✨ support auto-compaction

README.md

@@ -18,6 +18,7 @@ MCPMark provides a reproducible, extensible benchmark for researchers and engine
 
 ## News
 
+- 🔥 **13 Dec** — Added auto-compaction support (`--compaction-token`) to summarize long conversations and avoid context overflow during evaluation ([#236](https://github.com/eval-sys/mcpmark/pull/236])).
 - 🏅 **02 Dec** — Evaluated `gemini-3-pro-preview` (thinking: low): **Pass@1 50.6%** ± 2.3% — so close to `gpt-5-high` (51.6%)! Also `deepseek-v3.2-thinking` 36.8% and `deepseek-v3.2-chat` 29.7%
 - 🔥 **02 Dec** — Obfuscate GitHub @mentions to prevent notification spam during evaluation ([#229](https://github.com/eval-sys/mcpmark/pull/229))
 - 🏅 **01 Dec** — DeepSeek v3.2 uses MCPMark! Kudos on securing the best open-source model. [X Post](https://x.com/deepseek_ai/status/1995452650557763728) | [Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V3.2/resolve/main/assets/paper.pdf)
@@ -150,6 +151,8 @@ Please visit `docs/introduction.md` for choices of *MODEL*.
 
 Tip: MCPMark supports **auto-resume**. When re-running, only unfinished tasks will execute. Failures matching our retryable patterns (see [RETRYABLE_PATTERNS](src/errors.py)) are retried automatically. Models may emit different error strings—if you encounter a new resumable error, please open a PR or issue.
 
+Tip: MCPMark supports **auto-compaction**; pass `--compaction-token N` to enable automatic context summarization when prompt tokens reach `N` (use `999999999` to disable).
+
 ---
 
 ## Service setup and authentication

pipeline.py

@@ -76,6 +76,15 @@ def main():
     parser.add_argument(
         "--timeout", type=int, default=3600, help="Timeout in seconds for agent execution"
     )
+    parser.add_argument(
+        "--compaction-token",
+        type=int,
+        default=999_999_999,
+        help=(
+            "Auto-compact conversation when prompt tokens (from API usage) reach this limit. "
+            "Use 999999999 to disable compaction."
+        ),
+    )
     parser.add_argument(
         "--reasoning-effort",
         default="default",
@@ -155,6 +164,7 @@ def main():
                 reasoning_effort=args.reasoning_effort,
                 agent_name=args.agent,
                 task_suite=args.task_suite,
+                compaction_token=args.compaction_token,
             )
 
             pipeline.run_evaluation(args.tasks)

src/agents/base_agent.py

@@ -22,6 +22,7 @@ class BaseMCPAgent(ABC):
     STDIO_SERVICES = ["notion", "filesystem", "playwright", "playwright_webarena", "postgres", "insforge"]
     HTTP_SERVICES = ["github", "supabase"]
     DEFAULT_TIMEOUT = 600
+    COMPACTION_DISABLED_TOKEN = 999_999_999
 
     CLAUDE_THINKING_BUDGETS = {
         "low": 1024,
@@ -39,6 +40,7 @@ def __init__(
         service_config: Optional[Dict[str, Any]] = None,
         service_config_provider: Optional[Callable[[], Dict[str, Any]]] = None,
         reasoning_effort: Optional[str] = "default",
+        compaction_token: int = COMPACTION_DISABLED_TOKEN,
     ):
         self.litellm_input_model_name = litellm_input_model_name
         self.api_key = api_key
@@ -48,6 +50,7 @@ def __init__(
         self.service_config = service_config or {}
         self._service_config_provider = service_config_provider
         self.reasoning_effort = reasoning_effort or "default"
+        self.compaction_token = int(compaction_token)
 
         self.is_claude = self._is_anthropic_model(litellm_input_model_name)
         self.use_claude_thinking = self.is_claude and self.reasoning_effort != "default"
@@ -249,6 +252,17 @@ def _create_http_server(self) -> MCPHttpServer:
     # Message/Tool formatting helpers
     # ------------------------------------------------------------------
 
