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/CLAUDE.md

@@ -429,6 +429,12 @@ Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the
 4. Applies updates atomically (temp file + rename) with cache invalidation, skipping duplicate fact content before append
 5. Next interaction injects top 15 facts + context into `<memory>` tags in system prompt
 
+**Token counting** (`packages/harness/deerflow/agents/memory/prompt.py`):
+- `_count_tokens` budgets the injection. In default `tiktoken` mode, the encoding is loaded lazily and cached.
+- Failed tiktoken loads are cached with a timestamp. During the fixed cooldown (`_TIKTOKEN_RETRY_COOLDOWN_S`, 600s), callers fall back to char estimation immediately instead of re-triggering the blocking BPE download; after the cooldown, transient outages can self-heal without a restart.
+- In-flight loads are cached as a LOADING sentinel so concurrent callers fall back instead of spawning more blocking threads.
+- Set `memory.token_counting: char` to skip tiktoken entirely and use the network-free CJK-aware char estimate.
+
 Focused regression coverage for the updater lives in `backend/tests/test_memory_updater.py`.
 
 **Configuration** (`config.yaml` → `memory`):
@@ -438,6 +444,7 @@ Focused regression coverage for the updater lives in `backend/tests/test_memory_
 - `model_name` - LLM for updates (null = default model)
 - `max_facts` / `fact_confidence_threshold` - Fact storage limits (100 / 0.7)
 - `max_injection_tokens` - Token limit for prompt injection (2000)
+- `token_counting` - Token counting strategy for the injection budget: `tiktoken` (default, accurate but may download BPE data from a public endpoint on first use — can block for a long time in network-restricted environments, see issues #3402/#3429) or `char` (network-free CJK-aware char estimate, never touches tiktoken)
 
 ### Reflection System (`packages/harness/deerflow/reflection/`)