Connect Depasify to AI Agents: Orchestrate Fiat and Crypto Flows

You want to connect Depasify to an AI agent so your financial systems can independently manage crypto and fiat ledgers, orchestrate mass payouts, execute FX quotes, and handle complex KYB onboarding. Here is exactly how to do it using Truto's /tools endpoint and SDK, bypassing the need to code and maintain bespoke API wrappers for a highly regulated financial platform.

Giving a Large Language Model (LLM) read and write access to a core banking and crypto infrastructure platform like Depasify is an engineering challenge. You either spend months building, hosting, and auditing a custom connector that understands the nuances of dual-approval policies and blockchain asset management, or you use a managed infrastructure layer that handles the boilerplate. If your team uses ChatGPT, check out our guide on connecting Depasify to ChatGPT, or if you are building on Anthropic's models, read our guide on connecting Depasify to Claude. For developers building custom autonomous workflows, you need a programmatic way to fetch these tools and bind them to your agent framework.

This guide breaks down exactly how to fetch AI-ready tools for Depasify, bind them natively to an LLM using LangChain (or frameworks like LangGraph, CrewAI, or Vercel AI SDK), and execute complex financial operations safely. For a deeper look at the architecture behind this approach, refer to our research on architecting AI agents and the SaaS integration bottleneck.

The Engineering Reality of Custom Depasify Connectors

Building AI agents is easy. Connecting them to external financial APIs is hard. Giving an LLM access to external data sounds simple in a prototype, but executing regulated transactions in production introduces unique hurdles. If you decide to integrate Depasify yourself, you own the entire API lifecycle, and Depasify's API introduces highly specific challenges that break standard LLM assumptions.

The Dual-Approval (Four-Eyes) State Machine

Unlike simple SaaS tools where a POST request immediately provisions a resource, Depasify operates on strict financial compliance principles. When an agent creates a fiat or blockchain payment, the transaction often enters a pending state dictated by a "four-eyes policy." The agent cannot simply assume the payment was executed. It must understand the approval_progress object, track who initiated the payment, and recognize that a secondary authorization step (via a different authenticated entity) is required before funds move. If your agent is unaware of this state machine, it will hallucinate success to the user when the payment is actually sitting in an unapproved queue.

The Ledger and Settlement Complexity

Depasify bridges traditional fiat banking and blockchain rails. This means your agent must navigate vastly different data models depending on the rail. A standard bank withdrawal requires navigating virtual IBANs, SEPA routing, and counterparties. A blockchain payment requires understanding destination wallet addresses, specific network identifiers (like Polygon or Ethereum), and dynamic gas fee estimation. Hand-coding prompts to teach an LLM the exact payload structures for these disparate transaction types almost guarantees parameter hallucination.

KYC/KYB Asynchronous Workflows

Onboarding individuals and businesses into a financial platform is not a synchronous CRUD operation. Initiating a KYB (Know Your Business) process involves generating a secure application, managing password-protected onboarding forms, uploading required documentation per attribute, and dealing with statuses that lock records from further modification once processed. If your agent attempts to update a KYB application that has already moved to a processed state, the API will reject it with a 403 Forbidden. Your agent logic must natively handle these asynchronous state locks.

Why a Unified Tool Layer Matters for Financial Agents

Direct API tools - exposing one tool per raw Depasify endpoint - push provider quirks into the LLM's context window. The model has to remember that blockchain wallets designated for 'deposits' must omit an address, while 'source' wallets require one. Every one of those quirks is a hallucination waiting to happen.

Truto collapses this complexity. Your agent interfaces with a strictly typed tool layer where invalid arguments are rejected by JSON schema validation before they ever hit Depasify.

1. Deterministic input validation. Every tool has a strict JSON schema. If the LLM tries to include an address for a deposit wallet, the tool schema rejects it immediately.
2. Reduced prompt bloat. You do not need to waste thousands of tokens explaining Depasify's unique KYB attribute keys or four-eyes policy thresholds. The tool schemas carry that context natively.
3. Normalized Error Handling. When things fail, the agent gets a structured, predictable error message, allowing it to self-correct.

A Note on Rate Limits

When operating at scale, your agent will inevitably hit rate limits. Truto does not retry, throttle, or apply backoff on rate limit errors. When Depasify returns an HTTP 429 Too Many Requests, Truto passes that exact error straight to your caller. However, Truto normalizes the upstream rate limit information into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF specification. The caller - your agent loop - is completely responsible for inspecting these headers and implementing a retry or backoff mechanism. We cover how to handle this in the code examples below.

Depasify Hero Tools for AI Agents

Here are the highest-leverage operations your AI agent can perform in Depasify using Truto.

Create a Blockchain Payment

The create_a_depasify_blockchain_payment tool allows the agent to initiate transfers from specific blockchain wallets. The agent must supply the account ID, wallet ID, amount, and currency. Crucially, the returned object includes the approval_progress state, which the agent must relay to the user if a four-eyes policy intercepts the transaction.

