Stop hand-formatting log lines

agentcore-dashboard-metrics is a small Python package that logs exactly the metrics line your Grafana dashboards need — for agents on Bedrock AgentCore built with LangGraph, Strands, or CrewAI.

The problem it solves

CloudWatch Logs Insights' parse command is a literal, positional string matcher — it has no notion of a "field," only fixed text tokens in a fixed order. A dashboard panel built to read a specific shape returns a silent "No data" the moment any agent's log line drifts from it: a swapped field, a quoted number, one extra space. No error, no warning — just an empty chart.

This is the exact string every dashboard panel expects, and the one this package emits for you on every turn:

Install

Zero required runtime dependencies — the framework wrappers are fully duck-typed against whatever graph / agent / crew object you pass them, so installing this package never pulls in LangChain, Strands, or CrewAI on your behalf.

Quickstart, per framework

Pick your agent's framework. Each call replaces an entire streaming loop, TTFT/latency timing, and token accumulation — plus the log line itself.

from agentcore_dashboard_metrics import run_agent_turn

async def invoke(payload, context):
    # ...build graph, messages...
    output, turn_id = await run_agent_turn(
        graph, messages, log,
        user_id=user_id, session_id=session_id,
    )
    return {"result": output, "turn_id": turn_id}

from agentcore_dashboard_metrics import run_strands_turn

async def invoke(payload, context):
    agent = get_or_create_agent()
    output, turn_id = await run_strands_turn(
        agent, payload.get("prompt"), log,
        user_id=user_id, session_id=session_id,
    )
    return {"result": output, "turn_id": turn_id}

from agentcore_dashboard_metrics import run_crewai_turn

async def invoke(payload, context):
    crew = get_or_create_crew()
    output, turn_id = await run_crewai_turn(
        crew, {"prompt": payload.get("prompt")}, log,
        user_id=user_id, session_id=session_id,
    )
    return {"result": output, "turn_id": turn_id}

Strands multi-agent, CrewAI Flows, or a custom loop

Drop to the framework-agnostic context manager. It handles timing and the log line; you just report usage as you get it.

from agentcore_dashboard_metrics import TurnMetrics

with TurnMetrics(log, user_id=user_id, session_id=session_id) as m:
    result = my_own_agent_call(...)
    m.add_usage(input_tokens=result.input_tokens,
                output_tokens=result.output_tokens)
    m.mark_first_token()  # optional — only if you stream
# the log line is emitted automatically on exit, including on
# an exception (status is forced to "error"; nothing is swallowed)

Already computed everything yourself? Skip straight to the formatter:

from agentcore_dashboard_metrics import log_turn_metrics

log_turn_metrics(
    log, user_id=user_id, session_id=session_id, turn_id=turn_id,
    status="success", input_tokens=412, output_tokens=88,
    ttft_ms=640.12, latency_ms=1820.55,
)

Rules that keep every panel populated

The package enforces these for you — sanitizing bad input rather than corrupting the line — but it's worth knowing what it's protecting against if you ever log this by hand.

• 01 Line starts with Published metrics — (em dash, not a hyphen) — every panel filters on it.
• 02 Fields stay in order: UserID → SessionID → TurnID → Status → InputTokens → OutputTokens → TTFTMs → LatencyMs.
• 03 Separator is exactly " | " — space, pipe, space.
• 04 Numbers are bare and unquoted — stats sum()/avg() need real numerics.
• 05 Status is one lowercase word, no spaces or pipes.
• 06 Exactly one line per turn — double-logging breaks every count() panel.
• 07 IDs are always non-empty, stable strings — they double as click-through link values.

Four dashboards, one log group

Once your agent logs this shape, these dashboards read it directly — no code changes, no per-agent wiring.

Click-through navigation

Every ID rendered as a link jumps to the related dashboard with that value pre-filled and the time range carried over:

API

All three run_*_turn() functions are async def and return (output_text, turn_id).

"No data" checklist

1. Is your agent actually logging? Confirm Published metrics lines are reaching CloudWatch at all.
2. Does the line match the contract in this doc? Copy one real line out of CloudWatch and run it through the dashboard's parse pattern manually.
3. Is the time range wide enough? Dashboards default to now-24h → now.
4. Is the variable value exact? $SessionID/$UserID/$TurnID do exact string matches.