agentdiet¶

Why it exists¶

agentdiet is a comparable baseline, not a recommendation. It reproduces the method from "Reducing Cost of LLM Agents with Trajectory Reduction" (Xiao, Gao, Peng, Xiong — FSE 2026, arXiv:2509.23586), which the authors call AgentDiet, so the published approach can be A/B'd against context-guru's own reducers on the same traffic, agent and benchmark. The paper reports −39.9%…−59.7% input tokens and −21.1%…−35.9% total cost at unchanged task success on SWE-bench Verified and Multi-SWE-bench Flash.

How it works¶

Its unit is the step — one assistant message plus the tool results that answered it — not one tool output. Four things follow from that, and together they are what distinguishes it from extract_llm:

1. A fixed age chooses the target, not size or economics. When the agent has completed step s, only step s − a is eligible (delay_steps, a=2). The most recent a steps are never touched, so a bad reduction cannot corrupt what the agent is working on right now — the paper's protection against a malfunctioning reflection model.
2. The model gets a sliding window of neighbouring steps, serialized as XML (context_steps, b=1 ⇒ steps [s−a−b … s]). This is the part extract_llm structurally cannot do: seeing the steps around the target is what lets the model call content redundant (already stated nearby) or expired (mattered only to a finished sub-goal) rather than merely verbose.
3. Two thresholds bound the spend. A step below min_step_tokens (θ=500) never earns a call; and a reduction that comes back is applied only if it clears min_saved_tokens or max_keep_ratio, so a marginal rewrite does not pay a cache-write for a handful of tokens.
4. A reduction is made once, then frozen. Later turns replay the same bytes, so the request prefix stays stable and reductions accumulate over the session — which is what the paper gets for free by editing the agent's own trajectory in place.

The window is serialized in the shape the reflection prompt describes:

<step id="7">
<think>The fix works. Now run the existing suite to check nothing broke.</think>
<call tool="bash">{"command":"python -m pytest testing/test_collection.py -v"}</call>
<result id="0">… 74 collected, per-test PASSED lines, summary …</result>
</step>

Before → After¶

before:  <result id="0">  … 74 individual "PASSED" lines … 73 passed, 1 xfailed in 4.48s
after:   ... (individual test lines omitted; mostly PASSED)
         ======= 73 passed, 1 xfailed in 4.48s  <<cg:…>> [full output: call context_guru_expand]

Lossiness¶

Lossy but reversible — each reduced tool result is stashed under its own <<cg:…>> marker and recovered via context_guru_expand / GET /expand.

Configuration¶

CONTEXT_GURU_AGENTDIET_TIMEOUT (default 90s) bounds one reflection call; /stats reports agentdiet_timeouts, agentdiet_errors and agentdiet_call_timeout_ms beside it. A non-zero timeout count means the budget is too small for the server's load, and that arm's savings are an undercount rather than a measurement.

Faithfulness to the paper¶

Defaults are the paper's tuned values (a=2, b=1, θ=500). min_saved_tokens (400) and max_keep_ratio (0.8) come from the authors' artifact, whose apply-gate is saved >= 400 || keep < 0.8; Algorithm 1 in the paper states this more simply as l_orig − l_reduced > θ. The artifact's form is the default because it is what produced the published numbers — set min_saved_tokens: 500 and max_keep_ratio: 0 to reproduce the paper's stated gate exactly.

Three deviations, all forced by where context-guru sits:

• The reduction is written in place, into the step's tool-result messages. The paper's reflection module replaces the whole step with one assistant message; here only summarize changes the message count, and it must run alone for that reason. Tool results are where 63% of trajectory tokens live by the paper's own accounting (30.4K of 48.4K).
• Tool-call arguments are not reduced. On Anthropic traffic bifrost's schema does not model tool_use blocks, so their name and input are not visible to any component here and the assistant message is not even rewritable. That forgoes the paper's str_replace_editor redundancy case (~25% of trajectory tokens) and is the main reason to expect a smaller reduction here than the published 39.9%–59.7%.
• The prompt is written from the paper's description of its four parts (job, format, the three waste categories with the examples the paper names, and the anti-loss guidelines) rather than copied from the authors' artifact. components.Model is one prompt in / text out, so the paper's system + user + assistant-prefill + </step> stop sequence is folded into a single prompt and the reply is parsed defensively instead (prose, code fences and a missing close tag are all tolerated; an unparseable reply reduces nothing). One case is declined rather than guessed: a <result> block whose id is missing or unparseable is used only when the step has a single result. On a step with parallel tool calls it is dropped, because placing it at id 0 would put one tool's compressed output into another tool's message — smaller, so the never-worse check would pass it, and then frozen and replayed for the rest of the session.

When it shines¶

Long agentic coding sessions with verbose tool output — test runs, build logs, directory listings — where the same step is re-sent on every subsequent turn. Its expired category is the one signal no other component here computes.

When it's inert¶

Fewer than b + a completed steps; a target step below min_step_tokens; a reduction that fails the apply-gate; no model available (it then replays what it already froze and reduces nothing new); or a trajectory whose tool outputs are all small — GAIA-shaped traffic, where the median text tool output is well under θ.

Run it alone¶

The preset is [format, agentdiet, cachesplit] on purpose. The method's claim is what one age-targeted reflection achieves; stacking context-guru's own offloaders beside it would reduce the same tool outputs first, and there would be nothing left to attribute.

See also: Components overview · extract_llm · summarize · Choose a preset