Farcaster Data
Decentralized social network data from the Farcaster protocol for Web3 social intelligence and protocol analytics. The Farcaster data share provides comprehensive access to casts, profiles, channels, and engagement metrics from the decentralized social network. Perfect for tracking Web3 narratives, monitoring community sentiment, and analyzing protocol adoption.
Update Frequency: On-demand
What's Included
This data is collected by Heisenberg nodes running the data agent and organized into the following structure.
Cast Data
Posts (casts) with full content and threads:
| Field Name | Type | Nullable | Description |
|---|---|---|---|
| id | string | No | Unique cast identifier |
| content | text | No | Full cast text content |
| author | string | No | User who created the cast |
| author_id | string | No | Unique author identifier |
| created_at | timestamp | No | Cast timestamp |
| url | string | No | Direct link to the cast |
| parent_id | string | Yes | Parent cast for replies |
| thread_id | string | Yes | Thread identifier |
| reactions | object | Yes | Likes, recasts, and other reactions |
| reply_count | integer | No | Number of replies |
| recast_count | integer | No | Number of recasts |
| media | array | Yes | Attached media files |
Profile Information
User profiles and follower relationships:
| Field Name | Type | Nullable | Description |
|---|---|---|---|
| user_id | string | No | Unique user identifier |
| username | string | No | Farcaster username |
| display_name | string | Yes | Display name |
| bio | text | Yes | User biography |
| avatar | string | Yes | Profile image URL |
| follower_count | integer | No | Number of followers |
| following_count | integer | No | Number of following |
| verification_status | boolean | No | Verification badge status |
| created_at | timestamp | No | Account creation date |
Channel Data
Channel topics and membership:
| Field Name | Type | Nullable | Description |
|---|---|---|---|
| channel_id | string | No | Unique channel identifier |
| channel_name | string | No | Channel name |
| description | text | Yes | Channel description |
| member_count | integer | No | Number of members |
| activity_metrics | object | Yes | Posts, engagement, growth |
| trending_status | boolean | No | Whether channel is trending |
Engagement Metrics
Cast and network engagement data:
| Field Name | Type | Nullable | Description |
|---|---|---|---|
| cast_id | string | No | Associated cast identifier |
| likes | integer | No | Number of likes |
| recasts | integer | No | Number of recasts |
| replies | integer | No | Number of replies |
| engagement_rate | float | Yes | Calculated engagement rate |
| trending_score | float | Yes | Trending algorithm score |
Additional Features
| Feature | Benefit |
|---|---|
| Real-time Updates | Data refreshes multiple times per day, ensuring you always have the latest casts and engagement data |
| Thread Detection | Automatically identifies conversation threads and reply chains for context analysis |
| Media Extraction | Separate fields for images, videos, and other media attachments |
| Engagement Metrics | Comprehensive engagement data including likes, recasts, and replies |
| Channel Tracking | Channel-level data for tracking community activity and trends |
| Profile Analytics | User-level data for tracking key voices and their impact |
| Decentralized Context | Full protocol-level data for understanding decentralized social dynamics |
| Structured Format | Clean, normalized data ready for immediate use in AI applications |
On-Demand Context Generation
Want to create custom context pipelines from this Farcaster data? You can generate on-demand context tailored to your specific needs using our COOK platform. COOK allows you to build personalized Data Agents that process, filter, and transform this Farcaster data into custom insights perfect for your AI applications. Create context pipelines that combine Farcaster data with other sources, apply custom filters, and generate structured outputs that match your exact requirements.
Integration
Access Methods
REST API - Query casts, profiles, and channels programmatically
MCP Integration - Plug directly into AI agents and multi-cloud workflows
Direct Database Access - PostgreSQL connection for custom analytics
Example Queries
Filter by:
- Channel - Specific channels
- User - Specific accounts
- Time range - Recent casts or date ranges
- Keywords - Search in cast content
- Engagement threshold - Minimum likes, recasts
- Thread - Specific conversation threads
Analyze by:
- Trending casts - Highest engagement or fastest growth
- Channel activity - Most active channels
- User influence - Key voices and their impact
- Topic trends - Emerging discussions and themes
Next Steps
Ready to integrate Farcaster data into your application?