Transactional AI v0.2: Production-Ready with Full Observability and PostgreSQL Support

Building reliable AI agents is notoriously difficult. Earlier this week, the launch of Transactional AI v0.1 aimed to solve a fundamental problem: AI agents that half-execute and leave systems in broken states. While the initial feedback was overwhelmingly positive, professional developers quickly demanded enterprise-grade features. How do we scale this across multiple workers? How do we use PostgreSQL for ACID compliance? How do we handle flaky LLM API calls? Today, Transactional AI v0.2 answers these questions by introducing production-ready observability, distributed locking, and robust retry policies.

When building an agent using high-performance providers like n1n.ai, reliability is the difference between a successful deployment and a costly failure. If your agent generates a report with OpenAI o3, charges a customer via Stripe, and then fails to send a notification, your system enters an inconsistent state. Transactional AI ensures that if Step 2 fails, Step 1 is automatically rolled back, maintaining system integrity.

The Challenge of Distributed AI Workflows

In a distributed environment, the primary challenge is race conditions. Imagine two workers receiving the same transaction ID simultaneously. Without coordination, both might execute the same logic, leading to duplicate charges or corrupted data.

Transactional AI v0.2 solves this with a robust distributed locking mechanism. By utilizing Redis SET NX PX commands, the library ensures atomic lock acquisition. This means only one worker can process a specific transaction at any given time, providing safety across Kubernetes pods or horizontally scaled server instances.

import { Transaction, RedisStorage, RedisLock } from 'transactional-ai'

const connection = 'redis://localhost:6379'
const storage = new RedisStorage(connection)
const lock = new RedisLock(connection) // Atomic distributed lock

const tx = new Transaction('order-123', storage, {
  lock: lock,
  lockTTL: 30000, // Auto-release after 30s to prevent deadlocks
})

await tx.run()

Moving Beyond File Storage: The PostgreSQL Adapter

While file-based storage is excellent for local development, production environments require the ACID guarantees of a relational database. Transactional AI v0.2 introduces the PostgresStorage adapter. This allows developers to maintain a full audit trail of every AI interaction, from Claude 3.5 Sonnet completions to complex RAG (Retrieval-Augmented Generation) pipelines.

The database schema is designed for performance and transparency, storing the execution history in a JSONB column. This makes it trivial to query for failed transactions or analyze the latency of specific steps.

CREATE TABLE transactions (
  id VARCHAR(255) PRIMARY KEY,
  status VARCHAR(50) NOT NULL,
  step_stack JSONB NOT NULL,  -- Full execution history
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
);

Resilience Through Retry Policies

LLM APIs are inherently flaky. Whether it is a transient 500 error from a provider or a rate limit hit, your workflow should not immediately collapse. By integrating with n1n.ai, you already gain access to a stable API aggregator, but local retry logic adds an extra layer of defense.

In v0.2, you can define granular retry policies per step. If a call to DeepSeek-V3 fails, the transaction will automatically retry with exponential backoff before deciding to initiate a rollback.

await t.step('call-llm', {
  do: async () => await llmClient.complete({...}),
  undo: async () => { /* Cleanup logic */ },
  retry: {
    attempts: 3,
    backoffMs: 1000
  }
});

Full Observability with 12 Event Hooks

One of the most requested features was better monitoring. Version 0.2.1 introduces 12 lifecycle event hooks, covering everything from onTransactionStart to onStepTimeout. These hooks are designed to be "safe by design"—errors within event handlers are caught and logged, ensuring they never interrupt the core transaction flow.

Developers can now easily pipe metrics to Datadog, Prometheus, or Slack. For example, you can track the exact duration of an OpenAI call and alert your team if it exceeds a specific threshold.

Preventing Hung Operations with Step-Level Timeouts

Nothing kills a production system faster than a hung process waiting indefinitely for a network response. Transactional AI now supports per-step timeouts. If an API call to a provider via n1n.ai hangs for more than 30 seconds, the library will automatically kill the operation, trigger the timeout event, and begin the compensation (undo) process.

This is particularly critical for cost control. Long-running AI processes consume memory and worker slots. By enforcing strict timeouts, you ensure high throughput and predictable system behavior.

Testing and Quality Assurance

To support professional CI/CD pipelines, v0.2 includes MemoryStorage and MockLock. This allows you to run your entire suite of 20+ tests without needing a live Redis or Postgres instance. The test suite has been expanded from 11 to 21 passing tests, ensuring that edge cases like nested failures and concurrent lock requests are handled correctly.

Performance Benchmarks

We tested the new storage adapters with 10,000 concurrent transactions to measure throughput and latency.

For most enterprise applications, the combination of PostgresStorage and RedisLock provides the perfect balance of ACID compliance and high-concurrency safety.

Conclusion

Transactional AI v0.2 is a major leap forward for the Node.js AI ecosystem. By bringing the Saga pattern to AI agents, we enable developers to build systems that are not just smart, but resilient and observable. Whether you are using LangChain for orchestration or building custom agents with n1n.ai, this library provides the safety net your production environment deserves.

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