feat: /compress <focus> — guided compression with focus topic (#8017)

Adds an optional focus topic to /compress: `/compress database schema`
guides the summariser to preserve information related to the focus topic
(60-70% of summary budget) while compressing everything else more aggressively.
Inspired by Claude Code's /compact <focus>.

Changes:
- context_compressor.py: focus_topic parameter on _generate_summary() and
  compress(); appends FOCUS TOPIC guidance block to the LLM prompt
- run_agent.py: focus_topic parameter on _compress_context(), passed through
  to the compressor
- cli.py: _manual_compress() extracts focus topic from command string,
  preserves existing manual_compression_feedback integration (no regression)
- gateway/run.py: _handle_compress_command() extracts focus from event args
  and passes through — full gateway parity
- commands.py: args_hint="[focus topic]" on /compress CommandDef

Salvaged from PR #7459 (CLI /compress focus only — /context command deferred).
15 new tests across CLI, compressor, and gateway.

agent/context_compressor.py

@@ -306,13 +306,19 @@ class ContextCompressor(ContextEngine):
 
         return "\n\n".join(parts)
 
-    def _generate_summary(self, turns_to_summarize: List[Dict[str, Any]]) -> Optional[str]:
+    def _generate_summary(self, turns_to_summarize: List[Dict[str, Any]], focus_topic: str = None) -> Optional[str]:
         """Generate a structured summary of conversation turns.
 
         Uses a structured template (Goal, Progress, Decisions, Files, Next Steps)
         inspired by Pi-mono and OpenCode. When a previous summary exists,
         generates an iterative update instead of summarizing from scratch.
 
+        Args:
+            focus_topic: Optional focus string for guided compression.  When
+                provided, the summariser prioritises preserving information
+                related to this topic and is more aggressive about compressing
+                everything else.  Inspired by Claude Code's ``/compact``.
+
         Returns None if all attempts fail — the caller should drop
         the middle turns without a summary rather than inject a useless
         placeholder.
@@ -414,6 +420,14 @@ Target ~{summary_budget} tokens. Be specific — include file paths, command out
 
 Write only the summary body. Do not include any preamble or prefix."""
 
+        # Inject focus topic guidance when the user provides one via /compress <focus>.
+        # This goes at the end of the prompt so it takes precedence.
+        if focus_topic:
+            prompt += f"""
+
+FOCUS TOPIC: "{focus_topic}"
+The user has requested that this compaction PRIORITISE preserving all information related to the focus topic above. For content related to "{focus_topic}", include full detail — exact values, file paths, command outputs, error messages, and decisions. For content NOT related to the focus topic, summarise more aggressively (brief one-liners or omit if truly irrelevant). The focus topic sections should receive roughly 60-70% of the summary token budget."""
+
         try:
             call_kwargs = {
                 "task": "compression",
@@ -631,7 +645,7 @@ Write only the summary body. Do not include any preamble or prefix."""
     # Main compression entry point
     # ------------------------------------------------------------------
 
-    def compress(self, messages: List[Dict[str, Any]], current_tokens: int = None) -> List[Dict[str, Any]]:
+    def compress(self, messages: List[Dict[str, Any]], current_tokens: int = None, focus_topic: str = None) -> List[Dict[str, Any]]:
         """Compress conversation messages by summarizing middle turns.
 
         Algorithm:
@@ -643,6 +657,12 @@ Write only the summary body. Do not include any preamble or prefix."""
 
         After compression, orphaned tool_call / tool_result pairs are cleaned
         up so the API never receives mismatched IDs.
+
+        Args:
+            focus_topic: Optional focus string for guided compression.  When
+                provided, the summariser will prioritise preserving information
+                related to this topic and be more aggressive about compressing
+                everything else.  Inspired by Claude Code's ``/compact``.
         """
         n_messages = len(messages)
         # Only need head + 3 tail messages minimum (token budget decides the real tail size)
@@ -700,7 +720,7 @@ Write only the summary body. Do not include any preamble or prefix."""
             )
 
         # Phase 3: Generate structured summary
-        summary = self._generate_summary(turns_to_summarize)
+        summary = self._generate_summary(turns_to_summarize, focus_topic=focus_topic)
 
