amazing

ai_client.py

@@ -1,5 +1,7 @@
 # ai_client.py
 import tomllib
+import json
+import datetime
 from pathlib import Path
 
 _provider: str = "gemini"
@@ -16,8 +18,56 @@ _anthropic_history: list[dict] = []
 # Returns the output string if approved, None if rejected.
 confirm_and_run_callback = None
 
+# Injected by gui.py - called whenever a comms entry is appended.
+# Signature: (entry: dict) -> None
+comms_log_callback = None
+
 MAX_TOOL_ROUNDS = 5
 
+# ------------------------------------------------------------------ comms log
+
+_comms_log: list[dict] = []
+
+MAX_FIELD_CHARS = 400   # beyond this we show a truncated preview in the UI
+
+def _clamp(value, max_chars: int = MAX_FIELD_CHARS) -> tuple[str, bool]:
+    """Return (display_str, was_truncated)."""
+    if isinstance(value, (dict, list)):
+        s = json.dumps(value, ensure_ascii=False, indent=2)
+    else:
+        s = str(value)
+    if len(s) > max_chars:
+        return s[:max_chars], True
+    return s, False
+
+
+def _append_comms(direction: str, kind: str, payload: dict):
+    """
+    direction : "OUT" | "IN"
+    kind      : "request" | "response" | "tool_call" | "tool_result"
+    payload   : raw dict describing the event
+    """
+    entry = {
+        "ts":        datetime.datetime.now().strftime("%H:%M:%S"),
+        "direction": direction,
+        "kind":      kind,
+        "provider":  _provider,
+        "model":     _model,
+        "payload":   payload,
+    }
+    _comms_log.append(entry)
+    if comms_log_callback is not None:
+        comms_log_callback(entry)
+
+
+def get_comms_log() -> list[dict]:
+    return list(_comms_log)
+
+
+def clear_comms_log():
+    _comms_log.clear()
+
+
 def _load_credentials() -> dict:
     with open("credentials.toml", "rb") as f:
         return tomllib.load(f)
@@ -264,15 +314,33 @@ def _send_gemini(md_content: str, user_message: str, base_dir: str) -> str:
 
         full_message = f"<context>\n{md_content}\n</context>\n\n{user_message}"
 
+        _append_comms("OUT", "request", {
+            "message": full_message,
+        })
+
         response = _gemini_chat.send_message(full_message)
 
-        for _ in range(MAX_TOOL_ROUNDS):
+        for round_idx in range(MAX_TOOL_ROUNDS):
+            # Log the raw response candidates as text summary
+            text_parts_raw = [
+                part.text
+                for candidate in response.candidates
+                for part in candidate.content.parts
+                if hasattr(part, "text") and part.text
+            ]
             tool_calls = [
                 part.function_call
                 for candidate in response.candidates
                 for part in candidate.content.parts
                 if part.function_call is not None
             ]
+
+            _append_comms("IN", "response", {
+                "round":      round_idx,
+                "text":       "\n".join(text_parts_raw),
+                "tool_calls": [{"name": fc.name, "args": dict(fc.args)} for fc in tool_calls],
+            })
+
             if not tool_calls:
                 break
 
@@ -280,7 +348,15 @@ def _send_gemini(md_content: str, user_message: str, base_dir: str) -> str:
             for fc in tool_calls:
                 if fc.name == TOOL_NAME:
                     script = fc.args.get("script", "")
+                    _append_comms("OUT", "tool_call", {
+                        "name":   TOOL_NAME,
+                        "script": script,
+                    })
                     output = _run_script(script, base_dir)
+                    _append_comms("IN", "tool_result", {
+                        "name":   TOOL_NAME,
+                        "output": output,
+                    })
                     function_responses.append(
                         types.Part.from_function_response(
                             name=TOOL_NAME,
@@ -325,7 +401,11 @@ def _send_anthropic(md_content: str, user_message: str, base_dir: str) -> str:
         full_message = f"<context>\n{md_content}\n</context>\n\n{user_message}"
         _anthropic_history.append({"role": "user", "content": full_message})
 
-        for _ in range(MAX_TOOL_ROUNDS):
+        _append_comms("OUT", "request", {
+            "message": full_message,
+        })
+
+        for round_idx in range(MAX_TOOL_ROUNDS):
             response = _anthropic_client.messages.create(
                 model=_model,
                 max_tokens=8096,
@@ -338,6 +418,24 @@ def _send_anthropic(md_content: str, user_message: str, base_dir: str) -> str:
                 "content": response.content
             })
 
+            # Summarise the response content for the log
+            text_blocks = [b.text for b in response.content if hasattr(b, "text") and b.text]
+            tool_use_blocks = [
+                {"id": b.id, "name": b.name, "input": b.input}
+                for b in response.content
+                if b.type == "tool_use"
+            ]
+            _append_comms("IN", "response", {
+                "round":       round_idx,
+                "stop_reason": response.stop_reason,
+                "text":        "\n".join(text_blocks),
+                "tool_calls":  tool_use_blocks,
+                "usage":       {
+                    "input_tokens":  response.usage.input_tokens,
+                    "output_tokens": response.usage.output_tokens,
+                } if response.usage else {},
+            })
+
             if response.stop_reason != "tool_use":
                 break
 
@@ -345,7 +443,17 @@ def _send_anthropic(md_content: str, user_message: str, base_dir: str) -> str:
             for block in response.content:
                 if block.type == "tool_use" and block.name == TOOL_NAME:
                     script = block.input.get("script", "")
+                    _append_comms("OUT", "tool_call", {
+                        "name":   TOOL_NAME,
+                        "id":     block.id,
+                        "script": script,
+                    })
                     output = _run_script(script, base_dir)
+                    _append_comms("IN", "tool_result", {
+                        "name":   TOOL_NAME,
+                        "id":     block.id,
+                        "output": output,
+                    })
                     tool_results.append({
                         "type": "tool_result",
                         "tool_use_id": block.id,
@@ -360,6 +468,10 @@ def _send_anthropic(md_content: str, user_message: str, base_dir: str) -> str:
                 "content": tool_results
             })
 
+            _append_comms("OUT", "tool_result_send", {
+                "results": [{"tool_use_id": r["tool_use_id"], "content": r["content"]} for r in tool_results],
+            })
+
         text_parts = [
             block.text
             for block in response.content