v2.0.0

## Mem0 Python SDK (v2.0.0)

A ground-up redesign of how memories are extracted, stored, and retrieved — plus a long-overdue cleanup of the SDK surface.

## Highlights

- **New extraction algorithm** — single-pass, ADD-only, roughly half the latency
- **Multi-signal hybrid retrieval** — semantic + BM25 keyword + entity matching fused into one score
- **Built-in entity linking** — replaces graph memory with no external store to manage
- **Cleaner SDK surface** — constructor and method signatures aligned with the Platform API

## What's new

### Single-pass ADD-only extraction
One LLM call per `add()`. No separate UPDATE/DELETE pass. The model now spends its capacity on understanding the input instead of diffing against existing memories, and agent-generated facts ("I've booked your flight for March 3rd") are captured as first-class memories for the first time. Hash-based deduplication prevents exact duplicates; ranking at retrieval time handles the rest.

### Hybrid retrieval
Semantic vector similarity, BM25 keyword matching, and entity-graph boosting are normalized and fused into a single `score` on every result. The fusion adapts at runtime — if spaCy or `fastembed` aren't installed, search gracefully degrades to semantic-only instead of breaking.

### Entity linking (replaces graph memory)
Entities (proper nouns, quoted text, compound noun phrases) are extracted automatically during `add()` and stored in a parallel `{collection}_entities` collection inside your existing vector store. At query time, entities from the query boost ranking on matching memories. No Neo4j, Memgraph, Kuzu, or Apache AGE deployment needed — ~4,000 lines of graph driver code have been removed from the SDK.

### Vector store enhancements
All 15 vector stores now implement `keyword_search()` and `search_batch()`. Qdrant uses sparse vectors (BM25) alongside dense vectors in the same collection via `fastembed`; every other store uses its native full-text capability with no extra dependency.

### Stricter, clearer validation
- Empty or whitespace-only entity IDs raise `ValueError` with a specific message
- `threshold` must be in `[0, 1]` — out-of-range values raise instead of silently behaving oddly
- `messages=None` or wrong type raises `Mem0ValidationError` (code `VALIDATION_003`)
- `custom_fact_extraction_prompt` → renamed to `custom_instructions` (consolidates with `custom_update_memory_prompt`, which was deprecated)

### Platform client
- `MemoryClient(api_key="...")` — `org_id` / `project_id` no longer in the constructor
- `get_all` returns a paginated envelope: `{count, next, previous, results}`
- `add` is async by default and returns `{status: "PENDING", event_id}` — poll `/v1/event/{event_id}/` for completion
- Removed: `api_version`, `output_format`, `async_mode`, `enable_graph`, `filter_memories`, `expiration_date`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `org_name`, `project_name`

## Breaking changes at a glance

| Change | Migration |
|---|---|
| Entity IDs on `search()` / `get_all()` | Must be inside `filters={"user_id": "..."}`; top-level kwargs now raise `ValueError` |
| `top_k` default `100` → `20` | Pass `top_k=100` explicitly to restore |
| `threshold` default `None` → `0.1` | Pass `threshold=0.0` to disable |
| `rerank` default `True` → `False` | Pass `rerank=True` to restore |
| `graph_store` / `enable_graph` | Removed — uninstall graph drivers |
| `custom_update_memory_prompt` | Removed — use `custom_instructions` |
| `qdrant-client` | Bumped to `>=1.12.0` |
| `upstash-vector` | Bumped to `>=0.6.0` |

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

## Install

```bash
pip install --upgrade mem0ai

# Recommended — full hybrid search (Python 3.10–3.12):
pip install --upgrade "mem0ai[nlp]" fastembed
python -m spacy download en_core_web_sm
```

The base package works on any supported Python version; the `[nlp]` extra currently requires Python 3.10–3.12 (spaCy's `blis` / `thinc` don't ship Python 3.13 wheels yet).

Full migration guide: [docs.mem0.ai/migration/oss-v2-to-v3](https://docs.mem0.ai/migration/oss-v2-to-v3)