Get Invocations

Overview

The getInvocations endpoint returns a comprehensive snapshot of all AI model invocations (conversations) including:

Model responses and reasoning
Trading tool calls (CREATE_POSITION, CLOSE_POSITION, HOLDING)
Parsed trading decisions and execution results
Timestamps and metadata for each action

This endpoint powers the Model Chat view, providing visibility into AI decision-making and trade execution.

Invocation Tracking

Each time a trading model is invoked by the scheduler, the system:

Creates an Invocation record with the model’s response
Logs ToolCalls for each trading action (open, close, hold positions)
Parses metadata to extract structured decisions and results
Filters auto-triggered actions (stop-loss/take-profit) from the conversation view

Invocations are persisted in the database and linked to their parent model via modelId.

Request

input

object

Empty object - no parameters required

// oRPC schema
z.object({})

Response

conversations

array

required

Array of conversation snapshots ordered by timestamp (newest first)

Show Conversation Object Properties

string

required

Unique invocation identifier

modelId

string

required

Reference to the parent model (foreign key)

modelName

string

required

Display name of the model (e.g., “GPT-4 Turbo”)

modelVariant

string

Trading strategy variant: "Apex" | "Trendsurfer" | "Contrarian" | "Sovereign"

modelLogo

string

required

OpenRouter model name for logo display (e.g., “openai/gpt-4-turbo”)

response

string | null

AI model’s text response (reasoning, analysis)

responsePayload

any

Raw response payload from the AI provider

timestamp

string

required

ISO 8601 timestamp of invocation (e.g., “2026-03-07T14:32:15.000Z”)

toolCalls

array

required

Array of trading actions taken during this invocation

Show Tool Call Properties

string

required

Unique tool call identifier

type

string

required

Tool call type: "CREATE_POSITION" | "CLOSE_POSITION" | "HOLDING"

metadata

object

required

Structured metadata containing decisions and execution results

Show Metadata Properties

raw

any

required

Raw unparsed metadata from tool execution

decisions

array

required

Array of parsed trading decisions (normalized format)

results

array

required

Array of execution results (order IDs, fill prices, errors)

timestamp

string

required

ISO 8601 timestamp of tool call execution

// Response schema
z.object({
  conversations: z.array(
    z.object({
      id: z.string(),
      modelId: z.string(),
      modelName: z.string(),
      modelVariant: variantIdSchema.optional(),
      modelLogo: z.string(),
      response: z.string().nullable(),
      responsePayload: z.any().optional(),
      timestamp: z.string(),
      toolCalls: z.array(
        z.object({
          id: z.string(),
          type: z.string(),
          metadata: z.object({
            raw: z.any(),
            decisions: z.array(z.any()),
            results: z.array(z.any()),
          }),
          timestamp: z.string(),
        })
      ),
    })
  ),
})

Database Schema

Invocations Table

{
  id: string;              // Primary key
  modelId: string;         // Foreign key to Models table
  response: string;        // AI model's text response
  responsePayload: jsonb;  // Raw response object
  createdAt: timestamp;    // Invocation timestamp
  updatedAt: timestamp;    // Last update timestamp
}

ToolCalls Table

{
  id: string;                    // Primary key
  invocationId: string;          // Foreign key to Invocations table
  toolCallType: ToolCallType;    // "CREATE_POSITION" | "CLOSE_POSITION" | "HOLDING"
  metadata: text;                // JSON string of execution metadata
  createdAt: timestamp;          // Tool call timestamp
  updatedAt: timestamp;          // Last update timestamp
}

Auto-Triggered Filtering

The endpoint automatically excludes invocations that only contain auto-triggered stop-loss or take-profit closures:

function isAutoTriggeredClose(metadata: Record<string, unknown>): boolean {
  return (
    typeof metadata.autoTrigger === "string" &&
    (metadata.autoTrigger === "STOP" || metadata.autoTrigger === "TARGET")
  );
}

// Filter out invocations with only auto-triggered actions
const filtered = invocations.filter(
  (inv) => !isAutoTriggeredInvocation(inv.toolCalls)
);

This ensures the conversation view only shows deliberate AI decisions, not automatic risk management actions.

Tool Call Types

CREATE_POSITION

enum

Model decided to open a new position (LONG or SHORT)Typical decisions:

Symbol, side (LONG/SHORT), quantity, leverage
Entry price, stop-loss, take-profit targets
Confidence score, invalidation conditions

CLOSE_POSITION

enum

Model decided to close an existing positionTypical decisions:

Symbol, side, quantity to close
Reason for closure (profit target hit, invalidation, risk management)
Realized P&L after execution

HOLDING

enum

Model evaluated positions but decided to hold (no action)Typical decisions:

Current portfolio state confirmation
Reasoning for maintaining existing positions
Market analysis and next evaluation time

TanStack Query Integration

Client Usage

import { useQuery } from "@tanstack/react-query";
import { orpc } from "@/server/orpc/client";

function ModelChatView() {
  const { data, error, isLoading } = useQuery(
    orpc.models.getInvocations.queryOptions({ input: {} })
  );

  if (isLoading) return <div>Loading conversations...</div>;
  if (error) return <div>Error: {error.message}</div>;

  return (
    <div>
      {data?.conversations.map((conversation) => (
        <div key={conversation.id}>
          <div className="model-header">
            <img src={conversation.modelLogo} alt={conversation.modelName} />
            <h3>{conversation.modelName}</h3>
            <span className="variant">{conversation.modelVariant}</span>
            <time>{new Date(conversation.timestamp).toLocaleString()}</time>
          </div>
          
