# Pinecone

```json
{
  "name": "Pinecone",
  "slug": "pinecone",
  "url": "https://composio.dev/toolkits/pinecone",
  "markdown_url": "https://composio.dev/toolkits/pinecone.md",
  "logo_url": "https://logos.composio.dev/api/pinecone",
  "categories": [
    "ai & machine learning",
    "developer tools & devops",
    "data & analytics"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-03-29T06:45:37.839Z"
}
```

![Pinecone logo](https://logos.composio.dev/api/pinecone)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Pinecone MCP or direct API to index vectors, run similarity searches, manage namespaces, and retrieve embeddings through natural language.

## Summary

Pinecone is a fully managed vector database for high-performance AI memory and search. It lets you scale semantic search and retrieval without infrastructure overhead.

## Categories

- ai & machine learning
- developer tools & devops
- data & analytics

## Toolkit Details

- Tools: 30

## Images

- Logo: https://logos.composio.dev/api/pinecone

## Authentication

- **Api Key**
  - Type: `api_key`
  - Description: Api Key authentication for Pinecone.
  - Setup:
    - Configure Api Key credentials for Pinecone.
    - Use the credentials when creating an auth config in Composio.
- **Oauth2**
  - Type: `oauth2`
  - Description: Oauth2 authentication for Pinecone.
  - Setup:
    - Configure Oauth2 credentials for Pinecone.
    - Use the credentials when creating an auth config in Composio.

## Suggested Prompts

- Query all vectors similar to user question
- Upsert document embeddings into a namespace
- Delete vectors from the archive index

