add ollama version and instruction for teenages

.gitignore

@@ -225,3 +225,6 @@ test_providers.py
 # Internal analysis artifacts (not learning material)
 analysis/
 analysis_progress.md
+
+# macOS resource fork / metadata files
+._*

agents/s01_agent_loop_ollama.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+"""
+s01_agent_loop_ollama.py - The Agent Loop (Ollama version)
+
+Same pattern as s01_agent_loop.py but uses Ollama via its
+OpenAI-compatible API instead of Anthropic.
+
+    while stop_reason == "tool_calls":
+        response = LLM(messages, tools)
+        execute tools
+        append results
+
+    +----------+      +--------+      +---------+
+    |   User   | ---> | Ollama | ---> |  Tool   |
+    |  prompt  |      |        |      | execute |
+    +----------+      +---+----+      +----+----+
+                          ^               |
+                          |   tool_result |
+                          +---------------+
+                          (loop continues)
+
+Requirements:
+    pip install openai python-dotenv
+    ollama pull qwen2.5-coder:7b   # or any tool-capable model
+"""
+
+import json
+import os
+import subprocess
+
+from dotenv import load_dotenv
+from openai import OpenAI
+
+load_dotenv(override=True)
+
+client = OpenAI(
+    base_url=os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1"),
+    api_key=os.getenv("OLLAMA_API_KEY", "ollama"),  # Ollama ignores the key
+)
+MODEL = os.getenv("OLLAMA_MODEL_ID", "qwen2.5-coder:7b")
+
+SYSTEM = f"You are a coding agent at {os.getcwd()}. Use bash to solve tasks. Act, don't explain."
+
+# OpenAI tool format (different from Anthropic's)
+TOOLS = [{
+    "type": "function",
+    "function": {
+        "name": "bash",
+        "description": "Run a shell command.",
+        "parameters": {
+            "type": "object",
+            "properties": {"command": {"type": "string"}},
+            "required": ["command"],
+        },
+    },
+}]
+
+
+def run_bash(command: str) -> str:
+    dangerous = ["rm -rf /", "sudo", "shutdown", "reboot", "> /dev/"]
+    if any(d in command for d in dangerous):
+        return "Error: Dangerous command blocked"
+    try:
+        r = subprocess.run(command, shell=True, cwd=os.getcwd(),
+                           capture_output=True, text=True, timeout=120)
+        out = (r.stdout + r.stderr).strip()
+        return out[:50000] if out else "(no output)"
+    except subprocess.TimeoutExpired:
+        return "Error: Timeout (120s)"
+
+
+# -- The core pattern: same while loop, OpenAI message format --
+def agent_loop(messages: list):
+    while True:
+        response = client.chat.completions.create(
+            model=MODEL,
+            messages=messages,
+            tools=TOOLS,
+            tool_choice="auto",
+        )
+        msg = response.choices[0].message
+
+        # Append assistant turn (convert to dict for mutability)
+        messages.append(msg.model_dump(exclude_unset=False))
+
+        # If no tool calls, we're done
+        if response.choices[0].finish_reason != "tool_calls" or not msg.tool_calls:
+            return
+
+        # Execute each tool call, collect results
+        for tool_call in msg.tool_calls:
+            args = json.loads(tool_call.function.arguments)
+            print(f"\033[33m$ {args['command']}\033[0m")
+            output = run_bash(args["command"])
+            print(output[:200])
+            messages.append({
+                "role": "tool",
+                "tool_call_id": tool_call.id,
+                "content": output,
+            })
+
+
+if __name__ == "__main__":
+    print(f"\033[90mUsing model: {MODEL} via {client.base_url}\033[0m\n")
+    history = [{"role": "system", "content": SYSTEM}]
+    while True:
+        try:
+            query = input("\033[36ms01-ollama >> \033[0m")
+        except (EOFError, KeyboardInterrupt):
+            break
+        if query.strip().lower() in ("q", "exit", ""):
+            break
+        history.append({"role": "user", "content": query})
+        agent_loop(history)
+        last = history[-1]
+        content = last.get("content") or ""
+        if content:
+            print(content)
+        print()

