---
title: "Token efficiency"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Token efficiency}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!-- gptr-vignette-brand:start -->
<style>
html:not([data-bs-theme]) body { background: #fff; color: #1c1e22; }
.gptr-vignette-brand { float: right; width: 120px; margin: 0 0 1rem 1rem; }
.gptr-vignette-brand img { display: block; max-width: 100%; height: auto; }
html[data-bs-theme] .gptr-vignette-brand { display: none; }
@media (max-width: 600px) {
  .gptr-vignette-brand { float: none; width: 72px; margin: 0 0 1rem; }
}
</style>
<picture class="gptr-vignette-brand">
  <source media="(prefers-color-scheme: dark)" srcset="data:image/svg+xml;base64,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 439 508" width="439" height="508" xmlns:c2pa="http://c2pa.org/manifest"><metadata><c2pa:manifest>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</c2pa:manifest></metadata><defs></defs>
  <path d="M219.5 9 L430 130.5 L430 377.5 L219.5 499 L9 377.5 L9 130.5 Z" fill="#1c1e22" stroke="#1c1e22" style="stroke-width:16px;stroke-linejoin:round;"></path>
  <polyline points="124,174 180,224 124,274" fill="none" stroke="#efede7" style="stroke-width:24px;stroke-linecap:square;stroke-linejoin:miter;"></polyline>
  <rect x="208" y="224" width="16" height="50" rx="0" fill="#b85450"></rect>
  <rect x="240" y="174" width="72" height="100" rx="0" fill="#7d9dcb"></rect>
  <text x="219.5" y="385" fill="#efede7" style="font-family:'IBM Plex Mono',monospace;font-size:66px;font-weight:600;text-anchor:middle;">gptr</text>
  </svg>">
  <img src="data:image/svg+xml;base64,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 439 508" width="439" height="508" xmlns:c2pa="http://c2pa.org/manifest"><metadata><c2pa:manifest>AAAWgmp1bWIAAAAeanVtZGMycGEAEQAQgAAAqgA4m3EDYzJwYQAAABZcanVtYgAAAEdqdW1kYzJtYQARABCAAACqADibcQN1cm46YzJwYToxYjAyYjE3ZC1mZGQ2LTQ1ZTUtODUwZS1iYjMzMjZhNDY4OTAAAAADl2p1bWIAAAApanVtZGMyYXMAEQAQgAAAqgA4m3EDYzJwYS5hc3NlcnRpb25zAAAAALxqdW1iAAAARGp1bWRjYm9yABEAEIAAAKoAOJtxE2MycGEuaW5ncmVkaWVudC52MwAAAAAYYzJzaHiA/KGq6DrqmfBy+WXvF+MAAABwY2JvcqNpZGM6Zm9ybWF0bWltYWdlL3N2Zyt4bWxqaW5zdGFuY2VJRHgseG1wOmlpZDoyNDI3Y2VkNy01YmJiLTQyNjEtYjRhZS1jNzg3YmY4ZjkyZDJscmVsYXRpb25zaGlwaHBhcmVudE9mAAAB4mp1bWIAAABBanVtZGNib3IAEQAQgAAAqgA4m3ETYzJwYS5hY3Rpb25zLnYyAAAAABhjMnNosaVJETVsr+/Kf0HZzKUDawAAAZljYm9yomdhY3Rpb25zgqJmYWN0aW9ua2MycGEub3BlbmVkanBhcmFtZXRlcnOha2luZ3JlZGllbnRzgaJjdXJseC1zZWxmI2p1bWJmPWMycGEuYXNzZXJ0aW9ucy9jMnBhLmluZ3JlZGllbnQudjNkaGFzaFggo49tAnlmVsIgus2i+Zvz3B+fm5VfAGScYaSPusYxpdGkZmFjdGlvbngdY29tLmFudGhyb3BpYy5jbGF1ZGUucHJvdmlkZWRqcGFyYW1ldGVyc6F4H2NvbS5hbnRocm9waWMub3JpZ2luLWNvbmZpZGVuY2VndW5rbm93bmtkZXNjcmlwdGlvbnhmQ2xhdWRlIHByb3ZpZGVkIHRoaXMgZmlsZSBhdCB0aGUgcmVxdWVzdCBvZiBhIHVzZXIgYW5kIG1heSBoYXZlIGNyZWF0ZWQgb3IgbW9kaWZpZWQgdGhlIGZpbGUgY29udGVudHMubXNvZnR3YXJlQWdlbnShZG5hbWVmQ2xhdWRlcmFsbEFjdGlvbnNJbmNsdWRlZPUAAADIanVtYgAAAEBqdW1kY2JvcgARABCAAACqADibcRNjMnBhLmhhc2guZGF0YQAAAAAYYzJzaI45u2i2Stl+MGKFh0b+CsEAAACAY2JvcqVjYWxnZnNoYTI1NmNwYWRNAAAAAAAAAAAAAAAAAGRoYXNoWCC4kPfS+ttYYDYc7jR0lAPoFJfrway+yf8VDACCcJOmF2RuYW1lbmp1bWJmIG1hbmlmZXN0amV4Y2x1c2lvbnOBomVzdGFydBiWZmxlbmd0aBkeBAAAAj5qdW1iAAAAJ2p1bWRjMmNsABEAEIAAAKoAOJtxA2MycGEuY2xhaW0udjIAAAACD2Nib3KlY2FsZ2ZzaGEyNTZpc2lnbmF0dXJleE1zZWxmI2p1bWJmPS9jMnBhL3VybjpjMnBhOjFiMDJiMTdkLWZkZDYtNDVlNS04NTBlLWJiMzMyNmE0Njg5MC9jMnBhLnNpZ25hdHVyZWppbnN0YW5jZUlEeCx4bXA6aWlkOmNiNDE5MGRiLTE1YWUtNDFhOC04ZjExLTAwNTQyZmM4M2M5ZHJjcmVhdGVkX2Fzc2VydGlvbnODomN1cmx4LXNlbGYjanVtYmY9YzJwYS5hc3NlcnRpb25zL2MycGEuaW5ncmVkaWVudC52M2RoYXNoWCCjj20CeWZWwiC6zaL5m/PcH5+blV8AZJxhpI+6xjGl0aJjdXJseCpzZWxmI2p1bWJmPWMycGEuYXNzZXJ0aW9ucy9jMnBhLmFjdGlvbnMudjJkaGFzaFgg3g+CmYCj7lgOYi6hXlzJUuP3uRCxCps5K9sh1KuyVQ6iY3VybHgpc2VsZiNqdW1iZj1jMnBhLmFzc2VydGlvbnMvYzJwYS5oYXNoLmRhdGFkaGFzaFggQU17tHHdgIw2smKU0esDlnI/cVVcqx2ntXRZSni3+Ct0Y2xhaW1fZ2VuZXJhdG9yX2luZm+jZG5hbWVvQW50aHJvcGljIEZpbGVzZ3ZlcnNpb25lMS4wLjBrc3BlY1ZlcnNpb25lMi40LjAAABA4anVtYgAAAChqdW1kYzJjcwARABCAAACqADibcQNjMnBhLnNpZ25hdHVyZQAAABAIY2JvctKEWQISogEmGCFZAgowggIGMIIBjaADAgECAhRA5aAK7sI50L64g/oGQgU9Z1UTADAKBggqhkjOPQQDAzBJMRcwFQYDVQQKEw5BbnRocm9waWMsIFBCQzEuMCwGA1UEAxMlQW50aHJvcGljIENvbnRlbnQgQ3JlZGVudGlhbHMgUm9vdCBDQTAeFw0yNjA4MDcxODQzNTZaFw0yODA4MDYxOTQzNTZaMEQxFzAVBgNVBAoTDkFudGhyb3BpYywgUEJDMSkwJwYDVQQDEyBBbnRocm9waWMgQ2xhdWRlIENvbnRlbnQgU2lnbmluZzBZMBMGByqGSM49AgEGCCqGSM49AwEHA0IABJh6CmvLUBgFFNU0vUKlOVtE6djd17L5SuwX0LemFisBM3dkd/3cyjxFA3Qo5S46fX0/ihY0VZ7mfb9KF703t5OjWDBWMA4GA1UdDwEB/wQEAwIHgDAVBgNVHSUEDjAMBgorBgEEAYPoXgIBMAwGA1UdEwEB/wQCMAAwHwYDVR0jBBgwFoAUzlHiBIFOZFsj+OPEz5o+nMHXXMIwCgYIKoZIzj0EAwMDZwAwZAIwMXMdFJ4BetLLVY7ORuE9noqbbAZOZn/aArXyTwFAZfKrPzxF2vPoJNf1+UCdg1XGAjBwX1zd9WGqYkqmL5SFqw1QySjr1zJfpJM9+1rdDwSPLMOPOjKuiXjoU/pUUeG9RwmhY3BhZFkNngAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPZYQMkQJzvPLLfARxS3QS3T9b85SP1gEtHr2kSUjuqAm/T2+BuVeOFAWlYuoTxpZDiU0LKah2v2vnEc+TnefQylv7s=</c2pa:manifest></metadata><defs></defs>
  <path d="M219.5 9 L430 130.5 L430 377.5 L219.5 499 L9 377.5 L9 130.5 Z" fill="#f3f1ec" stroke="#1c1e22" style="stroke-width:16px;stroke-linejoin:round;"></path>
  <polyline points="124,174 180,224 124,274" fill="none" stroke="#1c1e22" style="stroke-width:24px;stroke-linecap:square;stroke-linejoin:miter;"></polyline>
  <rect x="208" y="224" width="16" height="50" rx="0" fill="#b85450"></rect>
  <rect x="240" y="174" width="72" height="100" rx="0" fill="#6c8ebf"></rect>
  <text x="219.5" y="385" fill="#1c1e22" style="font-family:'IBM Plex Mono',monospace;font-size:66px;font-weight:600;text-anchor:middle;">gptr</text>
  </svg>" width="120" height="139" alt="gptr logo">
</picture>
<!-- gptr-vignette-brand:end -->


