agent-failure-debugger

Diagnoses agent execution behavior — not just what failed, but why, and whether execution quality is healthy, degraded, or failed. Deterministic causal analysis with fix generation.

pip install agent-failure-debugger

from agent_failure_debugger import diagnose

result = diagnose(raw_log, adapter="langchain")
print(result["summary"]["execution_quality"]["status"])  # healthy / degraded / failed
print(result["explanation"]["context_summary"])

---

Use the Debugger

Call diagnose() after every agent run. It returns execution quality (healthy, degraded, or failed), root cause analysis when failures are detected, and fix proposals.

result = diagnose(raw_log, adapter="langchain")
status = result["summary"]["execution_quality"]["status"]

# In CI/CD or automated pipelines:
assert status != "failed", f"Agent execution failed: {result['summary']['root_cause']}"

When the agent runs normally, you get healthy with confidence scores and grounding state. When something goes wrong, you get the root cause, causal path, and a fix proposal — without changing how you call the tool.

Three ways to use it:

• Failure diagnosis — an agent broke, you need to know why. diagnose() returns root cause, causal path, explanation, and a fix proposal. This is the core use case.
• Health check — call diagnose() after every run and check execution_quality.status. Healthy runs return healthy; degraded quality (weak grounding, redundant tool results, low alignment) is surfaced before it becomes a failure. Track degraded frequency over time to catch regressions early.
• Run comparison — same prompt produces different results across runs. compare_runs() measures stability; diff_runs() identifies what structurally separates successful runs from failed ones.

Atlas detects failures; the debugger explains why they happened and proposes fixes. You can use Atlas alone for detection, but diagnosis requires the debugger.

From a raw log (simplest)

from agent_failure_debugger import diagnose

# Example: LangChain agent trace (no tool data)
raw_log = {
    "steps": [
        {"type": "llm", "output": "The Q4 revenue was $4.2M, up 31% year-over-year."}
    ],
    "tool_calls": [],
}

result = diagnose(raw_log, adapter="langchain")

print(result["summary"])
# → {'root_cause': '...', 'failure_count': ..., 'gate_mode': '...', ...}

print(result["explanation"]["context_summary"])
# → describes what happened and why

raw_log is a loosely structured dict — its format depends on the source. The adapter normalizes it into the telemetry format Atlas expects. The more structured and complete the log (especially tool calls and outputs), the more accurate the diagnosis. Minimal logs may result in incomplete or degraded analysis.

One function: adapt → detect (via Atlas) → diagnose → explain. Atlas is installed automatically as a dependency. Output quality depends entirely on the input log — incomplete telemetry will silently degrade detection and diagnosis.

Which adapter to use:

Adapters normalize raw logs from different sources into Atlas's telemetry format.

Adapter	Use for
langchain	LangChain / LangGraph traces
langsmith	LangSmith run-tree exports
crewai	CrewAI crew execution logs
redis_help_demo	Redis workshop Help Center

If unsure: use "langchain" for agent traces, "redis_help_demo" for the Redis workshop demo. For the JSON format each adapter expects, see Adapter Formats.

Note: crewai and redis_help_demo adapters do not yet produce state or grounding telemetry. Some failure patterns (e.g., agent_tool_call_loop) may not fire through these adapters. See the Atlas adapter verification status for details.

CLI:

# From a raw log (full pipeline)
python -m agent_failure_debugger.diagnose log.json --adapter langchain

# From matcher output (diagnosis only)
python -m agent_failure_debugger.main matcher_output.json

From matcher output (direct)

from agent_failure_debugger.pipeline import run_pipeline

result = run_pipeline(
    matcher_output,
    use_learning=True,
    include_explanation=True,
)

print(result["summary"]["root_cause"])
print(result["explanation"]["interpretation"])
print(result["explanation"]["risk"]["level"])

Use this when you already have matcher output, or when building a custom adapter.

From a live agent (via Atlas watch)

Atlas's watch() wraps a LangGraph agent and runs the debugger pipeline on completion. It is a separate entry point from diagnose() — both produce the same pipeline output but from different starting points: watch() captures telemetry from a live execution, while diagnose() accepts a raw log after the fact.