agents/s02_tool_use_ollama.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+"""
+s02_tool_use_ollama.py - Tools (Ollama version)
+
+Same dispatch-map pattern as s02_tool_use.py but uses Ollama
+via its OpenAI-compatible API.
+
+    +----------+      +--------+      +------------------+
+    |   User   | ---> | Ollama | ---> | Tool Dispatch    |
+    |  prompt  |      |        |      | {                |
+    +----------+      +---+----+      |   bash: run_bash |
+                          ^          |   read: run_read |
+                          |          |   write: run_wr  |
+                          +----------+   edit: run_edit |
+                          tool_result| }                |
+                                     +------------------+
+
+Key insight: "The loop didn't change at all. I just added tools."
+"""
+
+import json
+import os
+import subprocess
+from pathlib import Path
+
+from dotenv import load_dotenv
+from openai import OpenAI
+
+load_dotenv(override=True)
+
+WORKDIR = Path.cwd()
+client = OpenAI(
+    base_url=os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1"),
+    api_key=os.getenv("OLLAMA_API_KEY", "ollama"),
+)
+MODEL = os.getenv("OLLAMA_MODEL_ID", "qwen2.5-coder:7b")
+
+SYSTEM = f"You are a coding agent at {WORKDIR}. Use tools to solve tasks. Act, don't explain."
+
+
+def safe_path(p: str) -> Path:
+    path = (WORKDIR / p).resolve()
+    if not path.is_relative_to(WORKDIR):
+        raise ValueError(f"Path escapes workspace: {p}")
+    return path
+
+
+def run_bash(command: str) -> str:
+    dangerous = ["rm -rf /", "sudo", "shutdown", "reboot", "> /dev/"]
+    if any(d in command for d in dangerous):
+        return "Error: Dangerous command blocked"
+    try:
+        r = subprocess.run(command, shell=True, cwd=WORKDIR,
+                           capture_output=True, text=True, timeout=120)
+        out = (r.stdout + r.stderr).strip()
+        return out[:50000] if out else "(no output)"
+    except subprocess.TimeoutExpired:
+        return "Error: Timeout (120s)"
+
+
+def run_read(path: str, limit: int = None) -> str:
+    try:
+        lines = safe_path(path).read_text().splitlines()
+        if limit and limit < len(lines):
+            lines = lines[:limit] + [f"... ({len(lines) - limit} more lines)"]
+        return "\n".join(lines)[:50000]
+    except Exception as e:
+        return f"Error: {e}"
+
+
+def run_write(path: str, content: str) -> str:
+    try:
+        fp = safe_path(path)
+        fp.parent.mkdir(parents=True, exist_ok=True)
+        fp.write_text(content)
+        return f"Wrote {len(content)} bytes to {path}"
+    except Exception as e:
+        return f"Error: {e}"
+
+
+def run_edit(path: str, old_text: str, new_text: str) -> str:
+    try:
+        fp = safe_path(path)
+        content = fp.read_text()
+        if old_text not in content:
+            return f"Error: Text not found in {path}"
+        fp.write_text(content.replace(old_text, new_text, 1))
+        return f"Edited {path}"
+    except Exception as e:
+        return f"Error: {e}"
+
+
+# -- The dispatch map: {tool_name: handler} --
+TOOL_HANDLERS = {
+    "bash":       lambda **kw: run_bash(kw["command"]),
+    "read_file":  lambda **kw: run_read(kw["path"], kw.get("limit")),
+    "write_file": lambda **kw: run_write(kw["path"], kw["content"]),
+    "edit_file":  lambda **kw: run_edit(kw["path"], kw["old_text"], kw["new_text"]),
+}
+
+# OpenAI function format (vs Anthropic's input_schema format)
+TOOLS = [
+    {"type": "function", "function": {
+        "name": "bash", "description": "Run a shell command.",
+        "parameters": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}}},
+    {"type": "function", "function": {
+        "name": "read_file", "description": "Read file contents.",
+        "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "limit": {"type": "integer"}}, "required": ["path"]}}},
+    {"type": "function", "function": {
+        "name": "write_file", "description": "Write content to file.",
+        "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}}},
+    {"type": "function", "function": {
+        "name": "edit_file", "description": "Replace exact text in file.",
+        "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "old_text": {"type": "string"}, "new_text": {"type": "string"}}, "required": ["path", "old_text", "new_text"]}}},
+]
+
+
+def agent_loop(messages: list):
+    while True:
+        response = client.chat.completions.create(
+            model=MODEL, messages=messages,
+            tools=TOOLS, tool_choice="auto",
+        )
+        msg = response.choices[0].message
+        messages.append(msg.model_dump(exclude_unset=False))
+
+        if response.choices[0].finish_reason != "tool_calls" or not msg.tool_calls:
+            return
+
+        for tool_call in msg.tool_calls:
+            args = json.loads(tool_call.function.arguments)
+            handler = TOOL_HANDLERS.get(tool_call.function.name)
+            output = handler(**args) if handler else f"Unknown tool: {tool_call.function.name}"
+            print(f"> {tool_call.function.name}: {str(output)[:200]}")
+            messages.append({
+                "role": "tool",
+                "tool_call_id": tool_call.id,
+                "content": str(output),
+            })
+
+
+if __name__ == "__main__":
+    print(f"\033[90mUsing model: {MODEL} via {client.base_url}\033[0m\n")
+    history = [{"role": "system", "content": SYSTEM}]
+    while True:
+        try:
+            query = input("\033[36ms02-ollama >> \033[0m")
+        except (EOFError, KeyboardInterrupt):
+            break
+        if query.strip().lower() in ("q", "exit", ""):
+            break
+        history.append({"role": "user", "content": query})
+        agent_loop(history)
+        last = history[-1]
+        content = last.get("content") or ""
+        if content and last.get("role") == "assistant":
+            print(content)
+        print()