Eidentic: Build AI agents with self-improving memory and built-in production tools

The gap between prototype AI agents and production-ready systems often comes down to two critical components: memory that evolves with usage and infrastructure that scales securely. Most developers spend months stitching these together from disjointed tools, only to find their solutions arrive too late or come with prohibitive licensing. Enter Eidentic, a TypeScript SDK designed to eliminate that friction by bundling self-improving memory and production fundamentals into a single, open-source package.

Solving the two problems every AI agent faces

Building a functional AI agent usually starts with excitement—but quickly stalls when developers realize they’re forced to reinvent two essential layers. The first is persistent, adaptive memory, not just a static vector store that requires manual prompting. Eidentic’s memory engine learns across sessions, resolves inconsistencies, and refines its knowledge base over time, performing better with each interaction. The second is the production infrastructure: enforced cost limits, multi-tenant isolation, sandboxed tool execution, and evaluation pipelines that gate deployments. Most platforms introduce these features as afterthoughts in enterprise tiers, leaving developers to cobble together fragile solutions themselves.

By shipping both memory and production tools in one SDK, Eidentic removes the need for patchwork integrations. Its Apache-2.0 license ensures full transparency and flexibility, with no hidden tiers or restrictive terms. Support for Node, Bun, Deno, and edge environments means it adapts to modern JavaScript runtimes without sacrificing performance.

From zero to agent in under a minute

Getting started with Eidentic is intentionally simple. Install the package and define an agent with built-in memory and tooling constraints:

npm install eidentic

import { Agent, AIModel, SqliteStore } from "eidentic";
import { anthropic } from "@ai-sdk/anthropic";

const agent = new Agent({
  id: "support",
  instructions: "You are a support agent. Remember the user.",
  model: new AIModel(anthropic("claude-sonnet-4-5")),
  store: new SqliteStore("./eidentic.sqlite"),
});

for await (const ev of agent.query("What did we decide last week?", {
  sessionId: "u-42",
})) {
  if (ev.type === "stream.delta") process.stdout.write(ev.delta.text);
}

When the agent queries prior conversations using sessionId, it retrieves context with citations and consolidates learned insights while idle. Switching storage backends—from SQLite to LibSQL or PostgreSQL—requires only a single line change, thanks to Eidentic’s ports-and-adapters architecture. This consistency extends across the SDK, making it easier to migrate or scale without rewriting core logic.

Inside the Eidentic toolkit

Eidentic isn’t just another SDK; it’s a curated set of production-grade components designed to accelerate AI development without sacrificing control:

• Four-tier memory engine: Combines lexical recall, vector search, self-editing memory blocks, a temporal knowledge graph, and idle-time consolidation for smarter, more efficient reasoning.

• Durable execution: Supports checkpointing and resume capabilities, ensuring exactly-once tool dispatch even after interruptions or crashes.

• Strict resource controls: Implements cost ceilings, rate limits, and quotas to prevent runaway expenses, alongside multi-tenant isolation for secure deployments.

• Secure tooling: Sandboxed execution environments, deny-by-default permissions, and GDPR-compliant data erasure to protect user privacy.

• Built-in evaluation suite: Includes a CI pass-rate gate, an MCP host with OAuth integration, and support for A2A standards to standardize agent-to-agent communication.

• Framework integrations: First-class support for React hooks, a Next.js handler, and a CLI for streamlined development and deployment.

Transparency over hype

Eidentic’s team acknowledges the SDK is pre-1.0, focusing on stabilization rather than premature versioning. Instead of glossing over limitations, they publish raw benchmarks—including results where their approach doesn’t outperform baselines. On the LongMemEval benchmark, Eidentic’s memory-based method beats full-context retrieval by 14.2 points while using up to 39 times fewer tokens. However, on LoCoMo’s smaller datasets, full-context prompting still holds an edge. These trade-offs are openly documented, reinforcing Eidentic’s commitment to honesty in performance reporting.

Ready to build?

Eidentic is designed for developers who want to move from prototypes to production without vendor lock-in or hidden costs. The documentation provides step-by-step guides, while GitHub hosts the open-source codebase and example projects for Next.js, React, and Express. Whether you’re building a customer support agent, an internal tool, or a research prototype, Eidentic’s combination of memory and infrastructure could save months of integration work—without sacrificing flexibility or control.