167ef4512f
* 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 |
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| .. | ||
| __init__.py | ||
| agents.py | ||
| artifacts.py | ||
| assistants_compat.py | ||
| auth.py | ||
| channels.py | ||
| feedback.py | ||
| mcp.py | ||
| memory.py | ||
| models.py | ||
| runs.py | ||
| skills.py | ||
| suggestions.py | ||
| thread_runs.py | ||
| threads.py | ||
| uploads.py | ||