exoclaw-turn-budget 0.2.0

Per-turn iteration + token budget with progressive warnings, plus a daily token-rate guard with model fallback — keeps agents from blowing through provider weekly quotas

exoclaw-turn-budget

Per-turn and per-day token + iteration budgets for exoclaw, with progressive warnings before any cutoff and configurable enforcement actions.

Why

Provider weekly quotas are measured in tokens (or "prompts" that translate to tokens under the hood). A single runaway turn — say, an unbounded research loop chaining hundreds of expensive web_search calls — can consume an entire week's quota in 90 minutes.

A hard max_iterations cap stops the loop but kills useful work mid-task with no warning. This plugin instead provides:

• Turn budget — caps iterations and tokens per individual turn. Warnings inject as user-role messages at configurable thresholds (default 50/80/90%) so the agent can wrap up gracefully. Default enforcement is CUTOFF.
• Daily budget — tracks cumulative tokens for the configured primary models (e.g., the GLM family for z.ai's pooled weekly quota), auto-resets at the UTC day boundary. Default enforcement is FALLBACK — silently demote to a cheaper model when the daily allotment is spent so the bot keeps working through end-of-day instead of going dark.

Both layers are built on a single shared BudgetWrapper primitive and share the same Enforcement enum: OBSERVE, WARN, CUTOFF, FALLBACK.

Install

pip install exoclaw-turn-budget

Usage

Stack the wrappers (turn → daily → real provider):

from exoclaw import Exoclaw
from exoclaw_turn_budget import (
    BudgetWrapper,
    DailyBudgetConfig,
    DailyBudgetTracker,
    Enforcement,
    TurnBudgetConfig,
    TurnBudgetPolicy,
    TurnBudgetTracker,
)

turn_tracker = TurnBudgetTracker(TurnBudgetConfig(
    iteration_budget=50,
    token_budget=1_500_000,
    warning_thresholds=(0.5, 0.8, 0.9),
    enforcement=Enforcement.CUTOFF,
))

daily_tracker = DailyBudgetTracker(DailyBudgetConfig(
    daily_budget=35_000_000,
    primary_models=("glm-4.7", "glm-5.1"),  # what counts against the daily pool
    enforcement=Enforcement.FALLBACK,
    fallback_model="minimax/minimax-m2.7",
))

# Stack: real provider → daily layer → turn layer → AgentLoop
provider = BudgetWrapper(real_provider, daily_tracker)
provider = BudgetWrapper(provider, turn_tracker)

app = Exoclaw(
    provider=provider,
    iteration_policy=TurnBudgetPolicy(turn_tracker),  # bridges turn cutoff into the loop
    conversation=conversation,
)

The wrapper picks up enforcement and fallback_model from tracker.config automatically — no need to set them in two places.

Turn budget configuration

Parameter	Default	Description
iteration_budget	50	Hard cap on LLM iterations within one turn. None to disable.
token_budget	1_500_000	Hard cap on total tokens (input + output, summed across iterations). None to disable.
warning_thresholds	(0.5, 0.8, 0.9)	Fractions at which to inject a warning message. Each fires at most once per turn.
enforcement	Enforcement.CUTOFF	Action at 100% utilization. See "Enforcement modes" below.
fallback_model	None	Required when enforcement == FALLBACK.
warning_template	see config	Template for threshold warnings. Substitutions: {scope}, {pct}, {used}, {cap}, {unit}.
cutoff_template	see config	Template for the cutoff message used by CUTOFF/WARN. Same substitutions.

Both budgets are consumed simultaneously — whichever exhausts first triggers enforcement. The substitution variables describe whichever axis is closer to exhaustion when the message fires (so {used}/{cap} {unit} reads as either 40/50 iterations or 1200000/1500000 tokens).

Daily budget configuration

Parameter	Default	Description
daily_budget	35_000_000	Tokens allowed per day for the primary models. Aim slightly below weekly_quota / 7.
primary_models	()	Models that count against the budget. Empty tuple means "all models count".
warning_thresholds	()	Fractions at which to inject a warning. Default empty (silent).
enforcement	Enforcement.FALLBACK	Action at 100% utilization.
fallback_model	None	Required when enforcement == FALLBACK.
reset_hour_utc	0	Hour of day (0–23, UTC) at which the budget rolls over.
warning_template / cutoff_template / fallback_template	see config	Same substitutions as the turn config. fallback_template is used at the cutoff point when enforcement == FALLBACK.

