agentpause 0.2.0

Predictive scheduling for autonomous LLM agents: suspend gracefully before rate limits, resume without losing work.

agentpause

Predictive scheduling for autonomous LLM agents. Suspend an agent gracefully before it hits a provider rate limit, and resume it without redoing work.

The core works on any provider — cloud (OpenAI, Anthropic, Groq) or local — because it only serializes application-level state. True KV-cache warm start is an optional plugin for self-hosted runtimes (llama.cpp, vLLM).

Measured results (from the accompanying research)

Experiment	Result
Simulation, 300 runs/config	reactive baseline crashes up to 100% → predictive 0% (k=4), token waste −80%
Real provider (Groq free, 6K TPM)	multi-window task completed with zero 429 errors
vs LangGraph's MemorySaver (200 runs)	LangGraph: 7.8 × 429/run, 9,239 tokens wasted; predictive: 0 and 0
End-to-end A/B (thinking models, 4B/8B)	recovery 54×–93× faster; total task time −19%

Reproduce them yourself with a free key: python scripts/benchmark_groq.py.

Status: early alpha (0.1). Core components, the high-level PredictiveScheduler API, the LiteLLM adapter (any provider), the LangGraph adapter, runnable examples, and a full test suite are in place; the optional KV-cache plugin is next.

Quick example

from agentpause import PredictiveScheduler

sched = PredictiveScheduler(backend=my_llm_call, telemetry=my_rate_limit_reader)

with sched.session("task-1") as s:      # resumes automatically if interrupted before
    for question in questions[s.step:]:  # skip steps finished before a suspend
        s.add_user(question)
        if s.should_suspend():           # predictive check, *before* the call
            s.checkpoint()
            break                        # stop cleanly; rerun to resume
        reply = s.call()
    else:
        s.complete()                     # task done: drop the checkpoint

backend is any callable messages -> (reply, tokens_used); telemetry is any callable () -> remaining_tokens. See examples/quickstart.py for a runnable demo (no API keys needed) that suspends mid-task and resumes on the next run.

Real providers via LiteLLM

The LiteLLM adapter supplies both callables for 100+ providers (OpenAI, Groq, Anthropic, local servers, ...), reading the budget from each response's rate-limit headers and refreshing stale readings with a tiny telemetry ping:

from agentpause import PredictiveScheduler
from agentpause.adapters.litellm import LiteLLMAdapter

adapter = LiteLLMAdapter(model="groq/llama-3.1-8b-instant")
sched = PredictiveScheduler(backend=adapter.backend, telemetry=adapter.telemetry)

Install with pip install -e ".[litellm]"; see examples/litellm_groq.py. To validate against your own provider (frontier models included): python scripts/validate_provider.py gpt-4o-mini.

Rate-limit headers by provider (defaults target the OpenAI-style names):

Provider	remaining tokens	remaining requests	reset
OpenAI	x-ratelimit-remaining-tokens	x-ratelimit-remaining-requests	x-ratelimit-reset-tokens (1s, 6m0s)
Groq	x-ratelimit-remaining-tokens	x-ratelimit-remaining-requests	x-ratelimit-reset-tokens (7.66s, 2m59.56s)
Anthropic	anthropic-ratelimit-tokens-remaining	anthropic-ratelimit-requests-remaining	RFC 3339 timestamp — set remaining_header= etc.

Header names differ? Override them: LiteLLMAdapter(model=..., remaining_header="...", requests_header="...", reset_header="...").

Beyond tokens: RPM and wait-vs-suspend

Telemetry can be richer than a token count. adapter.budget reports all three dimensions providers expose — remaining tokens (TPM), remaining requests (RPM), and seconds until the window resets — and unlocks the three-valued decision:

sched = PredictiveScheduler(backend=adapter.backend, telemetry=adapter.budget)

d = session.next_action()   # "continue" | "wait" | "checkpoint"

wait fires when the budget does not fit but the window resets within wait_threshold_s (default 15 s): a short in-place pause is cheaper than a full suspend/resume cycle. Exhausted requests (RPM = 0) block the call even with plenty of tokens left. Plain-int telemetry keeps working unchanged.