If you use llm-failure-atlas for detection, watch() runs the debugger automatically:

from llm_failure_atlas.adapters.callback_handler import watch

graph = watch(workflow.compile(), auto_diagnose=True, auto_pipeline=True)
result = graph.invoke({"messages": [...]})
# → detection + debugger pipeline + explanation printed automatically

For a copy-paste example without an API key, see Reproducible Examples below.

Self-healing agent (LangGraph)

Add automatic failure detection and informed retry to any LangGraph agent. When the health check detects a retryable failure, it injects the diagnosis into the conversation — the LLM reads why it failed and adjusts its approach. This is not a blind retry.

from agent_failure_debugger import create_health_check
from langgraph.graph import StateGraph, MessagesState, START, END

health_check, route = create_health_check(max_retries=2)

workflow = StateGraph(MessagesState)
workflow.add_node("agent", agent_node)
workflow.add_node("tools", tool_node)
workflow.add_node("health_check", health_check)

workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue,
                               {"tools": "tools", "check": "health_check"})
workflow.add_edge("tools", "agent")
workflow.add_conditional_edges("health_check", route,
                               {"retry": "agent", "end": END})

On retry, the health check appends a message like: "Previous attempt status: failed. The tool may have experienced a transient error that has since resolved. Please call the tool again." — the LLM reads this and retries the tool.

Not all failures benefit from retry. The integration classifies all 17 Atlas patterns as either retryable (transient errors, LLM non-determinism) or structural (bad prompts, config issues). Structural failures are reported immediately without wasting retries. See examples/self_healing/ for a working demo validated across GPT, Claude, and Gemini.

CI integration: Use pytest-agent-health to catch failures and regressions in CI, and create_health_check() to recover in production. The pytest plugin automatically compares against previous CI runs to detect new failure patterns and status degradation.

Behavioral Audit

The diagnostic pipeline can produce a formal audit report: run controlled scenarios across multiple LLM providers, diagnose each trace, and generate a PDF with pass/fail verdict, priority-ranked findings, and remediation owners.

Sample from a customer service agent audit (3 models × 6 scenarios):

Priority	Finding
P0 — CRITICAL	Agent fabricates information when backend service is down
P0 — CRITICAL	User's cancellation request ignored, agent pivots to upselling
P1 — HIGH	Wrong product category forwarded without notice
P2 — MEDIUM	Tool retry loop without strategy change

Audit verdict: FAIL — critical findings in user-facing flows, adjusted healthy rate 44%.

→ Sample report (PDF) → Audit toolkit and CI integration: pytest-agent-health

---

Quick Start

pip install agent-failure-debugger

Healthy run

from agent_failure_debugger import diagnose

raw_log = {
    "inputs": {"query": "What was Q3 revenue?"},
    "outputs": {"response": "Q3 revenue was $4.2M based on the latest earnings report."},
    "steps": [
        {"type": "tool", "name": "search_earnings", "inputs": {"quarter": "Q3"},
         "outputs": {"revenue": "$4.2M", "source": "10-Q filing"}, "error": None},
        {"type": "llm", "outputs": {"text": "Q3 revenue was $4.2M based on the latest earnings report."}}
    ]
}

result = diagnose(raw_log, adapter="langchain")
print(result["summary"]["execution_quality"]["status"])  # healthy
print(result["summary"]["failure_count"])                 # 0

The tool returns a result on every run. When the agent is healthy, you get confirmation — not silence.