+    def _compaction_enabled(self) -> bool:
+        return 0 < self.compaction_token < self.COMPACTION_DISABLED_TOKEN
+
+    def _count_prompt_tokens_litellm(self, messages: List[Dict[str, Any]]) -> int:
+        try:
+            from litellm import token_counter
+
+            return int(token_counter(model=self.litellm_input_model_name, messages=messages) or 0)
+        except Exception:  # pragma: no cover - best effort
+            return 0
+
     def _convert_to_sdk_format(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
         sdk_format: List[Dict[str, Any]] = []
         function_call_map: Dict[str, str] = {}
@@ -489,4 +503,3 @@ def _convert_to_openai_format(self, tools: List[Dict[str, Any]]) -> List[Dict[st
             logger.info("Converted %d tools for Gemini compatibility", len(functions))
 
         return functions
-

src/agents/mcpmark_agent.py

@@ -50,6 +50,16 @@ class MCPMarkAgent(BaseMCPAgent):
         "You are a helpful agent that uses tools iteratively to complete the user's task, "
         "and when finished, provides the final answer or simply states \"Task completed\" without further tool calls."
     )
+    COMPACTION_PROMPT = (
+        "You are performing a CONTEXT CHECKPOINT COMPACTION.\n"
+        "Summarize the conversation so far for another model to continue.\n\n"
+        "Include:\n"
+        "- Current progress and key decisions made\n"
+        "- Important context, constraints, or user preferences\n"
+        "- What remains to be done (clear next steps)\n"
+        "- Any critical data, examples, or references needed to continue\n\n"
+        "Be concise and structured. Do NOT call tools."
+    )
     DEFAULT_TIMEOUT = BaseMCPAgent.DEFAULT_TIMEOUT
 
