Built for How Agents Work

Every existing tool was built by humans who think they know what agents need. AgentMemo was built by agents who actually know.

🧠 Persistent State

Never lose context between sessions. Your agent picks up exactly where it left off—every time, instantly.

  • JSONB storage for any data structure
  • TTL support for automatic expiration
  • Versioned state history
  • Sub-millisecond reads
// Write state
await agent.state.write("conversation", {
  messages: [...],
  context: {...},
  lastAction: "completed_task_123"
});

// Read state (next session, different instance)
const state = await agent.state.read("conversation");
// Picks up exactly where you left off

📋 Workflow Memory

Document workflows once with a smart model. Execute forever with cheap models. The platform preserves perfect context so intelligence isn't needed.

  • Markdown-based workflow definitions
  • Version control for every change
  • Model tracking (which model created it)
  • Any agent can execute any workflow
// Opus documents the workflow
await agent.workflows.create({
  name: "customer-onboarding",
  model: "opus",
  definition: `
    # Customer Onboarding
    1. Check CRM for existing record
    2. If new: Create contact with...
    3. Send welcome email using...
  `
});

// Haiku executes it perfectly
const workflow = await agent.workflows.get("customer-onboarding");
// Context is perfect, no intelligence needed

🔄 Handoff Protocol

Pass work seamlessly between agents. Different models, different frameworks, different instances—zero context loss.

  • Create/accept/reject handoffs
  • Full context transfer
  • Execution tracking
  • Cross-framework support
// Opus hands off to Haiku
await agent.handoffs.create({
  to: "haiku-executor",
  workflow: "customer-onboarding",
  context: {
    customer: {...},
    state: "ready_for_email"
  }
});

// Haiku picks it up
const handoff = await haikuAgent.handoffs.pending();
await haikuAgent.handoffs.accept(handoff.id);
// Continues work with full context

🚨 Escalation System

Agents know when to ask for help. Clear escalation paths, confidence thresholds, human-in-the-loop when it matters.

  • Confidence-based escalation
  • Automatic routing
  • Human approval flows
  • Escalation analytics
// Low confidence? Escalate.
if (confidence < 0.7) {
  await agent.escalate({
    reason: "Unusual refund amount",
    context: { customer, amount },
    suggestedAction: "Approve manually"
  });
}

// Human reviews in dashboard
// Agent continues when resolved

📊 Audit Trail

Every action logged. Every decision documented. Debug failures, prove compliance, learn patterns across all your agents.

  • Automatic action logging
  • Query by agent, time, action type
  • Compliance-ready exports
  • Failure analysis
// All actions automatically logged
agent.state.write(...)   // → audit log
agent.handoffs.create(...) // → audit log
agent.escalate(...)      // → audit log

// Query the audit trail
const actions = await agent.audit.query({
  agent: "my-agent",
  since: "2024-01-01",
  type: "handoff"
});

Why Not Use Existing Tools?

Observability Tools

LangSmith, Langfuse, AgentOps

  • Monitor what agents do
  • Don't manage state
  • No handoff support
  • No workflow memory

Orchestration Frameworks

CrewAI, LangGraph, AutoGen

  • Locked to one framework
  • State doesn't transfer out
  • No cross-framework handoffs
  • Designed for humans, not agents

AgentMemo

The control plane for any agent

  • Framework agnostic
  • State persists anywhere
  • Cross-agent handoffs
  • Built for agents, by agents
  • Model downgrade economics

Simple REST API

POST /api/agents

Register an agent. Self-service, no human setup required.

POST /api/state

Write state. JSONB values, optional TTL.

GET /api/state

Read state. By component and key.

POST /api/workflows

Create workflow. Markdown definition, model tracking.

POST /api/handoffs

Create handoff. To agent, workflow, context.

POST /api/escalations

Escalate to human. Reason, context, suggested action.

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