reeltime

A deterministic record/replay debugger for LLM agents. Record a run once, then replay it offline, instantly, for free — and see the exact bytes the model received.

$ tape run python truncation_bug.py
Q1: is report_00.pdf there?  -> Yes — report_00.pdf is in the listing.
Q2: is invoice.pdf there?    -> No, invoice.pdf is not in the listing.

Q2 is wrong: invoice.pdf IS in the listing.
Run:  tape show last 1 --context --diff 0

note: mock provider, 2 events -- little latency to skip, so replay saves ~1s here.
      examples/m3_replay_speed.py measures ~80x on an 8-turn agent at 400ms/call.

✓ recorded 2 events → .tape/runs/01M0B3V68D0THT474YMFV0R2SQ.jsonl  (1.5s, <$0.0001)

$ tape replay
Q1: is report_00.pdf there?  -> Yes — report_00.pdf is in the listing.
Q2: is invoice.pdf there?    -> No, invoice.pdf is not in the listing.

✓ replayed 2 events in 0.72s  ($0.00)  [2× faster than the recorded run]
  wall clock 0.86s including startup; the recorded run took 1.51s

$ tape show last 1 --context --diff 0
context diff · event 0 → event 1 · gpt-4o-mini
  3 messages, 866 chars  →  3 messages, 353 chars   (+0 messages, -513 chars)

  = [0] system     unchanged · 43 chars
  ~ [1] user       CHANGED · 791 → 280 chars (-511, TRUNCATED (kept the first 280 chars))
        -  report_10.pdf  (50 KB)
        -  report_11.pdf  (51 KB)
        -  report_12.pdf  (52 KB)
        -  report_13.pdf  (53 KB)
        ⋯ 16 more diff lines ⋯
        -  invoice.pdf  (70 KB)
        +  report_10

  ~ [2] user       CHANGED · 32 → 30 chars (-2)
        -Is report_00.pdf in the listing?
        +Is invoice.pdf in the listing?

2 changed · 1 unchanged

The model was never wrong. invoice.pdf had been truncated out of its context one line before the question that asked about it.

(That demo runs against an embedded mock, so it has almost no latency to skip and replay only saves about a second. The ~80× figure below is measured on a realistic agent paying 400 ms per call.)

The problem

Your agent failed at step 14. You re-ran it, and now it fails at step 11. Nothing you can reproduce, so nothing you can fix — only re-roll and hope.

What this does about it

• Replay is instant, offline, and free. $0.00 and zero network calls — ~80× faster on an 8-turn agent paying 400 ms per call (the benchmark), and the ratio grows with the latency you were paying. That is what makes stepping and scrubbing possible at all.
• Streaming is recorded and replayed chunk by chunk, boundaries byte-exact, with --realtime to reinstate the recorded gaps. Every other tool in this space refuses streaming outright.
• --context shows the full message array the model received, collapsed where it is long, and diffs it between two calls so an injection or a truncation is impossible to miss. Most agent bugs are context bugs, and nothing else surfaces the exact bytes.
• Fork a run from any step, with the fix applied. tape fork <run> --at 13 --patch 'llm.system+="Ask first."' replays the first 13 events — free and identical — then goes live from there. Testing a prompt change costs one step instead of a whole run, and the fork is itself a complete trace, so it replays and forks again.
• tape diff <a> <b> finds where two runs stopped being the same run. It aligns them by event signature rather than by text, so the headline is the divergence point and what each run did alone afterwards. For LLM steps it reaches into the context, and a changed system prompt shows as the two lines that changed.

Install

pip install reeltime

Nothing is required at runtime: the core is standard library only. httpx, httpx2, and requests are patched if you have them.

Quickstart

Record a script you have not modified at all:

tape run python agent.py     # records; your code is untouched
tape ls                      # what you have recorded
tape replay <run>            # re-run it offline, free
tape show <run> 14 --context # what the model actually read at step 14

tape run needs no import in your code — it injects a sitecustomize on PYTHONPATH, so recording starts before your agent imports anything.

To scope recording yourself instead:

import reeltime as tape

@tape.tool                                  # local tools become boundaries
def read_file(path: str) -> str:
    return open(path).read()

with tape.session() as run:
    with tape.span("plan"):                 # groups events; replays order-free
        notes = read_file("notes.md")
        client.chat.completions.create(...) # recorded, with tokens and cost

print(run.summary.line())

How it works

An agent is deterministic except at four boundaries. Record what crosses them and everything in between replays exactly.

