A high-performance vector database skill for building AI-driven recommendation engines and semantic search applications.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install integrate-pinecone
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install integrate-pinecone using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Pinecone?
Pinecone is a specialized vector database designed to store, index, and query high-dimensional vector embeddings with extreme efficiency. As part of the Openclaw Skills library, this integration allows AI agents to manage indexes and collections while offloading the complexities of vector search infrastructure.
By leveraging the Membrane framework, this skill simplifies the developer experience by handling authentication and credential refreshing automatically. This ensures that your AI coding agents can focus on semantic logic and data retrieval without getting bogged down by the underlying security plumbing of the Pinecone API.
Pinecone Use Cases
- Building long-term memory systems for AI agents to retrieve relevant past context.
- Implementing semantic search functionality across large-scale document repositories.
- Developing recommendation engines based on high-dimensional data embeddings.
- Managing Pinecone indexes, vectors, and collections via natural language intent within an AI workflow.
How Pinecone Works
- Environment Setup: The user installs the Membrane CLI and authenticates their specific agent type (e.g., Openclaw or Claude).
- Connection Establishment: A secure connection is created to the Pinecone domain, which handles OAuth or API key management server-side.
- Action Discovery: The agent uses natural language search to identify the correct Pinecone action (like querying or upserting vectors) based on current intent.
- Execution: The agent runs the selected action with JSON parameters or uses a proxy request for custom API endpoints.
- Data Handling: Membrane processes the request, injects authentication headers, and returns the vector search results to the agent.
Pinecone Setup
First, install the required CLI to manage your Openclaw Skills:
npm install -g @membranehq/cli@latest
Next, authenticate your environment and create a connection to Pinecone:
# Log in as an openclaw agent
membrane login --tenant --clientName=openclaw
# Ensure connection to Pinecone
membrane connection ensure "https://www.pinecone.io/" --json
Pinecone Data Schema & Taxonomy
The skill organizes interaction through a structured connection model that abstracts the Pinecone API components into manageable actions.
| Component | Description |
|---|---|
| Index | The top-level container for managing vector data and search configurations. |
| Vector | High-dimensional data points stored within an index, representing embeddings. |
| Collection | Static snapshots of an index that can be used to create new indexes. |
| Action Schema | Every action includes an inputSchema and outputSchema for strictly typed data exchange. |
Pinecone Advanced Features
- Natural Language Action Discovery: Use the
--intentflag to find specific Pinecone functions without reading documentation. - Automated Credential Refresh: Never manage local API keys; Membrane handles the full auth lifecycle server-side.
- Direct API Proxy: Access niche Pinecone endpoints not covered by pre-built actions using the
membrane requestcommand. - Headless Authentication: Support for remote or containerized environments via authorization URL polling.
SKILL.md
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