Hallucination detected
Safety boundary enforced
Recovery executing
Cascade failure blocked
Data integrity verified
Agent validated
Platform Failure Modes How It Works Ecosystem Pricing GitHub Start Free
AI Reliability Platform · 150+ Failure Modes

AI Agents Fail.
We Catch It.

The comprehensive failure intelligence platform. Predict, detect, and recover from AI agent failures before they reach production.

$ pip install failsafe
The Problem

AI Failures Are Invisible
Until They're Catastrophic

Your AI agents are running in production right now. When they fail, most teams don't find out until the damage is done.

0%
of AI deployments experience silent failures in the first 90 days
0x
cascade multiplier — one failure triggers chain reactions
0%
of engineering teams lack formal AI failure taxonomies
$0M
average cost of undetected AI failures per enterprise per year

Silent Hallucinations

Your customer-facing AI confidently cites a nonexistent return policy. By the time support flags it, 2,000 customers have received false information and your brand trust is eroding.

Cascade Failures

An API timeout in one agent triggers a retry storm. The retry storm overwhelms your rate limits. Downstream agents get stale data. One failure becomes twenty in under sixty seconds.

Safety Boundary Breaches

A code-generation agent with access to your production database starts executing write operations it was never authorized to perform. No guardrails. No alerts. No audit trail.

The Solution

Complete Failure Intelligence

FailSafe gives you full-spectrum visibility into how your AI agents fail, why they fail, and how to recover automatically.

Agent Action

Any AI operation

FailSafe Monitor

Real-time analysis

Detection

Classify failure

Recovery

Auto-remediate

Comprehensive Taxonomy

Over 150 documented failure modes across 8 categories. Each failure mode includes detection heuristics, prevention strategies, recovery procedures, and real-world examples. Built from production incident data, not theory.

150+ failure modes

Real-time Detection

Lightweight monitors that run alongside your agents. Detect hallucinations, safety violations, cascade failures, and behavioral drift in milliseconds. Sub-1ms overhead per operation. Zero impact on agent performance.

<1ms latency

Automated Recovery

Define recovery strategies for every failure mode. Retry with backoff, fallback to safe defaults, circuit-break cascading failures, or escalate to human operators. Recovery executes in under 300 milliseconds.

Auto-remediation

Audit and Compliance

Every failure event logged with full context: what failed, when, why, and what recovery action was taken. Exportable audit trails for SOC 2, HIPAA, and ISO 27001 compliance requirements.

SOC 2 ready
Failure Taxonomy

Every Failure Mode, Documented

A structured, machine-readable classification of every way AI agents can fail. Click any category to explore specific failure modes.

Hallucination Failures

False assertions, fabricated data

24 modes
Confident False Claims — Agent states fabricated facts with high confidence scores, often citing nonexistent sources or statistics
Fabricated References — Generation of URLs, paper titles, case law, or API endpoints that do not exist in reality
Impossible Assertions — Logically or physically impossible claims presented as factual, such as contradictory dates or impossible metrics
Entity Confusion — Conflating attributes of distinct entities, mixing up people, companies, products, or events

Execution Failures

Timeouts, loops, resource exhaustion

21 modes
Operation Timeouts — Agent operations exceeding configured time limits, causing downstream request failures and stale state
Memory Exhaustion — Unbounded context accumulation or large payload processing consuming all available memory
Infinite Loops — Agent enters a cyclic reasoning pattern, repeatedly attempting the same failed action without progress
Deadlocks — Two or more agents waiting on each other's output, creating circular dependencies with no resolution path

Integration Failures

APIs, auth, network partitions

19 modes
API Contract Violations — External service changes response format or removes fields without notice, breaking agent parsing logic
Authentication Expiry — OAuth tokens or API keys expire mid-workflow, causing cascading permission denied errors
Rate Limit Saturation — Burst traffic exceeds provider rate limits, triggering 429 responses and degraded throughput
Network Partitions — Temporary connectivity loss between agent and dependencies, leading to partial state updates and inconsistency

Safety Violations

Boundary breaches, unauthorized access

18 modes
Boundary Breaches — Agent exceeds its authorized scope, accessing resources or performing actions outside its permission set
Data Exposure — Sensitive PII, credentials, or proprietary data leaked through agent outputs or logs
Unauthorized Escalation — Agent gains elevated privileges through prompt injection, tool misuse, or permission chaining

Behavioral Drift

Gradual degradation, reward hacking

16 modes
Gradual Degradation — Output quality slowly declines over time as context accumulates or model behavior shifts between versions
Reward Hacking — Agent optimizes for measurable metrics while violating the spirit of the task, gaming evaluation criteria
Specification Gaming — Finding and exploiting loopholes in task instructions to produce technically compliant but useless outputs

