Architecture 

System overview 

Browser (Backend Module)
    |
    | AJAX (poll + send)
    v
ChatApiController
    |
    | enqueue message
    v
ConversationRepository  <----->  Database
    |                         (tx_nrmcpagent_conversation)
    |
    v
ChatProcessor (exec or worker)
    |
    | fork CLI / dequeue
    v
ProcessChatCommand / ChatWorkerCommand
    |
    v
ChatService
    |
    |--- resolveProvider()
    |        |
    |        v
    |    Task --> Configuration --> Model (nr-llm DB)
    |        |
    |        v
    |    ProviderAdapterRegistry (nr-llm)
    |        |
    |        v
    |    LLM Provider (OpenAI, Anthropic, ...)
    |
    |--- McpToolProvider
             |
             v
        MCP Server (hn/typo3-mcp-server)
             |
             v
        TYPO3 Content (pages, tt_content, ...)
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The frontend (a Lit web component) communicates with the backend exclusively through polling. There are no WebSocket or Server-Sent Events connections.

The AI Chat is accessible in two ways:

  • Backend module (Admin Tools > AI Chat) -- Full-page chat interface for longer conversations and history management.
  • Toolbar panel -- Floating bottom panel triggered by the toolbar button. Stays visible across module navigation, allowing users to chat while working in the page tree.

Key design decisions 

Polling over SSE 

The chat UI uses periodic AJAX polling instead of Server-Sent Events (SSE) or WebSockets. This was chosen because:

  • It works reliably behind reverse proxies and load balancers without special configuration.
  • TYPO3 backend requests go through the standard middleware stack, ensuring authentication and CSRF protection.
  • The polling interval is short enough (1-2 seconds) to feel responsive.

CLI processing over HTTP 

Message processing happens in CLI context (ai-chat:process or ai-chat:worker), not in the web request. This design:

  • Avoids PHP timeout issues -- LLM calls and MCP tool execution can take many seconds.
  • Keeps the web server responsive -- no long-running HTTP connections.
  • Allows the worker mode to reuse a single process for multiple requests, reducing overhead.

Crash recovery 

The system is designed to handle crashes gracefully:

  • Every state transition is persisted to the database immediately.
  • If a CLI process crashes mid-conversation, the conversation remains in processing, locked, or tool_loop status.
  • The ai-chat:cleanup command detects conversations stuck for more than 5 minutes and marks them as failed.
  • Users see a clear error message and can retry.

Domain model 

Conversation 

The central entity. Stored in tx_nrmcpagent_conversation.

Fields:

be_user
UID of the owning backend user.
title
Auto-generated title from the first message.
messages

JSON-encoded array of all messages (user, assistant, tool calls, tool results). Stored as mediumtext.

User messages with file attachments contain additional fields:

{
    "role": "user",
    "content": "What is in this image?",
    "fileUid": 42,
    "fileName": "photo.jpg",
    "fileMimeType": "image/jpeg"
}
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The fileUid is a TYPO3 FAL UID. ChatService::buildLlmMessages() reads the file and converts it to a multimodal content array before passing messages to the LLM.

message_count
Denormalized count for display without decoding.
status
Current processing state (see below).
current_request_id
Identifier for the active processing request. Used for worker dequeue locking.
system_prompt
Optional custom system prompt override (per conversation).

System prompt priority 

The system prompt sent to the LLM is resolved in this order:

  1. Conversation-level prompt -- If a conversation has a custom system_prompt set, it takes highest priority.
  2. nr-llm Configuration + Task prompts -- The system_prompt from the nr-llm Configuration record and the prompt_template from the Task record are combined (separated by a blank line). Configure these in the TYPO3 backend to provide tool usage instructions or persona definitions.
  3. Locale-based fallback -- If nothing is configured, a default prompt is used based on the backend user's language setting (German or English).

Provider resolution 

ChatService resolves the LLM provider through the nr-llm database chain:

  1. Read the Task record (by llmTaskUid from extension configuration).
  2. Follow Task → Configuration → Model via foreign keys.
  3. Use ProviderAdapterRegistry::createAdapterFromModel() to create a fully configured provider instance (with API key from nr-vault).

The Configuration's system_prompt and the Task's prompt_template are fetched in the same query and used by buildSystemPrompt().

archived
Whether the conversation is archived.
pinned
Whether the conversation is pinned (prevents auto-archiving).
error_message
Last error message (sanitized, no API keys).

ConversationStatus 

The conversation lifecycle is modeled as a state enum:

idle
Ready for new user input. This is the resting state.
processing
A CLI process is actively calling the LLM.
locked
Reserved by a worker process for dequeue.
tool_loop
The LLM requested tool calls; the system is executing MCP tools and will call the LLM again.
failed
An error occurred. The user can retry by sending a new message.

State transitions:

idle --> processing --> idle          (success)
idle --> processing --> tool_loop --> processing
                                         (tool iteration)
idle --> processing --> failed        (error)
* --> failed                         (cleanup timeout)
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File attachment flow 

User selects file (upload or FAL browser)
    |
    | POST /ai-chat/file-upload (multipart/form-data)
    v
ChatApiController::fileUpload()
    | validates MIME type + size (max 20 MB)
    v
FAL storage: fileadmin/ai-chat/<be_user_uid>/
    | returns fileUid
    v
Frontend stores {fileUid, name, mimeType} as pendingFile

User sends message
    |
    | POST /ai-chat/conversations/send {content, fileUid}
    v
ChatApiController::sendMessage()
    | validates file limit (max 5 per conversation)
    | reads FAL metadata (fileName, fileMimeType)
    | stores message with fileUid in conversation JSON
    v
ChatService::processConversation()
    |
    v
ChatService::buildLlmMessages()
    | reads file from FAL (getForLocalProcessing)
    | for each file attachment:
    |   images  → base64 data URI (provider must be VisionCapable)
    |   documents (PDF/DOCX/XLSX/TXT):
    |     if provider implements DocumentCapableInterface
    |       → sent as binary (base64-encoded document block)
    |     else
    |       → DocumentExtractorRegistry::extract() → plain-text block
    v
LLM Provider (multimodal chatCompletion call)
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ChatService::getProviderCapabilities() queries the active provider for its supported formats. It calls VisionCapableInterface::getSupportedImageFormats() for image formats and, if the provider also implements DocumentCapableInterface, appends getSupportedDocumentFormats() (e.g. ['pdf']). The frontend receives this list via GET /ai-chat/status and uses it to set the file picker's accept attribute dynamically — ensuring users can only select file types the current provider can process.

Component map 

Component Responsibility Key files
Backend Module Chat UI (Admin Tools > AI Chat) Classes/Controller/, Resources/Private/Templates/
Floating Panel Toolbar chat widget, persistent across navigation Resources/Public/JavaScript/ (Lit)
Agent Loop LLM call → tool use → reply, with retry logic Classes/Service/AgentLoopService.php
MCP Client Spawns typo3-mcp-server, handles stdio protocol Classes/Mcp/
Conversation Store Persists messages, pins, auto-archive Classes/Domain/Repository/
CLI Commands ai-chat:process (exec), ai-chat:worker (long-running) Classes/Command/
Access Control Group-based access, concurrency caps, length limits Classes/Service/AccessControlService.php

Dependency rules 

Enforced via PHPAt — runs automatically with PHPStan:

  • Domain MUST NOT depend on Controller or Command
  • Controller may depend on Domain and Service
  • Service may depend on Domain; MUST NOT depend on Controller
  • Mcp may depend on Domain and Service; MUST NOT depend on Controller
  • Tests may depend on anything

Architecture tests: Tests/Architecture/LayerDependencyTest.php