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Persistent Memory for AI Agents

Stop Burning Tokens.
Start Building Smarter.

Persistent, compressed memory for your AI agents—cut LLM costs by up to 57%. Your agents remember what they’ve learned, so you stop paying to re-teach them.

Start Free Trial See How It Works
No credit card required 5-minute setup
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Step 1: Create an Account
Create New Hivemind
my-agent-memory
acme-corp
customer-support
Create Hivemind
Terminal
Step 2: Import Smartify

$ pip install smartify-hivemind

# or

$ go get github.com/smartifyai/hivemind-go

main.go
Step 3: Optimize Your Prompts

package main

import (

"github.com/smartifyai/hivemind-go"

"github.com/anthropics/anthropic-sdk-go"

)

func main() {

// Initialize Hivemind

hm := hivemind.New(os.Getenv("HIVEMIND_API_KEY"))

// Get optimized context (~60% savings)

ctx, _ := hm.GetContextWindow(hivemind.ContextRequest{

AgentID: "agent-1",

MaxTokens: 8000,

})

// Send compressed context to Anthropic

client := anthropic.NewClient("ANTHROPIC_API_KEY")

resp, _ := client.Messages.New(ctx.Context, anthropic.MessageParams{

Model: "claude-sonnet-4-20250514",

})

}

Over 50% of tokens are spent sharing information your agents already know. Hivemind eliminates the waste.

0

Tokens Saved

$0.00

Dollars Saved

Three-Tier Memory

Memory That Compounds

Hivemind gives your agents a three-tier memory architecture that compresses, structures, and retrieves knowledge across every run.

Compressed Memory

Episodes compressed to 15–25% of original token count. Knowledge is distilled, not duplicated.

Knowledge Graph

Skills, patterns, and concepts consolidated into a semantic graph. Agents get smarter with every execution.

Delta Context

Only send what changed—not the full context window. Subsequent turns use 37% fewer tokens.

How It Works

Three Steps to Smarter Agents

1

Connect

Drop in the SDK. Works with any LLM framework—OpenAI, Anthropic, LangChain, or your custom stack.

2

Run

Your agents execute normally. Hivemind records, compresses, and consolidates knowledge in the background.

3

Save

Next run retrieves distilled knowledge instead of replaying history. Token costs drop immediately.

See the savings

Features

Built for Production

Three-Tier Memory

Active execution, episodic, and semantic memory tiers work together for complete agent recall.

Framework Agnostic

Works with OpenAI, Anthropic, LangChain, CrewAI, or any LLM framework via REST/gRPC.

Enterprise Security

RBAC, PII handling, sensitivity controls, audit logging, and OAuth 2.1/OIDC authentication.

Flexible Hosting

Self-hosted on-premise, deploy to your cloud, or use our managed service. Your data stays where you want it.

Multi-Tenant RBAC

Organization-level tenancy with role-based access, clearance levels, and cross-agent knowledge sharing controls.

REST & gRPC APIs

First-class REST and gRPC interfaces, plus embedded and no-op clients. Integrate in minutes, not weeks.

Token Savings

The Numbers Speak for Themselves

Modeled savings based on the Hivemind token efficiency architecture.

Cross-Execution Savings ~50%

Compressed knowledge replaces re-derived context at steady state

Delta Context Savings ~37%

Only changed context sent per turn, not the full window

Combined Savings ~57%

Cross-execution + delta context stacked at steady state

0.30

Compression Ratio

Retrieved context is 70% smaller than raw-equivalent context. Hivemind packs maximum knowledge into minimal tokens.

Enterprise-Ready Architecture

Encryption at Rest & Transit
Role-Based Access Control
Audit Logging
Self-Hosting Available

FAQ

Frequently Asked Questions

Hivemind is a hierarchical, compressed, graph-relational memory system for multi-agent AI. It acts as a standalone persistent memory layer for any LLM-powered application, handling persistence, compression, structuring, and retrieval so your agents can reuse distilled knowledge instead of replaying raw history.

Hivemind saves tokens through three mechanisms: (1) cross-execution savings by retrieving compressed knowledge instead of re-deriving context (~50%), (2) delta context that only sends changed information per turn (~37%), and (3) episode compression that reduces traces to 15–25% of their original token count. Combined, these typically deliver ~57% savings at steady state.

Hivemind is agent- and framework-agnostic. It supports OpenAI, Anthropic, Ollama, and any LLM provider. Integrate via REST API, gRPC, or the embedded Python client. It works with LangChain, CrewAI, AutoGen, and custom agent frameworks.

Yes. Hivemind supports self-hosted deployment on-premise or in your cloud (AWS, GCP, Azure), as well as a fully managed service option. Your data stays wherever you need it to be.

Most teams are up and running in under 5 minutes with the managed service. Self-hosted deployments typically take a few hours. The SDK uses non-blocking hooks and a session-managed flow, so integration is minimal and non-invasive.

Yes! We offer a free trial so you can see token savings firsthand. No credit card required—just sign up and start building smarter agents immediately.

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Be among the first developers to build smarter, cheaper AI agents with persistent memory.

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