         # Phase 4: Assemble compressed message list
         compressed = []

cli.py

@@ -5344,7 +5344,7 @@ class HermesCLI:
         elif canonical == "fast":
             self._handle_fast_command(cmd_original)
         elif canonical == "compress":
-            self._manual_compress()
+            self._manual_compress(cmd_original)
         elif canonical == "usage":
             self._show_usage()
         elif canonical == "insights":
@@ -6201,8 +6201,14 @@ class HermesCLI:
         self._reasoning_preview_buf = getattr(self, "_reasoning_preview_buf", "") + reasoning_text
         self._flush_reasoning_preview(force=False)
 
-    def _manual_compress(self):
+    def _manual_compress(self, cmd_original: str = ""):
-        """Manually trigger context compression on the current conversation."""
+        """Manually trigger context compression on the current conversation.
+
+        Accepts an optional focus topic: ``/compress <focus>`` guides the
+        summariser to preserve information related to *focus* while being
+        more aggressive about discarding everything else.  Inspired by
+        Claude Code's ``/compact <focus>`` feature.
+        """
         if not self.conversation_history or len(self.conversation_history) < 4:
             print("(._.) Not enough conversation to compress (need at least 4 messages).")
             return
@@ -6215,18 +6221,30 @@ class HermesCLI:
             print("(._.) Compression is disabled in config.")
             return
 
+        # Extract optional focus topic from the command (e.g. "/compress database schema")
+        focus_topic = ""
+        if cmd_original:
+            parts = cmd_original.strip().split(None, 1)
+            if len(parts) > 1:
+                focus_topic = parts[1].strip()
+
         original_count = len(self.conversation_history)
         try:
             from agent.model_metadata import estimate_messages_tokens_rough
             from agent.manual_compression_feedback import summarize_manual_compression
             original_history = list(self.conversation_history)
             approx_tokens = estimate_messages_tokens_rough(original_history)
-            print(f"🗜️  Compressing {original_count} messages (~{approx_tokens:,} tokens)...")
+            if focus_topic:
+                print(f"🗜️  Compressing {original_count} messages (~{approx_tokens:,} tokens), "
+                      f"focus: \"{focus_topic}\"...")
+            else:
+                print(f"🗜️  Compressing {original_count} messages (~{approx_tokens:,} tokens)...")
 
             compressed, _ = self.agent._compress_context(
                 original_history,
                 self.agent._cached_system_prompt or "",
                 approx_tokens=approx_tokens,
+                focus_topic=focus_topic or None,
             )
             self.conversation_history = compressed
             new_tokens = estimate_messages_tokens_rough(self.conversation_history)

gateway/run.py

@@ -5716,7 +5716,12 @@ class GatewayRunner:
             return f"{descriptions[new_mode]}\n_(could not save to config: {e})_"
 
     async def _handle_compress_command(self, event: MessageEvent) -> str:
-        """Handle /compress command -- manually compress conversation context."""
+        """Handle /compress command -- manually compress conversation context.
+
+        Accepts an optional focus topic: ``/compress <focus>`` guides the
+        summariser to preserve information related to *focus* while being
+        more aggressive about discarding everything else.
+        """
         source = event.source
         session_entry = self.session_store.get_or_create_session(source)
         history = self.session_store.load_transcript(session_entry.session_id)
@@ -5724,6 +5729,9 @@ class GatewayRunner:
         if not history or len(history) < 4:
             return "Not enough conversation to compress (need at least 4 messages)."
 
+        # Extract optional focus topic from command args
+        focus_topic = (event.get_command_args() or "").strip() or None
+
         try:
             from run_agent import AIAgent
             from agent.manual_compression_feedback import summarize_manual_compression
@@ -5765,7 +5773,7 @@ class GatewayRunner:
             loop = asyncio.get_event_loop()
             compressed, _ = await loop.run_in_executor(
                 None,
-                lambda: tmp_agent._compress_context(msgs, "", approx_tokens=approx_tokens)
+                lambda: tmp_agent._compress_context(msgs, "", approx_tokens=approx_tokens, focus_topic=focus_topic)
             )
 