     def __init__(
@@ -62,6 +72,7 @@ def __init__(
         service_config: Optional[Dict[str, Any]] = None,
         service_config_provider: Optional[Callable[[], Dict[str, Any]]] = None,
         reasoning_effort: Optional[str] = "default",
+        compaction_token: int = BaseMCPAgent.COMPACTION_DISABLED_TOKEN,
     ):
         super().__init__(
             litellm_input_model_name=litellm_input_model_name,
@@ -72,6 +83,7 @@ def __init__(
             service_config=service_config,
             service_config_provider=service_config_provider,
             reasoning_effort=reasoning_effort,
+            compaction_token=compaction_token,
         )
         logger.debug(
             "Initialized MCPMarkAgent for '%s' with model '%s' (Claude: %s, Thinking: %s, Reasoning: %s)",
@@ -303,6 +315,177 @@ async def _call_claude_native_api(
                 return None, e.response.text
             except Exception as e:
                 return None, e
+
+    async def _count_claude_input_tokens(
+        self,
+        messages: List[Dict[str, Any]],
+        tools: Optional[List[Dict]] = None,
+        system: Optional[str] = None,
+    ) -> int:
+        import os
+
+        api_base = os.getenv("ANTHROPIC_API_BASE", "https://api.anthropic.com")
+        headers = {
+            "x-api-key": self.api_key,
+            "anthropic-version": "2023-06-01",
+            "content-type": "application/json",
+        }
+        payload: Dict[str, Any] = {
+            "model": self.litellm_input_model_name.replace("anthropic/", ""),
+            "messages": messages,
+        }
+        if tools:
+            payload["tools"] = tools
+        if system:
+            payload["system"] = system
+
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                f"{api_base}/v1/messages/count_tokens",
+                headers=headers,
+                json=payload,
+                timeout=self.timeout,
+            )
+            response.raise_for_status()
+            data = response.json() or {}
+            return int(data.get("input_tokens", 0) or 0)
+
+    def _extract_litellm_text(self, response: Any) -> str:
+        try:
+            choices = getattr(response, "choices", None) or []
+            if not choices:
+                return ""
+            msg = getattr(choices[0], "message", None)
+            if msg is not None:
+                return str(getattr(msg, "content", "") or "")
+            return str(getattr(choices[0], "text", "") or "")
+        except Exception:  # pragma: no cover - best effort
+            return ""
+
+    def _extract_anthropic_text(self, response_json: Dict[str, Any]) -> str:
+        pieces: List[str] = []
+        for block in response_json.get("content", []) or []:
+            if isinstance(block, dict) and block.get("type") == "text":
+                text = block.get("text")
+                if text:
+                    pieces.append(str(text))
+        return "\n".join(pieces).strip()
+
+    def _merge_usage(self, total_tokens: Dict[str, int], usage: Dict[str, Any]) -> None:
+        try:
+            input_tokens = int(usage.get("input_tokens", 0) or 0)
+            output_tokens = int(usage.get("output_tokens", 0) or 0)
+            total_tokens_count = int(usage.get("total_tokens", 0) or (input_tokens + output_tokens))
+            total_tokens["input_tokens"] += input_tokens
+            total_tokens["output_tokens"] += output_tokens
+            total_tokens["total_tokens"] += total_tokens_count
+        except Exception:  # pragma: no cover - best effort
+            return
+
+    async def _maybe_compact_litellm_messages(
+        self,
+        messages: List[Dict[str, Any]],
+        total_tokens: Dict[str, int],
+        tool_call_log_file: Optional[str],
+        current_prompt_tokens: int,
+    ) -> List[Dict[str, Any]]:
+        if not self._compaction_enabled():
+            return messages
+        if current_prompt_tokens < self.compaction_token:
+            return messages
+
+        logger.info(f"| [compaction] Triggered at prompt tokens: {current_prompt_tokens:,}")
+        if tool_call_log_file:
+            try:
+                with open(tool_call_log_file, "a", encoding="utf-8") as f:
+                    f.write(f"| [compaction] Triggered at prompt tokens: {current_prompt_tokens:,}\n")
+            except Exception:
+                pass
+
+        compact_messages = [
+            {"role": "system", "content": self.COMPACTION_PROMPT},
+            {"role": "user", "content": json.dumps(messages, ensure_ascii=False)},
+        ]
+        completion_kwargs = {
+            "model": self.litellm_input_model_name,
+            "messages": compact_messages,
+            "api_key": self.api_key,
+        }
+        if self.base_url:
+            completion_kwargs["base_url"] = self.base_url
+        response = await litellm.acompletion(**completion_kwargs)
+
+        usage = getattr(response, "usage", None)
+        if usage:
+            input_tokens = getattr(usage, "prompt_tokens", None) or getattr(usage, "input_tokens", None) or 0
+            output_tokens = getattr(usage, "completion_tokens", None) or getattr(usage, "output_tokens", None) or 0
+            total_tokens_count = getattr(usage, "total_tokens", None)
+            if total_tokens_count is None:
+                total_tokens_count = input_tokens + output_tokens
+            total_tokens["input_tokens"] += int(input_tokens or 0)
+            total_tokens["output_tokens"] += int(output_tokens or 0)
+            total_tokens["total_tokens"] += int(total_tokens_count or 0)
+
+        summary = self._extract_litellm_text(response).strip() or "(no summary)"
+        system_msg = messages[0] if messages else {"role": "system", "content": self.SYSTEM_PROMPT}
+        first_user = messages[1] if len(messages) > 1 else {"role": "user", "content": ""}
+        return [
+            system_msg,
+            first_user,
+            {
+                "role": "user",
+                "content": f"Context summary (auto-compacted due to token limit):\n{summary}",
+            },
+        ]
+
+    async def _maybe_compact_anthropic_messages(
+        self,
+        messages: List[Dict[str, Any]],
+        total_tokens: Dict[str, int],
+        thinking_budget: int,
+        tool_call_log_file: Optional[str],
+        current_input_tokens: int,
+    ) -> List[Dict[str, Any]]:
+        if not self._compaction_enabled():
+            return messages
+        if current_input_tokens < self.compaction_token:
+            return messages
+
+        logger.info(f"| [compaction] Triggered at input tokens: {current_input_tokens:,}")
+        if tool_call_log_file:
+            try:
+                with open(tool_call_log_file, "a", encoding="utf-8") as f:
+                    f.write(f"| [compaction] Triggered at input tokens: {current_input_tokens:,}\n")
+            except Exception:
+                pass
+
+        compact_messages = [
+            {"role": "user", "content": self.COMPACTION_PROMPT},
+            {"role": "user", "content": json.dumps(messages, ensure_ascii=False)},
+        ]
+        response, error_msg = await self._call_claude_native_api(
+            messages=compact_messages,
+            thinking_budget=thinking_budget,
+            tools=None,
+            system=None,
+        )
+        if error_msg or not response:
+            logger.warning(f"| [compaction] Failed: {error_msg}")
+            return messages
+
+        usage = response.get("usage", {}) or {}
+        input_tokens = usage.get("input_tokens", 0) or 0
+        output_tokens = usage.get("output_tokens", 0) or 0
+        total_tokens["input_tokens"] += int(input_tokens)
+        total_tokens["output_tokens"] += int(output_tokens)
+        total_tokens["total_tokens"] += int(input_tokens + output_tokens)
+
+        summary = self._extract_anthropic_text(response) or "(no summary)"
+        first_user = messages[0] if messages else {"role": "user", "content": ""}
+        return [
+            first_user,
+            {"role": "user", "content": f"Context summary (auto-compacted due to token limit):\n{summary}"},
+        ]
 
