◈ 2026 is the year of agent harnesses

The data layer for
AI agent harnesses

db0 is the unified data SDK for AI agent systems —
state, memory, tool caching, guardrails and observability
through a single interface.

AI Agent Harness

🗂 Planner→

🔧 Tool Router→

🧠 Memory→

🔄 Execution Loop→

🛡 Guardrails→

📡 Monitoring→

db0

Backend

PostgreSQL + pgvector

SQLite

In-memory (tests)

Takes 30 seconds. Helps us build what you actually need.

// the problem

You're duct-taping
three systems together

✗ Without db0

📦 Redis — session state & tool cache

+ glue code

🔎 pgvector / Pinecone — semantic memory

+ glue code

🗄 PostgreSQL — history & audit logs

⚠️ Works — until it doesn't

✓ With db0

⚡ One SDK, one connection, one interface

unified harness context

🔗 Shared state across planner → executor

fork / rollback / promote

🛡 Guardrails + audit built-in, not bolted-on

harness-native primitives

✓ Works across every framework

// harness.component() — AI agent data layer

Six components.
One SDK instance.

🧠 memory

add · search · query · forget

Long-term knowledge across sessions. Vector semantic search and structured SQL in one call. Scoped at task, user, or app level.

🔄 state

checkpoint · restore · fork · rollback

Execution progress and cross-session continuity. Branch experiments, roll back safely, promote the winner to main.

🔧 tools

cache · getCached · log

Cache tool outputs to avoid redundant calls. Log every invocation with input, output, and duration for full replay.

🛡 safety

addRule · check · logViolation

Pattern-based guardrails evaluated before every tool call. Violations are blocked and written to an audit log automatically.

📋 log

event · trace · analyze

Structured event stream for every agent run. Query full traces per session or aggregate failure rates across all agents.

⏪ recovery

mark · rollback · list

Explicit rollback markers before risky operations. Recover to any safe point without full state replay.

// end-to-end example

One AI harness.
All six components.

agent.ts — @db0/sdk

import { DB0 } from "@db0/sdk"

// choose backend — swap without changing app code
const db = new DB0({ backend: 'postgres' })
// 'sqlite' | 'memory'

// one harness — shared context across all components
const harness = db.harness({
  agentId,
  sessionId,
  userId
})

// competitive landscape

Others solve one piece.
db0 is the whole layer.

	db0	Mem0	LangGraph	DIY Stack
Cross-session memory	✓	✓	Partial	Manual
Execution state / checkpointing	✓	✗	✓	Manual
Tool result caching	✓	✗	✗	Manual
Guardrails + audit log	✓	✗	Partial	Manual
Cross-agent observability	✓	✗	✗	✗
Framework-agnostic	✓	✓	✗	✓
Single SDK for all of the above	✓	✗	✗	✗

// roadmap

From SDK to Mission Control

NowOPEN

v0.1

—memory, state, tools, log

—PostgreSQL + SQLite + in-memory

—TypeScript SDK

—MIT license

Month 1–2

v0.2

—Guardrails component

—Additional backends

—Full TypeScript types

Month 2–3

v0.3

—Recovery component

—Cross-agent memory scoping

—Python SDK

Month 4–5

v1.0

—Mission Control dashboard

—Hosted db0 Cloud

—GA release

The data layer forAI agent harnesses

You're duct-tapingthree systems together

Six components.One SDK instance.

One AI harness.All six components.

Others solve one piece.db0 is the whole layer.