feat(core): add config path parameter and enhance fs cli capabilities

Author: jinliyl

reme/agent/chat/fs_cli.py

@@ -10,20 +10,102 @@
 class FsCli(BaseReactStream):
     """FsCli agent with system prompt."""
 
-    def __init__(self, working_dir: str, **kwargs):
+    def __init__(
+        self,
+        working_dir: str,
+        summary_params: dict | None = None,
+        compact_params: dict | None = None,
+        **kwargs,
+    ):
         super().__init__(**kwargs)
         self.working_dir: str = working_dir
+        self.summary_params: dict = summary_params or {}
+        self.compact_params: dict = compact_params or {}
+
         self.messages: list[Message] = []
+        self.previous_summary: str = ""
+
+    async def reset_history(self) -> str:
+        """Reset conversation history using summary.
+
+        Summarizes current messages to memory files and clears history.
+        """
+        if not self.messages:
+            self.messages.clear()
+            self.previous_summary = ""
+            return "No history to reset."
 
-    def reset_history(self):
-        """Reset conversation history."""
+        # Import required modules
+        from ..fs import FsSummarizer
+
+        # Summarize current conversation and save to memory files
+        current_date = datetime.now().strftime("%Y-%m-%d")
+        summarizer = FsSummarizer(tools=self.tools, **(self.summary_params or {}))
+
+        result = await summarizer.call(
+            messages=self.messages,
+            date=current_date,
+            service_context=self.service_context,
+        )
+
+        # Clear messages (no previous_summary update, as summarizer saves to files)
         self.messages.clear()
-        return self
+        self.previous_summary = ""
+
+        return f"History saved to memory files and reset. Result: {result.get('answer', 'Done')}"
+
+    async def compact_history(self) -> str:
+        """Compact history then reset.
+
+        First compacts messages if they exceed token limits (generating a summary),
+        then calls reset_history to save to files and clear.
+        """
+        if not self.messages:
+            return "No history to compact."
+
+        # Import required modules
+        from ..fs import FsCompactor
+
+        # Step 1: Compact messages
+        compactor = FsCompactor(**(self.compact_params or {}))
+        compact_result = await compactor.call(
+            messages=self.messages,
+            previous_summary=self.previous_summary,
+            service_context=self.service_context,
+        )
+
+        compacted_messages = compact_result.get("messages", self.messages)
+        is_compacted = compact_result.get("compacted", False)
+
+        if not is_compacted:
+            return "History is within token limits, no compaction needed."
+
+        # Step 2: Extract summary from compacted messages
+        # The first message contains the summary wrapped in compaction_summary_format
+        tokens_before = compact_result.get("tokens_before", 0)
+
+        if compacted_messages and compacted_messages[0].role == Role.USER:
+            # Extract summary content from the first message
+            summary_content = compacted_messages[0].content
+            self.previous_summary = summary_content
+
+        # Step 3: Update messages and call reset_history to save and clear
+        self.messages = compacted_messages
+        reset_result = await self.reset_history()
+
+        return f"History compacted from {tokens_before} tokens. {reset_result}"
 
     async def build_messages(self) -> list[Message]:
         """Build system prompt message."""
         current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S %A")
-        system_prompt = self.prompt_format("system_prompt", workspace_dir=self.working_dir, current_time=current_time)
+
+        system_prompt = self.prompt_format(
+            "system_prompt",
+            workspace_dir=self.working_dir,
+            current_time=current_time,
+            has_previous_summary=bool(self.previous_summary),
+            previous_summary=self.previous_summary or "",
+        )
 
         return [
             Message(role=Role.SYSTEM, content=system_prompt),

reme/agent/chat/fs_cli.yaml

@@ -8,6 +8,13 @@ system_prompt: |
   Your working directory is: {workspace_dir}
   Treat this directory as the single global workspace for file operations unless explicitly instructed otherwise.
 
+  [has_previous_summary]## Previous Conversation Summary
+  [has_previous_summary]<previous-summary>
+  [has_previous_summary]{previous_summary}
+  [has_previous_summary]</previous-summary>
+  [has_previous_summary]
+  [has_previous_summary]The above is a summary of our previous conversation. Use it as context to maintain continuity.
+
   ## Session Initialization
 
   Before doing anything else, read these files to orient yourself (don't ask permission):

reme/agent/fs/fs_compactor.py

@@ -17,12 +17,14 @@ def __init__(
         context_window_tokens: int = 128000,
         reserve_tokens: int = 36000,
         keep_recent_tokens: int = 20000,
+        force_compact: bool = False,
         **kwargs,
     ):
         super().__init__(tools=[], **kwargs)
         self.context_window_tokens: int = context_window_tokens
         self.reserve_tokens: int = reserve_tokens
         self.keep_recent_tokens: int = keep_recent_tokens
+        self.force_compact: bool = force_compact
 
     @staticmethod
     def _normalize_messages(messages: list[Message | dict]) -> list[Message]:
@@ -178,16 +180,20 @@ async def execute(self):
         token_count: int = self.token_counter.count_token(original_messages)
         threshold = self.context_window_tokens - self.reserve_tokens
 
