---
title: "Search"
sidebarTitle: "Search API"
description: "Semantic search across your memories and documents"
icon: "/icons/hugeicons/search-01.svg"
---

Search through your memories and documents with a single API call. Each search runs inside one `namespace` (what v3/v4 called a container tag).

<Tip>
**`searchMode: "hybrid"`** is the default and gives the best results. It searches both memories and document chunks, returning the most relevant content.
</Tip>

<Note>
**TypeScript SDK:** one call, `supermemory.search(namespace, { query, searchMode })`. `searchMode` (`"memories"`, `"chunks"`, or `"hybrid"`) picks what comes back. There is no v5 Python SDK yet; the Python tab shows the legacy client.
</Note>

## Quick start

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    import { Supermemory } from "supermemory";

    const supermemory = new Supermemory({ apiKey: process.env.SUPERMEMORY_API_KEY });

    const results = await supermemory.search("user_123", {
      query: "machine learning",
      searchMode: "hybrid",
      limit: 5,
    });

    results.results.forEach(result => {
      console.log(result.memory || result.chunk, result.similarity);
    });
    ```
  </Tab>
  <Tab title="Python">
    ```python
    from supermemory import Supermemory

    client = Supermemory()

    results = client.search(
        "user_123",
        query="machine learning",
        search_mode="hybrid",
        limit=5,
    )

    for result in results.results:
        print(result.memory or result.chunk, result.similarity)
    ```
  </Tab>
  <Tab title="cURL">
    ```bash
    curl -X POST "https://api.supermemory.ai/ns/user_123/search?searchMode=hybrid&limit=5" \
      -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{ "query": "machine learning" }'
    ```
  </Tab>
</Tabs>

**Response:**
```json
{
  "results": [
    {
      "id": "mem_xyz",
      "memory": "User is interested in machine learning for product recommendations",
      "similarity": 0.91,
      "metadata": { "topic": "interests" },
      "isLatest": true,
      "system": { "createdAt": "2024-01-15T10:30:00.000Z", "updatedAt": "2024-01-15T10:30:00.000Z" }
    },
    {
      "id": "chunk_abc",
      "chunk": "Machine learning enables personalized experiences at scale...",
      "similarity": 0.87,
      "metadata": { "source": "onboarding_doc" },
      "isLatest": true,
      "system": { "createdAt": "2024-01-14T09:15:00.000Z", "updatedAt": "2024-01-14T09:15:00.000Z" }
    }
  ],
  "searchTime": 92
}
```

<Info>
In hybrid mode, results contain either a `memory` field (extracted facts) or a `chunk` field (document content), depending on the source. Branch on field presence.
</Info>

---

## Parameters

`namespace` is the URL path. Every other option goes in the JSON body (the second argument in the SDKs).

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `namespace` | string | required | Namespace to search (URL path) |
| `query` | string | required | Search query |
| `searchMode` | string | `"hybrid"` | `"hybrid"` (recommended), `"memories"`, or `"chunks"` |
| `limit` | number | 10 | Max results (max 100) |
| `threshold` | 0-1 | 0.3 | Similarity cutoff (higher = fewer, better results) |
| `rerank` | string | `"none"` | `"none"`, `"order"` (re-score for relevance, +100ms), or `"aggregate"` |
| `rewriteQuery` | boolean | false | Generate multiple rewrites, search all of them, and merge results. No extra cost, but adds latency |
| `filter` | object | — | Metadata filter expression. See [Filtering](#filtering) |
| `include` | object | — | `{ documents, related, forgotten }` — opt in to extra context per result |

<Note>
Defaults changed from v4: `searchMode` was `memories` and is now `hybrid`; `threshold` was `0.6` and is now `0.3`. Set both explicitly if you are comparing against v4 results.
</Note>

