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mirrorkit is a lightweight, drop-in production trace collector for LLM agents. Add two lines to your existing LangChain / LangGraph / Anthropic / OpenAI script and your agent’s traces start streaming to Mirrors — non-blocking, background-batched, with negligible latency.

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Install

pip install mirrorkit
Zero required runtime dependencies — the sender uses only the Python stdlib. LangChain / Anthropic / OpenAI are instrumented only if they’re importable.

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Usage (2 lines)

import mirrorkit
mirrorkit.init(api_key="mk_live_...", project="my-agent")
That’s it. Run your agent normally — traces are captured automatically and shipped in the background. The endpoint defaults to the MIRROR_ENDPOINT environment variable, then to the production URL.

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Options

mirrorkit.init(
    api_key="mk_live_...",
    project="my-agent",
    endpoint="https://api.runmirrors.com",  # optional override
    flush_interval=2.0,                      # seconds between batch flushes
    max_batch=50,                            # max traces per POST
    instrument=True,                         # auto-hook LangChain/Anthropic/OpenAI
)

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Manual logging

For frameworks that aren’t auto-instrumented, enqueue a trace yourself. Messages are OpenAI-style chat dicts: 
import mirrorkit
mirrorkit.init(api_key="mk_live_...", project="my-agent")

mirrorkit.log_trace(
    [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the weather in Paris?"},
        {
            "role": "assistant",
            "content": None,
            "tool_calls": [
                {
                    "id": "call_1",
                    "function": {"name": "get_weather", "arguments": '{"city": "Paris"}'},
                }
            ],
        },
        {"role": "tool", "tool_call_id": "call_1", "content": "18C, sunny"},
        {"role": "assistant", "content": "It's 18C and sunny in Paris."},
    ],
    trace_id="optional-id",
    model="gpt-4o",
)

mirrorkit.flush()  # also runs automatically at interpreter exit

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LangChain global handler

init() registers a global LangChain callback handler automatically, so you don’t need to pass callbacks. If your setup doesn’t honor the global hook, pass the handler explicitly: 
from langchain_core.runnables import RunnableConfig
import mirrorkit

mirrorkit.init(api_key="mk_live_...", project="my-agent")
chain.invoke(inputs, config=RunnableConfig(callbacks=[mirrorkit.handler()]))

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API

FunctionDescription
mirrorkit.init(api_key, project="default", endpoint=None, *, flush_interval=2.0, max_batch=50, instrument=True)Start the collector.
mirrorkit.log_trace(messages, *, trace_id=None, model=None)Manually enqueue one trace.
mirrorkit.flush(timeout=5.0)Block until the queue drains.
mirrorkit.shutdown()Stop the collector.
mirrorkit.handler()LangChain callback handler for manual registration.
Failures (non-2xx / network errors) are retried a couple of times, then dropped — the collector never raises into your program. The wire format is documented in the collect API reference.
The same package also ships the mirrors CLI — install it with pip install "mirrorkit[cli]".