HotMesh

Integrate AI automation into your current stack — without breaking it

HotMesh modernizes existing business systems by introducing a durable workflow layer that connects AI, automation, and human-in-the-loop steps — without replacing your current stack. Each process runs with persistent memory in Postgres, surviving retries, crashes, and human delays.

npm install @hotmeshio/hotmesh

What It Solves

Modernization often stalls where systems meet people and AI. HotMesh builds a durable execution bridge across those seams — linking your database, APIs, RPA, and AI agents into one recoverable process.

• AI that can fail safely — retries, resumable state, and confidence tracking
• Human steps that don’t block — pause for days, resume instantly
• Legacy systems that stay connected — SQL and RPA coexist seamlessly
• Full visibility — query workflows and outcomes directly in SQL

Core Model

Entity — the Business Process Record

Every workflow writes to a durable JSON document in Postgres called an Entity. It becomes the shared memory between APIs, RPA jobs, LLM agents, and human operators.

const e = await MemFlow.workflow.entity();

// initialize from a source event
await e.set({
  caseId: "A42",
  stage: "verification",
  retries: 0,
  notes: []
});

// AI step adds structured output
await e.merge({
  aiSummary: { result: "Verified coverage", confidence: 0.93 },
  stage: "approval",
});

// human operator review
await e.append("notes", { reviewer: "ops1", comment: "ok to proceed" });

// maintain counters
await e.increment("retries", 1);

// retrieve current process state
const data = await e.get();

Minimal surface contract

| Command | Purpose | | ------------- | ---------------------------------- | | set() | Initialize workflow state | | merge() | Update any JSON path | | append() | Add entries to lists (logs, notes) | | increment() | Maintain counters or metrics | | get() | Retrieve current state |

Entities are stored in plain SQL tables, directly queryable:

SELECT id, context->>'stage', context->'aiSummary'->>'result'
FROM my_app.jobs
WHERE entity = 'claims-review'
  AND context->>'stage' != 'complete';

Hook — Parallel Work Units

Hooks are stateless functions that operate on the shared Entity. Each hook executes independently (API, RPA, or AI), retrying automatically until success.

await MemFlow.workflow.execHook({
  workflowName: "verifyCoverage",
  args: ["A42"]
});

To run independent work in parallel, use a batch execution pattern:

// Run independent research perspectives in parallel using batch execution
await MemFlow.workflow.execHookBatch([
  {
    key: 'optimistic',
    options: {
      taskQueue: 'agents',
      workflowName: 'optimisticPerspective',
      args: [query],
      signalId: 'optimistic-complete'
    }
  },
  {
    key: 'skeptical',
    options: {
      taskQueue: 'agents',
      workflowName: 'skepticalPerspective',
      args: [query],
      signalId: 'skeptical-complete'
    }
  }
]);

Each hook runs in its own recoverable context, allowing AI, API, and RPA agents to operate independently while writing to the same durable Entity.

Example — AI-Assisted Claims Review

export async function claimsWorkflow(caseId: string) {
  const e = await MemFlow.workflow.entity();
  await e.set({ caseId, stage: "intake", approved: false });

  // Run verification and summarization in parallel
  await MemFlow.workflow.execHookBatch([
    {
      key: 'verifyCoverage',
      options: {
        taskQueue: 'agents',
        workflowName: 'verifyCoverage',
        args: [caseId],
        signalId: 'verify-complete'
      }
    },
    {
      key: 'generateSummary',
      options: {
        taskQueue: 'agents',
        workflowName: 'generateSummary',
        args: [caseId],
        signalId: 'summary-complete'
      }
    }
  ]);

  // Wait for human sign-off
  const approval = await MemFlow.workflow.waitFor("human-approval");
  await e.merge({ approved: approval === true, stage: "complete" });

  return await e.get();
}

This bridges:

• an existing insurance or EHR system (status + audit trail)
• LLM agents for data validation and summarization
• a human reviewer for final sign-off

—all within one recoverable workflow record.

Why It Fits Integration Work

HotMesh is purpose-built for incremental modernization.

| Need | What HotMesh Provides | | ----------------------------- | ---------------------------------------- | | Tie AI into legacy apps | Durable SQL bridge with full visibility | | Keep human review steps | Wait-for-signal workflows | | Handle unstable APIs | Built-in retries and exponential backoff | | Trace process across systems | Unified JSON entity per workflow | | Store long-running AI results | Durable state for agents and automations |

License

Apache 2.0 — free to build, integrate, and deploy. Do not resell the core engine as a hosted service.