Keep data and computation in R, and send models enough context to choose useful actions.
For inline work, an object around 4 GB can remain in the same R session across turns. The model
receives size-limited descriptions, previews and tool output that may include data values, while
its R tool computes on the object. No repeated export and
reload cycle is required; R copies, computation time and request latency still depend on the
workflow. Workers receive serialized context rather than sharing the parent workspace.
Four practices help:

- Refer to live objects with a compact description rather than printing large tables into a
  prompt. The model can compute on those objects with the `r` tool.
- Compose several deterministic operations in one R call and return a bounded result.
- Reuse a session's stable prompt and growing conversation when the provider supports prompt
  caching. Changing models, tools, compaction or rewind can change that prefix.
- Inspect usage and set budgets before starting a costly workflow.

The executed examples use fake providers. Their request counts and estimates demonstrate the
interfaces; they are not measurements of a real model's speed, quality or bill.

## Inspecting the prompt


``` r
library(gptr)
view = gptr_prompt(preset = "minimal")
attr(view, "tool_names")
#> [1] "read"  "r"     "edit"  "write"
view$sections
#>       name tier tokens
#> 1 preamble   T0     42
#> 2    tools   T0     86
#> 3    rules   T0    348
#> 4    modes   T0     84
#> 5  context   T0    152
view$total_tokens
#> [1] 1846
```

`gptr_prompt()` inspects the system sections and tool definitions with estimated token counts;
printing it shows their text. It does not query a provider. The `standard` preset describes
most capabilities as functions in `peter$`, with the core model-visible `r`, `read`, `edit`
and `write` tools and `ask` when applicable. `view$total_tokens` already counts the tool array
and a preview of the first message; your prompt and the objects you attach add to it.

## Object descriptions and result size


``` r
writeLines(gptr_describe(iris))
#> <data.frame> 150 x 5, 6.8 KB
#>   $ Sepal.Length <dbl> min 4.3, median 5.8, max 7.9; no NA
#>   $ Sepal.Width <dbl> min 2, median 3, max 4.4; no NA
#>   $ Petal.Length <dbl> min 1, median 4.35, max 6.9; no NA
#>   $ Petal.Width <dbl> min 0.1, median 1.3, max 2.5; no NA
#>   $ Species <fct> 3 levels, top: setosa (50), versicolor (50), virginica (50); no NA
writeLines(gptr_describe(iris, budget = 60))
#> <data.frame> 150 x 5, 6.8 KB
#>   Sepal.Length:dbl Sepal.Width:dbl Petal.Length:dbl Petal.Width:dbl Species:fct
```

`gptr_describe()` summarizes class, shape and values within a budget (150 tokens by default).
Packages can register methods for their own classes. Generative-model context normally uses
these descriptions and names; **System 1 questions send their input states**, so their egress
and size rules differ. See [Typed decisions](system-one.html).


``` r
work = new.env()

session = peter("Compare mpg and weight by cylinder count.", mtcars,
                model = fake, envir = work, mode = auto)

work$slopes
#>         4         6         8
#> -5.647025 -2.780106 -2.192438
```

The calculation runs in R once the model requests it. Return a small table, statistic or plot
rather than a full intermediate object. Long `r` output keeps a bounded head and tail; the
full spilled text can be read through `peter$out(id)` when an output ID was supplied.

## Reported usage and estimated components


``` r
gptr_usage(session, by = "model")
#>   group        requests  input  output  cache_read  cache_write  cost
#> 1 fake/fake-1  2         3382   48      0           0            0
#>
#> # 1 row
#> 2 requests, 3.4k tokens in, $0.0000

ledger = gptr_usage(session, detail = TRUE)
aggregate(tokens ~ component, data = ledger, FUN = sum)
#>       component tokens
#> 1      attached    132
#> 2   environment    150
#> 3        images      0
#> 4         other    156
#> 5       project      0
#> 6            t0   2720
#> 7            t1    618
#> 8  tool_results     53
#> 9         tools   1926
#> 10   transcript     70
#> 11    workspace     58
```

The usage summary contains provider-reported input, output, cache reads/writes and cost.
`by = "session"`, `"agent"`, `"model"` or `"route"` groups it; child requests are included
once. `gptr_usage()` without a session also includes the process-level System 1 log.

