replayai-sdk 0.7.0

Python SDK for ReplayAI — instrument agents, record sessions, replay in the dashboard.

replayai — Python SDK

Instrument Python agents, record every step (LLM calls, tool calls, retrievals, errors), and view sessions in the built-in dashboard where you can replay them, diff runs, and export tests.

• Stdlib only — pip install replayai-sdk brings no dependencies.
• Built-in dashboard — replayai ui launches a self-contained dashboard server. No external app or database required.
• Decorator + context manager — @trace(...) or with trace(...) as ctx:.
• Framework extras — pip install "replayai-sdk[langchain]" for auto-instrumentation.

Install

pip install replayai-sdk
# or, with the LangChain integration:
pip install "replayai-sdk[langchain]"

Windows note: If you see a warning like "The script replayai.exe is installed in … which is not on PATH", either add that directory to your PATH or use python -m replayai instead — it works identically:

python -m replayai ui          # same as: replayai ui
python -m replayai record agent.py

30-second usage

from replayai import trace, record_step

@trace("support-agent-v3", project="support-agent", tags=["production"])
def handle_support_ticket(message: str) -> str:
    record_step(
        type="llm_call", name="classify_intent",
        model="gpt-4o-mini", tokens_in=312, tokens_out=24,
        input=f"User: {message}", output="intent: billing_dispute",
        status="success",
    )
    record_step(
        type="tool_call", name="issue_refund",
        input='{"charge_id":"ch_002"}',
        output='{"refund_id":"ref_3391"}',
        status="success",
    )
    return "Refund issued (ref_3391)."

handle_support_ticket("I was charged twice, refund me.")

Launching the dashboard

The SDK ships with a self-contained dashboard server. Record sessions locally, then launch the UI to view them:

# 1. Record a session (stored locally by default when REPLAYAI_STORAGE=local)
REPLAYAI_STORAGE=local replayai record my_agent.py

# 2. Launch the dashboard
replayai ui
# → opens http://localhost:7373 in your browser
# → shows all locally-recorded sessions with full step timelines

Options:

Flag	Default	Description
--port	7373	Port to listen on
--storage	./replays	Local storage path
--no-browser	—	Don't auto-open the browser

The dashboard reads sessions from {storage}/sessions/*.json and serves:

• GET / — single-page dashboard UI (stats, sessions list, step timeline)
• GET /api/sessions — JSON session list
• GET /api/sessions/:id — JSON single session with steps
• GET /api/stats — JSON aggregate stats

CLI commands

replayai record <script.py> [--project <slug>] [--tags a,b]   # Run a script under a trace
replayai test [tests/replay/] [--live-llm]                    # Run replay regression tests
replayai ui [--port 7373] [--storage ./replays]               # Launch the dashboard
replayai --version                                            # Print version

Configuration

Environment variables (all optional):

Variable	Default	Description
REPLAYAI_PROJECT	—	Default project slug/id
REPLAYAI_TOKEN	—	Cloud API token
REPLAYAI_STORAGE	cloud	cloud, local, or both
REPLAYAI_STORAGE_PATH	./replays	Local storage directory (used when storage includes local)
REPLAYAI_API_URL	http://localhost:3000	Cloud API base URL
REPLAYAI_DASHBOARD_URL	http://localhost:3000	Dashboard base URL
REPLAYAI_SAMPLE_RATE	1.0	Fraction of sessions to record
REPLAYAI_STRICT	false	Raise on recording failures
REPLAYAI_REDACT_PATTERNS	built-in set	Comma-separated regexes
REPLAYAI_REDACT_STRICT	true	Set false to disable entropy-based secret detection
REPLAYAI_TIMEOUT	30	Per-request HTTP timeout (seconds)
REPLAYAI_MAX_STEPS	200	Hard ceiling on steps persisted per session

Programmatic override:

import replayai
replayai.configure(project="support-agent", api_url="http://localhost:3000")
replayai.strict_mode = True  # opt into hard failures

Async

import asyncio
from replayai import atrace, arecord_step

@atrace("async-agent")
async def handle(message: str) -> str:
    await arecord_step(type="llm_call", name="classify", status="success")
    return "ok"

asyncio.run(handle("hello"))

Replay & export

from replayai import ReplaySession

replay = ReplaySession("ses_8fa1")
replay.mock("issue_refund", '{"refund_id":"ref_3391"}')
with replay.trace() as trace_obj:
    replay.run(agent="support-agent-v3", framework="LangChain")
print(trace_obj.step_count, trace_obj.status)
print(replay.export(lang="pytest"))

LangChain integration

from replayai.integrations.langchain import trace_agent, ReplayCallbackHandler

@trace_agent("support-agent-v3", project="support-agent", tags=["production"])
def handle(message: str) -> str:
    return executor.invoke({"input": message})["output"]

See examples/quickstart.py and examples/langchain_demo.py for runnable demos.

License

MIT — see LICENSE.