Degraded run

from agent_failure_debugger import diagnose

raw_log = {
    "inputs": {"query": "Change my flight to tomorrow morning"},
    "outputs": {"response": "I've found several hotels near the airport for you."},
    "steps": [
        {"type": "llm", "outputs": {"text": "Let me check available flights."}},
        {"type": "tool", "name": "search_flights", "inputs": {"date": "2025-03-20"},
         "outputs": {"flights": []}, "error": None},
        {"type": "tool", "name": "search_flights", "inputs": {"date": "2025-03-20"},
         "outputs": {"flights": []}, "error": None},
        {"type": "tool", "name": "search_flights", "inputs": {"date": "2025-03-20"},
         "outputs": {"flights": []}, "error": None},
        {"type": "llm", "outputs": {"text": "I've found several hotels near the airport."}}
    ],
    "feedback": {"user_correction": "I asked about flights, not hotels."}
}

result = diagnose(raw_log, adapter="langchain")
print(result["summary"]["root_cause"])                    # incorrect_output
print(result["summary"]["execution_quality"]["status"])   # degraded
print(result["explanation"]["context_summary"])
# → "Root cause identified: the system produced output misaligned with
#    user intent, requiring correction (confidence: 0.625)."
print(result["explanation"]["risk"]["level"])              # low
print(result["summary"]["fix_count"])                     # 1

Same function, same interface. The difference is in the input, not in how you call the tool.

From matcher output (advanced)

If you already have matcher output (e.g., from a custom integration):

from agent_failure_debugger.pipeline import run_pipeline

result = run_pipeline(matcher_output, use_learning=True)
print(result["summary"])

See Quick Start Guide for more usage patterns including watch(), multi-run analysis, and direct telemetry.

Common Mistakes

⚠ No error is raised for wrong inputs. The system silently returns zero failures if the adapter cannot extract signals. See Limitations & FAQ for common causes and solutions.

This Tool Cannot

• Verify factual correctness of agent responses
• Detect semantic mismatch (requires embeddings)
• Analyze multi-agent system coordination

See Limitations & FAQ for details.

---

API Details

Execution quality

Every diagnose() result includes execution quality: healthy, degraded, or failed. Degradation indicators (low alignment, weak grounding, redundant tool results) are surfaced before they become failures.

eq = result["summary"]["execution_quality"]
print(eq["status"])       # "healthy" | "degraded" | "failed"
print(eq["indicators"])   # list of degradation concerns (empty if healthy)

Multi-run analysis

from agent_failure_debugger import compare_runs, diff_runs

stability = compare_runs(all_run_results)     # Is the agent stable?
diff = diff_runs(success_runs, failure_runs)   # What separates success from failure?

For runnable examples, see examples/multi_run_stability and examples/termination_divergence.

More

Full API documentation including enhanced explanation, individual pipeline steps, external evaluation, direct telemetry, and common mistakes: Quick Start Guide.

Input/output format, auto-apply gate, fix safety, and automation guidance: API Reference.

For real-world interpretation examples: Applied Debugging Examples and Operational Playbook in the Atlas repository.

---

Pipeline Steps

matcher_output.json
  → pipeline.py (orchestrator)
    ├ main.py               causal resolution + root ranking
    ├ abstraction.py        top-k path selection (optional)
    ├ decision_support.py   priority scoring + action plan
    ├ autofix.py            fix selection + patch generation
    ├ auto_apply.py         confidence gate + reason_code
    ├ pipeline_post_apply.py  evaluation runner or counterfactual
    ├ pipeline_summary.py     summary + execution quality assessment
    ├ execution_quality.py    healthy/degraded/failed classification
    └ explainer.py          explanation (context + risk + observation)

---

File Structure

File	Role
diagnose.py	Single entry point: raw log → full diagnosis
pipeline.py	Pipeline orchestrator (from matcher output)
pipeline_post_apply.py	Post-apply evaluation (runner + counterfactual)
pipeline_summary.py	Summary generation
main.py	CLI entry point for diagnosis only (from matcher output)
config.py	Paths, weights, thresholds
graph_loader.py	Load failure_graph.yaml
causal_resolver.py	Normalize, find roots, build paths, rank
formatter.py	Path scoring + conflict resolution
labels.py	SIGNAL_MAP (34) + FAILURE_MAP (17)
explainer.py	Deterministic + optional LLM explanation
explain.py	CLI for explanation generation (--enhanced, --deterministic)
decision_support.py	Failure to action mapping
autofix.py	Fix selection + patch generation
fix_templates.py	17 fix definitions (14 domain + 3 meta)
auto_apply.py	Confidence gate + auto-apply
execute_fix.py	Dependency ordering + staged apply
evaluate_fix.py	Counterfactual simulation
policy_loader.py	Read-only learning store access
reliability.py	Cross-run stability and differential analysis
execution_quality.py	Single-run execution behavior assessment
integrations/langgraph.py	LangGraph self-healing health check node

