* 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
Add accurate token counting using tiktoken library and significantly enhance
memory update prompts with detailed section guidelines, multilingual support,
and improved fact extraction. Update deep-research skill to be more proactive
for research queries.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Ryker_Feng