Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/langchain-py-pack/skills/langchain-debug-bundle/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill langchain-debug-bundle. Copyright stays with the author (MIT).

LangChain Debug Bundle (Python)

Overview

An on-call engineer pages you at 2am: the production agent loops, ToolMessage outputs are empty strings, the user sees "I could not find the answer." Someone asks the right question — what state was the graph in when it gave up? — and there is no answer, because the terminal that caught the failure is already gone, the Kubernetes pod has restarted, and the LangSmith URL was never recorded.

This skill produces one artifact: a single bundle-<incident_id>.tar.gz (typically 1-10 MB) containing everything a second engineer needs to reproduce the failure without a live terminal — environment and version manifest, filtered astream_events(version="v2") JSONL, a propagating callback stack, the LangSmith trace URL, and a post-write sanitization pass.

Four pitfalls make naive bundles useless:

• P01 — ChatAnthropic.stream() reports token_usage only on stream close; token math read from on_llm_end lags by stream duration, so cost context in the bundle is wrong.
• P28 — BaseCallbackHandler.with_config(callbacks=[...]) does NOT propagate into subgraphs or inner create_react_agent loops. A debug callback bound that way silently captures zero events from the place the incident actually happened.
• P47 — astream_events(version="v2") emits 2,000+ events per invocation. A raw dump is 50 MB and unreadable; an SSE viewer crashes on it.
• P67 — astream_log() is soft-deprecated in 1.0. Diagnostic tooling built on it breaks on the next minor version.

The skill's answer: assemble the manifest, capture v2 events with a whitelist (drop lifecycle noise, keep on_chat_model_stream / on_tool_* / any *_error event), attach DebugCallbackHandler via config["callbacks"] at invoke time, pull the LangSmith URL from the active RunTree, run the sanitization pass, tar it up. Pinned: langchain-core 1.0.x, langgraph 1.0.x, langsmith 0.1.x. Pain-catalog anchors: P01, P28, P47, P67.

Prerequisites

• Python 3.10+
• langchain-core >= 1.0, < 2.0, langgraph >= 1.0, < 2.0
• langsmith >= 0.1.40 for RunTree access
• Active LangSmith project (LANGSMITH_TRACING=true, LANGSMITH_API_KEY=..., LANGSMITH_PROJECT=...) — canonical 1.0 env-var names, not the legacy LANGCHAIN_TRACING_V2 (see P26).
• Write access to a staging directory outside the repo tree.

Instructions

Step 1 — Assemble the environment manifest

Record the runtime snapshot that lets a colleague reproduce on a different host. See env-manifest-template.md for the exact YAML shape.

import platform, sys, os, subprocess, datetime

RELEVANT = [
    "langchain-core", "langchain", "langgraph",
    "langchain-anthropic", "langchain-openai",
    "langsmith", "anthropic", "openai", "pydantic",
]

def pip_show(name: str) -> str | None:
    try:
        out = subprocess.check_output(
            [sys.executable, "-m", "pip", "show", name],
            stderr=subprocess.DEVNULL, text=True,
        )
        for line in out.splitlines():
            if line.startswith("Version:"):
                return line.split(":", 1)[1].strip()
    except subprocess.CalledProcessError:
        return None

def build_manifest(incident_id: str, invoke_meta: dict) -> dict:
    return {
        "bundle_spec_version": "1.0",
        "generated_at": datetime.datetime.utcnow().isoformat() + "Z",
        "incident_id": incident_id,
        "runtime": {
            "python": sys.version.split()[0],
            "platform": platform.platform(),
            "cpu_count": os.cpu_count(),
        },
        "packages": [
            {"name": n, "version": pip_show(n)}
            for n in RELEVANT if pip_show(n) is not None
        ],
        # NAMES only — never values. Sanitized by design (P27 posture).
        "env_var_names_present": sorted(
            k for k in os.environ
            if k.startswith(("LANGSMITH_", "LANGCHAIN_", "ANTHROPIC_", "OPENAI_", "GOOGLE_"))
        ),
        "invocation": invoke_meta,
    }

Record env-var names, not values. Values go through the sanitization pass in Step 5, but the safest design is never to capture them.

