top_k=20,           # New default is 20 (was 100)
    threshold=0.1,      # New default (pass 0.0 to disable)
    rerank=False         # New default (pass True to restore)
)
for r in results:
    print(r["score"])

Passing user_id, agent_id, or run_id as a top-level kwarg to search() or get_all() now raises ValueError. They must be inside the filters dict. The change aligns the OSS SDK with the Platform API contract.

// Before: entity IDs as top-level options
const results = await m.search("what meetings did I attend?", {
  userId: "alice",
  limit: 20          // [REMOVED] Renamed to 'topK' for consistency
});

// After: entity IDs go inside `filters` (matches Platform API)
const results = await m.search("what meetings did I attend?", {
  filters: { userId: "alice" },   // [REMOVED top-level option, use filters]
  topK: 20                        // [OK] Renamed from 'limit'
});

from mem0 import MemoryClient
from mem0.client.types import SearchMemoryOptions

# Before
client = MemoryClient(api_key="...", org_id="org-1", project_id="proj-1")
results = client.search("query", user_id="alice", top_k=20, enable_graph=True)

# After
client = MemoryClient(api_key="...")  # org_id, project_id removed
results = client.search(
    "query",
    options=SearchMemoryOptions(
        filters={"user_id": "alice"},
        top_k=20
    )
)

// Before
const client = new MemoryClient({
  apiKey: "...",
  organizationId: "org-1",  // [REMOVED] Removed
  projectId: "proj-1"       // [REMOVED] Removed
});
const results = await client.search("query", {
  user_id: "alice",         // [REMOVED] snake_case
  top_k: 20,                // [REMOVED] snake_case
  enable_graph: true        // [REMOVED] Removed
});

// After
const client = new MemoryClient({ apiKey: "..." });
const results = await client.search("query", {
  filters: { userId: "alice" },
  topK: 20
});

4. Update Add Calls

Python OSS
Python Client SDK
TypeScript Client SDK

# Before: could return ADD, UPDATE, DELETE events
result = m.add("I love hiking and my dog's name is Max", user_id="alice")
for item in result["results"]:
    if item["event"] == "ADD":
        print("New memory:", item["memory"])
    elif item["event"] == "UPDATE":
        print("Updated:", item["memory"])     # [REMOVED] No longer returned
    elif item["event"] == "DELETE":
        print("Deleted:", item["memory"])      # [REMOVED] No longer returned

# After: only ADD events
result = m.add("I love hiking and my dog's name is Max", user_id="alice")
for item in result["results"]:
    print("New memory:", item["memory"])  # Only ADD events

from mem0.client.types import AddMemoryOptions

# Before
client.add(messages, user_id="alice", async_mode=True, output_format="v1.1")

# After: async_mode and output_format removed (async by default, v1.1 always)
client.add(
    messages,
    options=AddMemoryOptions(user_id="alice")
)
# Or using **kwargs
client.add(messages, user_id="alice")

// Before
await client.add(messages, {
  user_id: "alice",       // [REMOVED] snake_case
  async_mode: true,       // [REMOVED] Removed
  output_format: "v1.1",  // [REMOVED] Removed
  enable_graph: true      // [REMOVED] Removed
});

// After
await client.add(messages, {
  userId: "alice"         // [OK] camelCase
});

The ADD-only model means memories accumulate over time. When information changes, the new fact is stored alongside the old one. Retrieval handles ranking: the most relevant, current information surfaces first.

5. Update Vector Store Dependencies

If you’re using Qdrant or Upstash, update your client libraries:

# Qdrant users
pip install "qdrant-client>=1.12.0"

# Upstash users
pip install "upstash-vector>=0.6.0"

6. Entity Store Setup

The new algorithm automatically creates a parallel entity store collection named {your_collection}_entities. No manual setup is required: it’s created on first use.

Make sure your vector store user/credentials have permission to create new collections. If you’re using a managed vector database with restricted permissions, pre-create the {collection_name}_entities collection with the same embedding dimensions as your main collection.

