feat: use Codex-style compaction prompt for context compression

agent/context_compressor.py

@@ -102,22 +102,24 @@ def _generate_summary(self, turns_to_summarize: List[Dict[str, Any]]) -> Optiona
             parts.append(f"[{role.upper()}]: {content}")
 
         content_to_summarize = "\n\n".join(parts)
-        prompt = f"""Summarize these conversation turns concisely. This summary will replace these turns in the conversation history.
-
-Write from a neutral perspective describing:
-1. What actions were taken (tool calls, searches, file operations)
-2. Key information or results obtained
-3. Important decisions or findings
-4. Relevant data, file names, or outputs
-
-Keep factual and informative. Target ~{self.summary_target_tokens} tokens.
-
----
-TURNS TO SUMMARIZE:
-{content_to_summarize}
----
-
-Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix."""
+        prompt = (
+            "You are performing a CONTEXT CHECKPOINT COMPACTION. Create a handoff "
+            "summary for the AI assistant that will resume this conversation.\n\n"
+            "Include:\n"
+            "- Current progress and key decisions made\n"
+            "- Important context, constraints, or user preferences discovered\n"
+            "- What remains to be done (clear next steps)\n"
+            "- Any critical data: file paths, variable names, URLs, error messages, "
+            "or code snippets needed to continue\n"
+            "- Tool calls made and their key results\n\n"
+            "Be concise, structured, and focused on helping the assistant seamlessly "
+            "continue the work without re-doing what's already been done.\n\n"
+            f"Target roughly {self.summary_target_tokens} tokens.\n\n"
+            "---\n"
+            f"TURNS TO SUMMARIZE:\n{content_to_summarize}\n"
+            "---\n\n"
+            'Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix.'
+        )
 
         # Use the centralized LLM router — handles provider resolution,
         # auth, and fallback internally.

tests/agent/test_context_compressor.py

@@ -153,6 +153,33 @@ def test_none_content_in_system_message_compress(self):
         assert len(result) < len(msgs)
 
 
+class TestSummaryPrompt:
+    def test_generate_summary_uses_handoff_compaction_prompt(self):
+        mock_response = MagicMock()
+        mock_response.choices = [MagicMock()]
+        mock_response.choices[0].message.content = "[CONTEXT SUMMARY]: compacted"
+
+        with patch("agent.context_compressor.get_model_context_length", return_value=100000):
+            c = ContextCompressor(model="test", quiet_mode=True, summary_target_tokens=1234)
+
+        messages = [
+            {"role": "user", "content": "Investigate the failing test"},
+            {"role": "assistant", "content": "I'll inspect the traceback."},
+        ]
+
+        with patch("agent.context_compressor.call_llm", return_value=mock_response) as mock_call_llm:
+            c._generate_summary(messages)
+
+        prompt = mock_call_llm.call_args.kwargs["messages"][0]["content"]
+        assert "CONTEXT CHECKPOINT COMPACTION" in prompt
+        assert "Current progress and key decisions made" in prompt
+        assert "Important context, constraints, or user preferences discovered" in prompt
+        assert "What remains to be done (clear next steps)" in prompt
+        assert "Tool calls made and their key results" in prompt
+        assert "Target roughly 1234 tokens." in prompt
+        assert 'Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix.' in prompt
+
+
 class TestNonStringContent:
     """Regression: content as dict (e.g., llama.cpp tool calls) must not crash."""