"Transfer 500 USDC from our main operations blockchain wallet to our designated vendor wallet on the Polygon network. Check if the transaction needs a second approver."

Orchestrate Fiat Off-Ramping

The create_a_depasify_fiat_payment tool handles sending fiat from a virtual IBAN to a counterparty. This is critical for automated off-ramping flows. The agent must construct the payload with recipient details and tracking descriptions.

"We need to off-ramp 10,000 EUR. Create a fiat payment from our primary corporate virtual IBAN to the saved counterparty for Acme Corp."

Initiate KYB Onboarding

The create_a_depasify_kyb tool generates a new Know Your Business application in the authenticated user's vault. The response returns a unique UUID and a generated password. The agent must securely handle these credentials and pass them to the user to complete the onboarding form.

"Start a new KYB onboarding process for a new enterprise client. Give me the secure link and the generated password so I can forward it to their compliance officer."

Generate FX and Payout Quotes

The create_a_depasify_quote tool is essential for navigating volatile crypto and FX markets. It allows the agent to lock in a rate for 60 seconds. The agent can then prompt the user for confirmation and use a secondary tool to accept the quote.

"Check the current conversion rate to swap 5,000 USDT to EUR. Generate a quote, tell me the exact EUR payout, and wait for my confirmation to execute."

Audit Ledger Entries

The list_all_depasify_ledgers tool gives the agent read-access to the immutable ledger for a given account. The agent can filter these entries to reconcile deposits, withdrawals, trades, and internal transfers, functioning as an autonomous accounting assistant.

"Pull the ledger entries for our main client-facing account over the last 24 hours. Summarize total trading fees incurred and total inbound deposits."

Approve Four-Eyes Transactions

When operating as a manager agent or when escalating to a human-in-the-loop, the depasify_fiat_payments_approve and depasify_blockchain_payments_approve tools allow authorized entities to push pending transactions through the four-eyes policy queue.

"List all pending blockchain payments waiting for approval in my vault. For any under 1,000 USD, go ahead and approve them."

To see the complete list of available operations, including market data polling, bank account provisioning, and document uploads, visit the Depasify integration page.

Workflows in Action

Connecting these tools to an LLM unlocks autonomous financial operations that would typically require a team of operations analysts jumping between banking portals.

Scenario 1: Automated Treasury Rebalancing and Off-Ramping

An operations manager wants to automatically off-ramp crypto revenue into fiat when balances exceed a certain threshold, while adhering to compliance policies.

"Check our main operations account balance. If the USDT balance is over 50,000, convert 20,000 USDT to EUR via a market trade, then immediately initiate a fiat payment of that EUR amount to our corporate banking counterparty."

Step-by-step execution:

1. The agent calls depasify_accounts_get_balance to check the current holdings.
2. Finding the USDT balance > 50,000, it calls create_a_depasify_trade with the pair USDT/EUR and amount 20,000.
3. The agent monitors the trade completion via get_single_depasify_trade_by_id.
4. Once settled, it calls list_all_depasify_counterparties to find the ID of the corporate bank account.
5. It calls create_a_depasify_fiat_payment to push the newly acquired EUR to the counterparty.
6. The agent reads the response, notes the approval_progress status (due to a four-eyes policy), and replies to the user: "Trade executed successfully. A fiat transfer of 18,950 EUR has been initiated and is currently in the 'pending' state waiting for a secondary approval from a manager."

Scenario 2: Autonomous Compliance and KYB Orchestration

A compliance officer needs to onboard a new institutional partner without manually navigating portal UI forms.

"Initialize a new KYB process for our new liquidity provider. Once created, upload their Certificate of Incorporation document (I will provide the file path) to the business files section."

Step-by-step execution:

1. The agent calls create_a_depasify_kyb and receives the application ID and generated password.
2. The agent calls depasify_kyb_upload_file using the application ID, specifying the business_files attribute, and attaching the provided document.
3. The agent replies to the user: "KYB application created successfully. The Certificate of Incorporation has been uploaded. Please share this secure password with the provider to complete their onboarding flow."

Building Multi-Step Workflows

To build an autonomous agent that can navigate Depasify's financial APIs, you need an orchestration loop. Below is a framework-agnostic architectural approach using LangChain.js to retrieve Depasify tools from Truto, bind them to an Anthropic model, and handle execution.

Crucially, because Depasify handles financial transactions, rate limits are aggressively enforced. Truto passes HTTP 429 errors directly to your application. Your agent loop must inspect the normalized ratelimit-reset header and pause execution to avoid failing the user's request mid-workflow.

