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* fix(summarization): resolve fraction triggers from declared context_window, degrade instead of crashing the agent build A fraction trigger/keep clause requires profile["max_input_tokens"], which any third-party OpenAI-compatible model lacks, so SummarizationMiddleware construction raised ValueError out of create_summarization_middleware and failed the whole agent build (#3103). - factory: translate a declared model context_window into the langchain profile (metadata-only, never reaches the provider payload); explicit caller/override profiles win - summarization factory: drop unusable fraction trigger clauses (absolute clauses survive), fall a fraction keep back to the messages default, and disable compaction with an actionable warning only when no usable trigger clause remains — the agent build never dies from summarization config - docs: config.example.yaml, ModelConfig.context_window, summarization.md * refactor(summarization): share the default keep constant with the fraction fallback The fraction-keep degradation fallback hardcoded ("messages", 20), duplicating SummarizationConfig.keep's default_factory literal. Move the value to a shared DEFAULT_KEEP constant so the two cannot drift apart. * fix(summarization): keep trigger-null + fraction-keep constructing after degradation A trigger of None with a fraction keep hit the all-clauses-dropped branch (has_usable_trigger=False) and disabled compaction, and the accompanying warning claimed configured triggers were all fraction-based when none were configured. Only report nothing-usable when trigger clauses actually existed; trigger:null keeps constructing the never-firing middleware with the degraded keep, matching its behavior outside the degradation path. * fix(summarization): address review — keep manual compaction, validate ContextSize, pin wiring Review follow-ups on #4901: - When every configured trigger is a dropped fraction clause, keep constructing the never-firing middleware (trigger=None) instead of returning None: manual /compact runs with force=True and never consults trigger clauses, so it must keep working for a profile-less model rather than reporting 'compaction is disabled'. The warning now says auto-compaction will not fire while manual compaction remains. - ContextSize gains a config-load validator: fraction values must be in (0,1] (a percent-style 80 instead of 0.8 previously produced a threshold the context could never reach — a silently inert trigger), absolute values must be positive. - New un-monkeypatched integration test pins the shipped wiring (context_window declared -> real factory attaches profile -> fraction clause survives -> middleware constructs), which the stubbed middleware-side tests and kwarg-capturing factory-side tests each stopped short of. - Docs (summarization.md + config.example.yaml) clarify that the fraction resolves against the summary/anchor model's context_window (summarization.model_name when set, else the run model), including the mismatch caveat for a larger-window summary model. * fix(summarization): reject non-finite ContextSize values at config load YAML .nan / .inf pass pydantic's float parsing, and nan <= 0 is False, so the positivity check alone let them through as dead thresholds (count >= nan is always False) — the same silent-inert-trigger class the range validator was added to close. Guard with math.isfinite first, consistent with the existing non-finite guards on mem0 timeout_seconds and poll_after_seconds. * fix(summarization): merge context_window into inferred profile, require whole message counts - construct the model first, then merge max_input_tokens into the provider-inferred langchain profile: passing profile= to the constructor replaced the whole inferred metadata (tool_calling, structured_output, io capabilities, output limits) with the single key. An explicitly configured profile is still never clobbered. - reject non-integral ContextSize values for type=messages at config load: langchain slices the message list with them, so a float index raised TypeError mid-compaction. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>