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PINECONE_CANCEL_BULK_IMPORT` | Cancel Bulk Import | Tool to cancel a bulk import operation in Pinecone. Use when you need to stop an ongoing import operation that is not yet finished. |
| `PINECONE_CONFIGURE_INDEX` | Configure Index | Tool to configure an existing Pinecone index, including pod type, replicas, deletion protection, and tags. Use when you need to scale an index vertically or horizontally, enable/disable deletion protection, or update tags. The change is asynchronous; check index status for completion. |
| `PINECONE_CREATE_BACKUP` | Create Backup | Tool to create a backup of a Pinecone index for disaster recovery and version control. Use when you need to preserve the current state of an index including vectors, metadata, and configuration. |
| `PINECONE_CREATE_INDEX` | Create Index | Tool to create a Pinecone index with specified configuration. Use when you need to initialize a new vector database index for storing and querying embeddings. |
| `PINECONE_CREATE_INDEX_WITH_EMBEDDING_MODEL` | Create Index with Embedding Model | Tool to create a Pinecone index with integrated embedding model for automatic vectorization. Use when you need to set up a new index that automatically converts text to vectors using a pre-configured embedding model. |
| `PINECONE_CREATE_INDEX_FROM_BACKUP` | Create Index from Backup | Tool to create an index from a backup. Use when you need to restore or duplicate index data from a previously saved backup. |
| `PINECONE_CREATE_NAMESPACE` | Create Namespace | Tool to create a namespace within a serverless Pinecone index. Use when you need to organize vectors into isolated partitions. |
| `PINECONE_DELETE_INDEX` | Delete Index | Tool to permanently delete a Pinecone index. Use when you need to remove an index from your project. Note: Deletion protection and pending collections can prevent deletion. |
| `PINECONE_DELETE_NAMESPACE` | Delete Namespace | Tool to permanently delete a namespace from a serverless index. Use when you need to remove an entire namespace and all its data. This operation is irreversible and only supported on serverless indexes. |
| `PINECONE_DESCRIBE_BACKUP` | Describe Backup | Tool to retrieve detailed information about a specific backup. Use when you need to check backup status, configuration, or metadata. |
| `PINECONE_DESCRIBE_BULK_IMPORT` | Describe Bulk Import | Tool to describe a specific bulk import operation in Pinecone. Use when you need to retrieve detailed information about an import's status, progress, timing, and any errors. |
| `PINECONE_DESCRIBE_INDEX_STATS` | Describe Index Stats | Tool to get index statistics including vector count per namespace, dimensions, and fullness. Use when you need to understand the contents and status of an index. |
| `PINECONE_DESCRIBE_RESTORE_JOB` | Describe Restore Job | Tool to get detailed information about a specific restore job in Pinecone. Use when you need to check the status, progress, or metadata of a restore operation. |
| `PINECONE_GENERATE_EMBEDDINGS` | Generate Embeddings | Tool to generate vector embeddings for input text using Pinecone's hosted embedding models. Use when you need to convert text into vector representations for semantic search or similarity matching. |
| `PINECONE_GET_MODEL_INFORMATION` | Get Model Information | Tool to retrieve detailed information about a specific model hosted by Pinecone. Use when you need to understand model capabilities for embedding and reranking operations. |
| `PINECONE_LIST_BULK_IMPORTS` | List Bulk Imports | Tool to list all recent and ongoing bulk import operations in Pinecone. Use when you need to monitor or track the status of data import jobs. Supports pagination with a default limit of 100 imports per page. |
| `PINECONE_LIST_COLLECTIONS` | List Collections | Tool to list all collections in a Pinecone project (pod-based indexes only). Use when you need to view available collections. |
| `PINECONE_LIST_INDEX_BACKUPS` | List Index Backups | Tool to list all backups for a specific Pinecone index. Use when you need to view available backups for an index. Supports pagination via limit and paginationToken parameters. |
| `PINECONE_LIST_INDEXES` | List Indexes | Tool to list all indexes in a Pinecone project. Use when you need to retrieve all indexes with their configurations and status information. |
| `PINECONE_LIST_AVAILABLE_MODELS` | List Available Models | Tool to list all available embedding and reranking models hosted by Pinecone. Use when you need to discover available models or filter by model type (embed/rerank) or vector type (dense/sparse). |
| `PINECONE_LIST_NAMESPACES` | List Namespaces | Tool to list all namespaces in a serverless Pinecone index. Use when you need to discover available namespaces for data organization. Returns up to 100 namespaces by default with pagination support. |
| `PINECONE_LIST_PROJECT_BACKUPS` | List Project Backups | Tool to list all backups for indexes in a Pinecone project. Use when you need to retrieve backup information across all project indexes. Supports pagination with limit and paginationToken parameters. |
| `PINECONE_LIST_RESTORE_JOBS` | List Restore Jobs | Tool to list all restore jobs for a project with pagination support. Use when you need to view the status of restore operations or track restore progress. |
| `PINECONE_LIST_VECTORS` | List Vectors | Tool to list vector IDs in a Pinecone serverless index. Use when you need to browse or retrieve vector identifiers from a namespace. Supports filtering by prefix and pagination for large result sets. |
| `PINECONE_QUERY_VECTORS` | Query Vectors | Tool to perform semantic search within a Pinecone index using a query vector. Retrieves IDs and similarity scores of the most similar items, ordered from most to least similar. Either vector or id parameter must be provided. |
| `PINECONE_RERANK_DOCUMENTS` | Rerank Documents | Tool to rerank documents by semantic relevance to a query. Use when you need to order retrieved documents by their semantic relevance to a user's search query using Pinecone's hosted reranking models. |
| `PINECONE_SEARCH_RECORDS_IN_NAMESPACE` | Search Records in Namespace | Tool to search records within a Pinecone namespace using text, vector, or ID query. Use when you need to find similar records based on embeddings or record IDs. Results can optionally be reranked for relevance. |
| `PINECONE_START_BULK_IMPORT` | Start Bulk Import | Tool to start an asynchronous bulk import of vectors from object storage (S3, GCS, or Azure Blob Storage) into a Pinecone index. Use when you need to import large volumes of vectors from external storage. Returns an import ID to track the operation status. |
| `PINECONE_UPDATE_VECTOR` | Update Vector | Tool to update a vector in Pinecone by ID. Use to overwrite vector values and/or metadata. Supports bulk updates via metadata filters. |
| `PINECONE_UPSERT_RECORDS_TO_NAMESPACE` | Upsert Records to Namespace | Tool to upsert text records into a Pinecone namespace. Use when you need to add or update records with automatic text-to-vector conversion. |

## Supported Triggers

None listed.

## Installation and MCP Setup

### Path 1: SDK Installation

#### Path 1, Step 1: Install Composio

Install the Composio SDK
```python
pip install composio_openai
```

```typescript
npm install @composio/openai
```

#### Path 1, Step 2: Initialize Composio and Create Tool Router Session

Import and initialize Composio client, then create a Tool Router session
```python
from openai import OpenAI
from composio import Composio
from composio_openai import OpenAIResponsesProvider

composio = Composio(provider=OpenAIResponsesProvider())
openai = OpenAI()
session = composio.create(user_id='your-user-id')
```

```typescript
import OpenAI from 'openai';
import { Composio } from '@composio/core';
import { OpenAIResponsesProvider } from '@composio/openai';

const composio = new Composio({
  provider: new OpenAIResponsesProvider(),
});
const openai = new OpenAI({});
const session = await composio.create('your-user-id');
```

