Built-in `wait` tool capped at ~50s causes MASSIVE token burn on long waits (multi_agent_v2 re-samples every 50s)
What version of Codex CLI is running?
codex-cli 0.144.1
What subscription do you have?
Plus Plan
Which model were you using?
gpt-5.6-luna
What platform is your computer?
Darwin 25.5.0 arm64 arm
What terminal emulator and version are you using (if applicable)?
Terminal.app
Codex doctor report
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What issue are you seeing?
Massive, avoidable token/quota burn during long waits.
When the agent waits for a long-running external job (e.g. an ~11–36 min
remote build+boot driven through a custom MCP server), it uses the
built-in wait tool. That tool is hard-capped per call atmax_wait_timeout_ms (~50000 ms observed). Because of the 50s cap the
model cannot wait once — it is forced to call wait(50s) back-to-back,
and every 50s call returns to the model and triggers a fresh sampling
request over the ENTIRE context.
For a high-reasoning model with a large context this turns a single
"wait for the job" into ~12+ full re-samples in 10 minutes.
Concrete impact: one ~36 min job dropped my weekly quota from 84% to
65% remaining — for what should have been 1–3 blocking calls. The cost
scales roughly linearly with wait duration. A purpose-built,
server-side blocking MCP wait existed (one call, 840s, zero
intermediate re-samples), but the built-in wait tool was used instead,
and its 50s cap makes the re-sample storm unavoidable even when the tool
is "used correctly".
Environment:
- codex-cli: 0.144.1
- model: gpt-5.6-luna,
model_reasoning_effort = high - OS: macOS (darwin 25.5.0)
- Feature:
features.multi_agent_v2(provides the built-inwaittool)
Evidence (from ~/.codex/logs_2.sqlite, single session, single thread_id):
12 consecutive model-invoked wait calls in one ~10 min window:
18:59:22 tool_name="wait" handler_duration_ms=50005
19:00:15 tool_name="wait" handler_duration_ms=50006
19:01:08 tool_name="wait" handler_duration_ms=50003
19:02:00 tool_name="wait" handler_duration_ms=50005
19:02:53 tool_name="wait" handler_duration_ms=50004
19:03:45 tool_name="wait" handler_duration_ms=50005
19:04:39 tool_name="wait" handler_duration_ms=50004
19:05:37 tool_name="wait" handler_duration_ms=50004
19:06:30 tool_name="wait" handler_duration_ms=50004
19:07:22 tool_name="wait" handler_duration_ms=50004
19:08:15 tool_name="wait" handler_duration_ms=50005
19:09:07 tool_name="wait" handler_duration_ms=50004
- Each
waitis model-invoked (function_call,from="output_item_done",
tool_name="wait") — 24 such events in the window.
- Model is NOT suspended between waits:
codex_api::sse::responsesruns
continuously at ~21 events/min for the whole 10 min (active sampling
between every wait).
current_time_reminder: 0 injections in the window — NOT the trigger.- Subagent spawns in the entire session: 0 —
multi_agent_v2was never
used for actual multi-agent work; only its wait tool was pulled in.
What steps can reproduce the bug?
- Enable
features.multi_agent_v2(default in this build). - Start a long-running external job (>= 5 min) via a tool (e.g. an MCP
server that runs a build/boot).
- Ask the agent to wait for completion.
- Observe repeated
tool_name="wait"calls at ~50s each, with a full
model sampling request between each, for the whole duration.
- Check token/quota usage: it grows roughly linearly with the wait time
instead of staying near the cost of a single blocking call.
Log query used to confirm (SQLite over ~/.codex/logs_2.sqlite):
Look for rows where feedback_log_body containstool_name="wait" and handler_duration_ms=50004, spaced ~50s apart,
while codex_api::sse::responses rows continue throughout.
What is the expected behavior?
Waiting for a 10-minute job should NOT cost ~12 full re-samples. Any of:
- The built-in
waittool should allow a single long wait (respect a
caller-supplied duration up to a high ceiling), so waiting for a
10-minute job is ONE tool call and ONE re-sample.
- When a
waitcall hits the internal cap, the runtime should
transparently re-arm the timer and keep waiting WITHOUT round-tripping
through a full model sampling request, unless something actually
changed.