The detailed ledger estimates components such as tools, workspace, attached objects,
transcript and tool results. Its sum can differ from reported usage because tokenizers,
provider accounting, images and fake-provider behavior differ. Unknown reported usage or
cost stays `NA`, not zero. A local model's known zero metered charge does not imply zero
hardware cost or latency.

## Budgets and partial results


``` r
budget_error = tryCatch(
  peter("Summarize mpg.", model = small, budget = list(tokens = 500)),
  gptr_error_budget = function(e) e
)
class(budget_error)[1]
#> [1] "gptr_error_budget_tokens"
budget_error$session$status
#> [1] "budget"
```

The estimated input for the first request already exceeds 500 tokens, so this call stops
before that request. A top-level run's defaults are 2 million tokens and 5 US dollars;
`budget = list(tokens = ..., cost = ..., turns = ...)` supplies limits for that call. Change
settings deliberately when different defaults are needed:


``` r
gptr_config(budget = list(tokens = 20000, cost = 1, turns = 10), .scope = "session")
session = peter("Review this analysis.", budget = list(tokens = 12000, turns = 6))
```

Checks happen before requests using estimated next-input tokens and usage already recorded.
The next output or final provider charge can exceed a remaining allowance; unknown usage
cannot establish a hard monetary ceiling. A budget is a stopping policy, not a billing
reservation. At the console the UI can ask to extend it; without a person present the call
signals a budget condition carrying the partial session as `e$session`. Inspect that
session's status, transcript, workspace and usage before deciding whether to continue.

Teams and fan-outs share the root call's budget; launching more children does not give each
one a fresh root allowance. With `gptr_parallel()`, member `budget =`, `tools =` and per-run
`.opts` are not applied. Set the intended limits through the governing settings or enclosing
run, cap concurrency and inspect the returned children's statuses. See
[Teams and background work](teams-and-background.html).

## Compaction

At the chat prompt, `/compact` replaces older model context with a summary. Add a focus when
particular details must survive, for example `/compact retain the model comparison and next
steps`. Automatic compaction follows the configured context and cold-cache thresholds; see
[Configuration](configuration.html).

Compaction can make a model request and add usage. It keeps the R objects already in your
session, while the model no longer sees every older turn verbatim. Preserve important decisions
in project files and named R objects. A changed request prefix can also affect provider cache
reuse.

## Three different caches

| Mechanism | What is reused | Inspect |
|---|---|---|
| Provider prompt cache | A repeated request prefix | `gptr_usage(session)` |
| System 1 answer cache | Matching input, question and model | Metadata and `gptr_cache()` |
| Replay / System 2 cache | Recorded code and saved answer | `gptr_blocks(path)`, `gptr_cache()` |

A stable prefix makes provider cache reuse possible. Providers decide whether a cache hit
occurs, subject to their support, minimum size, expiry and pricing; gptr does not guarantee a
hit after the first request. Compaction, image elision, changed resources or rewind can break
the prefix. Document replay makes no model request for a usable recorded call but still runs
its R code. Clearing an answer cache changes what can be reused.

## Explicit ways to reduce context

These live-call examples assume a configured model. For unattended use of a remote provider,
save its egress acknowledgment first, as in [Language models](language-models.html).
`context = "none"` still requires that consent because project instructions and environment
details remain; offline providers and local loopback routes are exempt (Ollama only while
local-only inference is on).


``` r
peter("Summarize the model fit.", fit, .opts = list(context = "names"))
peter("Find the mean of x. Return one number.", .opts = list(context = "none"))
peter("Compute the mean.", x, .opts = list(preset = "minimal"))
```

`context = "names"` lists workspace names and uses headers for explicitly attached objects.
`"none"` omits both built-in workspace and attached-object descriptions; the R bindings still
exist and can be used by tool code, as `x` is in the second example. Prompt text, tool results,
images and other context blocks can still reach the model. System 1 input states use a separate
encoding route. [Execution model](execution-model.html) explains these boundaries.
`preset = "minimal"` reduces the tool/prompt surface; it does not authorize actions or remove
required safety checks.

`{identifier}` in a literal prompt can insert a short atomic value when that is sufficient.
For a long workflow, continue one session, keep intermediate data in R and request concise
reports between stages. Use a new session or an explicit fork when a separate analysis is
intended.