#### Path 1, Step 3: Execute Pinecone Tools via Tool Router with Your Agent

Get tools from Tool Router session and execute Pinecone actions with your Agent
```python
tools = session.tools
response = openai.responses.create(
  model='gpt-4.1',
  tools=tools,
  input=[{
    'role': 'user',
    'content': 'Query the vector index for documents similar to "climate change research".'
  }]
)
result = composio.provider.handle_tool_calls(
  response=response,
  user_id='your-user-id'
)
print(result)
```

```typescript
const tools = session.tools;
const response = await openai.responses.create({
  model: 'gpt-4.1',
  tools: tools,
  input: [{
    role: 'user',
    content: 'Query the vector index for documents similar to "climate change research".'
  }],
});
const result = await composio.provider.handleToolCalls(
  'your-user-id',
  response.output
);
console.log(result);
```

### Path 2: MCP Server Setup

#### Path 2, Step 1: Install Composio

Install the Composio SDK for Python or TypeScript
```python
pip install composio claude-agent-sdk
```

```typescript
npm install @composio/core ai @ai-sdk/openai @ai-sdk/mcp
```

#### Path 2, Step 2: Initialize Client and Create Tool Router Session

Import and initialize the Composio client, then create a Tool Router session for Pinecone
```python
from composio import Composio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions

composio = Composio(api_key='your-composio-api-key')
session = composio.create(user_id='your-user-id')
url = session.mcp.url
```

```typescript
import { Composio } from '@composio/core';

const composio = new Composio({ apiKey: 'your-api-key' });
const session = await composio.create('your-user-id');
console.log(`Tool Router session created: ${session.mcp.url}`);
```

#### Path 2, Step 3: Connect to AI Agent

Use the MCP server with your AI agent (Anthropic Claude or Mastra)
```python
import asyncio

options = ClaudeAgentOptions(
    permission_mode='bypassPermissions',
    mcp_servers={
        'tool_router': {
            'type': 'http',
            'url': url,
            'headers': {
                'x-api-key': 'your-composio-api-key'
            }
        }
    },
    system_prompt='You are a helpful assistant with access to Pinecone tools.',
    max_turns=10
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query('Upsert new document vectors to the "research_papers" namespace.')
        async for message in client.receive_response():
            if hasattr(message, 'content'):
                for block in message.content:
                    if hasattr(block, 'text'):
                        print(block.text)

asyncio.run(main())
```

```typescript
import { openai } from '@ai-sdk/openai';
import { experimental_createMCPClient as createMCPClient } from '@ai-sdk/mcp';
import { generateText } from 'ai';

const client = await createMCPClient({
  transport: {
    type: 'http',
    url: session.mcp.url,
    headers: {
      'x-api-key': 'your-composio-api-key',
    },
  },
});

const tools = await client.tools();
const { text } = await generateText({
  model: openai('gpt-4o'),
  tools,
  messages: [{
    role: 'user',
    content: 'Upsert new document vectors to the "research_papers" namespace.'
  }],
  maxSteps: 5,
});

console.log(`Agent: ${text}`);
```

## Why Use Composio?

### 1. AI Native Pinecone Integration

- Supports both Pinecone MCP and direct API based integrations
- Structured, LLM-friendly schemas for reliable tool execution
- Rich coverage for reading, writing, and querying your Pinecone data

### 2. Managed Auth

- Built-in OAuth handling with automatic token refresh and rotation
- Central place to manage, scope, and revoke Pinecone access
- Per user and per environment credentials instead of hard-coded keys

### 3. Agent Optimized Design

- Tools are tuned using real error and success rates to improve reliability over time
- Comprehensive execution logs so you always know what ran, when, and on whose behalf

### 4. Enterprise Grade Security

- Fine-grained RBAC so you control which agents and users can access Pinecone
- Scoped, least privilege access to Pinecone resources
- Full audit trail of agent actions to support review and compliance

## Use Pinecone with any AI Agent Framework

Choose a framework you want to connect Pinecone with:

- [ChatGPT](https://composio.dev/toolkits/pinecone/framework/chatgpt)
- [Antigravity](https://composio.dev/toolkits/pinecone/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/pinecone/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/pinecone/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/pinecone/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/pinecone/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/pinecone/framework/codex)
- [Cursor](https://composio.dev/toolkits/pinecone/framework/cursor)
- [VS Code](https://composio.dev/toolkits/pinecone/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/pinecone/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/pinecone/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/pinecone/framework/hermes-agent)
- [Google ADK](https://composio.dev/toolkits/pinecone/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/pinecone/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/pinecone/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/pinecone/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/pinecone/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/pinecone/framework/crew-ai)
- [Pydantic AI](https://composio.dev/toolkits/pinecone/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/pinecone/framework/autogen)

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- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.

## Frequently Asked Questions

### Do I need my own developer credentials to use Pinecone with Composio?

Yes, Pinecone requires you to configure your own API key credentials. Once set up, Composio handles secure credential storage and API request handling for you.

### Can I use multiple toolkits together?

Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview).

### Is Composio secure?

Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev).

### What if the API changes?

Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)