- At minimum,
max_wait_timeout_ms/default_wait_timeout_msshould be
documented and user-configurable in ~/.codex/config.toml, and the
built-in wait tool should be independently disable-able, so a
workflow that relies on a server-side blocking MCP wait can opt out of
the re-sample storm.
Actual behavior: wait silently caps each call at ~50s and re-enters
model sampling on every expiry, multiplying token/quota usage roughly
linearly with wait duration, even when a zero-cost blocking wait is
available.
Additional information
Same MCP config on a different client shows NO problem (isolates the bug to codex-cli):
With the EXACT same MCP server and the same server-side blocking wait
tool, another agent CLI (Anthropic's Claude Code) makes ONE blocking MCP
call and suspends cleanly for the whole job — no periodic re-sampling, no
measurable token burn. Only codex-cli reaches for its built-in sleep/wait
tool and loops it. This strongly indicates the burn is codex-cli's
client-side behavior (the model reaching for the built-in sleep tool plus
its short per-call cap), NOT the MCP server or the workload.
Underlying mechanism — code_mode (verified): MCP tools are invoked
through a JS runtime ("cells"): tool dispatch is logged asdispatch_tool_call_with_code_mode_result. A long-running MCP call (here
a server-side blocking wait) becomes a background cell, and the model
polls it with the built-in wait tool, whose args are{"cell_id": ..., "yield_time_ms": ..., "max_tokens": ...}. Eachyield_time_ms chunk (observed 10s / 25s / 30s / 50s) ends the tool call,
returns to the model, and triggers a fresh full sampling request. This is
exactly why a client WITHOUT code-mode (calling MCP directly/blocking)
shows no burn on the same MCP server.
Feature toggles tried:
multi_agent_v2 = false(verified by restart + probe): tool still
fires; it only removed a cap override, dropping the per-call yield from
~50s to the ~30s default — i.e. MORE frequent re-samples
(counterproductive).
current_time_reminder = false(verified by restart + probe): tool
still fires afterwards (observed
wait {"cell_id":"1","yield_time_ms":25000,...} in a post-restart
session).
The wait tool args (cell_id / yield_time_ms / max_tokens) and thedispatch_tool_call_with_code_mode_result dispatch tie the tool tocode_mode, not to those features.
Relevant config schema (extracted from the codex binary):
MultiAgentV2ConfigToml:
min_wait_timeout_ms
max_wait_timeout_ms <-- observed effective cap ~50000
default_wait_timeout_ms
...
FeatureToml variants include: multi_agent_v2, current_time_reminder, ...
Suggested fixes:
- Expose
max_wait_timeout_ms/default_wait_timeout_msas documented
config.toml keys.
- Allow disabling the built-in
waittool independently (confirm the
untagged FeatureToml bool form disables it).
- Prefer re-arming the wait timer internally over a full re-sample when
nothing has changed.
WORKING WORKAROUND (live-verified): route the MCP namespace directly
(not through a code-mode cell) via direct_only_tool_namespaces:
[features.code_mode]
direct_only_tool_namespaces = ["mcp__chimera_bridge"]
(Namespace string is exactly what Codex logs internally:namespace: Some("mcp__chimera_bridge").) After this, a long blocking MCP
call dispatches via codex_mcp::connection_manager (direct) and the model
blocks silently on it — 0 SSE sampling, 0 wait/cell polls — until it
returns, i.e. ONE call, ONE re-sample. This confirms the burn is caused by
code-mode wrapping MCP calls into polled cells.
NOTE what does NOT work: code_mode = false (still wrapped — the MCP call
still dispatched via code_mode_result and was polled), multi_agent_v2 =,
falsecurrent_time_reminder = false (all verified ineffective, see
above). The per-namespace direct_only_tool_namespaces is the only lever
that worked. Request: please document this as the supported way to make a
long-running MCP tool call block instead of being polled, and/or make
blocking (wait=true-style) MCP calls not incur a re-sample per cell
yield by default.
Possibly related (different bug): #23807 (codex-cli stalls for
exactly 300s between tool result and next model request). Not the same
root cause, but also in the wait/timeout area.
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