feat(memory): add memory.token_counting config to avoid tiktoken network dependency (#3429) (#3465)

* feat(memory): add memory.token_counting config to avoid tiktoken network dependency (#3429)

Add a `memory.token_counting` option (`tiktoken` | `char`) so deployments in
network-restricted environments can opt out of tiktoken entirely. In `char`
mode the memory-injection budget uses a network-free character-based estimate
and never triggers the BPE download from openaipublic.blob.core.windows.net,
which could otherwise block for tens of minutes (see #3402).

Also harden the default `tiktoken` path:
- cache an in-flight LOADING sentinel so concurrent callers fall back
  immediately instead of spawning more blocking get_encoding threads when the
  first load is still running (e.g. under the 5s startup warm-up timeout);
- cache failures with a timestamp and retry after a cooldown so a transient
  network outage self-heals back to accurate counting without a restart;
- skip startup warm-up entirely in char mode.

The new config is surfaced via the memory config API and config.example.yaml
(config_version bumped). Default remains `tiktoken`, so existing deployments
are unaffected.

* fix(memory): use CJK-aware char token estimate and address review feedback

- Replace the flat len(text)//4 fallback with a CJK-aware estimate so
  Chinese/Japanese/Korean memory content does not over-fill the injection budget
- Document the internal tiktoken retry cooldown and char-mode escape hatch
- Sync CLAUDE.md / config.example.yaml / MEMORY_IMPROVEMENTS.md wording
- Fix MemoryConfigResponse mocks/assertions and add CJK estimate tests

backend/packages/harness/deerflow/agents/lead_agent/prompt.py

@@ -586,7 +586,11 @@ def _get_memory_context(agent_name: str | None = None, *, app_config: AppConfig
             return ""
 
         memory_data = get_memory_data(agent_name, user_id=get_effective_user_id())
-        memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
+        memory_content = format_memory_for_injection(
+            memory_data,
+            max_tokens=config.max_injection_tokens,
+            use_tiktoken=(config.token_counting == "tiktoken"),
+        )
 
         if not memory_content.strip():
             return ""