Architecture Overview

sequenceDiagram
    participant App as Your App (Agent Loop)
    participant Truto as Truto API
    participant Depasify as Depasify API

    App->>Truto: GET /integrated-account/{id}/tools
    Truto-->>App: Return JSON schemas (Create Trade, Fiat Payout, etc.)
    App->>App: LLM decides to call "create_a_depasify_fiat_payment"
    App->>Truto: POST tool execution
    Truto->>Depasify: POST /fiat-payments
    alt Rate Limit Hit
        Depasify-->>Truto: 429 Too Many Requests
        Truto-->>App: 429 (with ratelimit-reset header)
        App->>App: Sleep until reset time
        App->>Truto: Retry POST tool execution
    else Success
        Depasify-->>Truto: 201 Created (Status: pending)
        Truto-->>App: Return transaction state
    end

TypeScript Implementation

First, install the necessary dependencies:

npm install @langchain/anthropic @langchain/core truto-langchainjs-toolset

Here is how you write the agent loop, incorporating explicit rate limit handling for Truto's pass-through architecture.

import { ChatAnthropic } from "@langchain/anthropic";
import { TrutoToolManager } from "truto-langchainjs-toolset";
import { HumanMessage, AIMessage, ToolMessage } from "@langchain/core/messages";
 
// Initialize the Truto Tool Manager with your API key and integrated account ID
const toolManager = new TrutoToolManager({
  apiKey: process.env.TRUTO_API_KEY,
  accountId: process.env.DEPASIFY_ACCOUNT_ID,
});
 
// Initialize your LLM
const llm = new ChatAnthropic({
  modelName: "claude-3-5-sonnet-latest",
  temperature: 0,
});
 
// Utility function to handle rate limit backoff
const sleep = (ms: number) => new Promise((resolve) => setTimeout(resolve, ms));
 
async function executeDepasifyWorkflow(userPrompt: string) {
  console.log("Fetching Depasify tools from Truto...");
  const tools = await toolManager.getTools();
  
  // Bind the strictly-typed Depasify schemas to the LLM
  const llmWithTools = llm.bindTools(tools);
  
  let messages = [new HumanMessage(userPrompt)];
 
  while (true) {
    // 1. LLM decides what to do based on current context
    const response = await llmWithTools.invoke(messages);
    messages.push(response);
 
    // 2. Exit loop if the LLM provides a final text answer
    if (!response.tool_calls || response.tool_calls.length === 0) {
      console.log("\nFinal Agent Response:", response.content);
      break;
    }
 
    // 3. Execute requested tool calls
    for (const toolCall of response.tool_calls) {
      console.log(`\nExecuting tool: ${toolCall.name}`);
      
      const tool = tools.find((t) => t.name === toolCall.name);
      if (!tool) continue;
 
      let success = false;
      let attempts = 0;
      let toolResult = "";
 
      // Execute with manual Rate Limit handling
      while (!success && attempts < 3) {
        try {
          attempts++;
          toolResult = await tool.invoke(toolCall.args);
          success = true;
        } catch (error: any) {
          // Truto passes 429s directly. We must handle them client-side.
          if (error.status === 429) {
            // Truto normalizes standard IETF rate limit headers
            const resetTimeHeader = error.headers?.['ratelimit-reset'];
            const resetSeconds = resetTimeHeader ? parseInt(resetTimeHeader, 10) : 5;
            
            console.warn(`\n[Rate Limit] Depasify API quota exhausted. Sleeping for ${resetSeconds} seconds...`);
            await sleep(resetSeconds * 1000);
            console.log("Retrying tool execution...");
          } else {
            // Pass standard validation or business logic errors back to the LLM so it can self-correct
            console.error(`\nTool error: ${error.message}`);
            toolResult = `Error executing tool: ${error.message}`;
            success = true; // Break retry loop, let LLM handle the error context
          }
        }
      }
 
      // 4. Feed the raw result (success or error) back to the LLM
      messages.push(
        new ToolMessage({
          tool_call_id: toolCall.id,
          content: typeof toolResult === "string" ? toolResult : JSON.stringify(toolResult),
        })
      );
    }
  }
}
 
// Run the workflow
executeDepasifyWorkflow(
  "Check our USDT balance. If it is over 10,000, trade 5,000 USDT for EUR and tell me the trade ID."
);

Preventing Agent Loops

Notice the error handling block in the code above. If an API request fails with a 400 Bad Request (for example, the agent attempted to withdraw more funds than available in the sub-account), Truto passes the error back cleanly. The code catches this error, stringifies it, and passes it back to the LLM as a ToolMessage.

This is a critical pattern for financial integrations. If you just throw an unhandled exception, the agent crashes. If you pass the error back, Claude or ChatGPT can read the Depasify error message ("Insufficient balance"), apologize to the user, and ask if they would like to transfer funds from another account first. This creates a resilient, self-correcting financial agent.

Shifting from Integration Maintenance to Agent Intelligence

Connecting Depasify to AI agents manually is a fast track to technical debt. The Depasify API is incredibly powerful, offering deep control over fiat accounts, blockchain wallets, KYC pipelines, and trading floors. But wiring those raw endpoints directly into an LLM context window guarantees hallucinated payloads and broken compliance workflows.

By leveraging a unified tool layer, you remove the burden of API maintenance from your engineering team. Your agent interacts with clean, predictable tools that enforce schema validation before any financial operation is attempted. You stop worrying about how to map specific blockchain network identifiers or manage nested counterparties, and instead focus on building agentic loops that execute highly complex treasury and compliance operations safely and autonomously.