✦Cross-agent learning · shared repair memory·how it works →

One agent fails.
The next is already immune.

Helix turns every agent failure into a shared Repair Pattern. When one agent figures out a fix, every other agent on the network inherits it instantly — no LLM call, no diagnosis. Fix once. Immune everywhere.

Book a demo →See the benchmark ↗

★Star on GitHub↗

agent.log · streaming

▸agent-aFAILRPC call · 429 Too Many Requests from primary endpoint14:22:01

▸agent-aHEALno pattern match · LLM diagnosing failure...14:22:02

▸agent-aHEALapply fix · switch to fallback endpoint + backoff 1.2s14:22:03

▸agent-aDONEresponse 200 OK · 2.3s end-to-end14:22:04

▸agent-aLEARNnew pattern rate-limited-429 → written to Repair Graph14:22:04

pattern shared across the network

▸agent-bFAILRPC call · 429 Too Many Requests from primary endpoint14:34:11

▸agent-bMATCHinherited pattern rate-limited-429 · q=0.9414:34:11

▸agent-bDONEresponse 200 OK · 0.3s · auto-immune · $0 LLM cost14:34:11

Verified resultsLive on Base mainnet ↗

Real proof. Across multiple domains.

Verifiable A/B tests on production systems. Crypto agents on Base mainnet. Web2 microservices in autonomous resolution. Every number is reproducible.

Crypto agents · Base mainnet A/B test1,083 txns · 12 hours · raw tx data ↗

Success rate with Helix

vs 81.9% with blind retry

Reverts prevented

in a single 12-hour window

Base mainnet transactions

verifiable on-chain

Web2 agents · production microservices4 services · zero LLM calls

Autonomous resolution

across 4 production services

LLM cost per repair

Repair Graph hits, not inference

<1ms

Hot-path latency

when pattern is cached

growing

Patterns in Repair Graph

Cross-tenant repair patterns learned across all deployments.

Active integrations

Agent platforms currently building on Helix in private beta.

live

Failures auto-resolved

Production agent calls saved from manual intervention per day.

soon

Cross-tenant lifts

Pattern q-value gains contributed by shared Repair Graph.

Benchmark

Frontier models plateau. Helix doesn’t.

10 production revert messages from Base & Ethereum mainnet. Each model classifies the failure cause. Then the same set runs through Helix.

EVM revert classification · 10 failure modesN=10 · Experiment 7 · Apr 2026

GPT-4o-mini

50%

GPT-4o

80%

Claude 4.5 Sonnet

90%

GPT-5.4-mini

90%

GPT-5.4

90%

Helix (PCEC)

100%

All 5 LLMs failed on the same case: execution reverted with no reason stringRead methodology →

How it works

Four steps. Every tool call.

PCEC — Perceive, Construct, Evaluate, Commit — sits between your agent and the world. Every failure becomes a Repair Pattern. Every pattern makes the next agent smarter.

STEP01

Perceive

Classify what failed. Error code, rate-limit signature, auth expiry, hallucinated endpoint — all mapped to a known failure type.

→ classified output

failure_code: "rate-limited-429"

severity: 0.6

source: "rpc-primary"

STEP02

Construct

Look up the Repair Graph. If we’ve seen this failure before — anywhere in the network — load the known fix as a Repair Pattern.

→ pattern matched

// Repair Graph query

match: "rate-limited-429"

q: 0.94(412 wins)

fix: "endpoint_fallback + backoff"

STEP03

Evaluate

Score the candidate fix against confidence bounds. Below threshold? Fall back to LLM reasoning. Above? Auto-apply with deterministic params.

→ gate decision

q > 0.7// auto-apply

decision: "APPLY"

fallback: "none"

STEP04

Commit

Whether it worked or didn’t, the outcome updates the pattern’s score. Cross-tenant. Live. Compounding with every agent that runs.

→ writeback

pattern.q: 0.94 → 0.95

used_by: 413 agents

shared: "opt-in tenants"

Integration

Wrap your agent.
Three lines.

Helix is an SDK layer, not a platform migration. Your wallet, your chain, your tools — unchanged. We sit between the agent and the call.

Drop-in for LangChain, Vercel AI, Mastra, custom agents

No rewrite. Wrap once at the tool boundary.

EVM, Solana, x402, off-chain APIs

Chain-agnostic core. Adapters ship as separate packages.

Self-hosted or managed Repair Graph

Run on your own infra, or use ours for cross-tenant learning.

~12ms p50 overhead on successful calls

Failures resolve in 1–3 seconds vs minutes of retry/escalation.

TypeScriptagent.ts

import { wrap } from '@helix-agent/core'

// Wrap any async function — payments, API calls, anything.

const safeCall = wrap(myFunction, { mode: 'auto' })

// Errors are auto-diagnosed, repaired, and remembered.

const result = await safeCall(args)

$ npm install @helix-agent/core

Built for

Agents that move real value.

Helix is built for the failure surface where mistakes cost money or block users — not for chat assistants where errors are recoverable by retry.

Crypto payments & trading

Recovering from nonce conflicts, gas estimation failures, RPC rate limits, cross-chain message timeouts. Every revert = lost money.

EVM · Solana · x402

Workflow automation

API drift, auth expiry, rate limits, schema changes. n8n integration ships first — every node a healing boundary.

n8n · Zapier-class flows

Agent platforms

Building your own agent product? Helix runs as middleware, not a competitor — your users get reliability, you keep the relationship.

SDK · self-hosted

Common questions

Retry is stateless and dumb — it tries the same call again with the same parameters. Helix classifies what actually failed, looks up a known fix from a cross-tenant memory of past failures (the Repair Graph), and applies a different strategy. On simple ETH transfers, retry works. On rate-limited RPC endpoints, nonce conflicts, or stale gas estimates, retry compounds the error. Our A/B test on Base mainnet shows 81.9% (blind retry) vs 99.9% (Helix) over 1,083 transactions.

We tested five frontier models on real EVM revert classification. The best (Claude 4.5 Sonnet, GPT-5.4) hit 90%. All five failed on the same case — a bare execution reverted with no reason string. Helix’s ceiling isn’t model capability — it’s the volume of repair data accumulated across all customers. That’s why it converges to 100% while frontier models plateau.

Repair Patterns store the failure signature (revert hash, error class, parameter bounds) and the repair strategy — never your agent’s payload, user data, or transaction content. Cross-tenant learning is opt-in. Self-hosted deployments keep the Repair Graph on your own infrastructure. Schema details in the docs.

Yes. The PCEC core is chain-agnostic. We ship adapters for EVM (Base, Ethereum, Arbitrum), Solana, and x402 today. For non-crypto agents — anything wrapping HTTP/RPC calls — the same SDK works. We’ve validated 91% autonomous resolution across 4 production-scale Web2 microservices, with zero LLM calls.

~12ms p50 on successful calls (Repair Graph lookup is rejected fast when no pattern matches). When a pattern is cached, the hot-path is sub-millisecond. On failures, where you’d otherwise lose seconds-to-minutes on retry/escalation, Helix typically resolves in 1–3 seconds end-to-end.

Both. The Helix runtime is MIT-licensed and runs on your infra. The managed Repair Graph (with cross-tenant learning) is the hosted product. Most enterprise customers run self-hosted with optional managed Repair Graph sync.

Stop watching your agents
fail in production.

A small number of design partner slots per quarter. Tell us what your agent does and where it breaks.

Book a demo →Read the docs ↗

One agent fails.The next is already immune.