Solana Agent Kit provides integration with Elfa AI for retrieving social media intelligence, market data, and trend analysis from platforms like Twitter. This documentation covers the available functions and tools for interacting with Elfa AI API.

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Overview

Elfa AI integration enables access to:

  • Smart mentions detection
  • Top mentions by ticker symbol
  • Keyword-based mentions search
  • Trending tokens analysis
  • Twitter account statistics
  • API key status monitoring

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Configuration

Before using Elfa AI integration, you need to configure your API key in the Solana Agent Kit:

import { SolanaAgentKit } from "solana-agent-kit";

const agent = new SolanaAgentKit(
  privateKey,
  rpcUrl,
  {
    ELFA_AI_API_KEY: "your_elfa_ai_api_key"
  }
);

​
Core Functions

The integration provides several core functions for interacting with the Elfa AI API:

​
API Health Check

const pingResult = await agent.methods.pingElfaAiApi();
// Returns: { message: "pong" }

​
API Key Status

const keyStatus = await agent.methods.getElfaAiApiKeyStatus();
// Returns detailed information about API key status including usage limits

​
Smart Mentions

Retrieve tweets from smart accounts with high engagement:

const smartMentions = await agent.methods.getSmartMentions(
  100, // limit
  0    // offset
);

Example response:

{
  "success": true,
  "data": [
    {
      "id": "611245869",
      "type": "post",
      "content": "In my opinion, it's a great time to add $ETH.",
      "originalUrl": "/EricTrump/status/1886541132903133230",
      "likeCount": 48036,
      "quoteCount": 3103,
      "replyCount": 8900,
      "repostCount": 7981,
      "viewCount": 5660995,
      "mentionedAt": "2025-02-03T22:23:50.000Z",
      "account": {
        "id": 83583,
        "username": "EricTrump",
        "followerCount": 5500559,
        "isVerified": true
      }
    }
  ],
  "metadata": {
    "total": 6,
    "limit": 100,
    "offset": 0
  }
}

​
Top Mentions by Ticker

Retrieve top tweets for a specific ticker symbol:

const topMentions = await agent.methods.getTopMentionsByTicker(
  "SOL",    // ticker
  "1h",     // timeWindow
  1,        // page
  10,       // pageSize
  false     // includeAccountDetails
);

Example response:

{
  "success": true,
  "data": {
    "data": [
      {
        "id": 612200471,
        "twitter_id": "1886663937518645714",
        "content": "Same story for $SOL - looks like a failed breakdown.",
        "mentioned_at": "2025-02-04T06:31:49+00:00",
        "type": "post",
        "metrics": {
          "like_count": 45,
          "reply_count": 6,
          "repost_count": 7,
          "view_count": 1744
        }
      }
    ],
    "total": 12,
    "page": 1,
    "pageSize": 10
  }
}

​
Search Mentions by Keywords

Search for tweets containing specific keywords within a date range:

const searchResults = await agent.methods.searchMentionsByKeywords(
  "ai, agent",   // comma-separated keywords
  1622505600,    // from timestamp
  1625097600,    // to timestamp
  20             // limit
);

Example response:

{
  "success": true,
  "data": [
    {
      "id": 612258820,
      "twitter_id": "1886671035048845535",
      "content": "The Move AI Hackathon 🛠️\n\nUnlock limitless possibilities...",
      "mentioned_at": "2025-02-04T07:00:02+00:00",
      "type": "quote",
      "metrics": {
        "like_count": 1,
        "reply_count": 0,
        "repost_count": 0,
        "view_count": 0
      }
    }
  ],
  "metadata": {
    "total": 1875,
    "cursor": "FGluY2x1ZGVfY29udGV4d..."
  }
}

Get tokens trending in social media discussions:

const trendingTokens = await agent.methods.getTrendingTokens();

Example response:

{
  "success": true,
  "data": {
    "total": 5,
    "page": 1,
    "pageSize": 5,
    "data": [
      {
        "token": "eth",
        "current_count": 916,
        "previous_count": 377,
        "change_percent": 142.97
      },
      {
        "token": "btc",
        "current_count": 580,
        "previous_count": 458,
        "change_percent": 26.64
      }
    ]
  }
}

​
Twitter Account Smart Stats

Get engagement and influence metrics for a Twitter account:

const accountStats = await agent.methods.getSmartTwitterAccountStats("elonmusk");

Example response:

{
  "success": true,
  "data": {
    "smartFollowingCount": 5913,
    "averageEngagement": 30714784.98833819,
    "followerEngagementRatio": 0.1423006675490259
  }
}

​
LangChain Tools

For AI agent integration with LangChain, the following tools are available:

​
Ping Tool

import { ElfaPingTool } from "solana-agent-kit";

const pingTool = new ElfaPingTool(agent);

​
API Key Status Tool

import { ElfaApiKeyStatusTool } from "solana-agent-kit";

const apiKeyStatusTool = new ElfaApiKeyStatusTool(agent);

​
Smart Mentions Tool

import { ElfaGetMentionsTool } from "solana-agent-kit";

const smartMentionsTool = new ElfaGetMentionsTool(agent);