             # _compress_context already calls end_session() on the old session
@@ -5789,7 +5797,10 @@ class GatewayRunner:
                 approx_tokens,
                 new_tokens,
             )
-            lines = [f"🗜️ {summary['headline']}", summary["token_line"]]
+            lines = [f"🗜️ {summary['headline']}"]
+            if focus_topic:
+                lines.append(f"Focus: \"{focus_topic}\"")
+            lines.append(summary["token_line"])
             if summary["note"]:
                 lines.append(summary["note"])
             return "\n".join(lines)

hermes_cli/commands.py

@@ -69,7 +69,8 @@ COMMAND_REGISTRY: list[CommandDef] = [
                args_hint="[name]"),
     CommandDef("branch", "Branch the current session (explore a different path)", "Session",
                aliases=("fork",), args_hint="[name]"),
-    CommandDef("compress", "Manually compress conversation context", "Session"),
+    CommandDef("compress", "Manually compress conversation context", "Session",
+               args_hint="[focus topic]"),
     CommandDef("rollback", "List or restore filesystem checkpoints", "Session",
                args_hint="[number]"),
     CommandDef("stop", "Kill all running background processes", "Session"),

run_agent.py

@@ -6548,17 +6548,23 @@ class AIAgent:
             if messages and messages[-1].get("_flush_sentinel") == _sentinel:
                 messages.pop()
 
-    def _compress_context(self, messages: list, system_message: str, *, approx_tokens: int = None, task_id: str = "default") -> tuple:
+    def _compress_context(self, messages: list, system_message: str, *, approx_tokens: int = None, task_id: str = "default", focus_topic: str = None) -> tuple:
         """Compress conversation context and split the session in SQLite.
 
+        Args:
+            focus_topic: Optional focus string for guided compression — the
+                summariser will prioritise preserving information related to
+                this topic.  Inspired by Claude Code's ``/compact <focus>``.
+
         Returns:
             (compressed_messages, new_system_prompt) tuple
         """
         _pre_msg_count = len(messages)
         logger.info(
-            "context compression started: session=%s messages=%d tokens=~%s model=%s",
+            "context compression started: session=%s messages=%d tokens=~%s model=%s focus=%r",
             self.session_id or "none", _pre_msg_count,
             f"{approx_tokens:,}" if approx_tokens else "unknown", self.model,
+            focus_topic,
         )
         # Pre-compression memory flush: let the model save memories before they're lost
         self.flush_memories(messages, min_turns=0)
@@ -6570,7 +6576,7 @@ class AIAgent:
             except Exception:
                 pass
 
-        compressed = self.context_compressor.compress(messages, current_tokens=approx_tokens)
+        compressed = self.context_compressor.compress(messages, current_tokens=approx_tokens, focus_topic=focus_topic)
 
         todo_snapshot = self._todo_store.format_for_injection()
         if todo_snapshot:

