Fit every model request to its context window
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Every turn re-sent the whole transcript with nothing checking it against the model's window. On a local model that fails silently: Ollama drops the start of an over-long prompt, which is where the system prompt and the plan are. Measured on the Jetson's llama3.2, cut at 4096 tokens with no error. - The model catalog reads max_input_tokens and max_output_tokens from LiteLLM's model_info, and startup refuses a role on a model with no declared window - BudgetedClient wraps both routes. It fits the canonical events before rendering: older tool results cut to head and tail, then the oldest turns dropped with a note, keeping calls with their results; a request that still cannot fit fails with ContextOverflow and is never sent. The output ceiling is the smaller of config and the model's limit - Tokens are estimated from characters, pessimistically, and the estimate is corrected from reported prompt sizes, only ever upward - The executor takes its scope from the log: the latest plan, which is the approved milestone plan, goes in its system prompt, and it sees only events after it - Tool output keeps its head and, mostly, its tail, so a test runner's failure summary survives truncation - tuning.context.keep_recent_turns and safety_margin are new knobs - scripts/probe_context.py checks a declared window against what the server does; it confirmed llama3.2 cuts at the declared 4096 - The tuning guide covers the budget, declaring a local model's limits, and the probe Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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"""Check a model's declared context window against what its server does.
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uv run python scripts/probe_context.py ALIAS [--config PATH]
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ALIAS is a LiteLLM model alias. The LiteLLM URL and token come from the agent
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config (NISSE_CONFIG, ./nisse-dev.yaml, or --config), with its secrets from
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the environment or .env. See docs/Tuning-Guide.md, "Declaring a local model's
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limits", and nisse_dev/probe.py for what the verdicts mean.
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"""
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import argparse
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import os
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import sys
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from pathlib import Path
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import httpx
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from nisse_dev.catalog import ModelCatalog
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from nisse_dev.config import AgentConfig, ConfigError
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from nisse_dev.envfile import load_env_file
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from nisse_dev.probe import probe
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def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
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parser.add_argument("alias", help="LiteLLM model alias to probe")
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parser.add_argument("--config", help="agent config file to take LiteLLM from")
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parser.add_argument(
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"--max-probe-tokens",
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type=int,
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default=64_000,
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help="refuse to send a prompt larger than this (default: %(default)s)",
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)
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args = parser.parse_args()
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load_env_file(Path(os.environ.get("NISSE_ENV", ".env")).expanduser())
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try:
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config = (
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AgentConfig.from_file(Path(args.config))
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if args.config
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else AgentConfig.load()
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)
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except ConfigError as exc:
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print(exc, file=sys.stderr)
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return 2
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catalog = ModelCatalog.discover(config.llm_base_url, config.llm_token)
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declared = catalog.facts(args.alias).max_input_tokens
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if declared is None:
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print(
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f"LiteLLM declares no max_input_tokens for {args.alias}; set one first",
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file=sys.stderr,
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)
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return 1
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if declared * 1.5 > args.max_probe_tokens:
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print(
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f"{args.alias} declares {declared} tokens; probing would send about "
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f"{int(declared * 1.5)}. Raise --max-probe-tokens to allow it.",
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file=sys.stderr,
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)
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return 1
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with httpx.Client(
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base_url=config.llm_base_url.rstrip("/"),
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headers={"Authorization": f"Bearer {config.llm_token}"},
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timeout=600,
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) as http:
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result = probe(http, args.alias, declared)
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print(f"{args.alias}: {result.message}")
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return 0 if result.verdict in ("matches", "refuses", "declared_conservative") else 1
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if __name__ == "__main__":
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raise SystemExit(main())
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