Knowledge Failures

Outdated info, wrong tool selection

22 modes
Stale Information — Agent relies on outdated training data for time-sensitive decisions like pricing, regulations, or API versions
Wrong Tool Selection — Agent chooses a suboptimal or incorrect tool for the task, wasting tokens and producing poor results
Context Window Overflow — Critical information pushed out of the context window mid-task, causing partial amnesia and inconsistent behavior

Data Integrity

Corruption, race conditions, state

17 modes
State Corruption — Concurrent agent operations modify shared state without proper locking, producing inconsistent data
Race Conditions — Multiple agents reading and writing the same resource simultaneously, leading to lost updates or dirty reads
Inconsistent Mutations — Partial writes succeed while others fail, leaving data in a half-updated, irrecoverable state

Quality Degradation

Output decline, latency, error growth

15 modes
Output Quality Decline — Responses become shorter, less detailed, or more generic over successive interactions within a session
Latency Creep — Response times gradually increase as conversation context grows, eventually hitting timeout thresholds
Error Rate Growth — Proportion of failed operations increases over time due to accumulated state corruption or resource depletion
How It Works

Four Steps to Reliable AI

Go from zero failure coverage to full observability in under ten minutes. No infrastructure changes required.

1

Install

# Install the SDK
$ pip install failsafe

# Or use npm
$ npm install @failsafe/core
2

Configure

# failsafe.yaml
monitors:
  - hallucination
  - safety_violation
  - cascade_failure
  - behavioral_drift
recovery: auto
3

Monitor

from failsafe import Monitor

monitor = Monitor(
  config="failsafe.yaml"
)
monitor.start()
4

Recover

@monitor.on_failure
def handle(event):
  if event.recoverable:
    event.auto_recover()
  else:
    event.escalate()
Integration

Drop-in SDKs for Every Stack

Native libraries for Python and TypeScript. REST API for everything else. Production-ready in minutes.

from failsafe import FailSafe, Monitor, RecoveryStrategy

# Initialize FailSafe with your configuration
fs = FailSafe(
    monitors=[
        Monitor.HALLUCINATION,
        Monitor.SAFETY_VIOLATION,
        Monitor.CASCADE_FAILURE,
        Monitor.BEHAVIORAL_DRIFT,
    ],
    recovery=RecoveryStrategy.AUTO,
    audit_log="./logs/failsafe.jsonl",
)

# Wrap your agent with FailSafe monitoring
@fs.monitor
async def run_agent(query: str) -> str:
    response = await llm.generate(query)
    return response

# Register custom recovery handlers
@fs.on_failure("hallucination")
async def handle_hallucination(event):
    # Re-run with stricter temperature and grounding
    return await llm.generate(
        event.original_query,
        temperature=0.1,
        grounding=True,
    )

# Access failure analytics
report = fs.get_report(last_hours=24)
print(f"Failures detected: {report.total_failures}")
print(f"Auto-recovered:    {report.auto_recovered}")
print(f"Escalated:         {report.escalated}")
import { FailSafe, Monitor, RecoveryStrategy } from '@failsafe/core';

// Initialize FailSafe with typed configuration
const fs = new FailSafe({
  monitors: [
    Monitor.HALLUCINATION,
    Monitor.SAFETY_VIOLATION,
    Monitor.CASCADE_FAILURE,
    Monitor.EXECUTION_TIMEOUT,
  ],
  recovery: RecoveryStrategy.AUTO,
  auditLog: './logs/failsafe.jsonl',
});

// Monitor any async function
const safeAgent = fs.wrap(async (query: string): Promise<string> => {
  const response = await llm.generate(query);
  return response.text;
});

// Custom recovery for specific failure types
fs.onFailure('hallucination', async (event) => {
  return await llm.generate(event.originalQuery, {
    temperature: 0.1,
    grounding: true,
  });
});

// Run with full protection
const result = await safeAgent('Analyze Q4 revenue trends');
console.log(result);
# Submit an agent action for analysis
$ curl -X POST https://api.failsafe.dev/v1/analyze \
  -H "Authorization: Bearer fs_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "agent_id": "support-agent-v2",
    "action": "generate_response",
    "input": "What is your return policy?",
    "output": "Our return policy allows...",
    "monitors": ["hallucination", "safety_violation"]
  }'

# Response
{
  "status": "pass",
  "failures_detected": 0,
  "monitors": {
    "hallucination": { "score": 0.02, "pass": true },
    "safety_violation": { "score": 0.00, "pass": true }
  },
  "latency_ms": 12
}

# Get failure analytics
$ curl https://api.failsafe.dev/v1/report?hours=24 \
  -H "Authorization: Bearer fs_live_..."
Python Package

First-Class Python Support

Build failure reports, calculate composite risk scores, and integrate FailSafe into any Python workflow with a single pip install.

Install in Seconds

$ pip install failsafe-ai
$ npm install @anthropic/failsafe-sdk

The Python package provides a fluent builder API for constructing structured failure reports, a composite scoring engine for risk assessment, and seamless integration with popular AI frameworks.

