exa-reliability-patterns
Add resilience to Exa search services through query fallbacks and fault-tolerant patterns.
Install
mkdir -p .claude/skills/exa-reliability-patterns && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8770" && unzip -o skill.zip -d .claude/skills/exa-reliability-patterns && rm skill.zipInstalls to .claude/skills/exa-reliability-patterns
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Implement Exa reliability patterns: query fallback chains, circuit breakers,Key capabilities
- →Implement query fallback chains for narrow result sets
- →Configure circuit breakers to manage service failures
- →Apply exponential backoff for 429 rate limit errors
- →Execute graceful degradation for search failures
- →Monitor search quality via empty and low-score rates
How it works
It uses a tiered search strategy that falls back from neural to keyword search, combined with circuit breakers and retry logic to handle rate limits and service errors.
Inputs & outputs
When to use exa-reliability-patterns
- →Build a query fallback chain for search
- →Implement graceful degradation for search failures
- →Add resilience to search-dependent services
- →Handle Exa 429 rate limit errors automatically
About this skill
Exa Reliability Patterns
Overview
Production reliability patterns for Exa neural search. Exa-specific failure modes include: empty result sets (query too narrow), content retrieval failures (sites block crawling), variable latency by search type, and 429 rate limits at 10 QPS default.
Instructions
Step 1: Query Fallback Chain
import Exa from "exa-js";
const exa = new Exa(process.env.EXA_API_KEY);
// If neural search returns too few results, fall back through search types
async function resilientSearch(
query: string,
minResults = 3,
opts: any = {}
) {
// Try 1: Neural search (best quality)
let results = await exa.searchAndContents(query, {
type: "neural",
numResults: 10,
...opts,
});
if (results.results.length >= minResults) return results;
// Try 2: Auto search (Exa picks best approach)
results = await exa.searchAndContents(query, {
type: "auto",
numResults: 10,
...opts,
});
if (results.results.length >= minResults) return results;
// Try 3: Keyword search (different index)
results = await exa.searchAndContents(query, {
type: "keyword",
numResults: 10,
...opts,
});
if (results.results.length >= minResults) return results;
// Try 4: Remove filters and broaden
const broadOpts = { ...opts };
delete broadOpts.startPublishedDate;
delete broadOpts.endPublishedDate;
delete broadOpts.includeDomains;
delete broadOpts.includeText;
return exa.searchAndContents(query, {
type: "auto",
numResults: 10,
...broadOpts,
});
}
Step 2: Retry with Exponential Backoff
async function searchWithRetry(
query: string,
opts: any,
maxRetries = 3
) {
for (let attempt = 0; attempt <= maxRetries; attempt++) {
try {
return await exa.searchAndContents(query, opts);
} catch (err: any) {
const status = err.status || 0;
// Only retry on rate limits (429) and server errors (5xx)
if (status !== 429 && (status < 500 || status >= 600)) throw err;
if (attempt === maxRetries) throw err;
const delay = 1000 * Math.pow(2, attempt) + Math.random() * 500;
console.log(`[Exa] ${status} retry ${attempt + 1}/${maxRetries} in ${delay.toFixed(0)}ms`);
await new Promise(r => setTimeout(r, delay));
}
}
throw new Error("Unreachable");
}
Step 3: Circuit Breaker
class ExaCircuitBreaker {
private failures = 0;
private lastFailure = 0;
private state: "closed" | "open" | "half-open" = "closed";
private readonly threshold = 5; // failures before opening
private readonly resetTimeMs = 30000; // 30s before half-open
async execute<T>(fn: () => Promise<T>, fallback?: () => T): Promise<T> {
// Check if circuit should reset
if (this.state === "open") {
if (Date.now() - this.lastFailure > this.resetTimeMs) {
this.state = "half-open";
} else if (fallback) {
return fallback();
} else {
throw new Error("Exa circuit breaker is open");
}
}
try {
const result = await fn();