tests/agent/test_compress_focus.py

@@ -0,0 +1,139 @@
+"""Tests for focus_topic flowing through the compressor.
+
+Verifies that _generate_summary and compress accept and use the focus_topic
+parameter correctly.  Inspired by Claude Code's /compact <focus>.
+"""
+
+from unittest.mock import MagicMock, patch
+
+from agent.context_compressor import ContextCompressor
+
+
+def _make_compressor():
+    """Create a ContextCompressor with minimal state for testing."""
+    compressor = ContextCompressor.__new__(ContextCompressor)
+    compressor.protect_first_n = 2
+    compressor.protect_last_n = 5
+    compressor.tail_token_budget = 20000
+    compressor.context_length = 200000
+    compressor.threshold_percent = 0.80
+    compressor.threshold_tokens = 160000
+    compressor.max_summary_tokens = 10000
+    compressor.quiet_mode = True
+    compressor.compression_count = 0
+    compressor.last_prompt_tokens = 0
+    compressor._previous_summary = None
+    compressor._summary_failure_cooldown_until = 0.0
+    compressor.summary_model = None
+    return compressor
+
+
+def test_focus_topic_injected_into_summary_prompt():
+    """When focus_topic is provided, the LLM prompt includes focus guidance."""
+    compressor = _make_compressor()
+    turns = [
+        {"role": "user", "content": "Tell me about the database schema"},
+        {"role": "assistant", "content": "The schema has tables: users, orders, products."},
+    ]
+
+    captured_prompt = {}
+
+    def mock_call_llm(**kwargs):
+        captured_prompt["messages"] = kwargs["messages"]
+        resp = MagicMock()
+        resp.choices = [MagicMock()]
+        resp.choices[0].message.content = "## Goal\nUnderstand DB schema."
+        return resp
+
+    with patch("agent.context_compressor.call_llm", mock_call_llm):
+        result = compressor._generate_summary(turns, focus_topic="database schema")
+
+    assert result is not None
+    prompt_text = captured_prompt["messages"][0]["content"]
+    assert 'FOCUS TOPIC: "database schema"' in prompt_text
+    assert "PRIORITISE" in prompt_text
+    assert "60-70%" in prompt_text
+
+
+def test_no_focus_topic_no_injection():
+    """Without focus_topic, the prompt doesn't contain focus guidance."""
+    compressor = _make_compressor()
+    turns = [
+        {"role": "user", "content": "Hello"},
+        {"role": "assistant", "content": "Hi"},
+    ]
+
+    captured_prompt = {}
+
+    def mock_call_llm(**kwargs):
+        captured_prompt["messages"] = kwargs["messages"]
+        resp = MagicMock()
+        resp.choices = [MagicMock()]
+        resp.choices[0].message.content = "## Goal\nGreeting."
+        return resp
+
+    with patch("agent.context_compressor.call_llm", mock_call_llm):
+        result = compressor._generate_summary(turns)
+
+    prompt_text = captured_prompt["messages"][0]["content"]
+    assert "FOCUS TOPIC" not in prompt_text
+
+
+def test_compress_passes_focus_to_generate_summary():
+    """compress() passes focus_topic through to _generate_summary."""
+    compressor = _make_compressor()
+
+    # Track what _generate_summary receives
+    received_kwargs = {}
+    original_generate = compressor._generate_summary
+
+    def tracking_generate(turns, **kwargs):
+        received_kwargs.update(kwargs)
+        return "## Goal\nTest."
+
+    compressor._generate_summary = tracking_generate
+
+    messages = [
+        {"role": "system", "content": "System prompt"},
+        {"role": "user", "content": "first"},
+        {"role": "assistant", "content": "reply1"},
+        {"role": "user", "content": "second"},
+        {"role": "assistant", "content": "reply2"},
+        {"role": "user", "content": "third"},
+        {"role": "assistant", "content": "reply3"},
+        {"role": "user", "content": "fourth"},
+        {"role": "assistant", "content": "reply4"},
+    ]
+
+    compressor.compress(messages, current_tokens=100000, focus_topic="authentication flow")
+
+    assert received_kwargs.get("focus_topic") == "authentication flow"
+
+
+def test_compress_none_focus_by_default():
+    """compress() passes None focus_topic by default."""
+    compressor = _make_compressor()
+
+    received_kwargs = {}
+
+    def tracking_generate(turns, **kwargs):
+        received_kwargs.update(kwargs)
+        return "## Goal\nTest."
+
+    compressor._generate_summary = tracking_generate
+
+    messages = [
+        {"role": "system", "content": "System prompt"},
+        {"role": "user", "content": "first"},
+        {"role": "assistant", "content": "reply1"},
+        {"role": "user", "content": "second"},
+        {"role": "assistant", "content": "reply2"},
+        {"role": "user", "content": "third"},
+        {"role": "assistant", "content": "reply3"},
+        {"role": "user", "content": "fourth"},
+        {"role": "assistant", "content": "reply4"},
+    ]
+
+    compressor.compress(messages, current_tokens=100000)
+
+    assert received_kwargs.get("focus_topic") is None

