stakeout-agent 0.0.2a0

Drop-in monitoring for GenAI applications

stakeout-agent

Drop-in monitoring for LangGraph applications. Captures every graph run, node execution, and tool call into MongoDB with no changes to your graph code.

Why stakeout-agent?

When building LangGraph applications, understanding how your graphs execute is critical for debugging and optimization. stakeout-agent provides:

• Zero code changes — just add a callback to your graph config
• Complete visibility — captures node starts/ends, tool calls, and errors
• Resilient by default — MongoDB failures are logged and never crash your application
• MongoDB storage — leverage your existing infrastructure
• Framework-agnostic core — easily extensible to other frameworks

Installation

pip install stakeout-agent

Requires Python 3.10+ and a running MongoDB instance.

Quick start

Sync (graph.invoke)

from stakeout_agent import LangGraphMonitorCallback

monitor = LangGraphMonitorCallback(graph_id="my_graph", thread_id="thread_123")
result = graph.invoke(inputs, config={"callbacks": [monitor]})

Async (graph.ainvoke / graph.astream)

from stakeout_agent import AsyncLangGraphMonitorCallback

monitor = AsyncLangGraphMonitorCallback(graph_id="my_graph", thread_id="thread_123")
result = await graph.ainvoke(inputs, config={"callbacks": [monitor]})

Configuration

Environment variable	Default	Description
MONGO_URI	mongodb://localhost:27017	MongoDB connection string
MONGO_DB	stakeout	Database name

What gets recorded

runs collection

One document per graph invocation.

{
  "_id": "<run_id>",
  "graph_id": "my_graph",
  "thread_id": "thread_123",
  "status": "completed",
  "started_at": "2026-04-25T10:00:00Z",
  "ended_at": "2026-04-25T10:00:05Z",
  "error": null,
  "metadata": {}
}

status is one of running, completed, or failed.

events collection

One document per node start/end, tool call, or error within a run.

{
  "run_id": "<run_id>",
  "graph_id": "my_graph",
  "event_type": "node_end",
  "node_name": "agent",
  "timestamp": "2026-04-25T10:00:03Z",
  "latency_ms": 1240.5,
  "payload": {},
  "error": null
}

event_type	When
node_start	A graph node begins execution
node_end	A graph node completes
tool_call	A tool is invoked
tool_result	A tool returns a result
error	A node or tool raises an exception

Error handling

All MongoDB write operations catch PyMongoError and log the failure rather than propagating the exception. A monitoring failure will never take down your application. Enable DEBUG logging on stakeout_agent to see these errors:

import logging
logging.getLogger("stakeout_agent").setLevel(logging.DEBUG)

Using MonitorDB directly

from stakeout_agent import MonitorDB

db = MonitorDB()

# fetch all runs for a graph
runs = list(db.runs.find({"graph_id": "my_graph"}).sort("started_at", -1))

# fetch events for a specific run
events = list(db.events.find({"run_id": "<run_id>"}).sort("timestamp", 1))

Package structure

stakeout_agent/
├── callback_handler/
│   ├── base.py        # _MonitorBase — framework-agnostic core logic
│   ├── langgraph.py   # LangGraphMonitorCallback, AsyncLangGraphMonitorCallback
│   └── __init__.py
└── db.py              # MonitorDB

To add support for another LLM framework, create a file under callback_handler/ that inherits from _MonitorBase and implements the target framework's callback protocol.

Dashboard

The recorded data can power a dashboard to visualize graph runs, node execution timelines, and tool call details:

License

MIT