Async

Every entry point has an async twin — same rules, same guarantees, never blocks the event loop:

adapter = LiteLLMAdapter(model="groq/llama-3.1-8b-instant")
sched = PredictiveScheduler(backend=None, async_backend=adapter.abackend,
                            telemetry=adapter.telemetry)
reply = await session.acall()          # retry/backoff via asyncio.sleep
await guard.acheck(state["messages"])  # async LangGraph nodes

For sharper input estimates, wire the per-model tokenizer: PredictiveScheduler(..., count_tokens=adapter.count_tokens) (falls back to the ~4 chars/token heuristic if the tokenizer is unavailable).

When prediction fails anyway

Estimates are statistical — a 429 can still slip through. agentpause survives it instead of crashing:

• typed errors: everything derives from AgentPauseError (RateLimitHit, TelemetryError, CheckpointError, BackendError);
• retry with backoff: unexpected 429s are retried (provider retry-after honored, else exponential backoff — see RetryPolicy);
• hits are feedback: each one bumps safety_k up (capped at k_max), so the scheduler grows more cautious on workloads it underestimates;
• clean failure: a failed call leaves the session state untouched — no phantom steps, resumes stay consistent.

LangGraph integration

AgentPauseGuard adds the predictive gate to any LangGraph node — LangGraph persists reactively (after nodes), the guard pauses before the call that would hit the rate limit, via LangGraph's own interrupt() + checkpointer:

from agentpause.adapters.langgraph import AgentPauseGuard

guard = AgentPauseGuard(telemetry=adapter.telemetry)   # e.g. from LiteLLMAdapter

def agent_node(state):
    guard.check(state["messages"])       # pauses the graph here if needed
    reply = llm.invoke(state["messages"])
    guard.record(state["messages"], reply.usage_metadata["total_tokens"])
    ...

Resume the paused thread with graph.invoke(Command(resume=True), config). On resume the guard re-reads telemetry fresh — never from the checkpoint. Install with pip install -e ".[langgraph]"; see examples/langgraph_quickstart.py.

Why

Current agent frameworks persist state reactively: they checkpoint after a step completes and crash when the provider returns HTTP 429. agentpause adds a predictive layer that estimates the next step's cost, compares it against the remaining rate-limit budget, and suspends cleanly before the error — then resumes from the exact step.

This library is the engineering counterpart of the research preprint "A Resource-Aware Predictive Scheduler for Autonomous LLM Agents".

Components (v0.1)

Module	Role
PredictiveScheduler / Session	the high-level API: session(), should_suspend(), call(), checkpoint()
Estimator	predicts next-step token cost with a moving-average error correction (ε) and tracks σ
should_checkpoint / RiskModel	the suspension rule (remaining < estimated + k·σ) and a diagnostic risk score
Budget / decide	three-dimensional telemetry (TPM, RPM, reset time) and the three-valued rule: continue / wait / checkpoint
StateStore / Checkpoint	atomic logical checkpointing with idempotency keys — works on any provider
adapters.litellm.LiteLLMAdapter	backend + telemetry for any LiteLLM-supported provider (headers → budget, stale reading → 1-token ping)
adapters.langgraph.AgentPauseGuard	predictive gate for LangGraph nodes: check() interrupts the graph before the fatal call, record() trains the estimator

Install (from source, during development)

git clone https://github.com/<user>/agentpause
cd agentpause
pip install -e ".[dev]"
pytest

Roadmap

• Core components + test suite
• PredictiveScheduler high-level API (session(), should_suspend(), call())
• Runnable quickstart example (no keys)
• LiteLLM adapter (works with any provider)
• LangGraph adapter (interrupt + checkpointer)
• Optional KV-cache plugin for llama.cpp / vLLM (true warm start)

License

MIT