Graph Memory: Now Built-In

External graph store support has been removed from the open-source SDK and replaced by built-in graph memory (entity linking), which runs natively with no external dependencies. What was removed:

enable_graph / enableGraph config flag
graph_store / graphStore configuration block (Neo4j, Memgraph, Kuzu, Apache AGE, Neptune)
All external graph store code paths (~4000 lines)

What replaces it: Mem0 now builds the graph itself. It extracts entities (proper nouns, quoted text, compound noun phrases) from every memory during the add pipeline and stores them in a parallel collection ({collection}_entities) inside your existing vector store. Memories that share an entity are linked, and at search time entities from the query are matched against this collection to boost connected memories. The boost is folded into the combined score on each result. Migration:

Remove enable_graph / enableGraph from your config
Remove the graph_store / graphStore block: it is no longer read
Uninstall external graph drivers (neo4j, memgraph, etc.) if you were using them only for Mem0
No data migration is required. Built-in graph memory activates automatically on the next add() call.

The old relations field on search results (populated by the external graph store) is no longer returned. Entity connections are now applied through retrieval ranking rather than exposed as a separate, directly traversable structure. If your application read or traversed the relations array, you will need to redesign that part against the new API.

How the New Algorithm Works

Extraction: Single-Pass ADD-Only

Input conversation
    → Retrieve top-10 related existing memories (for deduplication context)
    → Single LLM call: extract all distinct new facts
    → Batch embed extracted memories
    → Hash-based deduplication (MD5, prevents exact duplicates)
    → Batch insert into vector store
    → Entity extraction + linking

The previous algorithm used two LLM calls: one to extract candidate facts, one to decide ADD/UPDATE/DELETE actions against existing memories. The new algorithm collapses this into a single call that only adds. The model spends its capacity on understanding the input rather than diffing against existing state.

Retrieval: Multi-Signal Hybrid Search

Query
    → Preprocess (lemmatize keywords, extract entities)
    → Parallel scoring:
        1. Semantic search (vector similarity)
        2. BM25 keyword search (normalized term matching)
        3. Entity matching (entity graph boost)
    → Score fusion → Top-K selection

Scoring: The three signals are normalized and fused into a single combined score per result. The fusion adapts based on which signals are available at runtime (semantic-only, semantic + BM25, or all three when spaCy + the entity store are active). BM25 is a boost signal, not a recall expander. Only semantic search results are candidates: BM25 and entity scores boost ranking but don’t add new candidates.

Vector Store Compatibility

All 15 supported vector stores have been enhanced with two new capabilities:

Capability	Purpose	Fallback if Unsupported
`keyword_search()`	BM25/full-text keyword matching	Falls back to semantic-only search
`search_batch()`	Batch search for entity matching	Falls back to sequential search

Qdrant-specific changes:

Now uses sparse vectors (BM25) alongside dense vectors in the same collection
Requires fastembed library for BM25 encoding (lazy-loaded, gracefully degrades)
Install: pip install fastembed

All other vector stores:

Enhanced with keyword_search() methods using their native full-text capabilities
No additional dependencies required

Graceful Degradation

The new features degrade gracefully when optional dependencies are missing:

Missing Dependency	Impact	Search Still Works?
spaCy (`mem0ai[nlp]`)	No entity extraction, no BM25 lemmatization	Yes (semantic-only)
`fastembed` (Qdrant)	No BM25 keyword search	Yes (semantic + entity)
Entity store unavailable	No entity boosting	Yes (semantic + BM25)

You always get semantic search. Hybrid search features layer on top when available.

Removed Parameters Reference

These parameters have been removed across all SDKs. Remove them from your code:

Python Client SDK: Removed parameters

Constructor: org_id, project_id All methods: api_version, output_format, async_mode, org_name, project_name, org_id, project_id add(): enable_graph, immutable, filter_memories, batch_size, force_add_only, includes, excludes, keyword_search search(): enable_graph get_all(): enable_graph project.update(): enable_graph

TypeScript Client SDK: Removed parameters

Constructor: organizationId, projectId, organizationName, projectName All methods: OutputFormat enum, API_VERSION enum add(): enable_graph / enableGraph, async_mode / asyncMode, output_format / outputFormat, immutable, filter_memories / filterMemories, batch_size / batchSize, force_add_only / forceAddOnly, includes, excludes, keyword_search / keywordSearch search(): enable_graph / enableGraph get_all(): enable_graph / enableGraph

Python OSS: Removed/renamed parameters

Config: custom_fact_extraction_prompt → renamed to custom_instructions Config: custom_update_memory_prompt → deprecated, use custom_instructions Config: enable_graph + graph_store → removed (graph store support removed entirely)

