diff --git a/cortex/openai_orchestrator.py b/cortex/openai_orchestrator.py
index dcf8282..881fc57 100644
--- a/cortex/openai_orchestrator.py
+++ b/cortex/openai_orchestrator.py
@@ -190,6 +190,66 @@ async def resume(checkpoint: OrchestrateCheckpoint, confirmed: bool) -> Orchestr
     )
 
 
+_CHARS_PER_TOKEN = 4
+# Fixed token overhead budget for sending 40 tool schemas per call
+_TOOL_SCHEMA_OVERHEAD = 3_000
+# Chars to keep per truncated old tool result
+_TRUNC_RESULT_CHARS = 400
+# Always keep the last N tool-result messages uncompacted
+_KEEP_RECENT_TOOL_MSGS = 6  # ~2 rounds of 3 tools each
+
+
+def _estimate_tokens(messages: list[dict]) -> int:
+    total = sum(len(json.dumps(m)) for m in messages)
+    return total // _CHARS_PER_TOKEN + _TOOL_SCHEMA_OVERHEAD
+
+
+def _compact_messages(messages: list[dict], budget_tokens: int) -> list[dict]:
+    """
+    Truncate old tool result content when approaching the context budget.
+
+    Strategy: keep system message, recent assistant/tool rounds, and the
+    original user task intact. Truncate content of old tool results in the
+    middle of the conversation — the model only needs recent results to reason.
+    """
+    if _estimate_tokens(messages) <= budget_tokens:
+        return messages
+
+    tool_indices = [i for i, m in enumerate(messages) if m.get("role") == "tool"]
+    n_to_compact = max(0, len(tool_indices) - _KEEP_RECENT_TOOL_MSGS)
+    if n_to_compact == 0:
+        return messages  # nothing old enough to trim
+
+    compact_set = set(tool_indices[:n_to_compact])
+    result = []
+    chars_saved = 0
+    for i, msg in enumerate(messages):
+        if i in compact_set:
+            content = msg.get("content", "")
+            if len(content) > _TRUNC_RESULT_CHARS:
+                msg = dict(msg)
+                saved = len(content) - _TRUNC_RESULT_CHARS
+                chars_saved += saved
+                msg["content"] = (
+                    content[:_TRUNC_RESULT_CHARS]
+                    + f" …[{len(content) - _TRUNC_RESULT_CHARS} chars omitted]"
+                )
+        result.append(msg)
+
+    new_est = _estimate_tokens(result)
+    logger.info(
+        "context compaction: saved ~%d tokens (%d chars), now ~%d / %d tokens",
+        chars_saved // _CHARS_PER_TOKEN, chars_saved, new_est, budget_tokens,
+    )
+    return result
+
+
+def _context_budget(model_cfg: dict | None) -> int:
+    """Return the usable token budget (75% of context window, min 16k, default 32k)."""
+    context_k = (model_cfg or {}).get("context_k") or 32
+    return max(16_000, int(context_k * 1000 * 0.75))
+
+
 async def _run_from_messages(
     client,
     messages: list[dict],
@@ -211,10 +271,13 @@ async def _run_from_messages(
     Returns (final_response, checkpoint) — checkpoint is set if confirmation is needed.
     """
     final_response = ""
+    budget = _context_budget(model_cfg)
 
     for round_num in range(starting_round, settings.orchestrator_max_rounds):
-        logger.info("OpenAI orchestrator round %d / %d  model=%s",
-                    round_num + 1, settings.orchestrator_max_rounds, model_name)
+        messages = _compact_messages(messages, budget)
+        est = _estimate_tokens(messages)
+        logger.info("OpenAI orchestrator round %d / %d  model=%s  ~%d tokens",
+                    round_num + 1, settings.orchestrator_max_rounds, model_name, est)
 
         response = await client.chat.completions.create(
             model=model_name,
@@ -240,7 +303,8 @@ async def _run_from_messages(
             ]
         messages.append(assistant_msg)
 
-        if choice.finish_reason == "tool_calls" and msg.tool_calls:
+        # Some models set finish_reason="stop" even when tool_calls are present
+        if msg.tool_calls and (choice.finish_reason in ("tool_calls", "stop", None)):
             # Snapshot state before tool responses for potential checkpoint
             pre_fn_state = list(messages)
 
@@ -249,10 +313,14 @@ async def _run_from_messages(
 
             for tc in msg.tool_calls:
                 name = tc.function.name
+                raw_args = tc.function.arguments or "{}"
                 try:
-                    args_parsed = json.loads(tc.function.arguments)
-                except json.JSONDecodeError:
-                    args_parsed = {"raw": tc.function.arguments}
+                    args_parsed = json.loads(raw_args)
+                    if not isinstance(args_parsed, dict):
+                        raise ValueError("args must be a JSON object")
+                except (json.JSONDecodeError, ValueError) as e:
+                    logger.warning("Malformed tool args for %s: %s — args: %.200s", name, e, raw_args)
+                    args_parsed = {}
 
                 if name in effective_confirm:
                     pending_tools.append({"name": name, "args": args_parsed, "tool_call_id": tc.id})