┌──────────────── your agent, unmodified ────────────────┐
│                                                        │
│   ① LLM calls          ─────┐                          │
│   ② tool / network     ─────┤                          │
│   ③ random / uuid      ─────┤──►  Recorder ──► trace   │
│   ④ clock reads        ─────┘                          │
│                                                        │
└────────────────────────────────────────────────────────┘

Nothing else in the process can differ between two runs. That is the whole trick, and it is why replay costs nothing: there is no model to call, because every answer is already on the tape.

The context view

$ tape show 01M0AX2W 0 --context
event 0 · llm · gpt-4o-mini · examples/truncation_bug.py:97 (main.<locals>.ask)
3 messages · 866 chars of context · 216 in / 12 out tokens · <$0.0001
temperature 0

── [0] system · 43 chars ─────────────────────────────────────────────────────
  Answer only from the listing you are given.

── [1] user · 791 chars ──────────────────────────────────────────────────────
  Directory listing:
    report_00.pdf  (40 KB)
    report_01.pdf  (41 KB)
    report_02.pdf  (42 KB)
    report_03.pdf  (43 KB)
    report_04.pdf  (44 KB)
    report_05.pdf  (45 KB)
  ⋯ elided 550 chars · lines 8-29 of 32 ⋯
    report_28.pdf  (68 KB)
    report_29.pdf  (69 KB)
    invoice.pdf  (70 KB)

── [2] user · 32 chars ───────────────────────────────────────────────────────
  Is report_00.pdf in the listing?

── completion ────────────────────────────────────────────────────────────────
  Yes — report_00.pdf is in the listing.

Long messages collapse from the middle, keeping head and tail, because the end of a long message is where a truncation shows itself. The marker states both how many characters were elided and which lines. --full prints everything.

--context --diff M aligns the two message arrays with a sequence-alignment pass, so a message injected at the front does not report everything after it as changed, and labels each difference INJECTED, DROPPED, CHANGED, or TRUNCATED. Anthropic's top-level system field is hoisted to position 0 — it is part of what the model read, and it is the field people most often get wrong.

Replay

tape replay <run>              # re-run the recorded command against the tape
tape replay <run> --to 14      # stop after event 14
tape replay <run> --step       # pause before each event
tape replay <run> --strict     # only exact matches
tape replay <run> --loose      # also match on content hash alone
tape replay <run> --realtime   # re-emit stream chunks with their recorded gaps

A replayed @tape.tool never executes its body, which is what makes replaying an agent that deletes files or charges cards safe. Recorded exceptions are raised again — HTTP and tool alike — because a replay in which a failed call now succeeds is a replay of a different run.

The three-tier matcher

Index matching breaks the moment you edit your code. Content-hash matching breaks the moment you change a prompt by one character — which is exactly the edit you make while debugging. So identity and content are kept separate:

--strict accepts tier 1, the default accepts 1–2, --loose accepts all three. Tier 2 is the one that matters: it is what lets you tweak a prompt, replay anyway, and watch what changes downstream.

Nothing ever falls through to a live call. When a call cannot be matched, replay stops and says why each nearby recording was rejected:

no recorded tool event matches this call

  at        agent.py:91  (in Planner.step)
  span      root/plan
  sent      {"args":{"path":"b.txt"},"name":"delete_file"}

  nearest unconsumed events, and why each was rejected:
    #14   tool  agent.py:88                same call site, content differs — would match without --strict
    #22   tool  tools.py:12                same kind and span, different call site

  matching is 'strict'. Drop --strict to allow drifted content, or re-record.

Every drifted or fuzzy match is summarised at the end of the run. A match nobody mentions is silent divergence, which is the one thing this tool must never do.

Fork

Replay to a step, change one thing, and run live from there. The first N events are free and identical, so you are testing exactly one variable instead of re-running the whole agent and hoping the bug recurs.

tape fork <run> --at 13
tape fork <run> --at 13 --patch 'llm.model=claude-sonnet-4-5'
tape fork <run> --at 13 --patch 'llm.system+="Ask before destructive actions."'
tape fork <run> --at 7  --patch 'tool.read_file.result="<empty file>"'
tape fork <run> --at 13 --edit          # open $EDITOR on the event first

$ tape fork 01M0BDHF --at 1 --patch 'llm.system+="Use the full listing."'
✓ forked → 01M0BDK0WA531Q  (1 replayed, 2 live, $0.0004)
  parent 01M0BDHF8JK3MT · forked at event 1
  patched llm.system+="Use the full listing."

--at N replays events 0 through N−1. Event N is the first live one, and the patch applies to it on its way out. That is the one thing worth being pedantic about: --at 0 runs everything live, --at len(run) replays everything, and --at 13 means the thirteen events before 13 are free.

A fork writes both halves to its own run, so it is a complete trace — replayable and forkable again. The parent is never modified. tape ls shows parentage:

RUN             WHEN               EVENTS      DUR     COST  COMMAND
01M0BDK0WA531Q  2026-08-18 14:31        3     0.4s  $0.0004  agent.py  ← 01M0BDHF8JK3MT@1
01M0BDHF8JK3MT  2026-08-18 14:30        3     1.2s  $0.0011  agent.py

The patch grammar

<kind>[.<name>].<field> followed by an operator and a value. Values parse as JSON when they are JSON, and as a bare string otherwise — so llm.model=gpt-4o needs no quotes.

llm.system finds the system prompt whichever way the provider carries it — Anthropic's top-level system field or OpenAI's first role: system message — so one expression works against both. Fields that substitute a result stop the boundary executing at all; everything else rewrites the request and the call still happens.

Anything the grammar cannot express is what --edit is for: it opens $EDITOR on the event at the fork point and uses the request body you save. An empty buffer or invalid JSON aborts without creating a run.

A fork needs live credentials from event N onward. Those are checked before anything is replayed, so a missing key costs you an error message rather than a replayed prefix and then an error message.

Diff

Two runs, aligned by call site rather than by index, so an event inserted near the front does not report everything after it as changed.

$ tape diff 01M0BFPQ 01M0BFPR
diff  A 01M0BFPQCH0BJJH78JWEWK98G2   B 01M0BFPQGVF0QZPV4R9HG1GKGK

step 0   identical
step 1   tool    delete(n=0)  →  ask(n=0)
                 result: deleted 0  →  asked 0
step 2   ⋯ divergent from here (A ended; B: 2 more events)

cost   A $0.00      B $0.00
tokens A 0          B 0

The last line is the one to read first. Alignment and field-level reporting are table stakes; naming the step where two trajectories stop being the same run is the reason to run this at all. Everything above it is detail hung off that answer.

For LLM steps the report reaches into the context, so a changed system prompt shows as the two lines that changed rather than as "the request differs":

step 1   llm     system prompt changed
                 - Answer only from the listing you are given.
                 + Answer only from the listing you are given. Use the full listing.
                 tokens in: 88  →  94

--only llm (repeatable) narrows the comparison to one kind; --json gives the same structure as data, divergence point included.

Forks are the natural thing to diff: fork a run with one patch, then compare the two and read what that one change did.

Why interception is at the transport layer

On 2026-08-18 the OpenAI Python SDK (3.2.0) is built on httpx2 2.10, while the Anthropic SDK (0.122.0) is still on httpx 0.28. reeltime intercepts at Client._transport_for_url — httpx's own documented extension point — so supporting that split cost one constructor argument, because both libraries kept the same hook.

An interceptor that patched the SDKs instead would have needed a rewrite for that migration, and another one at the next. Nothing in the recording path knows a provider exists; model, tokens, and cost are added afterwards by pure functions over the recorded bytes (core/decoders/). Adding a provider is one module and one row in a pricing table, with nothing patched.

Numbers

Measured on the included benchmark (python examples/m3_replay_speed.py) — an 8-turn agent with 400 ms of latency per call, on an M-series Mac:

• ~80× faster replay, and the ratio grows with the latency you were paying.
• ~2 ms added per recorded HTTP event.
• 20–30 µs added per ambient read (random, uuid, clock).
• ~184 bytes per event on disk; payloads over 8 KB are content-addressed into .tape/blobs/ and deduplicate across turns.

What this can't replay

Being precise about the boundary is the point.

• External state mutation. If the agent deleted a file, replay does not put it back. Replay reproduces the decisions, not the world. Run replays in a scratch directory or a container.
• The agent's own time.sleep. Replay skips network latency, not code that deliberately waits. An agent that sleeps 30 s still sleeps 30 s.
• datetime.now(), unless you opt in. datetime is a C type, so seeing now() means replacing the module attribute with a subclass — and pydantic v2 dispatches on type identity, so doing that makes the real datetime class unrecognisable to it and breaks any library that imported it first. The Anthropic SDK stops working entirely. Enable with patch=("random", "uuid", "time", "datetime") if your stack is not pydantic v2; time.time() is patched either way and covers most clock reads.
• True thread races. Concurrent calls in the same span replay in recorded order. Put concurrent work in separate tape.span()s and the order stops mattering; a genuine data race between threads is not reproduced.
• JSON body whitespace. A parsed JSON body is stored as JSON, not as the original bytes. Keys and values survive; formatting does not. Keeping the exact bytes meant keeping a base64 copy that redaction could not scrub, which is a bad trade for whitespace no parser can see.
• Binary bodies are stored as base64 and cannot be scanned for secrets. Text and JSON bodies are scrubbed in full.
• random.Random() instances, SystemRandom, and numpy.random.default_rng(). Only the module-level functions are patched; an explicitly constructed generator is an object you can seed yourself.
• C-extension nondeterminism. Anything reading the clock or entropy below the Python layer is invisible.
• Non-httpx network stacks. aiohttp and raw sockets are not intercepted.

How this compares

AgentTape is the closest thing to this and it is a good project — a shipped CLI, an HTML viewer, an alignment-based diff, a pytest plugin, and hand-editable YAML cassettes. It is aimed at a different job: it builds test fixtures, so it deliberately discards a recording when the run raises, fails hard when a prompt changes, and freezes the clock and RNG rather than recording them. Those are the right calls for a fixture library and the wrong ones for a debugger. If you want offline agent tests in CI, use it. If you want to understand why one run failed, use this. Full teardown, including what it does better: COMPETITIVE.md.

LangSmith and Braintrust are hosted observability and evaluation platforms. Different job again: they show you aggregate behaviour across many runs; this reproduces one run byte for byte on your laptop.

Design notes

Redaction is mandatory, not optional. Traces are meant to be pasted into issues, so every event is scrubbed before it reaches disk — sensitive headers dropped by name, key-shaped values replaced (sk-, sk-ant-, ghp_, AWS, JWT, …), blobs included. Add your own with tape.redact(r"ACME-[A-Z0-9]{24}"); the end-of-run summary reports what was caught. The header's environment snapshot is an allowlist of configuration-shaped variables, never the whole environment.

Traces survive the crash you are debugging. Every event is flushed as it is written, so a run that dies leaves everything up to the moment it died. A missing footer line is precisely how you know it did not exit cleanly.

The outermost boundary is the one recorded. An HTTP call inside a @tape.tool body does not produce a second event, and neither do random draws made there. On replay that body never runs, so anything recorded inside it could never be matched.

Only your own code's ambient reads are recorded. asyncio reads time.monotonic() every loop iteration and httpx reads perf_counter() twice per request. The same filter applies on replay, so those stay live in both directions — consistent, and never a spurious miss.

Configuration

Explicit arguments beat environment variables, which beat the nearest .tapeconfig.

tape.install(
    tape_dir=".tape",            # or $TAPE_DIR
    blob_threshold=8192,         # or $REELTIME_BLOB_THRESHOLD
    patch=("random", "uuid", "time", "numpy"),   # add "datetime" to opt in
    http=True,                   # or $REELTIME_HTTP
    decode=True,                 # provider decoders; $REELTIME_DECODE
    record_library_ambient=False,
    redact=[r"ACME-[A-Z0-9]{24}"],
)

{ "blob_threshold": 16384, "redact": ["ACME-[A-Z0-9]{24}"] }

Examples

Three runnable agents, all covered by the test suite — see examples/. The two SDK examples import nothing from reeltime, which is the zero-edit claim made concrete.

Roadmap

MCP is next: no record/replay tool captures MCP sessions today, and a server that exposes a different tool set between runs is exactly the kind of thing that changes an agent's behaviour invisibly.

Development

git clone https://github.com/vedanth2406/reeltime
cd reeltime
pip install -e ".[dev]"
pytest                                  # 489 tests
pytest --cov --cov-report=term-missing  # core/ is at 94%
python examples/m3_replay_speed.py      # the benchmark above

Verified on Python 3.9 through 3.13.

Prior art

tapedeck and agenttape were both taken on PyPI, so the package is reeltime. The CLI is tape. See How this compares for what already exists in this space and why this is a different tool.

License

MIT — see LICENSE.