When a user is idle for longer than a configured TTL, nanobot **proactively** compresses the session context into a summary. This reduces token cost and first-token latency when the user returns — instead of re-processing a long stale context with an expired KV cache, the model receives a compact summary and fresh input.
Introduce a disabled_skills option in the config schema that allows
users to specify a list of skill names to be excluded. The setting is
threaded from config through Nanobot -> AgentLoop -> ContextBuilder ->
SkillsLoader. Disabled skills are filtered out from list_skills,
get_always_skills, and build_skills_summary. Four new test cases cover
the filtering behavior.
Allow config.json to reference environment variables via ${VAR_NAME}
syntax. Variables are resolved at runtime by resolve_config_env_vars(),
keeping the raw templates in the Pydantic model so save_config()
preserves them. This lets secrets live in a separate env file
(e.g. loaded by systemd EnvironmentFile=) instead of plain text
in config.json.
Xubin Ren
chengyongruandXubin Ren
chenyahui
whsandXubin Ren
Ben LenartsandXubin Ren