Extract skills.py and tools.py from agent.py

agent/tools.py

@@ -0,0 +1,132 @@
+"""LLM interaction, tool dispatch, and memory management."""
+
+import os
+import re
+import json
+import urllib.request
+import urllib.error
+
+
+def log(msg):
+    print(f"[tools] {msg}", flush=True)
+
+
+def set_logger(fn):
+    global log
+    log = fn
+
+
+# ─── Memory ──────────────────────────────────────────────────────────
+
+def load_memory(workspace):
+    """Load all memory files from workspace."""
+    memory = ""
+    try:
+        with open(f"{workspace}/MEMORY.md") as f:
+            memory = f.read().strip()
+        mem_dir = f"{workspace}/memory"
+        if os.path.isdir(mem_dir):
+            for fname in sorted(os.listdir(mem_dir)):
+                if fname.endswith(".md"):
+                    try:
+                        with open(f"{mem_dir}/{fname}") as f:
+                            topic = fname.replace(".md", "")
+                            memory += f"\n\n## {topic}\n{f.read().strip()}"
+                    except Exception:
+                        pass
+    except FileNotFoundError:
+        pass
+    return memory
+
+
+# ─── Tool Call Parsing ───────────────────────────────────────────────
+
+def try_parse_tool_call(text):
+    """Parse text-based tool calls (model dumps JSON as text)."""
+    text = re.sub(r"</?tool_call>", "", text).strip()
+    for start in range(len(text)):
+        if text[start] == "{":
+            for end in range(len(text), start, -1):
+                if text[end - 1] == "}":
+                    try:
+                        obj = json.loads(text[start:end])
+                        name = obj.get("name")
+                        args = obj.get("arguments", {})
+                        if name and isinstance(args, dict):
+                            return (name, args)
+                    except json.JSONDecodeError:
+                        continue
+    return None
+
+
+# ─── LLM Interaction ────────────────────────────────────────────────
+
+def ollama_request(ollama_url, payload):
+    data = json.dumps(payload).encode("utf-8")
+    req = urllib.request.Request(
+        f"{ollama_url}/api/chat",
+        data=data,
+        headers={"Content-Type": "application/json"},
+    )
+    with urllib.request.urlopen(req, timeout=120) as resp:
+        return json.loads(resp.read(2_000_000))
+
+
+def query_ollama(messages, runtime, tools, skill_scripts, dispatch_fn, ollama_url, max_rounds):
+    """Call Ollama chat API with skill-based tool support."""
+    payload = {
+        "model": runtime["model"],
+        "messages": messages,
+        "stream": False,
+        "options": {"num_predict": 512},
+    }
+
+    if tools:
+        payload["tools"] = tools
+
+    for round_num in range(max_rounds):
+        remaining = max_rounds - round_num
+        try:
+            data = ollama_request(ollama_url, payload)
+        except (urllib.error.URLError, TimeoutError) as e:
+            return f"[error: {e}]"
+
+        msg = data.get("message", {})
+
+        # Structured tool calls
+        tool_calls = msg.get("tool_calls")
+        if tool_calls:
+            messages.append(msg)
+            for tc in tool_calls:
+                fn = tc.get("function", {})
+                result = dispatch_fn(
+                    fn.get("name", ""),
+                    fn.get("arguments", {}),
+                    round_num + 1,
+                )
+                if remaining <= 2:
+                    result += f"\n[warning: {remaining - 1} tool rounds remaining — wrap up]"
+                messages.append({"role": "tool", "content": result})
+            payload["messages"] = messages
+            continue
+
+        # Text-based tool calls
+        content = msg.get("content", "").strip()
+        parsed_tool = try_parse_tool_call(content)
+        if parsed_tool:
+            fn_name, fn_args = parsed_tool
+            if fn_name in skill_scripts:
+                messages.append({"role": "assistant", "content": content})
+                result = dispatch_fn(fn_name, fn_args, round_num + 1)
+                if remaining <= 2:
+                    result += f"\n[warning: {remaining - 1} tool rounds remaining — wrap up]"
+                messages.append({
+                    "role": "user",
+                    "content": f"Tool result:\n{result}\n\nNow respond to the user based on this result.",
+                })
+                payload["messages"] = messages
+                continue
+
+        return content
+
+    return "[max tool rounds reached]"