Tokens spent on the fallback_model do not count against the daily budget — when primary_models is set, only matches deplete it.

Durability — the BudgetStateStore protocol

By default the daily counter lives in process memory. A container restart at 14:30 UTC after spending 7M of 10M would reset the counter to zero — bad for long-running server deploys.

DailyBudgetTracker accepts an optional store= argument implementing the BudgetStateStore protocol:

class BudgetStateStore(Protocol):
    def load(self) -> dict | None: ...   # called once at construction
    def save(self, state: dict) -> None: ...   # called on every record/reset
    def clear(self) -> None: ...   # ops use only

Three implementations:

Class	Source	When to use
InMemoryBudgetStore	this package, default	Chip deploys, tests, anything where restart-resets-counter is fine
FileBudgetStore(path)	this package	Server deploys — JSON file with atomic write (temp + os.rename); a SIGKILL mid-write leaves the previous state intact rather than corrupting the counter
DBOSBudgetStore	exoclaw-executor-dbos (planned)	Deployments that already use DBOS for durable agent-loop state — wraps reads/writes in @DBOS.step() for full workflow-replay safety

from exoclaw_turn_budget import DailyBudgetTracker, FileBudgetStore

tracker = DailyBudgetTracker(
    config,
    store=FileBudgetStore("/var/lib/luna/budget-state.json"),
)

Security: when using FileBudgetStore with an agent that has filesystem tools (read/write/edit/exec), put the state path outside the agent's workspace. Both WorkspaceTool's allowed_dir and ExecTool's restrict_to_workspace block paths outside the workspace boundary, so a state file inside workspace/ would let a prompt-injected or self-correcting agent edit its own quota counter.

A typical layout:

/root/.nanobot/                  ← volume-mounted, durable across restarts
├── budget-state.json            ← FileBudgetStore path (outside workspace)
└── workspace/                   ← agent's filesystem tools allowed here
    ├── skills/
    └── ...

Enforcement modes

Mode	Behavior at 100% utilization
OBSERVE	Track only — nothing happens. Useful for measurement before flipping on real enforcement.
WARN	Inject the cutoff message as a one-time user-role notice, then forward unchanged. Continues working past the limit.
CUTOFF	Synthesize a final response containing the cutoff message and stop. The inner provider is not called.
FALLBACK	Rewrite the model argument to fallback_model and forward. Tokens used on the fallback don't count against the budget.

Threshold warnings (50/80/90%) are orthogonal — they always fire if warning_thresholds is non-empty, regardless of the enforcement mode chosen for the cutoff.

How warnings reach the agent

When utilization crosses a threshold, the next chat() call has the warning appended as a synthetic user-role message at the end of messages. The agent sees:

[Budget notice] You've used 80% of your turn budget
(40/50 iterations). Wrap up your current line of work and
respond to the user soon.

The injection is ephemeral — only that one model call sees it, but the agent's response (the wrap-up plan it adopts) gets persisted as normal. Each threshold fires at most once per turn; subsequent iterations don't repeat the same warning. Threshold warnings are also suppressed once the budget is exhausted, so the agent doesn't get a stale "you're at 50%" notice after it has already hit the limit.

Subagents

Each AgentLoop instance gets its own TurnBudgetTracker. To budget a parent turn together with its subagent spawns, share the same tracker (and policy) when constructing the subagent loop. The DailyBudgetTracker is naturally shared across all loops in a process since it's keyed on wall-clock time, not turn boundaries.

Composition with other policies

TurnBudgetPolicy only handles turn-boundary detection and the optional cutoff. Pair it with exoclaw-loop-detection when you also want pattern-based detection of degenerate tool-call loops — they don't conflict, but exoclaw only accepts a single iteration_policy. A meta-policy that delegates to both is left as an exercise.

MicroPython compatibility

This package opts into the workspace's MP CI gate via [tool.exoclaw] mp_compat = true. The runtime branches (plain-class Enforcement constants instead of enum.Enum, dual @dataclass / hand-written __init__ configs, time.time() day-key) are exercised by tests/micro/test_imports.py.