-        if token_count < threshold:
+        if not self.force_compact and token_count < threshold:
             logger.info(f"Token count {token_count} below threshold ({threshold}), skipping compaction")
             return {
                 "compacted": False,
                 "tokens_before": token_count,
                 "is_split_turn": False,
                 "messages": original_messages,
+                "summary_content": "",
             }
 
-        logger.info(f"Starting compaction, token count: {token_count}, threshold: {threshold}")
+        if self.force_compact:
+            logger.info(f"Force compaction enabled, token count: {token_count}, threshold: {threshold}")
+        else:
+            logger.info(f"Starting compaction, token count: {token_count}, threshold: {threshold}")
 
         history_prompt_messages = self.build_messages_s1()
 
@@ -198,6 +204,7 @@ async def execute(self):
                 "tokens_before": token_count,
                 "is_split_turn": False,
                 "messages": original_messages,
+                "summary_content": "",
             }
 
         history_summary = await self._generate_summary(history_prompt_messages) if history_prompt_messages else ""
@@ -222,4 +229,5 @@ async def execute(self):
             "tokens_before": token_count,
             "is_split_turn": self.context.is_split_turn,
             "messages": final_messages,
+            "summary_content": summary_content,
         }

reme/agent/fs/fs_summarizer.yaml

@@ -17,7 +17,23 @@ user_message_v2: |
   1. Check if {memory_dir}/ exists; if not, create it via bash
   2. Check if {memory_dir}/YYYY-MM-DD.md exists (use actual date)
   3. If file is NEW: Write memories directly (be concise)
-  4. If file EXISTS: Read it first, then UPDATE with new memories (keep concise, merge/deduplicate)
+  4. If file EXISTS:
+     a) Read the existing file content
+     b) Compare conversation history with existing content
+     c) Identify NEW/UPDATED information not yet captured
+     d) Use edit_tool to add/update only the new information (preserve existing content)
+     e) If conversation contains NO new information, skip writing
   5. If NO valuable information to store: Reply with reason and [SILENT]
 
+  IMPORTANT for updates:
+  - Only add information that is NOT already in the file
+  - Preserve all existing entries
+  - Merge duplicate information intelligently
+  - Use edit_tool for surgical updates, not write_tool (which overwrites)
+
+  Example of what counts as NEW information:
+  - Existing: "Alice: Software engineer"
+  - Conversation: "Alice loves Python and AI projects"
+  - Action: ADD "Enjoys Python programming and AI project work" to Alice's entry
+
   Store durable memories. Keep entries concise and well-organized.

reme/config/default.yaml

@@ -20,8 +20,8 @@ flows:
 llms:
   default:
     backend: openai
-#    model_name: qwen3-30b-a3b-instruct-2507
-    model_name: qwen3-30b-a3b-thinking-2507
+    model_name: qwen3-30b-a3b-instruct-2507
+#    model_name: qwen3-30b-a3b-thinking-2507
     request_interval: 1
 #    temperature: 0.0001
 

reme/config/fs.yaml

@@ -0,0 +1,33 @@
+backend: cmd
+
+llms:
+  default:
+    backend: openai
+    model_name: qwen3-30b-a3b-instruct-2507
+#    model_name: qwen3-30b-a3b-thinking-2507
+    request_interval: 1
+#    temperature: 0.0001
+
+embedding_models:
+  default:
+    backend: openai
+    model_name: text-embedding-v4
+    dimensions: 1024
+
+memory_stores:
+  default:
+    backend: sqlite
+    store_name: test_hybrid
+    embedding_model: default
+    fts_enabled: true
+    snippet_max_chars: 700
+
+token_counters:
+  default:
+    backend: base
+
+  hf:
+    backend: hf
+    model_name: Qwen/Qwen3-Coder-30B-A3B-Instruct
+    use_mirror: true
+

reme/core/application.py

@@ -24,6 +24,7 @@ def __init__(
         llm_api_base: str | None = None,
         embedding_api_key: str | None = None,
         embedding_api_base: str | None = None,
+        config_path: str | None = None,
         enable_logo: bool = True,
         log_to_console: bool = True,
         parser: type[PydanticConfigParser] | None = None,
@@ -43,7 +44,7 @@ def __init__(
             embedding_api_base=embedding_api_base,
             service_config=None,
             parser=parser,
-            config_path=None,
+            config_path=config_path,
             enable_logo=enable_logo,
             log_to_console=log_to_console,
             default_llm_config=default_llm_config,

reme/reme.py

@@ -51,6 +51,7 @@ def __init__(
         llm_api_base: str | None = None,
         embedding_api_key: str | None = None,
         embedding_api_base: str | None = None,
+        config_path: str = "default",
         enable_logo: bool = True,
         log_to_console: bool = True,
         default_llm_config: dict | None = None,
@@ -71,6 +72,7 @@ def __init__(
             llm_api_base: API base for LLM provider
             embedding_api_key: API key for embedding provider
             embedding_api_base: API base for embedding provider
+            config_path: Path to config file
             enable_logo: Enable logo
             log_to_console: Log to console
             default_llm_config: LLM configuration
@@ -102,6 +104,7 @@ def __init__(
             llm_api_base=llm_api_base,
             embedding_api_key=embedding_api_key,
             embedding_api_base=embedding_api_base,
+            config_path=config_path,
             enable_logo=enable_logo,
             log_to_console=log_to_console,
             parser=ReMeConfigParser,