### Search modes

- **`hybrid`** (default, recommended) — Searches both memories and document chunks, and returns both in the response
- **`memories`** — Only searches extracted memories
- **`chunks`** — Only searches raw document/chunk content, skipping extracted memories

```typescript
// Hybrid: memories + document chunks (default)
await supermemory.search("user_123", {
  query: "quarterly goals",
});

// Memories only: just extracted facts
await supermemory.search("user_123", {
  query: "user preferences",
  searchMode: "memories",
});

// Chunks only: RAG over documents
await supermemory.search("user_123", {
  query: "refund policy",
  searchMode: "chunks",
});
```

---

## Filtering

The `namespace` scopes results to a user or project. Use `filter` for metadata-based filtering inside that namespace:

```typescript
const results = await supermemory.search("user_123", {
  query: "meeting notes",
  filter: {
    operator: "and",
    operands: [
      { field: "type", operator: "eq", value: "meeting" },
      { field: "year", operator: "eq", value: 2024 },
    ],
  },
});
```

<Accordion title="Filter Types">
  - **Equality:** `{ field: "status", operator: "eq", value: "active" }` (also `neq`; strings, numbers, booleans)
  - **String contains:** `{ field: "title", operator: "contains", value: "react" }` (also `notContains`; optional `caseSensitive`)
  - **Numeric:** `{ field: "priority", operator: "gte", value: 5 }` (`gt`, `gte`, `lt`, `lte`)
  - **Array contains:** `{ field: "tags", operator: "arrayContains", value: "important" }` (also `arrayNotContains`)
  - **Logic:** `{ operator: "and" | "or", operands: [...] }`, nested up to 5 levels

  Keys are literal: `customer.plan` is one field name, not a nested path. See [Organizing & Filtering](/concepts/filtering) for full syntax.
</Accordion>

---

## Query optimization

### Reranking

Re-scores results for better relevance. Adds ~100ms latency.

```typescript
const results = await supermemory.search("user_123", {
  query: "complex technical question",
  rerank: "order",
});
```

### Threshold

Control result quality vs quantity:

```typescript
// Broad search — more results
await supermemory.search("user_123", { query: "...", threshold: 0.3 });

// Precise search — fewer, better results
await supermemory.search("user_123", { query: "...", threshold: 0.8 });
```

### Attachments

- `attach.documents` adds the most relevant source document to each result.
- `attach.related` adds parent, child, and sibling memories (see [graph memory](/concepts/graph-memory)).
- `attach.forgotten` allows forgotten memories in related context. By default, search excludes memories that have been forgotten or have passed their `forgetAfter` expiration.

```typescript
await supermemory.search("user_123", {
  query: "old project notes",
  include: { related: true, forgotten: true },
  searchMode: "memories",
});
```

Attached context arrives under `result.included` (`included.document`, `included.related.{parents,children,siblings}`).

---

## Chatbot example

Optimal configuration for conversational AI:

```typescript
async function getContext(userId: string, message: string) {
  const results = await supermemory.search(userId, {
    query: message,
    threshold: 0.6,
    searchMode: "hybrid",
    limit: 5,
  });

  return results.results
    .map(r => r.memory || r.chunk)
    .join('\n\n');
}
```

<Accordion title="Response schema">
  ```typescript
  interface SearchResult {
    id: string;
    memory?: string;        // Present for memory results
    chunk?: string;         // Present for document chunk results
    similarity: number;     // 0-1
    metadata: object | null;
    isLatest: boolean;
    system: { createdAt: string; updatedAt: string };
    included?: {            // Only when you pass attach
      document?: object;
      related?: { parents: object[]; children: object[]; siblings: object[] };
    };
  }

  interface SearchResponse {
    results: SearchResult[];
    searchTime: number;     // ms
  }
  ```
</Accordion>

---

## Next steps

- [Ingesting Content](/ingestion/add-memories) — Add content to search
- [User Profiles](/recall/user-profiles) — Get user context
- [Organizing & Filtering](/concepts/filtering) — Namespaces and metadata