tests/cli/test_compress_focus.py

@@ -0,0 +1,118 @@
+"""Tests for /compress <focus> — guided compression with focus topic.
+
+Inspired by Claude Code's /compact <focus> feature.
+"""
+
+from unittest.mock import MagicMock, patch
+
+from tests.cli.test_cli_init import _make_cli
+
+
+def _make_history() -> list[dict[str, str]]:
+    return [
+        {"role": "user", "content": "one"},
+        {"role": "assistant", "content": "two"},
+        {"role": "user", "content": "three"},
+        {"role": "assistant", "content": "four"},
+    ]
+
+
+def test_focus_topic_extracted_and_passed(capsys):
+    """Focus topic is extracted from the command and passed to _compress_context."""
+    shell = _make_cli()
+    history = _make_history()
+    compressed = [history[0], history[-1]]
+    shell.conversation_history = history
+    shell.agent = MagicMock()
+    shell.agent.compression_enabled = True
+    shell.agent._cached_system_prompt = ""
+    shell.agent._compress_context.return_value = (compressed, "")
+
+    def _estimate(messages):
+        if messages is history:
+            return 100
+        return 50
+
+    with patch("agent.model_metadata.estimate_messages_tokens_rough", side_effect=_estimate):
+        shell._manual_compress("/compress database schema")
+
+    output = capsys.readouterr().out
+    assert 'focus: "database schema"' in output
+
+    # Verify focus_topic was passed through
+    shell.agent._compress_context.assert_called_once()
+    call_kwargs = shell.agent._compress_context.call_args
+    assert call_kwargs.kwargs.get("focus_topic") == "database schema"
+
+
+def test_no_focus_topic_when_bare_command(capsys):
+    """When no focus topic is provided, None is passed."""
+    shell = _make_cli()
+    history = _make_history()
+    shell.conversation_history = history
+    shell.agent = MagicMock()
+    shell.agent.compression_enabled = True
+    shell.agent._cached_system_prompt = ""
+    shell.agent._compress_context.return_value = (list(history), "")
+
+    with patch("agent.model_metadata.estimate_messages_tokens_rough", return_value=100):
+        shell._manual_compress("/compress")
+
+    shell.agent._compress_context.assert_called_once()
+    call_kwargs = shell.agent._compress_context.call_args
+    assert call_kwargs.kwargs.get("focus_topic") is None
+
+
+def test_empty_focus_after_command_treated_as_none(capsys):
+    """Trailing whitespace after /compress does not produce a focus topic."""
+    shell = _make_cli()
+    history = _make_history()
+    shell.conversation_history = history
+    shell.agent = MagicMock()
+    shell.agent.compression_enabled = True
+    shell.agent._cached_system_prompt = ""
+    shell.agent._compress_context.return_value = (list(history), "")
+
+    with patch("agent.model_metadata.estimate_messages_tokens_rough", return_value=100):
+        shell._manual_compress("/compress   ")
+
+    shell.agent._compress_context.assert_called_once()
+    call_kwargs = shell.agent._compress_context.call_args
+    assert call_kwargs.kwargs.get("focus_topic") is None
+
+
+def test_focus_topic_printed_in_compression_banner(capsys):
+    """The focus topic shows in the compression progress banner."""
+    shell = _make_cli()
+    history = _make_history()
+    compressed = [history[0], history[-1]]
+    shell.conversation_history = history
+    shell.agent = MagicMock()
+    shell.agent.compression_enabled = True
+    shell.agent._cached_system_prompt = ""
+    shell.agent._compress_context.return_value = (compressed, "")
+
+    with patch("agent.model_metadata.estimate_messages_tokens_rough", return_value=100):
+        shell._manual_compress("/compress API endpoints")
+
+    output = capsys.readouterr().out
+    assert 'focus: "API endpoints"' in output
+
+
+def test_no_focus_prints_standard_banner(capsys):
+    """Without focus, the standard banner (no focus: line) is printed."""
+    shell = _make_cli()
+    history = _make_history()
+    compressed = [history[0], history[-1]]
+    shell.conversation_history = history
+    shell.agent = MagicMock()
+    shell.agent.compression_enabled = True
+    shell.agent._cached_system_prompt = ""
+    shell.agent._compress_context.return_value = (compressed, "")
+
+    with patch("agent.model_metadata.estimate_messages_tokens_rough", return_value=100):
+        shell._manual_compress("/compress")
+
+    output = capsys.readouterr().out
+    assert "focus:" not in output
+    assert "Compressing" in output

