fix: proactive compression after large tool results + Anthropic error detection

Two fixes for context overflow handling:

1. Proactive compression after tool execution: The compression check now
   estimates the next prompt size using real token counts from the last API
   response (prompt_tokens + completion_tokens) plus a conservative estimate
   of newly appended tool results (chars // 3 for JSON-heavy content).
   Previously, should_compress() only checked last_prompt_tokens which
   didn't account for tool results — so a 130k prompt + 100k chars of tool
   output would pass the 140k threshold check but fail the 200k API limit.

2. Safety net: Added 'prompt is too long' to context-length error detection
   phrases. Anthropic returns 'prompt is too long: N tokens > M maximum'
   on HTTP 400, which wasn't matched by existing phrases. This ensures
   compression fires even if the proactive check underestimates.

Fixes #813

tests/test_413_compression.py

@@ -396,3 +396,73 @@ class TestPreflightCompression:
             result = agent.run_conversation("hello", conversation_history=big_history)
 
         mock_compress.assert_not_called()
+
+
+class TestToolResultPreflightCompression:
+    """Compression should trigger when tool results push context past the threshold."""
+
+    def test_large_tool_results_trigger_compression(self, agent):
+        """When tool results push estimated tokens past threshold, compress before next call."""
+        agent.compression_enabled = True
+        agent.context_compressor.context_length = 200_000
+        agent.context_compressor.threshold_tokens = 140_000
+        agent.context_compressor.last_prompt_tokens = 130_000
+        agent.context_compressor.last_completion_tokens = 5_000
+
+        tc = SimpleNamespace(
+            id="tc1", type="function",
+            function=SimpleNamespace(name="web_search", arguments='{"query":"test"}'),
+        )
+        tool_resp = _mock_response(
+            content=None, finish_reason="stop", tool_calls=[tc],
+            usage={"prompt_tokens": 130_000, "completion_tokens": 5_000, "total_tokens": 135_000},
+        )
+        ok_resp = _mock_response(
+            content="Done after compression", finish_reason="stop",
+            usage={"prompt_tokens": 50_000, "completion_tokens": 100, "total_tokens": 50_100},
+        )
+        agent.client.chat.completions.create.side_effect = [tool_resp, ok_resp]
+        large_result = "x" * 100_000
+
+        with (
+            patch("run_agent.handle_function_call", return_value=large_result),
+            patch.object(agent, "_compress_context") as mock_compress,
+            patch.object(agent, "_persist_session"),
+            patch.object(agent, "_save_trajectory"),
+            patch.object(agent, "_cleanup_task_resources"),
+        ):
+            mock_compress.return_value = (
+                [{"role": "user", "content": "hello"}], "compressed prompt",
+            )
+            result = agent.run_conversation("hello")
+
+        mock_compress.assert_called_once()
+        assert result["completed"] is True
+
+    def test_anthropic_prompt_too_long_safety_net(self, agent):
+        """Anthropic 'prompt is too long' error triggers compression as safety net."""
+        err_400 = Exception(
+            "Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', "
+            "'message': 'prompt is too long: 233153 tokens > 200000 maximum'}}"
+        )
+        err_400.status_code = 400
+        ok_resp = _mock_response(content="Recovered", finish_reason="stop")
+        agent.client.chat.completions.create.side_effect = [err_400, ok_resp]
+        prefill = [
+            {"role": "user", "content": "previous"},
+            {"role": "assistant", "content": "answer"},
+        ]
+
+        with (
+            patch.object(agent, "_compress_context") as mock_compress,
+            patch.object(agent, "_persist_session"),
+            patch.object(agent, "_save_trajectory"),
+            patch.object(agent, "_cleanup_task_resources"),
+        ):
+            mock_compress.return_value = (
+                [{"role": "user", "content": "hello"}], "compressed",
+            )
+            result = agent.run_conversation("hello", conversation_history=prefill)
+
+        mock_compress.assert_called_once()
+        assert result["completed"] is True