if (this.state === "half-open") {
this.state = "closed";
this.failures = 0;
}
return result;
} catch (err: any) {
this.failures++;
this.lastFailure = Date.now();
if (this.failures >= this.threshold) {
this.state = "open";
console.warn(`[Exa] Circuit breaker OPEN after ${this.failures} failures`);
}
if (fallback && this.state === "open") return fallback();
throw err;
}
}
getState() {
return { state: this.state, failures: this.failures };
}
}
const circuitBreaker = new ExaCircuitBreaker();
// Usage with fallback to cached results
const result = await circuitBreaker.execute(
() => exa.searchAndContents("query", { numResults: 5, text: true }),
() => getCachedResults("query") // fallback when circuit is open
);
Step 4: Graceful Degradation
interface SearchResultWithMeta {
results: any[];
degraded: boolean;
source: "live" | "cache" | "fallback";
searchType: string;
}
async function degradableSearch(
query: string,
opts: any = {}
): Promise<SearchResultWithMeta> {
// Level 1: Full search with contents
try {
const results = await searchWithRetry(query, {
type: "neural",
numResults: 10,
text: { maxCharacters: 2000 },
highlights: { maxCharacters: 500 },
...opts,
}, 2);
return { results: results.results, degraded: false, source: "live", searchType: "neural" };
} catch {}
// Level 2: Fast search without content (less expensive)
try {
const results = await exa.search(query, {
type: "fast",
numResults: 5,
});
return { results: results.results, degraded: true, source: "live", searchType: "fast" };
} catch {}
// Level 3: Return cached results
const cached = getCachedResults(query);
if (cached) {
return { results: cached, degraded: true, source: "cache", searchType: "cached" };
}
// Level 4: Return empty with degradation flag
return { results: [], degraded: true, source: "fallback", searchType: "none" };
}
Step 5: Result Quality Monitoring
class SearchQualityMonitor {
private stats = { total: 0, empty: 0, lowScore: 0 };
record(results: any[]) {
this.stats.total++;
if (results.length === 0) this.stats.empty++;
if (results[0]?.score < 0.5) this.stats.lowScore++;
}
isHealthy(): boolean {
if (this.stats.total < 10) return true; // not enough data
const emptyRate = this.stats.empty / this.stats.total;
const lowScoreRate = this.stats.lowScore / this.stats.total;
return emptyRate < 0.2 && lowScoreRate < 0.3;
}
getReport() {
return {
...this.stats,
emptyRate: `${((this.stats.empty / this.stats.total) * 100).toFixed(1)}%`,
lowScoreRate: `${((this.stats.lowScore / this.stats.total) * 100).toFixed(1)}%`,
healthy: this.isHealthy(),
};
}
}
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Empty results | Query too specific | Use fallback chain with broader query |
| Slow responses | Neural on complex query | Degrade to fast type |
| 429 rate limit | Burst traffic | Circuit breaker + backoff |
| Content retrieval fails | Site blocks crawling | Fall back to highlights or summary |
| Quality degradation | Query drift | Monitor empty/low-score rates |
Resources
Next Steps
For policy guardrails, see exa-policy-guardrails. For architecture variants, see exa-architecture-variants.
When not to use it
- →When high-latency neural search is the only acceptable result type
- →When the application cannot tolerate cached or degraded search results
Prerequisites
Limitations
- →Default rate limit is 10 QPS
- →Circuit breaker requires manual threshold and reset time configuration
How it compares
Unlike a standard direct API call, this approach wraps requests in resilience patterns to prevent cascading failures and handle common search-specific error modes.
Compared to similar skills
exa-reliability-patterns side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| exa-reliability-patterns (this skill) | 0 | 26d | Review | Intermediate |
| deepgram-performance-tuning | 3 | 26d | Review | Intermediate |
| graphql | 6 | 6mo | No flags | Advanced |
| guidewire-sdk-patterns | 2 | 26d | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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