j#SrSSKJr SSKrSSKrSSKJrJr SSKJrJ r SSK J r J r J r Jr SSKJr SSKJr SS jrSS jr\R*"S 5rSS jrSS jr"SS5r"SS5rg)u Native LangSmith tracer for the extraction agents and the playground's RAG agents. Each pydantic-ai agent class in `quber.agents` holds its own instance. The playground's agents share one process-wide instance from `quber.playground.tracing`. The cli backend (`ClaudeCLIClient`) and the `quber.processors` classes built through `AgentFactory` are not traced. Posts runs to LangSmith's first-class `/runs/multipart` API with explicit `inputs` and `outputs` JSON — the same pattern as `~/.claude/hooks/stop_hook.sh`. We do this instead of OTLP because the OTel adapter on LangSmith's side has to introspect span attributes to derive inputs/outputs, and Logfire/pydantic-ai emit attribute keys that the adapter doesn't recognize. The result: traces land, but the LangSmith UI shows empty input/output panels. This tracer skips the translation entirely. We hand LangSmith the data in the shape its UI renders directly. Toggle via `TRACE_TO_LANGSMITH` env var; configured via `CC_LANGSMITH_API_KEY`, `CC_LANGSMITH_PROJECT`, `CC_LANGSMITH_DEBUG`. ) annotationsN)asynccontextmanagercontextmanager)datetimetimezone)AnyDict GeneratorOptional)logger) get_settingscz[5RnUR=(a [UR5$N)r obstrace_to_langsmithboollangsmith_api_key)rs ;/home/mande/repo/quber/src/quber/agents/langsmith_tracer.pylangsmith_enabledr$s+ .  C  ! ! Ad3+@+@&AAcJ[R"[R5$r)rnowrutcrrutc_nowr)s << %%rz-\d{8}$c@[RSU=(d S5$)aStrip the trailing `-YYYYMMDD` dated suffix from an Anthropic model identifier. LangSmith's pricing catalog and many of its UI affordances key on the bare model name (e.g. `claude-haiku-4-5`, `claude-sonnet-4-6`). The dated form Anthropic returns (`claude-haiku-4-5-20251001`) doesn't match the catalog, so cost attribution silently breaks. Same approach as `~/.claude/hooks/stop_hook.sh`'s sed. ) _DATED_SUFFIXsub)models rcanonical_model_namer!0s   R" --rc \[USS5=(d S[USS5=(d S-[USS5=(d S-n[U5[[USS5=(d S5[[USS5=(d S5[[USS5=(d S5S.S.$)aTranslate a pydantic-ai `RunUsage` into LangSmith's `usage_metadata` schema (the LangChain standard). The reported `input_tokens` is the usage object's `input_tokens` plus its `cache_read_tokens` and `cache_write_tokens`. A pydantic-ai `RunUsage` built from an Anthropic response already counts cache reads and writes in `input_tokens`, so with prompt caching active the cached input is counted twice. input_tokensrcache_read_tokenscache_write_tokens output_tokens) cache_readcache_creation)r#r&input_token_details)getattrint)usager#s rusage_metadata_fromr->s  * /a 5-q 1 6Q 8 5. 2 7a 9 L)WUOQ?D1Ege-@!DIJ!'%1Eq"I"NQO  rc\rSrSrSrS S SjjrS Sjr\S S Sjj5r\ S SSjj5r Sr g)LangSmithTracerWu;Authors LangSmith runs directly via the SDK. Each pydantic-ai agent class in `quber.agents` holds its own instance. The playground's agents share one from `quber.playground.tracing`. The tracer is a no-op if `TRACE_TO_LANGSMITH` is unset/false or `CC_LANGSMITH_API_KEY` is missing — callers don't need to guard. Nc[5UlUR(dSUlSUlSUlgSSKJn [5RnU"SU=(d URS9UlU=(d URUlU=(d SUlUR(a-[R"SURUR5 gg)Nr)Clientzhttps://api.smith.langchain.com)api_urlapi_keyquberz4LangSmith tracer initialized: project={} run_name={})renabledclientprojectrun_name langsmithr2r rrlangsmith_projectlangsmith_debugr info)selfr8r4r9r2rs r__init__LangSmithTracer.__init__`s )* ||DKDL DM $n  54s44  7#"7"7  +G    KKF    rcU(a [U5OSnSS0nU(aX4S'U(a X:waXS'U(aURU5 SU0$)N ls_provider anthropic ls_model_namels_model_full_namemetadata)r!update)r>r extra_metadata canonicalrFs r build_extraLangSmithTracer.build_extra~sS 49(/d $1;#? (1_ % U'-2) *  OON +H%%rc #~# [5nUR(a URcUv g[[R "55n[ 5nU(a [U5OSnURX45n URRUUSUUURU U(aU/OSS9 Uv URRUUR=(d 0[ 5S9 g![a%n [R"SU 5 Uv Sn A gSn A ff=f![a!n [R"SU 5 Sn A gSn A ff=f![aan URRU[!U 5[ 5S9 e![a!n [R"SU 5 Sn A eSn A ff=fSn A ff=f7f) aOpen an LLM-type run. Caller sets `.outputs` on the yielded handle, and the tracer patches LangSmith on exit with the outputs and the end timestamp. The run is recorded as failed only when an exception escapes the `with` block. The tracer records its `repr` and re-raises it. Nllmidnamerun_typeinputs start_time project_nameextratagsLangSmith create_run failed: {}run_idoutputsend_time$LangSmith update_run (ok) failed: {}rYerrorr['LangSmith update_run (error) failed: {} RunHandler6r7struuiduuid4rr!rJ create_runr8 Exceptionr warning update_runrZrepr r>rPrRr rHhandlerYrSrIrUexc patch_excs r llm_run_syncLangSmithTracer.llm_run_syncs||t{{2L TZZ\"Y 38(/d   7  KK " "%!\\$-i[4 #  LL L &&!"NN0b$Y''  NN4C/3E86D!.F=/ D9DF=DF=! E +EF=E  F= F:,FF5 F2F-(F5-F22F55F::F=c # [5nUR(a URcU7v g[[R "55n[ 5nU(a [U5OSnURX45n URRUUSUUURU U(aU/OSS9 U7v URRXeR=(d 0[ 5S9 g![a&n [R"SU 5 U7v Sn A gSn A ff=f![a!n [R"SU 5 Sn A gSn A ff=f![aan URRU[!U 5[ 5S9 e![a!n [R"SU 5 Sn A eSn A ff=fSn A ff=f7f) a Async sibling of `llm_run_sync`. The langsmith Client's `create_run` / `update_run` are non-blocking in practice (they enqueue onto a background batched sender), so we can call them from async code without awaiting. The trace-side I/O happens off the request path. NrMrNrWrXr\r]r_r`rjs rllm_runLangSmithTracer.llm_runs||t{{2L TZZ\"Y 38(/d   7  KK " "%!\\$-i[4 #  LL L &&fnn>RPR]d]f&g  NNs rr?RunHandle.__init__s ') rN)rtru)rxryrzr{r| __slots__r?r}rrrrarasII*rra)rtr)rtr)r rbrtrb)r,rrtrw)r| __future__rrerc contextlibrrrrtypingrr r r logurur quber.settingsr rrcompilerr!r-r/rarrrrse,# :'11'B & :&  .2iLiLX**r