fix(middleware): offload memory injection off event loop to prevent tiktoken blocking (#3402) (#3411)

* fix(middleware): offload memory injection off event loop to prevent tiktoken blocking (#3402)

  DynamicContextMiddleware.abefore_agent() called _inject() synchronously
  on the asyncio event loop.  The first time memory is injected (second
  request), _inject() → format_memory_for_injection() → _count_tokens()
  → tiktoken.get_encoding("cl100k_base") needs to download the BPE data
  from openaipublic.blob.core.windows.net.  In network-restricted
  environments this download blocks until the OS TCP timeout (~26 min),
  starving ALL concurrent handlers including /api/v1/auth/me.

  Fix:
  - abefore_agent now uses asyncio.to_thread(self._inject, state) so
    file I/O and tiktoken never block the event loop.
  - Extract _get_tiktoken_encoding() with a module-level cache so
    tiktoken.get_encoding() is called at most once per encoding name.
  - Add warm_tiktoken_cache() startup helper; gateway lifespan pre-warms
    the cache via asyncio.to_thread so the first request never triggers a
    cold download.
  - _count_tokens falls back to len(text) // 4 on any encoding failure.

  Tests:
  - tests/test_tiktoken_cache_and_count_tokens.py (12 tests): cache
    hit/miss, fallback paths, warm-up helper.
  - tests/blocking_io/test_dynamic_context_middleware.py (2 tests):
    Blockbuster gate verifies abefore_agent does not block the event
    loop; async/sync parity check.

  Fixes #3402

* Apply suggestions from code review

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

* fix the lint error

* fix(memory): use future annotations to avoid NameError when tiktoken is absent

Add `from __future__ import annotations` to prompt.py so that
tiktoken.Encoding type hints are never evaluated at runtime.  Without
this, environments where tiktoken is not installed could raise NameError
on the module-level cache and function return annotations.

Addresses Copilot review comment on PR #3411.

* fix(middleware): bound abefore_agent injection with timeout to prevent hung requests

Wrap the asyncio.to_thread(self._inject) offload in asyncio.wait_for()
with a 5-second cap.  If the startup warm-up failed silently (e.g.
network blip during deploy), a cold tiktoken BPE download on the first
request can block until the OS TCP timeout (~26 min).  The bounded
timeout ensures the request degrades gracefully (no memory/date context
for that turn) rather than hanging.

Adds test_abefore_agent_returns_none_on_timeout to the blocking-IO
regression anchors.

Addresses review feedback from xg-gh-25 on PR #3411.

---------

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

backend/app/gateway/app.py

@@ -179,6 +179,25 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
     config = get_gateway_config()
     logger.info(f"Starting API Gateway on {config.host}:{config.port}")
 
+    # Pre-warm tiktoken encoding cache so the first memory-injection request
+    # never blocks on the BPE data download (which hits an OpenAI/Azure URL
+    # that may be unreachable in restricted networks — see issue #3402).
+    try:
+        from deerflow.agents.memory.prompt import warm_tiktoken_cache
+
+        warmed = await asyncio.wait_for(
+            asyncio.to_thread(warm_tiktoken_cache),
+            timeout=5,
+        )
+        if warmed:
+            logger.info("tiktoken encoding cache warmed successfully")
+        else:
+            logger.warning("tiktoken encoding cache warm-up failed; token counting will use character-based fallback")
+    except TimeoutError:
+        logger.warning("tiktoken encoding cache warm-up timed out; token counting will use character-based fallback")
+    except Exception:
+        logger.warning("tiktoken warm-up skipped", exc_info=True)
+
     # Initialize LangGraph runtime components (StreamBridge, RunManager, checkpointer, store)
     async with langgraph_runtime(app, startup_config):
         logger.info("LangGraph runtime initialised")