Step 2 — Capture astream_events(version="v2") with a filter

Raw v2 events flood 2,000+ per invocation (P47). A server-side filter drops lifecycle noise (on_chain_start/on_chain_end) and keeps model, tool, and error events — yielding 50-200 events per invocation and a ~500 KB JSONL.

import json, itertools
from pathlib import Path

KEEP = {
    "on_chat_model_start", "on_chat_model_end",
    "on_tool_start", "on_tool_end", "on_tool_error",
    "on_retriever_start", "on_retriever_end",
    "on_custom_event",
}
# Additionally: any event whose name ends in "_error"
# Additionally: 1-in-10 sampled on_chat_model_stream (for response reconstruction)

async def capture_events(graph, inputs, config, out_path: Path) -> int:
    sample = itertools.count()
    written = 0
    with out_path.open("w") as f:
        async for evt in graph.astream_events(inputs, config=config, version="v2"):
            name = evt["event"]
            if name == "on_chat_model_stream" and next(sample) % 10 != 0:
                continue
            if name not in KEEP and not name.endswith("_error"):
                continue
            f.write(json.dumps({
                "event": name,
                "name": evt.get("name"),
                "run_id": str(evt.get("run_id")),
                "tags": evt.get("tags"),
                "metadata": evt.get("metadata"),
                "data": _json_safe(evt.get("data", {})),
            }, default=str) + "\n")
            written += 1
    return written

Never use astream_log() (P67). The full event taxonomy and _json_safe helper live in astream-events-capture.md.

Step 3 — Attach callbacks that propagate into subgraphs (P28)

Callbacks bound via Runnable.with_config(callbacks=[...]) fire on the outer chain only. They go silent the moment the graph crosses into a subgraph or an inner create_react_agent loop — exactly where incidents happen. Pass them via config["callbacks"] at invoke time instead.

from langchain_core.callbacks import BaseCallbackHandler

class DebugCallbackHandler(BaseCallbackHandler):
    def __init__(self): self.records: list[dict] = []
    def on_tool_start(self, serialized, input_str, *, run_id, parent_run_id=None, **kw):
        self.records.append({
            "kind": "tool_start", "run_id": str(run_id),
            "parent_run_id": str(parent_run_id) if parent_run_id else None,
            "tool": serialized.get("name"), "input": input_str[:500],
        })
    def on_tool_error(self, error, *, run_id, **kw):
        self.records.append({
            "kind": "tool_error", "run_id": str(run_id),
            "error_type": type(error).__name__, "error_message": str(error)[:1000],
        })

debug = DebugCallbackHandler()

result = await agent.ainvoke(
    {"messages": [("user", reproducer_prompt)]},
    config={
        "configurable": {"thread_id": thread_id},
        "callbacks": [debug],                      # propagates into subgraphs
        "tags": ["debug-bundle", incident_id],
        "metadata": {"incident_id": incident_id},
    },
)

The full handler (LLM + retriever + tool lifecycle) and a propagation smoke test live in callback-propagation.md.

Step 4 — Record the LangSmith trace URL

A trace URL is cheaper than any local artifact — one click and the colleague sees the full run with latency, token counts, and input/output per node. Pull it from the active RunTree if you have a live handle; otherwise construct it from the invoke's run_id:

from langsmith.run_helpers import get_current_run_tree

def capture_langsmith_url() -> str | None:
    rt = get_current_run_tree()
    if rt is None:
        return None  # tracing not enabled or run already closed
    return rt.get_url()  # https://smith.langchain.com/o/.../r/<run_id>

# Write to langsmith.url in the bundle:
url = capture_langsmith_url()
(staging / "langsmith.url").write_text(url or "(no trace URL available)")

The URL requires the colleague to have access to the LangSmith project. For public sharing, use RunTree.share() to generate a public snapshot URL. Never paste a non-shared URL into a public Discord thread — the page redirects to a login and leaks the project name.

Step 5 — Sanitize before packaging

Every file in the staging dir passes through the redaction pass before the tar.gz is written. This is the last-mile guard; upstream redaction middleware should already have caught credential material, but the bundle cannot assume that.

import re

PATTERNS = [
    ("openai_key",    r"sk-proj-[A-Za-z0-9_-]{16,}|sk-[A-Za-z0-9_-]{32,}"),
    ("anthropic_key", r"sk-ant-[A-Za-z0-9_-]{16,}"),
    ("google_key",    r"AIza[A-Za-z0-9_-]{35}"),
    ("langsmith_key", r"lsv2_(?:pt|sk)_[A-Za-z0-9]{32,}"),
    ("bearer",        r"(?i)bearer\s+[A-Za-z0-9._~+/=-]{20,}"),
    ("db_uri",        r"[a-z]+://[^:/\s]+:[^@\s]+@[^/\s]+"),
    ("private_key",   r"-----BEGIN [A-Z ]*PRIVATE KEY-----[\s\S]*?-----END [A-Z ]*PRIVATE KEY-----"),
]

def sanitize_file(path, patterns=PATTERNS) -> dict[str, int]:
    text, counts = path.read_text(), {}
    for name, pat in patterns:
        new, n = re.subn(pat, f"[REDACTED:{name}]", text)
        if n: counts[name] = n; text = new
    path.write_text(text)
    return counts

The full pattern catalog (credentials, session tokens, PII, internal URLs) and the pre-upload tar -xzf ... && grep scan live in sanitization-checklist.md. For the production-grade upstream redaction middleware, use the forthcoming langchain-security-basics skill.