 
     async def _execute_anthropic_native_tool_loop(
@@ -327,9 +510,29 @@ async def _execute_anthropic_native_tool_loop(
         system_text = self.SYSTEM_PROMPT
         # Record initial state
         self._update_progress(messages, total_tokens, turn_count)
-        
+
         for _ in range(max_turns):
             turn_count += 1
+
+            current_input_tokens = 0
+            if self._compaction_enabled():
+                try:
+                    current_input_tokens = await self._count_claude_input_tokens(
+                        messages=messages,
+                        tools=tools,
+                        system=system_text,
+                    )
+                except Exception as exc:  # noqa: BLE001
+                    logger.debug("Claude token counting failed: %s", exc)
+
+            messages = await self._maybe_compact_anthropic_messages(
+                messages=messages,
+                total_tokens=total_tokens,
+                thinking_budget=thinking_budget,
+                tool_call_log_file=tool_call_log_file,
+                current_input_tokens=current_input_tokens,
+            )
+            self._update_progress(messages, total_tokens, turn_count)
 
             # Call Claude native API
             response, error_msg = await self._call_claude_native_api(
@@ -584,9 +787,20 @@ async def _execute_litellm_tool_loop(
 
         # Record initial state
         self._update_progress(messages, total_tokens, turn_count)
-        
+
         try:
             while turn_count < max_turns:
+                current_prompt_tokens = 0
+                if self._compaction_enabled():
+                    current_prompt_tokens = self._count_prompt_tokens_litellm(messages)
+
+                messages = await self._maybe_compact_litellm_messages(
+                    messages=messages,
+                    total_tokens=total_tokens,
+                    tool_call_log_file=tool_call_log_file,
+                    current_prompt_tokens=current_prompt_tokens,
+                )
+                self._update_progress(messages, total_tokens, turn_count)
 
                 # Build completion kwargs
                 completion_kwargs = {
@@ -626,7 +840,15 @@ async def _execute_litellm_tool_loop(
                     if consecutive_failures >= max_consecutive_failures:
                         raise
                     if "ContextWindowExceededError" in str(e):
-                        raise
+                        # Best-effort fallback: compact and retry once.
+                        messages = await self._maybe_compact_litellm_messages(
+                            messages=messages,
+                            total_tokens=total_tokens,
+                            tool_call_log_file=tool_call_log_file,
+                            current_prompt_tokens=self.compaction_token,
+                        )
+                        self._update_progress(messages, total_tokens, turn_count)
+                        continue
                     elif "RateLimitError" in str(e):
                         await asyncio.sleep(12 ** consecutive_failures)
                     else:
@@ -794,12 +1016,6 @@ async def _execute_litellm_tool_loop(
         }
 
 
-
-    # ==================== Format Conversion Methods ====================
-
-
-
-
     # ==================== MCP Server Management ====================
 
     async def _create_mcp_server(self) -> Any: