promptecho 0.1.2

Record & replay for LLM API calls — like vcrpy/nock, built for LLM traffic.

promptecho

Record & replay for LLM API calls. Like vcrpy / nock, but built for the way LLM traffic actually behaves.

Your LLM tests have three problems: they're flaky (non-deterministic outputs), slow (real network round-trips), and expensive (burning tokens in CI on every run). promptecho records each real API call once to a cassette file, then replays it forever — deterministically, instantly, for free.

import promptecho
from anthropic import Anthropic

@promptecho.use_cassette("cassettes/summarize.yaml")
def test_summarize():
    client = Anthropic()
    msg = client.messages.create(
        model="claude-opus-4-8",
        max_tokens=100,
        messages=[{"role": "user", "content": "Summarize: the cat sat on the mat."}],
    )
    assert "cat" in msg.content[0].text.lower()

First run: one real call, recorded to cassettes/summarize.yaml. Every run after: replayed from disk. No network, no tokens, no flake.

Proof, not marketing. The end-to-end test that gates every release records against a local server, shuts the server down, then replays. Same response, zero network. If the response can come back with the upstream gone, the cassette is genuinely doing the work — not a partial proxy. See tests/test_record_replay.py.

---

Why not just use vcrpy?

You can — at the HTTP layer, vcrpy works on LLM calls today. promptecho exists because LLM traffic breaks vcrpy's assumptions in five specific ways:

1. Matching. vcrpy matches on raw request bytes. LLM bodies carry volatile fields (client-injected IDs, reordered tools, whitespace) that change the bytes without changing the meaning — so byte-matching misses on replay. promptecho matches on a normalized fingerprint of the fields that determine the response, and canonicalizes across providers: it knows content: "hi" equals content: [{"type":"text","text":"hi"}], an Anthropic top-level system equals an OpenAI system-role message, and an Anthropic input_schema tool def equals an OpenAI function.parameters. A raw-bytes VCR can't.
2. Streaming. Most LLM calls are SSE streams. promptecho records the event stream and faithfully re-emits it on replay, so stream=True and token-by-token iteration work identically against a cassette — including reasoning deltas.
3. Binary / multimodal responses. vcrpy's text-based cassettes silently corrupt raw image/* / audio/* / octet-stream bodies. promptecho detects them by Content-Type and base64-encodes them in the cassette, so image-out and audio-out responses round-trip byte-exact.
4. Debuggable CI failures. When a vcrpy cassette miss happens, you get "no match". promptecho prints the exact path that changed: messages[1].content: recorded "summarize the cat" / incoming "summarize the dog". Test failures are actionable, not detective work.
5. Secrets. API keys live in headers on every call. promptecho redacts them by default — a cassette is safe to commit.

What promptecho is not

• Not a cache. Replay matching is exact/normalized and deterministic, on purpose. It does not semantically match "different prompt, close enough" — that would put non-determinism back into the harness you're using to remove it. (A separate opt-in fuzzy mode is on the roadmap as a dev-loop convenience; it will never be the default and never used in CI.)
• Not an eval. It freezes a response so your surrounding code is testable. Judging whether the response is good is a different tool (see roadmap: toMatchLLMSnapshot()).

---

What it covers

promptecho intercepts at the httpx transport layer. If the SDK uses httpx, promptecho sees the call — which is almost everything modern.

You're calling	Covered?
Anthropic, OpenAI, Mistral, Cohere, google-genai SDKs	✅
OpenAI SDK with custom base_url → OpenRouter, Together, Fireworks, Cerebras, Groq, DeepInfra, Perplexity	✅
Self-hosted vLLM / TGI / SGLang / LM Studio / Ollama (OpenAI-compatible mode)	✅
Your own fine-tune behind any of the above	✅
Reasoning models — o1/o3, Claude extended thinking, DeepSeek-R1	✅ (incl. reasoning_effort / thinking in default match-on)
Multimodal — base64-in-JSON (vision, Claude image-out, GPT-4o) and raw binary (image/*, audio/*)	✅ (byte-exact round-trip)
Bedrock via boto3, HF InferenceClient, in-process transformers	❌ (see workarounds in SUPPORT.md)

Full matrix with caveats and workarounds: SUPPORT.md. For practical recipes by scenario (startup / enterprise / research), see TUTORIAL.md.

Hosted open-source via the OpenAI SDK

This is the dominant pattern for non-Anthropic/non-OpenAI usage, and it Just Works:

from openai import OpenAI
client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key="...")

@promptecho.use_cassette("cassettes/openrouter.yaml")
def test_via_openrouter():
    r = client.chat.completions.create(
        model="meta-llama/llama-3.1-70b-instruct",
        messages=[{"role": "user", "content": "hi"}],
    )
    assert r.choices[0].message.content

Detection falls back to body shape when the host is unknown, so localhost gateways, in-house proxies, and self-hosted vLLM/TGI behave the same way as the brand-name hosts.

---

Install

pip install promptecho   # not yet on PyPI — install from source for now

git clone <repo> && cd promptecho
pip install -e .

Requires Python ≥ 3.9 and httpx ≥ 0.24.

---

Usage

Decorator

@promptecho.use_cassette("cassettes/foo.yaml")
def test_foo(): ...

Context manager

with promptecho.use_cassette("cassettes/foo.yaml"):
    client.messages.create(...)

pytest fixture (auto-named per test)

def test_bar(promptecho_cassette):   # records to cassettes/test_bar.yaml
    client.messages.create(...)

The fixture defaults to mode="once" locally and mode="none" when CI=true — so a forgotten recording fails the build instead of making a live call.

Record modes

Borrowed from vcrpy, so the mental model is free:

mode	absent cassette	present cassette	use for
once (default)	record	replay	normal dev
none	error	replay	CI — guarantees no live calls
new_episodes	record	replay + record new	evolving tests
all	record	re-record everything	refreshing fixtures

@promptecho.use_cassette("cassettes/foo.yaml", mode="none")

Choosing what to match on

Defaults to ["model", "messages", "system", "tools", "tool_choice", "reasoning_effort", "reasoning", "thinking"] — everything that determines the response for a chat-shaped call, including reasoning-model knobs.

@promptecho.use_cassette(
    "cassettes/foo.yaml",
    match_on=["model", "messages", "system", "temperature"],  # add temperature
)

For non-chat shapes (raw TGI /generate, embeddings) you'll want to override, e.g. match_on=["model", "input"] for an embeddings endpoint. See SUPPORT.md → Request shapes.

Async

Works identically with httpx.AsyncClient and the async surfaces of Anthropic / OpenAI / Mistral SDKs — the async transport is patched the same way as sync.

---

Cassette format

Human-readable YAML, designed to diff cleanly in PRs:

version: 1
match_on: [model, messages, system, tools, tool_choice, reasoning_effort, reasoning, thinking]
interactions:
  - request:
      method: POST
      url: https://api.anthropic.com/v1/messages
      match_key: ef43f6acaed95b2f        # fingerprint of matched fields
      matched_on: [model, messages, system, tools, tool_choice]
      body:                              # canonical (provider-normalized) body
        model: claude-opus-4-8
        messages:
          - {role: user, content: "Summarize: the cat sat on the mat."}
    response:
      status: 200
      headers: {content-type: application/json}
      streaming: false
      body:
        content: [{type: text, text: "A cat sat on a mat."}]
        usage: {input_tokens: 14, output_tokens: 8}

• Streamed responses store the ordered SSE events under response.events with streaming: true; replay re-emits them in order.
• Binary responses (image/audio/octet-stream) get binary: true and the body is base64-encoded; replay decodes and returns the original bytes.
• The stored body is the canonical, provider-normalized shape — not the raw provider JSON. That makes cassettes provider-agnostic and easier to skim in code review.

Auto-redacted on record: authorization, x-api-key, openai-organization. Configurable.

See examples/cassettes/example.yaml for a real one.

---

Status

v0.1.0, working core. 19 tests, all green. Not yet on PyPI.

Records and replays real httpx traffic — sync, async, SSE streaming, binary responses, cross-provider request shapes — verified end-to-end against a local server that gets shut down between record and replay.

Roadmap (build-in-public)

Done:

• httpx sync + async transport interception
• SSE streaming record/replay
• pytest plugin + auto-naming
• Per-provider request normalizers (Anthropic / OpenAI / generic)
• Reasoning-model match defaults (reasoning_effort, thinking, reasoning)
• Binary response round-trip (image/audio/octet-stream — base64 in cassette)
• Field-level diff on cassette miss (CI mode=none errors pinpoint the changed path, not just the field name)
• on_record_error policy (warn / raise / record) — prevents silently baking transient 4xx/5xx into cassettes

Next:

• requests / urllib3 interception backend — unlocks boto3-Bedrock and HF InferenceClient
• promptecho lint — find un-recorded calls in a test suite
• toMatchLLMSnapshot() sibling — semantic snapshot assertions on top of recorded calls

Design

For the why-not-the-other-way decisions — fingerprint vs raw bytes, why semantic matching is fenced off, how SSE re-emission works, how cross-provider normalization is structured — see DESIGN.md.

License

MIT