feat(agents): research loop with budget guards, compaction, depth nudge, finalize

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016DvdRZkyCy9NJpHYDdPqmS

scripts/smoke_toolcalling.py

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+"""Phase-1 GATE: does the local model reliably emit tool calls over MULTIPLE rounds?
+
+This is the single load-bearing risk of the whole design. A single tool call often works;
+the failure mode is multi-turn (the model stops calling tools, or emits malformed calls).
+Run this with LM Studio up and a model loaded:
+
+    lms server start && lms load gpt-oss-20b
+    uv run python scripts/smoke_toolcalling.py
+
+PASS = the model chained >=3 real tool calls and produced a final answer.
+"""
+
+from __future__ import annotations
+
+import pathlib
+import sys
+
+sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent))
+
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
+
+from llm.client import build_llm, list_loaded_models, resolve_model_id
+from tools.market_data import get_quote
+from tools.python_exec import run_python
+from tools.web import web_search
+
+TOOLS = [run_python, web_search, get_quote]
+TOOLS_BY_NAME = {t.name: t for t in TOOLS}
+
+SYSTEM = (
+    "You are a test agent. Use a tool for EVERY factual or numeric step — never compute "
+    "in your head. Call tools one step at a time, then give a final summary."
+)
+TASK = (
+    "Do these steps, each via a tool call, then summarize the results:\n"
+    "1) Get the last price of AAPL.\n"
+    "2) With run_python, compute a 7% stop-loss price (price * 0.93).\n"
+    "3) With run_python, compute a 15% take-profit price (price * 1.15).\n"
+    "4) web_search for one recent Apple (AAPL) news headline.\n"
+    "Report the stop price, take price, and the headline."
+)
+MAX_ROUNDS = 10
+
+
+def main() -> int:
+    loaded = list_loaded_models()
+    if not loaded:
+        print("FAIL: LM Studio not reachable at /v1/models.")
+        print("Start it:  lms server start   then   lms load <model>")
+        return 2
+    print("Loaded models :", loaded)
+    print("Resolved model:", resolve_model_id())
+    print("-" * 60)
+
+    llm = build_llm(TOOLS)
+    messages: list = [SystemMessage(content=SYSTEM), HumanMessage(content=TASK)]
+    tool_calls_made = 0
+    rounds = 0
+
+    while rounds < MAX_ROUNDS:
+        rounds += 1
+        ai: AIMessage = llm.invoke(messages)
+        messages.append(ai)
+        if not ai.tool_calls:
+            print(f"\n[round {rounds}] FINAL:\n{ai.content}\n")
+            break
+        for tc in ai.tool_calls:
+            name, args = tc["name"], tc["args"]
+            print(f"[round {rounds}] call {name}({args})")
+            tool = TOOLS_BY_NAME.get(name)
+            if tool is None:
+                result = f"ERROR: unknown tool {name}"
+            else:
+                try:
+                    result = tool.invoke(args)
+                except Exception as e:
+                    result = f"ERROR: {type(e).__name__}: {e}"
+            tool_calls_made += 1
+            print(f"          -> {str(result)[:180]}")
+            messages.append(ToolMessage(content=str(result), tool_call_id=tc["id"]))
+
+    print("=" * 60)
+    print(f"rounds={rounds}  tool_calls_executed={tool_calls_made}")
+    ok = tool_calls_made >= 3
+    print("RESULT:", "PASS — multi-turn tool calling works" if ok else "FAIL — tool calling unreliable")
+    return 0 if ok else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())