From 3b4b27de02b5e61e8628a60661bb2194868f5090 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=83=84=E3=83=B3=E3=83=87=E3=83=AC?=
 <vpeilivanidis@gmail.com>
Date: Sat, 21 Mar 2026 14:54:22 +0200
Subject: [PATCH] docs: restore custom model integrations section and fix
 architecture diagram

---
 docs/ARCHITECTURE.md | 47 ++++++++++++++++++++++++++------------------
 1 file changed, 28 insertions(+), 19 deletions(-)

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index f9f60ca..78ae0ce 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -49,30 +49,39 @@ Agents can respond with just `PASS` if they genuinely have nothing meaningful to
 Beyond the configured turn interval, the `needs_human_input()` method performs an additional check: if the **last agent message explicitly addresses the user by name**, the HITL prompt fires immediately. This ensures the conversation never inadvertently "speaks for" the human participant.
 
 **Decision Flow:**
-```
-generate_next_turn()
-    ↓
-Orchestrator due? → speak (or PASS → skip)
-    ↓
-_forced_next_agent set? → use it, clear it
-    ↓
-session_type = DYNAMIC?
-    → @mention in last message? → forced agent
-    → Score all agents by expertise → pick best
-    → fallback: round robin
-    ↓
-agent.generate_response()
-    ↓
-response == "PASS"? → skip, return {skipped: True}
-    ↓
-append to history with timestamp, return turn_data
+```mermaid
+flowchart TD
+
+A["generate_next_turn()"] --> B{"Orchestrator due?"}
+B -->|Yes| C["Speak or PASS"]
+C -->|Speak| EndOrch["Append to history & return turn"]
+C -->|PASS| D
+
+B -->|No| D{"_forced_next_agent set?"}
+D -->|Yes| E["Use forced agent & clear flag"] --> L
+D -->|No| F{"session_type = DYNAMIC?"}
+
+F -->|No| K["Fallback: round robin / argumentative"] --> L
+F -->|Yes| G{"@mention in last message?"}
+
+G -->|Yes| H["Use mentioned agent"] --> L
+G -->|No| I["Score agents by expertise"]
+
+I --> J{"Top score > 0?"}
+J -->|Yes| Best["Pick best matching agent"] --> L
+J -->|No| K
+
+L["agent.generate_response()"] --> M{"response == PASS?"}
+
+M -->|Yes| N["Skip turn, return skipped: True"]
+M -->|No| O["Append to history with timestamp"] --> P["Return turn_data"]
 ```
 
 ## Custom Model Integrations (Bring Your Own Code)
 
-If you do not want to use LiteLLM at all, the framework allows you to inject arbitrary Python scripts as the "brain" for an agent.
+If you do not want to use LiteLLM at all, the framework allows you to inject any arbitrary python script as the "brain" for an agent.
 
-1. Create a python file (e.g. `my_model.py`).
+1. Create a python file (e.g. `my_model.py`). 
 2. Write a function that accepts a `List[Dict[str, str]]` (the conversation history) and returns a `str` (the agent's reply).
 3. In the CLI wizard, select `custom_function` as the Model Type.
 4. Provide the path to `my_model.py` and the exact name of the function you wrote.