TypeScript OSS: Removed/renamed parameters

Config: customPrompt → renamed to customInstructions Config: enableGraph + graphStore → removed (graph store support removed entirely) search(): limit → renamed to topK

Common Issues

TypeScript: `limit` is not a valid parameter

The limit parameter has been renamed to topK in the TypeScript OSS:

// Before
const results = await m.search("query", { userId: "alice", limit: 20 });

// After
const results = await m.search("query", { filters: { userId: "alice" }, topK: 20 });

TypeScript Client: snake_case params no longer work

All TypeScript Client SDK parameters now use camelCase. The SDK handles conversion to/from the API automatically:

// Before
await client.search("query", { user_id: "alice", top_k: 20 });

// After
await client.search("query", { filters: { userId: "alice" }, topK: 20 });

`ValueError: Top-level entity parameters not supported in search() / get_all()`

search() and get_all() now require entity IDs inside filters. Top-level kwargs raise ValueError. This aligns the OSS SDK with the Platform API.

# Before
results = m.search("query", user_id="alice", top_k=20)

# After
results = m.search("query", filters={"user_id": "alice"}, top_k=20)

add() and delete_all() continue to accept entity IDs as top-level kwargs.

Search returns fewer results than before

The default threshold changed from None to 0.1. Low-relevance results that were previously included are now filtered out. To restore the old behavior:

results = m.search("query", filters={"user_id": "alice"}, threshold=0.0)

spaCy model not found

If you see errors about missing spaCy models, download the required model:

python -m spacy download en_core_web_sm

If spaCy is not installed at all, install the NLP extras:

pip install "mem0ai[nlp]"

Entity store collection creation fails

The entity store tries to create a {collection_name}_entities collection automatically. If your vector database has restricted permissions, pre-create this collection with the same embedding dimensions as your main collection.

Score values are different from before

The top-level score still ranges [0, 1], but it is computed differently in v3. Relative ranking between results stays comparable, but absolute numbers shift: retune any hard thresholds in your app against representative queries. If you need the raw cosine similarity for a specific use case, run an unboosted vector query directly against your vector store via vector_store.search(...).

Need Help?

Join our Discord community for real-time support
Open an issue on GitHub
Check the evaluation docs to benchmark the new algorithm on your data

​Overview

​Breaking Changes

​Python Open Source

​TypeScript Open Source

​Python Client SDK

​TypeScript Client SDK

​Step-by-Step Migration

​1. Update Installation

​2. Update Configuration

​3. Update Search Calls

​4. Update Add Calls

​5. Update Vector Store Dependencies

​6. Entity Store Setup

​Graph Memory: Now Built-In

​How the New Algorithm Works

​Extraction: Single-Pass ADD-Only

​Retrieval: Multi-Signal Hybrid Search

​Vector Store Compatibility

​Graceful Degradation

​Removed Parameters Reference

​Python Client SDK: Removed parameters

​TypeScript Client SDK: Removed parameters

​Python OSS: Removed/renamed parameters

​TypeScript OSS: Removed/renamed parameters

​Common Issues

​TypeScript: limit is not a valid parameter

​TypeScript Client: snake_case params no longer work

​ValueError: Top-level entity parameters not supported in search() / get_all()

​Search returns fewer results than before

​spaCy model not found

​Entity store collection creation fails

​Score values are different from before

​Need Help?

Overview

Breaking Changes

Python Open Source

TypeScript Open Source

Python Client SDK

TypeScript Client SDK

Step-by-Step Migration

1. Update Installation

2. Update Configuration

3. Update Search Calls

4. Update Add Calls

5. Update Vector Store Dependencies

6. Entity Store Setup

Graph Memory: Now Built-In

How the New Algorithm Works

Extraction: Single-Pass ADD-Only

Retrieval: Multi-Signal Hybrid Search

Vector Store Compatibility

Graceful Degradation

Removed Parameters Reference

Python Client SDK: Removed parameters

TypeScript Client SDK: Removed parameters

Python OSS: Removed/renamed parameters

TypeScript OSS: Removed/renamed parameters

Common Issues

TypeScript: `limit` is not a valid parameter

TypeScript Client: snake_case params no longer work

`ValueError: Top-level entity parameters not supported in search() / get_all()`

Search returns fewer results than before

spaCy model not found

Entity store collection creation fails

Score values are different from before

Need Help?