background-injection.mdx

title	Background injection
sidebarTitle	Background injection
description	Inject context from background work into the agent's conversation — self-review, RAG augmentation, or any async analysis.

Overview

chat.inject() queues model messages for injection into the conversation. Messages are picked up at the start of the next turn or at the next prepareStep boundary (between tool-call steps).

This is the backend counterpart to pending messages — pending messages come from the user via the frontend, while chat.inject() comes from your task code.

Basic usage

import { chat } from "@trigger.dev/sdk/ai";

// Queue a system message for injection
chat.inject([
  {
    role: "system",
    content: "The user's account was just upgraded to Pro.",
  },
]);

Messages are appended to the model messages before the next LLM inference call. The LLM sees them as part of the conversation context.

Common pattern: defer + inject

The most powerful pattern combines chat.defer() (background work) with chat.inject() (inject results). Background work runs in parallel with the idle wait between turns, and results are injected before the next response.

export const myChat = chat.agent({
  id: "my-chat",
  onTurnComplete: async ({ messages }) => {
    // Kick off background analysis — doesn't block the turn
    chat.defer(
      (async () => {
        const analysis = await analyzeConversation(messages);
        chat.inject([
          {
            role: "system",
            content: `[Analysis of conversation so far]\n\n${analysis}`,
          },
        ]);
      })()
    );
  },
  run: async ({ messages, signal }) => {
    return streamText({
      ...chat.toStreamTextOptions({ registry }),
      messages,
      abortSignal: signal,
    });
  },
});

Timing

1. Turn completes, onTurnComplete fires
2. chat.defer() registers the background work
3. The run immediately starts waiting for the next message (no blocking)
4. Background work completes, chat.inject() queues the messages
5. User sends next message, turn starts
6. Injected messages are appended before run() executes
7. The LLM sees the injected context alongside the new user message

If the background work finishes during a tool-call loop (not between turns), the messages are picked up at the next prepareStep boundary instead.

Example: self-review

A cheap model reviews the agent's response after each turn and injects coaching for the next one. Uses Prompts for the review prompt and generateObject for structured output.

import { chat } from "@trigger.dev/sdk/ai";
import { prompts } from "@trigger.dev/sdk";
import { streamText, generateObject, createProviderRegistry } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";

const registry = createProviderRegistry({ openai });

const selfReviewPrompt = prompts.define({
  id: "self-review",
  model: "openai:gpt-4o-mini",
  content: `You are a conversation quality reviewer. Analyze the assistant's most recent response.

Focus on:
- Whether the response answered the user's question
- Missed opportunities to use tools or provide more detail
- Tone mismatches

Be concise. Only flag issues worth fixing.`,
});

export const myChat = chat.agent({
  id: "my-chat",
  onTurnComplete: async ({ messages }) => {
    chat.defer(
      (async () => {
        const resolved = await selfReviewPrompt.resolve({});

        const review = await generateObject({
          model: registry.languageModel(resolved.model ?? "openai:gpt-4o-mini"),
          ...resolved.toAISDKTelemetry(),
          system: resolved.text,
          prompt: messages
            .filter((m) => m.role === "user" || m.role === "assistant")
            .map((m) => {
              const text =
                typeof m.content === "string"
                  ? m.content
                  : Array.isArray(m.content)
                    ? m.content
                        .filter((p: any) => p.type === "text")
                        .map((p: any) => p.text)
                        .join("")
                    : "";
              return `${m.role}: ${text}`;
            })
            .join("\n\n"),
          schema: z.object({
            needsImprovement: z.boolean(),
            suggestions: z.array(z.string()),
          }),
        });

        if (review.object.needsImprovement) {
          chat.inject([
            {
              role: "system",
              content: `[Self-review]\n\n${review.object.suggestions.map((s) => `- ${s}`).join("\n")}\n\nApply these naturally.`,
            },
          ]);
        }
      })()
    );
  },
  run: async ({ messages, signal }) => {
    return streamText({
      ...chat.toStreamTextOptions({ registry }),
      messages,
      abortSignal: signal,
    });
  },
});

The self-review runs on gpt-4o-mini (fast, cheap) in the background. If the user sends another message before it completes, the coaching is still injected — chat.inject() persists across the idle wait.

Other use cases

• RAG augmentation: After each turn, fetch relevant documents and inject them as context for the next response
• Safety checks: Run a moderation model on the response, inject warnings if issues are detected
• Fact-checking: Verify claims in the response using search tools, inject corrections
• Context enrichment: Look up user/account data based on what was discussed, inject it as system context

How it differs from pending messages

	chat.inject()	Pending messages
Source	Backend task code	Frontend user input
Triggered by	Your code (e.g. onTurnComplete + chat.defer())	User sending a message during streaming
Injection point	Start of next turn, or next prepareStep boundary	Next prepareStep boundary only
Message role	Any (system, user, assistant)	Typically user
Frontend visibility	Not visible unless you write custom data-* chunks	Visible via usePendingMessages hook

API reference

chat.inject()

chat.inject(messages: ModelMessage[]): void

Queue model messages for injection at the next opportunity. Messages persist across the idle wait between turns — they are not reset when a new turn starts.

Parameters:

Parameter	Type	Description
messages	ModelMessage[]	Model messages to inject (from the ai package)

Messages are drained (consumed) when:

1. A new turn starts — before run() executes
2. A prepareStep boundary is reached — between tool-call steps during streaming

`chat.inject()` writes to an in-memory queue in the current process. It works from any code running in the same task — lifecycle hooks, deferred work, tool execute functions, etc. It does not work from subtasks or other runs.