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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.

No credit card required 5-minute setup
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Step 1: Create a Hivemind
Create Hivemind
Coding Assistant
Software Development
30 days
Create Hivemind
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Step 2: Copy Your Credentials

Hivemind ID

hm_a1b2c3d4e5f6g7h8

API key

sk_live_abcde12345...

Quickstart

export HIVEMIND_API_KEY="sk_live_abcde12345..."
export HIVEMIND_HIVEMIND_ID="hm_a1b2c3d4e5f6g7h8"
Terminal
Step 3: Make Your First Call

# OpenAI-compatible. Point any chat client at Hivemind.

$ curl https://api.hivemind.smartify.ai/v1/chat/completions \

-H "Authorization: Bearer $HIVEMIND_API_KEY" \

-H "Content-Type: application/json" \

-d '{

"model": "openai/gpt-5.4-mini",

"messages": [

{"role": "user", "content": "Hello, Hivemind"}

]

}'

# Memory hydrates server-side. Your agent

# remembers what it’s already learned.

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

Point your OpenAI-compatible client at the Hivemind gateway. Two lines of config—no rewrite, no SDK lock-in.

2

Run

Your agents call the gateway like any LLM. Hivemind hydrates context, records turns, and consolidates knowledge in the background.

3

Save

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

Features

Built for Production

Three-Tier Memory

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

Framework Agnostic

Works with the official OpenAI SDKs, LangChain, CrewAI, AutoGen, LiteLLM, or anything that speaks chat completions. Bring any provider key.

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.

OpenAI-Compatible API

Drop-in chat completions endpoint. Swap the base URL on your existing OpenAI client and your scoped sk_live_ key—memory routing is automatic.

Token Savings

The Numbers Speak for Themselves

Modeled savings based on the Hivemind token efficiency architecture.

Cross-Execution Savings ~50%

Persistent 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

Benchmarks

Benchmarking Hivemind’s token-efficient memory algorithm

Validated across LoCoMo, LongMemEval, and ConvoMem. Powered by single-pass hierarchical extraction and multi-signal retrieval.

88.5

LoCoMo

R@10 · 1,986 questions

94.8

LongMemEval

R@5 · 500 questions

88.5

ConvoMem

avg recall · 500 items

Retrieval recall on hybrid semantic + keyword retrieval, no LLM rerank. Reported on the same benchmark frameworks published by Mem0 and MemPalace, so numbers are directly comparable to public benchmark tables.

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 exposes an OpenAI-compatible chat completions API, so any client that lets you set a base URL works out of the box: the official OpenAI SDKs (Python, JavaScript, Go), LangChain, CrewAI, AutoGen, LiteLLM, or plain curl. Bring your own provider key for OpenAI, Anthropic, Ollama, and more—or use Hivemind credits while you trial it.

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. You typically only change two things in your existing OpenAI-compatible client: the base URL (https://api.hivemind.smartify.ai/v1) and the API key (your scoped sk_live_ token from the dashboard). Memory hydration and consolidation happen server-side, so there’s nothing extra to wire up.

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.

Ready to cut your token bill in half?

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

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