---
title: "Open Source: Migrating to the New Memory Algorithm"
description: "Guide for self-hosted Mem0 users to upgrade to the new memory algorithm with ADD-only extraction, hybrid search, and entity linking."
icon: "arrow-right"
iconType: "solid"
---

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

## 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` | — | 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` | — | 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

<Tabs>
  <Tab title="Python">
    ```bash
    # 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
    ```

    <Info>
    **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.
    </Info>
  </Tab>
  <Tab title="TypeScript">
    ```bash
    npm install mem0ai@latest
    ```
  </Tab>
</Tabs>

<Info>
The Python `[nlp]` extra installs [spaCy](https://spacy.io/) for entity extraction and keyword lemmatization. Without it, Mem0 still works but falls back to semantic-only search (no entity linking, no BM25 lemmatization).
</Info>

<Warning>
**Qdrant users — install `fastembed` to enable BM25 keyword search.** The Qdrant backend uses [fastembed](https://github.com/qdrant/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 — you'll see a log warning `"fastembed not installed — BM25 keyword search disabled"` on the first search call. Other vector stores use their native full-text capabilities and don't need `fastembed`.

```bash
pip install fastembed
```
</Warning>

### 2. Update Configuration

<Tabs>
  <Tab title="Python OSS">
    ```python
    # 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
    }
    ```
  </Tab>
  <Tab title="TypeScript OSS">
    ```typescript
    // 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
    };
    ```
  </Tab>
</Tabs>

### 3. Update Search Calls

<Tabs>
  <Tab title="Python OSS">
    ```python
    # 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"])
    ```

    <Warning>
    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.
    </Warning>
  </Tab>
  <Tab title="TypeScript OSS">
    ```typescript
    // 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'
    });
    ```
  </Tab>
  <Tab title="Python Client SDK">
    ```python
    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
        )
    )
    ```
  </Tab>
  <Tab title="TypeScript Client SDK">
    ```typescript
    // 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
    });
    ```
  </Tab>
</Tabs>

### 4. Update Add Calls

<Tabs>
  <Tab title="Python OSS">
    ```python
    # 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
    ```
  </Tab>
  <Tab title="Python Client SDK">
    ```python
    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")
    ```
  </Tab>
  <Tab title="TypeScript Client SDK">
    ```typescript
    // 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
    });
    ```
  </Tab>
</Tabs>

<Tip>
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.
</Tip>

### 5. Update Vector Store Dependencies

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

```bash
# 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.

<Warning>
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.
</Warning>

## 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.

<Warning>
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.
</Warning>

## 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:

```typescript
// 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:

```typescript
// 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.

```python
# 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:

```python
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:

```bash
python -m spacy download en_core_web_sm
```

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

```bash
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](https://mem0.ai/discord) for real-time support
- Open an issue on [GitHub](https://github.com/mem0ai/mem0/issues)
- Check the [evaluation docs](/core-concepts/memory-evaluation) to benchmark the new algorithm on your data
