kadari — the local capture client

kadari is the only thing Kadari ships to you. It's a thin, zero-dependency Python library that records the LLM calls you already make to a local log. Kadari's engine (which stays on Kadari's side) then analyses that log and shows you, with proof, where you could safely route to a smaller in-family model — without ever re-running a call or crossing a quality bar you set.

This package carries none of Kadari's analysis engine — no scoring, no classifier model, no benchmarks. It is capture glue, by design. Read every line.

Install

Zero runtime dependencies (stdlib only):

pip install kadari

Or install from a checkout / a wheel your Kadari contact hands you:

pip install ./client
# or:  pip install kadari-0.3.0-py3-none-any.whl

Use

from kadari import LiveRecorder, wrap

rec = LiveRecorder("kadari_capture.jsonl")   # a local path you control

# Option A — record explicitly, right after a call you already make:
resp = client.messages.create(model="claude-opus-4-8", messages=[{"role": "user", "content": text}])
rec.record_anthropic(id=req_id, input=text, response=resp.to_dict())
# OpenAI: rec.record_openai(id=req_id, input=text, response=resp.to_dict())

# Option B — wrap the call once and capture every call automatically:
create = wrap(client.chat.completions.create, provider="openai", recorder=rec)
resp = create(model="gpt-5.4-nano", messages=[{"role": "user", "content": text}])

See what it costs

You don't need to capture anything to try it. The package ships a synthetic sample:

kadari demo -o report.html

Then on your own log:

kadari analyze kadari_capture.jsonl -o report.html

You get a single self-contained HTML file — no JavaScript, no network, opens offline and prints clean — showing spend by model, spend per day, how concentrated the bill is, what share of it we could actually price, and how much of it is metered rather than estimated.

Already have months of billing history? Import it instead of instrumenting anything:

kadari import openai-usage-export.csv --from openai-usage -o kadari_capture.jsonl
kadari analyze kadari_capture.jsonl -o report.html

Sources: openai-usage, anthropic-usage, csv (any table with model and token columns — names are matched loosely), and gateway (JSONL from LiteLLM, Helicone and similar). One thing worth knowing before you try: a provider usage export has no prompt text. It makes a complete spend report and it cannot be evaluated for savings, because an evaluation has to re-run your calls and compare against the answer you already paid for. A gateway log often does carry the text, in which case it works for both.

The part the free report can't do

The report tells you what you spent, and — from published rates — what the same tokens would have cost on the smallest model in your provider's family. That difference is a ceiling, not a saving. Nothing local has looked at whether the smaller model would have given an acceptable answer on your task.

That is the question Kadari measures. If you want it answered:

kadari bundle kadari_capture.jsonl --redact

This writes a zip next to your log and sends nothing — the client opens no network connection, and it is not going to start now. It prints exactly what went into the archive, runs a preflight that tells you up front whether the log is one we can actually evaluate, and points you at the upload page. You decide whether to send it.

Set expectations before you do: the first report's proven savings figure is $0.00 by design. Until enough of your own calls have been checked, we have not earned the right to claim a number on your task, and we would rather say so than estimate one.

Safety contract

• Fail open on the host, fail closed on the data. Recording is a side-channel: a bug here will never raise into your production call path — record() warns and returns False instead of throwing (unless you pass strict=True). What does get written is always valid and loadable.
• Local-first. This library never opens a network connection. Your prompts and outputs stay in the local file you named; nothing is uploaded by kadari.
• Privacy controls. redact= scrubs each input — and each tool-call argument value — before it is written; sample= records only a fraction of calls to bound log size.

rec = LiveRecorder("kadari_capture.jsonl", sample=0.1, redact=my_scrubber)

Wire format — the stable SDK contract

The on-disk JSONL log is the contract between this client and Kadari's engine. It is near-immutable: backward-compatible within a major version; a breaking change requires a new major version and a documented migration path (Kadari rule 6). One JSON object per line:

// {"kadari_log": {"version": "0.4", "client": "kadari/0.3.0", "sample": 1.0, "timestamps": true, "created": "2026-08-05T09:12:44Z"}}
{"id": "req-123", "model": "gpt-5.4-nano", "input": "...", "output": "Electronics",
 "usage": {"input_tokens": 64, "output_tokens": 2}, "ts": "2026-08-05T09:12:44Z"}

field	type	required	meaning
id	string	yes	unique per log — a captured log is rejected wholesale on a duplicate id. If you don't pass one, a uuid is generated, and a reused id is refused at write time so one retry can't make the whole log unloadable.
model	string	yes	the premium provider model string you actually called (e.g. gpt-5.5, claude-opus-4-8) — priced verbatim.
input	string	yes (may be empty)	the request text that gets scored/re-classified cheaply.
output	string	yes	the label or prose the premium model already returned. The provider adapters (record_openai/record_anthropic/wrap) unwrap a single-field structured object {"category": "X"} to X; the raw record(output=...) path writes the string verbatim, so pass a bare label there. When a response carries a tool call and no prose, the adapters write tool:<name> (see below).
usage	object	optional	{"input_tokens", "output_tokens"}, both non-negative integers. Attached only when both are present — a half block is dropped, since the engine honours usage only if both are set. When omitted, Kadari estimates spend from text length and marks the figure as estimated — measured and estimated tokens are never summed into one number presented as measured.
usage.cached_input_tokens	int	optional (since 0.4.0)	prompt tokens served from the provider's cache. Disjoint from input_tokens — see below.
usage.cache_write_tokens	int	optional (since 0.4.0)	prompt tokens written into the cache. Also disjoint from input_tokens.
ts	string	optional (since 0.3.0)	second-resolution UTC, 2026-08-05T09:12:44Z. On by default; LiveRecorder(timestamps=False) turns it off. Without it a report can describe what you spend on, but never how it is trending.
input_omitted	string	optional (since 0.3.0)	why input is empty — currently only oversize. See "Records too large to write" below. Valid only when input is empty.
output_omitted	string	optional (since 0.3.0)	why output is the sentinel kadari:omitted. Valid only alongside that sentinel.
tool_calls	array	optional (since 0.2.0)	the structured decision(s) the model already emitted, in provider order. Never written empty — a log with no tool call is byte-identical to one written before this field existed.
tool_calls[].name	string	yes (per entry)	the tool/function name — which decision was made.
tool_calls[].arguments	object	optional	the arguments, verbatim, with their original JSON types. Present unless arguments_omitted is.
tool_calls[].arguments_omitted	string	optional	why the payload is absent: oversize, unparsed, not_an_object, unserializable. Mutually exclusive with arguments. Readers must tolerate a reason they don't recognise.