tests/gateway/test_compress_focus.py

@@ -0,0 +1,118 @@
+"""Tests for gateway /compress <focus> — focus topic on the gateway side."""
+
+from datetime import datetime
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from gateway.config import GatewayConfig, Platform, PlatformConfig
+from gateway.platforms.base import MessageEvent
+from gateway.session import SessionEntry, SessionSource, build_session_key
+
+
+def _make_source() -> SessionSource:
+    return SessionSource(
+        platform=Platform.TELEGRAM,
+        user_id="u1",
+        chat_id="c1",
+        user_name="tester",
+        chat_type="dm",
+    )
+
+
+def _make_event(text: str = "/compress") -> MessageEvent:
+    return MessageEvent(text=text, source=_make_source(), message_id="m1")
+
+
+def _make_history() -> list[dict[str, str]]:
+    return [
+        {"role": "user", "content": "one"},
+        {"role": "assistant", "content": "two"},
+        {"role": "user", "content": "three"},
+        {"role": "assistant", "content": "four"},
+    ]
+
+
+def _make_runner(history: list[dict[str, str]]):
+    from gateway.run import GatewayRunner
+
+    runner = object.__new__(GatewayRunner)
+    runner.config = GatewayConfig(
+        platforms={Platform.TELEGRAM: PlatformConfig(enabled=True, token="***")}
+    )
+    session_entry = SessionEntry(
+        session_key=build_session_key(_make_source()),
+        session_id="sess-1",
+        created_at=datetime.now(),
+        updated_at=datetime.now(),
+        platform=Platform.TELEGRAM,
+        chat_type="dm",
+    )
+    runner.session_store = MagicMock()
+    runner.session_store.get_or_create_session.return_value = session_entry
+    runner.session_store.load_transcript.return_value = history
+    runner.session_store.rewrite_transcript = MagicMock()
+    runner.session_store.update_session = MagicMock()
+    runner.session_store._save = MagicMock()
+    return runner
+
+
+@pytest.mark.asyncio
+async def test_compress_focus_topic_passed_to_agent():
+    """Focus topic from /compress <focus> is passed through to _compress_context."""
+    history = _make_history()
+    compressed = [history[0], history[-1]]
+    runner = _make_runner(history)
+    agent_instance = MagicMock()
+    agent_instance.context_compressor.protect_first_n = 0
+    agent_instance.context_compressor._align_boundary_forward.return_value = 0
+    agent_instance.context_compressor._find_tail_cut_by_tokens.return_value = 2
+    agent_instance.session_id = "sess-1"
+    agent_instance._compress_context.return_value = (compressed, "")
+
+    def _estimate(messages):
+        return 100
+
+    with (
+        patch("gateway.run._resolve_runtime_agent_kwargs", return_value={"api_key": "***"}),
+        patch("gateway.run._resolve_gateway_model", return_value="test-model"),
+        patch("run_agent.AIAgent", return_value=agent_instance),
+        patch("agent.model_metadata.estimate_messages_tokens_rough", side_effect=_estimate),
+    ):
+        result = await runner._handle_compress_command(_make_event("/compress database schema"))
+
+    # Verify focus_topic was passed
+    agent_instance._compress_context.assert_called_once()
+    call_kwargs = agent_instance._compress_context.call_args
+    assert call_kwargs.kwargs.get("focus_topic") == "database schema"
+
+    # Verify focus is mentioned in response
+    assert 'Focus: "database schema"' in result
+
+
+@pytest.mark.asyncio
+async def test_compress_no_focus_passes_none():
+    """Bare /compress passes focus_topic=None."""
+    history = _make_history()
+    runner = _make_runner(history)
+    agent_instance = MagicMock()
+    agent_instance.context_compressor.protect_first_n = 0
+    agent_instance.context_compressor._align_boundary_forward.return_value = 0
+    agent_instance.context_compressor._find_tail_cut_by_tokens.return_value = 2
+    agent_instance.session_id = "sess-1"
+    agent_instance._compress_context.return_value = (list(history), "")
+
+    with (
+        patch("gateway.run._resolve_runtime_agent_kwargs", return_value={"api_key": "***"}),
+        patch("gateway.run._resolve_gateway_model", return_value="test-model"),
+        patch("run_agent.AIAgent", return_value=agent_instance),
+        patch("agent.model_metadata.estimate_messages_tokens_rough", return_value=100),
+    ):
+        result = await runner._handle_compress_command(_make_event("/compress"))
+
+    agent_instance._compress_context.assert_called_once()
+    call_kwargs = agent_instance._compress_context.call_args
+    assert call_kwargs.kwargs.get("focus_topic") is None
+
+    # No focus line in response
+    assert "Focus:" not in result