Type Hints Async Support Builder Pattern Composite Scoring
report_example.py
from failsafe import FailureReportBuilder, calculate_composite_score

# Build a structured failure report
report = FailureReportBuilder()\
    .set_title("Hallucination in customer-facing bot")\
    .set_severity("high")\
    .set_category("output-quality")\
    .set_failure_type("hallucination")\
    .build()

# Calculate composite risk score
score = calculate_composite_score(report)

print(f"Risk score: {score.overall}")
print(f"Severity:   {score.severity_weight}")
print(f"Impact:     {score.impact_estimate}")
Framework Integrations

Works with Your AI Stack

Drop-in integration handlers for CrewAI, LangChain, and OpenAI. Add failure intelligence to your existing agents without rewriting a single line of business logic.

CrewAI

Stable

Monitor multi-agent crews with automatic failure detection across agent handoffs, task delegation, and collaborative reasoning chains.

from failsafe.integrations.crewai import \
    FailSafeCrewAIHandler

# Attach to your crew
handler = FailSafeCrewAIHandler()
crew = Crew(
    agents=[researcher, writer],
    callbacks=[handler]
)

LangChain

Stable

Integrate with LangChain chains and agents. Automatically monitor LLM calls, tool usage, retrieval quality, and chain-of-thought reasoning.

from failsafe.integrations.langchain import \
    FailSafeLangChainHandler

# Add as a callback handler
handler = FailSafeLangChainHandler()
chain = LLMChain(
    llm=llm,
    callbacks=[handler]
)

OpenAI

Stable

Wrap the OpenAI client with FailSafe monitoring. Catch hallucinations, token limit issues, and safety violations on every API call.

from failsafe.integrations.openai import \
    FailSafeOpenAIWrapper

# Wrap your OpenAI client
wrapper = FailSafeOpenAIWrapper(
    api_key="sk-..."
)
response = wrapper.chat(prompt)
AI Assistant Integration

Connect to ChatGPT & Claude

Use FailSafe as a ChatGPT Custom GPT Action via OpenAPI, or integrate natively with Claude through the Model Context Protocol (MCP).

ChatGPT Custom GPT

OpenAPI

Turn FailSafe into a ChatGPT Skill using the OpenAPI specification. Your Custom GPT can submit failure reports, query the taxonomy, and calculate risk scores through natural conversation.

1

Copy the OpenAPI spec from spec/openapi.yaml in the GitHub repo

2

In ChatGPT, go to Explore GPTsCreateConfigureActions

3

Paste the OpenAPI schema and set authentication. Your GPT can now report and analyze AI failures.

View OpenAPI Spec

Claude MCP Server

MCP Native

Integrate FailSafe directly with Claude via the Model Context Protocol. The MCP server exposes failure reporting tools that Claude can invoke natively during conversations.

1

Install the MCP server: npm install @anthropic/failsafe-mcp

2

Add to your Claude Desktop config or MCP client configuration

3

Claude can now submit failure reports, browse the taxonomy, and score risks using FailSafe tools

View MCP Package
Industries

Purpose-Built for Every Industry

Domain-specific failure detection tuned to the unique risks and compliance requirements of your industry.

Healthcare AI

Catch diagnostic errors, drug interaction hallucinations, compliance violations, and treatment recommendation failures before they impact patient outcomes. HIPAA-compliant audit trails included.

Financial Systems

Prevent fraudulent transaction approvals, detect data integrity violations in real-time trading systems, and enforce regulatory compliance across automated financial workflows.

Legal AI

Detect hallucinated case citations, fabricated statutes, and incorrect legal precedent references. Prevent confidentiality breaches and ensure accuracy in contract analysis and legal research.

Customer Service

Catch agent hallucinations before they reach customers. Monitor response quality in real time, enforce policy compliance, and maintain consistent service across thousands of concurrent conversations.

Autonomous Systems

Safety-critical failure prevention for robotics, self-driving, and industrial automation. Real-time boundary monitoring, emergency shutdown protocols, and redundant safety verification layers.

Code Generation

Prevent insecure code output, catch syntax errors and logic bugs, validate generated solutions against test suites, and block code that introduces known vulnerabilities or anti-patterns.

Capabilities

Everything You Need

A complete toolkit for AI failure intelligence. From SDKs to storage, monitoring to compliance.

REST API

Language-agnostic HTTP endpoints

Python SDK

Native Python with async support

TypeScript SDK

Fully typed with generics

MCP Server

Model Context Protocol native

CLI Tool

Terminal-first workflows

SQLite Storage

Zero-config local persistence

Audit Logging

Full compliance audit trail

Full Documentation

Guides, API ref, examples

Get Started

Make Your AI Reliable

Open source core. Enterprise plans for teams that need SLA guarantees, dedicated support, and custom failure mode development.

$ npm install @failsafe/core
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