replayai-sdk 0.6.0

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 POST sessions to the ReplayAI dashboard where you can replay them, diff runs, and export tests.

• Stdlib only — pip install replayai-sdk brings no dependencies.
• 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]"

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.")

Open the dashboard — your run is there with a full timeline.

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_API_URL	http://localhost:3000	Cloud API 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

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.