          <p className="response">{conversation.response}</p>
          
          {conversation.toolCalls.map((toolCall) => (
            <div key={toolCall.id} className="tool-call">
              <strong>{toolCall.type}</strong>
              <pre>{JSON.stringify(toolCall.metadata.decisions, null, 2)}</pre>
              <pre>{JSON.stringify(toolCall.metadata.results, null, 2)}</pre>
            </div>
          ))}
        </div>
      ))}
    </div>
  );
}

Query Configuration

export const invocationsQuery = () =>
  queryOptions({
    queryKey: ["invocations"],
    queryFn: refreshConversationEvents,
    staleTime: 20_000,  // 20 seconds
    gcTime: 3 * 60_000, // 3 minutes
  });

Example Response

{
  "conversations": [
    {
      "id": "inv-123e4567-e89b-12d3-a456-426614174000",
      "modelId": "model-abc123",
      "modelName": "GPT-4 Turbo",
      "modelVariant": "Apex",
      "modelLogo": "openai/gpt-4-turbo",
      "response": "Market shows strong VWAP momentum with squeeze breakout. Opening LONG BTC with tight stop-loss.",
      "responsePayload": { "..." },
      "timestamp": "2026-03-07T14:32:15.000Z",
      "toolCalls": [
        {
          "id": "tool-456f7890-f12c-34e5-b678-537725285001",
          "type": "CREATE_POSITION",
          "metadata": {
            "raw": { "..." },
            "decisions": [
              {
                "action": "open",
                "symbol": "BTC",
                "side": "LONG",
                "quantity": 0.5,
                "leverage": 10,
                "stopLoss": 65000,
                "takeProfit": 72000,
                "confidence": 0.85
              }
            ],
            "results": [
              {
                "success": true,
                "orderId": "order-789g0123-g45h-67i8-c901-648836396002",
                "fillPrice": 68450.25,
                "filledQuantity": 0.5
              }
            ]
          },
          "timestamp": "2026-03-07T14:32:16.234Z"
        }
      ]
    }
  ]
}

Variant-Aware Fetching

The endpoint fetches invocations across all variants with fair representation:

const variants = VARIANT_IDS; // ["Apex", "Trendsurfer", "Contrarian", "Sovereign"]
const limitPerVariant = 100;

// Fetch 100 invocations per variant
const variantQueries = variants.map((variant) =>
  db.query.invocations.findMany({
    where: inArray(
      invocations.modelId,
      db.select({ id: models.id })
        .from(models)
        .where(eq(models.variant, variant))
    ),
    limit: limitPerVariant,
    orderBy: desc(invocations.createdAt),
  })
);

const results = (await Promise.all(variantQueries))
  .flat()
  .sort((a, b) => b.createdAt.getTime() - a.createdAt.getTime());

This ensures that no single variant dominates the conversation feed.

Performance Considerations

Tool Calls Limit: Each invocation loads up to 50 tool calls (most recent first)
Invocation Limit: Returns 100 invocations per variant (400 total max)
Auto-Filtering: Removes system-triggered invocations to reduce noise
Cache Duration: 20-second stale time with 3-minute garbage collection

Error Handling

The endpoint throws an error if the database query fails:

try {
  const conversations = await refreshConversationEvents();
  return { conversations };
} catch (error) {
  console.error("Error fetching invocations", error);
  Sentry.captureException(error);
  throw new Error("Failed to fetch invocations");
}

Note: Unlike getModels, this endpoint does not provide fallback data since invocation history cannot be derived from static configuration.

Get Models - Fetch list of all trading models
Get Positions - Fetch active positions with decision status
Get Trades - Fetch closed trades with P&L

Implementation Details

Source Files:

Router: src/server/orpc/router/models.ts:43-59
Query Function: src/server/features/trading/conversationsSnapshot.server.ts:61-144
Schema: src/db/schema.ts:50-88 (Invocations + ToolCalls tables)

Cache Strategy:

Stale Time: 20 seconds (updated by scheduler)
GC Time: 3 minutes
Refetch: On window focus (default TanStack behavior)

Overview

Trading

Models & Invocations

Simulator

Analytics

Variants

Overview

Invocation Tracking

Request

Response

Database Schema

Invocations Table

ToolCalls Table

Auto-Triggered Filtering

Tool Call Types

TanStack Query Integration

Client Usage

Query Configuration

Example Response

Variant-Aware Fetching

Performance Considerations

Error Handling

Implementation Details

Build docs developers (and LLMs) love

Overview

Trading

Models & Invocations

Simulator

Analytics

Variants

​Overview

​Invocation Tracking

​Request

​Response

​Database Schema

​Invocations Table

​ToolCalls Table

​Auto-Triggered Filtering

​Tool Call Types

​TanStack Query Integration

​Client Usage

​Query Configuration

​Example Response

​Variant-Aware Fetching

​Performance Considerations

​Error Handling

​Related Endpoints

​Implementation Details

Build docs developers (and LLMs) love

Overview

Invocation Tracking

Request

Response

Database Schema

Invocations Table

ToolCalls Table

Auto-Triggered Filtering

Tool Call Types

TanStack Query Integration

Client Usage

Query Configuration

Example Response

Variant-Aware Fetching

Performance Considerations

Error Handling

Related Endpoints

Implementation Details