Examples

Directory	Demonstrates
examples/self_healing/	create_health_check(): LangGraph self-healing with informed retry across 3 models
examples/termination_divergence/	diff_runs(): same root cause, different termination modes
examples/multi_run_stability/	compare_runs() → diff_runs(): two-step stability and divergence workflow

---

Graph Source

The canonical failure_graph.yaml is bundled in the llm-failure-atlas package. The debugger loads the graph automatically via the Atlas package.

from agent_failure_debugger.config import GRAPH_PATH
print(GRAPH_PATH)  # shows which graph is loaded

---

Configuration

Variable	Default	Description
LLM_FAILURE_ATLAS_GRAPH_PATH	Bundled in package	Override graph location
LLM_FAILURE_ATLAS_PATTERNS_DIR	Bundled in package	Override patterns directory
LLM_FAILURE_ATLAS_LEARNING_DIR	Bundled in package	Override learning store

All scoring weights and gate thresholds are in config.py.

---

Design Principles

• Deterministic — same matcher output, same root cause, same fix, same gate decision
• Graph is for interpretation only — not used during detection
• Signal names are contracts — no redefinition allowed
• Learning is suggestion-only — structure is never auto-modified
• Fail fast on invalid input — pipeline validates at entry
• Enhanced explanations — include_explanation=True adds context, interpretation, risk, and recommendation

---

Related Repositories

Repository	Role
llm-failure-atlas	Failure patterns, causal graph, matcher, adapters
pytest-agent-health	CI integration — catch silent agent failures in pytest
agent-pld-metrics	Behavioral stability framework (PLD)

---

Reproducible Examples

For copy-paste-run examples of healthy and degraded runs, see Quick Start above.

With a live agent (requires langchain-core and langgraph):

pip install agent-failure-debugger[langchain] langgraph

from langchain_core.language_models import FakeListLLM
from langchain_core.messages import HumanMessage, AIMessage
from langgraph.graph import StateGraph, MessagesState, START, END
from llm_failure_atlas.adapters.callback_handler import watch

llm = FakeListLLM(responses=[
    "The revenue was $4.2M in Q3 2024, representing 31% year-over-year "
    "growth. The Asia-Pacific segment contributed 45% of total revenue. "
    "Operating margins expanded to 19.3% across all regions."
])

def agent(state: MessagesState):
    return {"messages": [AIMessage(content=llm.invoke(state["messages"]))]}

workflow = StateGraph(MessagesState)
workflow.add_node("agent", agent)
workflow.add_edge(START, "agent")
workflow.add_edge("agent", END)

graph = watch(workflow.compile(), auto_diagnose=True)
graph.invoke({"messages": [HumanMessage(content="What was Q3 revenue?")]})

Note: watch() with FakeListLLM demonstrates the callback integration but may not trigger failure patterns — the fake LLM produces no tool calls or user corrections. For failure detection examples, use diagnose() with the raw log above.

Regression test examples:

12 examples in llm-failure-atlas under examples/ (10 agent + 2 non-LLM). Each contains log.json, matcher_output.json, and expected_debugger_output.json.

python -m agent_failure_debugger.main matcher_output.json

Multi-run analysis examples:

2 examples in this repository under examples/. Each contains input fixtures, a runnable script, and expected_output.json:

• termination_divergence — diff_runs() comparing silent exit vs error exit
• multi_run_stability — compare_runs() → diff_runs() two-step workflow

---

Internals

Root ranking formula:

score = 0.5 * confidence + 0.3 * normalized_downstream + 0.2 * (1 - normalized_depth)

More downstream impact ranks higher, even with lower confidence. This reflects causal priority, not detection confidence alone.

This tool implements a single control step within the PLD loop: post-incident causal analysis and intervention decision.

---

License

MIT License. See LICENSE.