Agentpause suspends LLM agents before rate limits, resumes without redoing work

Agentpause is an open-source library (version 0.4.0) that automates the suspension and resumption of autonomous LLM agents to prevent them from hitting provider rate limits. The core works on any provider — cloud (OpenAI, Anthropic, Groq) or self-hosted (llama.cpp, vLLM) — because it only serializes application-level state; true KV-cache warm start is an optional plugin for self-hosted runtimes. Predictive suspension requires a budget signal: on cloud providers, this comes from rate-limit headers; on self-hosted servers, the developer must wire in an equivalent budget read (e.g., from ollama-gateway). The library's three core features are before-call prediction, checkpoint on demand, and automatic resume on rerun.

Measured results from the accompanying research show the impact across multiple scenarios. In a simulation of 300 runs, the reactive baseline crashed up to 100% of the time while the predictive scheduler achieved 0% crashes (with k=4 safety margin) and reduced token waste by 80%. In real testing on Groq's free tier (6K tokens per minute limit), a multi-window task completed with zero 429 errors. Compared to LangGraph's MemorySaver over 200 runs, LangGraph suffered 7.8 rate-limit errors per run and wasted 9,239 tokens per run, while Agentpause achieved 0 errors and 0 waste. In an end-to-end A/B test on thinking models (4B and 8B parameters), recovery was 54×–93× faster when 429s did occur, and total task time dropped 19%. A live benchmark on 2026-07-08 against Groq's free tier running a 12-step task with ~1k-token context showed the reactive baseline suffered 7 errors, redid 7 steps, and wasted 12,840 tokens; Agentpause achieved zero errors, zero steps redone, and zero token waste, at the cost of 259 telemetry pings (about 2% of what crashes waste).

The library also addresses a secondary challenge: when an agent's checkpoint is resumed, the conversation history may need to fit within reduced token budget. Testing shows that blind truncation (cutting the history to fit) loses facts and causes the model to invent plausible but false replacements — sometimes confidently and sometimes by declining, making the failure unpredictable and dangerous. Semantic summarization (issuing one extra LLM call to compress the history) preserves facts at about a third of the original prompt size, though it rewrites everything in the model's own voice, so personality traits and tone do not survive. A 20-step live stress test on 2026-07-10 confirmed the full loop: at the token budget wall, the scheduler suspended, compacted the checkpoint offline, resumed with a slim history, and completed all 20 steps with zero 429s.

Integration is designed to be minimal. The simplest pattern requires three calls: should_suspend() before every LLM call (a local computation with no network cost), checkpoint() if it returns true, and re-running the code with the same session ID to resume automatically. Adapters are provided for LiteLLM (covering 100+ providers), direct HTTP-compatible providers (Groq, OpenAI, etc.), Anthropic's Messages API, and LangGraph; for custom loops or frameworks without an adapter, the developer writes the backend and telemetry callables by hand. The core has zero dependencies; optional extras (LiteLLM adapter, LangGraph adapter) install only if used. Installation is via pip install "agentpause[litellm]" or pip install "agentpause[langgraph]". Runnable examples include a quickstart that suspends mid-task on the first run and resumes on the second, validation scripts for frontier models, and end-to-end tests. Error handling is typed (all errors derive from AgentPauseError with specific subclasses like RateLimitHit, TelemetryError, CheckpointError, BackendError), and the scheduler grows more cautious over time by bumping the safety margin (k) up after each unexpected 429, capped at a maximum (k_max).

Agentpause addresses a real operational friction in production AI agents: when an autonomous task hits a provider's rate limit, it crashes or retries blindly, wasting tokens and requiring the developer to rebuild state or restart work from scratch. The library's core insight is predictive — it reads rate-limit headers (or other budget signals) and asks "will the next call fit?" before issuing it, allowing a clean checkpoint and graceful pause rather than a 429 error. Testing confirms the payoff: in a live Groq experiment with 12 steps, the reactive baseline incurred 7 errors and re-sent 12,840 tokens, while the predictive scheduler avoided both, at the cost of only 259 telemetry pings (about 2% of the waste it prevented). The tool also explores a second concern — when a checkpoint is resumed, how should a long conversation history be condensed to fit remaining token budget? The research shows blind truncation loses facts and causes hallucinations, while semantic summarization (a single extra LLM call) preserves facts but rewrites voice and persona, requiring the developer to choose what matters most. Recovery time is 54×–93× faster when 429s do occur, and full task time drops 19%, making it economically sensible even on paid tiers where the telemetry pings have cost.

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