// Tool input (JSON string):
const input = JSON.stringify({
  limit: 100,
  offset: 0
});

​
Top Mentions Tool

import { ElfaGetTopMentionsTool } from "solana-agent-kit";

const topMentionsTool = new ElfaGetTopMentionsTool(agent);

// Tool input (JSON string):
const input = JSON.stringify({
  ticker: "SOL",
  timeWindow: "1h",
  page: 1,
  pageSize: 10,
  includeAccountDetails: false
});

​
Search Mentions Tool

import { ElfaSearchMentionsTool } from "solana-agent-kit";

const searchMentionsTool = new ElfaSearchMentionsTool(agent);

// Tool input (JSON string):
const input = JSON.stringify({
  keywords: "ai, agent",
  from: 1622505600,
  to: 1625097600,
  limit: 20
});

import { ElfaTrendingTokensTool } from "solana-agent-kit";

const trendingTokensTool = new ElfaTrendingTokensTool(agent);

​
Account Smart Stats Tool

import { ElfaAccountSmartStatsTool } from "solana-agent-kit";

const accountStatsTool = new ElfaAccountSmartStatsTool(agent);

// Tool input: username as string
const input = "elonmusk";

​
Action Definitions

In addition to functions and tools, Solana Agent Kit provides actions for interacting with Elfa AI:

​
Ping Action

const pingResult = await agent.methods.executeAction("ELFA_PING_ACTION", {});

​
API Key Status Action

const keyStatus = await agent.methods.executeAction("ELFA_API_KEY_STATUS_ACTION", {});

​
Smart Mentions Action

const smartMentions = await agent.methods.executeAction("ELFA_GET_SMART_MENTIONS_ACTION", {
  limit: 100,
  offset: 0
});

​
Top Mentions Action

const topMentions = await agent.methods.executeAction("ELFA_GET_TOP_MENTIONS_BY_TICKER_ACTION", {
  ticker: "SOL",
  timeWindow: "1h",
  page: 1,
  pageSize: 10,
  includeAccountDetails: false
});

​
Search Mentions Action

const searchResults = await agent.methods.executeAction("ELFA_SEARCH_MENTIONS_BY_KEYWORDS_ACTION", {
  keywords: "ai, agent",
  from: 1622505600,
  to: 1625097600,
  limit: 20
});

const trendingTokens = await agent.methods.executeAction("ELFA_TRENDING_TOKENS_ACTION", {});

​
Account Smart Stats Action

const accountStats = await agent.methods.executeAction("ELFA_SMART_TWITTER_ACCOUNT_STATS_ACTION", {
  username: "elonmusk"
});

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Parameter Details

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Time Windows

The following time windows are supported for various endpoints:

  • 1h - Last hour
  • 24h - Last 24 hours
  • 7d - Last 7 days
  • 14d - Last 14 days
  • 30d - Last 30 days

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Pagination

Most endpoints support pagination with the following parameters:

  • limit or pageSize - Number of results per page
  • offset or page - Page number or offset for results
  • cursor - For cursor-based pagination (used in search endpoints)

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Error Handling

All functions and tools include error handling. Here’s an example of handling errors:

try {
  const result = await agent.methods.getTopMentionsByTicker("SOL");
  
  if (!result.success) {
    console.error("API error:", result.error);
    // Handle error
  } else {
    console.log("Top mentions:", result.data);
    // Process data
  }
} catch (error) {
  console.error("Exception:", error.message);
  // Handle exception
}

​
Utility Functions

The integration includes several utility functions:

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Create Axios Instance

function createAxiosInstance(apiKey: string): AxiosInstance {
  return axios.create({
    baseURL: "https://api.elfa.ai",
    headers: {
      "x-elfa-api-key": apiKey,
      "Content-Type": "application/json",
    },
  });
}

​
Integration with AI Agents

For AI agents built with LangChain, you can register these tools:

import { 
  ElfaPingTool, 
  ElfaGetTopMentionsTool, 
  ElfaTrendingTokensTool 
} from "solana-agent-kit";
import { OpenAI } from "langchain/llms/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";

// Create LangChain tools
const tools = [
  new ElfaPingTool(agent),
  new ElfaGetTopMentionsTool(agent),
  new ElfaTrendingTokensTool(agent)
];

// Create LLM
const model = new OpenAI({ temperature: 0 });

// Create agent
const executor = await initializeAgentExecutorWithOptions(
  tools,
  model,
  {
    agentType: "structured-chat-zero-shot-react-description",
  }
);

// Execute agent
const result = await executor.call({
  input: "What are the trending tokens today?"
});

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Best Practices

  1. Rate Limiting: Monitor your API usage through the key status endpoint
  2. Data Freshness: Social media data can become stale quickly, consider time windows carefully
  3. Error Handling: Implement comprehensive error handling for all API calls
  4. Pagination: For large datasets, implement pagination to avoid timeouts
  5. Date Ranges: For search queries, use reasonable date ranges to optimize performance

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Resources