stakeout-agent 0.0.7.1

Drop-in monitoring for GenAI applications

stakeout-agent

Drop-in observability for LangGraph and CrewAI.

One callback. Every run, node, and tool call — captured automatically into MongoDB or PostgreSQL. No changes to your agent code.

---

Install and go

pip install stakeout-agent

from stakeout_agent import LangGraphMonitorCallback

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

That's it. Every node execution, tool call, latency, and error is now in your database.

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How it works

graph LR
    A[Your LangGraph / CrewAI app] -->|callback| B[stakeout-agent]
    B --> C[(MongoDB)]
    B --> D[(PostgreSQL)]
    C --> E[Dashboard / your queries]
    D --> E

stakeout-agent hooks into your framework's event system. It records a run document for each invocation and an event document for every node start/end, tool call, tool result, and error — with latency tracked at every step.

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Why stakeout-agent?

	stakeout-agent
Lines of integration code	3
Crashes your app on DB failure	Never — errors are logged, not raised
Node-level latency (P95)	Yes — tracked per node and per tool
Frameworks	LangGraph + CrewAI
Backends	MongoDB + PostgreSQL
Dashboard included	Yes — Streamlit, zero config

---

Installation

# MongoDB backend (default)
pip install stakeout-agent

# PostgreSQL backend
pip install 'stakeout-agent[postgres]'

# CrewAI support
pip install 'stakeout-agent[crewai]'

Requires Python 3.10+.

---

Quick start

LangGraph — Sync

from stakeout_agent import LangGraphMonitorCallback

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

LangGraph — Async

from stakeout_agent import AsyncLangGraphMonitorCallback

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

CrewAI — Sync

from stakeout_agent import CrewAIMonitorCallback

monitor = CrewAIMonitorCallback(crew_id="my_crew", thread_id="thread_123")
crew.kickoff(inputs={...})

CrewAIMonitorCallback registers itself with CrewAI's event bus automatically — no extra wiring needed.

CrewAI — Async

from stakeout_agent import AsyncCrewAIMonitorCallback

monitor = AsyncCrewAIMonitorCallback(crew_id="my_crew", thread_id="thread_123")
await crew.akickoff(inputs={...})

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Dashboard

Visualise runs, node timelines, and tool call details with the included Streamlit dashboard:

docker compose up -d mongo
cd stakeout-agent
uv run python examples/seed_demo_data.py   # optional: load demo data
uv run --with streamlit streamlit run examples/dashboard.py

Open http://localhost:8501. The dashboard shows:

• Run History — recent runs, status, duration, and a runs-over-time chart
• Node Performance — average and P95 latency per node and tool, error counts
• Run Inspector — full event timeline for any individual run
• Thread Deep Dive — multi-turn conversation view across all runs in a thread

---

Try the examples

LangGraph

A self-contained example that requires no LLM API key — nodes are pure Python functions.

docker compose up -d mongo
cd stakeout-agent
uv run python examples/dummy_app.py

CrewAI

Requires a running MongoDB instance and an OpenAI API key (or configure a different provider via the llm parameter on each Agent).

Sync:

docker compose up -d mongo
cd stakeout-agent
OPENAI_API_KEY=sk-... uv run --with crewai python examples/dummy_crewai_app.py

Async:

docker compose up -d mongo
cd stakeout-agent
OPENAI_API_KEY=sk-... uv run --with crewai python examples/dummy_crewai_async_app.py

Each example runs a two-agent crew (Researcher + Writer) with a MultiplyTool, then prints the runs and events documents written to MongoDB.

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Configuration

Environment variable	Default	Description
STAKEOUT_BACKEND	mongodb	Backend to use: mongodb or postgres
MONGO_URI	mongodb://localhost:27017	MongoDB connection string
MONGO_DB	stakeout	MongoDB database name
POSTGRES_URI	postgresql://localhost/stakeout	PostgreSQL connection string (also reads DATABASE_URL)

PostgreSQL

export STAKEOUT_BACKEND=postgres
export POSTGRES_URI=postgresql://user:password@localhost/stakeout

Tables are created automatically on first connection — no migration needed.

docker compose up -d postgres
# connection string: postgresql://stakeout:stakeout@localhost/stakeout

You can also inject a backend instance directly:

from stakeout_agent import LangGraphMonitorCallback, PostgresMonitorDB

monitor = LangGraphMonitorCallback(
    graph_id="my_graph",
    thread_id="thread_123",
    db=PostgresMonitorDB(),
)

---

What gets recorded

runs

One document per graph/crew 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

One document per node/task start/end, tool call, or error.

{
  "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": {"outputs": "..."},
  "error": null
}

event_type	When	latency_ms
node_start	A graph node or crew task begins	absent
node_end	A graph node or crew task completes	present
tool_call	A tool is invoked	absent
tool_result	A tool returns a result	present
error	A node, task, or tool raises an exception	present

---

Error handling

All database writes catch exceptions and log them — a monitoring failure will never crash your application. Enable DEBUG logging to see them:

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

---

Querying the database directly

MongoDB

from stakeout_agent import MongoMonitorDB

db = MongoMonitorDB()
runs = list(db.runs.find({"graph_id": "my_graph"}).sort("started_at", -1))
events = list(db.events.find({"run_id": "<run_id>"}).sort("timestamp", 1))

PostgreSQL

import psycopg2

conn = psycopg2.connect("postgresql://user:password@localhost/stakeout")
with conn.cursor() as cur:
    cur.execute("SELECT * FROM runs WHERE graph_id = %s ORDER BY started_at DESC", ("my_graph",))
    runs = cur.fetchall()

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Extending stakeout-agent

New framework: create a file under callback_handler/ that inherits _MonitorBase and implements the target framework's callback protocol.

New database: create a class that inherits AbstractMonitorDB and implement create_run, complete_run, fail_run, and insert_event.

stakeout_agent/
├── backends/
│   ├── base.py        # AbstractMonitorDB — shared interface
│   ├── mongodb.py     # MongoMonitorDB
│   ├── postgres.py    # PostgresMonitorDB
│   └── __init__.py    # get_backend() factory
├── callback_handler/
│   ├── base.py        # _MonitorBase — framework-agnostic core logic
│   ├── langgraph.py   # LangGraphMonitorCallback, AsyncLangGraphMonitorCallback
│   ├── crewai.py      # CrewAIMonitorCallback, AsyncCrewAIMonitorCallback
│   └── __init__.py

---

Roadmap

• Sync LangGraph callback support
• Async LangGraph callback support
• Sync CrewAI callback support
• Async CrewAI callback support
• MongoDB persistence
• PostgreSQL persistence
• Run and event collections
• Streamlit dashboard (Run History, Node Performance, Run Inspector, Thread Deep Dive)
• Additional agentic frameworks (PydanticAI, SemanticKernel, AutoGen etc.)
• Additional storage backends (SQLite, Redis, ...)

License

MIT