Comment lines (//…) and blank lines are ignored by the reader.

The cache buckets are disjoint from input_tokens

input_tokens means the uncached billable input. The three input counts are mutually exclusive and add up to the prompt:

prompt = input_tokens + cached_input_tokens + cache_write_tokens

This matters because the two providers report the same facts under opposite conventions, and one of them has to be normalised somewhere:

• Anthropic already reports disjoint buckets — its input_tokens excludes both cache_read_input_tokens and cache_creation_input_tokens. Passed through unchanged.
• OpenAI reports prompt_tokens inclusive of prompt_tokens_details.cached_tokens. record_openai subtracts, once, on the way in.

record_openai / record_anthropic / wrap handle this for you. If you are wiring a provider by hand through the raw record() call, pass the uncached remainder as input_tokens — not the prompt total. Getting it backwards double-counts the cached prefix, and on cache-heavy traffic that prefix is most of the bill.

A cache read bills at roughly a tenth of an uncached input token and a cache write at more than one, so these are never summed into a single "input tokens" figure: the sum would correspond to no charge on your invoice. Both fields are optional and are never zero-filled — an absent count means "not reported", whereas a 0 would assert the traffic was uncached. A log written before 0.4.0 therefore loads unchanged.

The manifest line — what the log says about itself

Since 0.3.0 a fresh log opens with a single //-prefixed JSON line describing the log rather than any one call. Because both readers already skipped // lines, this is invisible to every reader that predates it.

sample is the field that matters. A log captured at sample=0.1 holds a tenth of your traffic and therefore a tenth of your spend — and without the manifest it is indistinguishable from a complete one, so any total computed from it is wrong by 10× with nothing on the page to say so. A log with no manifest reports its sample rate as unknown, never as 1.0: "we weren't told" and "we were told it's complete" are different claims and we won't collapse them.

The manifest is written only into an empty file. If you re-open an existing log with a different sample, the original manifest still stands and will misdescribe the new records — give each sampling regime its own log file.

Records too large to write

A record that would exceed the 200,000-character line ceiling sheds content before it sheds the call, in this order: tool-call arguments → input → output. Each step records why (arguments_omitted / input_omitted / output_omitted), and the priced facts — model and usage — survive every step.

The reason is arithmetic, not tidiness. Dropping the record entirely would remove your largest prompts from the total, and those are your most expensive calls — so the error would be both silent and biased low, which is the worst combination a spend figure can have. Nothing is ever truncated: half a prompt is evidence of nothing, and a later check that "verified" a decision against a truncated input would be lying. If even the priced skeleton won't fit, the record is refused outright and nothing partial is written.

Compatibility guarantees within a major version:

• the five REQUIRED fields above keep their names, types and meaning;
• new optional fields may be added; readers ignore unknown fields, so a newer client log still loads in an older engine and vice-versa;
• no required field is removed or repurposed, and no field type changes.

Tool calls — the structured decision

If your premium call already emits a tool call (an agentic support agent deciding to issue a refund, look up an order, escalate), that tool call is the decision — and it is the part worth measuring. The adapters record it automatically; nothing to configure:

{"id": "req-9", "model": "gpt-5.5", "input": "where is my order 118?",
 "output": "I've started your refund — 3–5 business days.",
 "usage": {"input_tokens": 812, "output_tokens": 96},
 "tool_calls": [{"name": "refund_order", "arguments": {"order_id": "A-118", "amount_cents": 4000}}]}

• A reply that explains and acts keeps both halves — prose in output, decision in tool_calls. Nothing about output changed for calls that already captured.
• A reply that only acts has no prose, so output becomes tool:<first tool name> — output is required and non-empty, and the tool: prefix keeps an agentic call from being mistaken for a classification label.
• Every tool call is recorded, in the order the provider returned them — not just the first.
• We only record a decision the model already emitted. Kadari never infers one from prose: an inferred decision is a guess wearing a proof's clothing.
• Arguments are recorded verbatim or not at all — never truncated. If they can't be represented faithfully (malformed JSON from the model, a value that isn't JSON, a payload too large for the line ceiling), the entry keeps the tool name and states the reason in arguments_omitted. Losing the payload never loses the call.
• redact= applies to argument values as well as input (keys and tool names are structure, not content, and are left alone). One consequence worth knowing: if you redact a value that also appears in the reply text, Kadari's checks can no longer match the two — which is the honest outcome, not a silent one.

This is enforced, not promised: the client never imports engine code, but a contract test in the engine repo (tests/test_kadari_client.py) writes a log with this client and asserts it loads unchanged through the engine's reader — so the two halves can never silently drift. The engine accepts one additional input shape (a nested Anthropic Messages {"id","request","response"} transcript); this client always emits the flattened shape above.