Step 6 — Bundle with an index

Write a top-level MANIFEST.yaml that describes every file and records the sanitization summary. Then tar.gz the staging dir.

import tarfile, yaml
from pathlib import Path

def write_bundle(staging: Path, manifest: dict, sanitize_report: dict,
                 out: Path, events_count: int, callback_count: int) -> Path:
    index = {
        "bundle_spec_version": "1.0",
        "incident_id": manifest["incident_id"],
        "generated_at": manifest["generated_at"],
        "files": [
            {"name": "manifest.yaml",  "purpose": "env + version snapshot"},
            {"name": "events.jsonl",   "purpose": f"filtered astream_events(v2), {events_count} events"},
            {"name": "callbacks.txt",  "purpose": f"DebugCallbackHandler records, {callback_count} entries"},
            {"name": "langsmith.url",  "purpose": "trace URL (shared) or (none)"},
            {"name": "notes.txt",      "purpose": "free-form engineer notes, sanitized"},
        ],
        "sanitization": sanitize_report,
    }
    (staging / "MANIFEST.yaml").write_text(yaml.safe_dump(index, sort_keys=False))
    with tarfile.open(out, "w:gz") as tar:
        tar.add(staging, arcname=out.stem)
    return out

bundle = write_bundle(
    staging=Path("/tmp/bundle-INC-2026-0421-A"),
    manifest=m, sanitize_report=report,
    out=Path("/tmp/bundle-INC-2026-0421-A.tar.gz"),
    events_count=n_events, callback_count=len(debug.records),
)

Output

Typical size: 1-10 MB compressed. Typical event count after filter: 50-200 per invocation (down from 2,000+ raw). Bundle is self-contained — no external dependencies beyond tar -xzf and a text editor.

Error Handling

Examples

Triage decision tree — "which file in the bundle do I read first?"

Give a colleague this table with the bundle so they know where to start:

Incident-driven capture — reproducing and bundling in one script

See callback-propagation.md for the full invoke-time config pattern. The skeleton:

async def reproduce_and_bundle(agent, reproducer, incident_id: str) -> Path:
    debug = DebugCallbackHandler()
    staging = Path(f"/tmp/bundle-{incident_id}"); staging.mkdir(exist_ok=True)

    try:
        result = await agent.ainvoke(
            reproducer,
            config={"configurable": {"thread_id": f"debug-{incident_id}"},
                    "callbacks": [debug], "tags": ["debug-bundle", incident_id]},
        )
        invoke_meta = {"status": "success"}
    except Exception as e:
        invoke_meta = {"status": "error",
                       "error_class": type(e).__name__, "error_message": str(e)[:500]}

    # Step 2 — capture events (separate invocation with same inputs OR replay
    # from RunTree if already in LangSmith)
    n_events = await capture_events(agent, reproducer, {...}, staging / "events.jsonl")

    # Step 1 — manifest, Step 3 — callbacks, Step 4 — LangSmith URL, Step 5 — sanitize
    # Step 6 — bundle
    return write_bundle(staging, build_manifest(incident_id, invoke_meta), ..., ...)

Discord / forum bug report checklist

Before posting to the LangChain Discord or GitHub Issues:

1. Run the pre-upload tar -xzf && grep scan (sanitization-checklist.md)
2. Confirm langsmith.url is a shared URL (public), not a project-internal one
3. Strip the incident_id if it maps to internal ticket numbers you cannot disclose
4. Include in the post: bundle attachment, a 3-sentence symptom description, the exact reproducer prompt, the first line of MANIFEST.yaml (spec version and versions of langchain-core + langgraph)

Resources

• LangChain callbacks concept
• astream_events v2
• LangSmith observability
• LangSmith RunTree API
• LangGraph streaming modes
• LangChain 1.0 release notes
• Pack pain catalog: docs/pain-catalog.md (entries P01, P26, P28, P47, P67)
• Companion skills: langchain-observability, langchain-security-basics (upstream redaction middleware), langchain-model-inference (token accounting)