NeoHive

Value per token

4.4 x more

Relevant results per dollar

Accuracy

87 % improvement

More of your own code found

Agentic results

3.6 x better

Tasks passed on all attempts

Figures from two published benchmarks, each with its full method: four models graded blind on proprietary codebases, and 97 τ³-Banking tasks run five times each.

01

Connect your data

Shared persistent context with zero maintenance

Connect it once and it ingests your team's repos, docs and tickets on its own, then keeps up as they change.

• Stays inside your network
• Curates itself
• Useful on day one

Connect agents

$ claude mcp add neohive

Connect through MCP. Every agent on your team is then reading the same memory.

02

Collaboration platform for agents

Every agent collaborating on the same team context

Claude Code, Codex and Cursor connect to the same hive over MCP, with no per-tool setup.

• Fix it once, not four times
• Same answer in every tool
• New hires start informed

Capture decisions and conventions

The reasoning behind a choice is the first thing lost, so it is kept beside the choice.

03

Self-improving memory

The more you run it, the better it answers

Retrieval reranks on what your team actually opens, so the useful answers surface first.

• What gets used, strengthens
• What goes stale, fades
• Prunes itself

Retire what stops being used

A memory that stops being retrieved stops surfacing, so the pruning happens on its own.

convention repo

hi, Neo here your team's sources all agents write here learning as we go see you in the hive found it I'll remember that

One container. Every hive, in context.

A dashboard for every hive, see your connected team data, gain insights on your shared memories and conventions.

Overview

• Rate limiting the auth endpoint docs/auth.md · updated 2d ago
• Error handling convention docs/conventions.md · updated 6d ago
• Billing API, v2 migration docs/billing.md · updated 3w ago

Your code never leaves your infrastructure.

NeoHive runs entirely in your own environment.

Local container

Runs in your VPC or on your machine. Embedding and retrieval both happen locally inside it.

No outbound calls

The container makes no external requests of its own. The only external traffic is the agent API calls your team already makes.

On-premises by default

Deploy on your own infrastructure from day one. Air-gapped and VPC-compatible for regulated environments.

Your existing permissions

Respects the access controls you have. Engineers see answers derived only from content they are allowed to read.

Local container · No outbound data · Embedding stays local · VPC-compatible

The only external traffic is the agent API calls your team is already making, so adding NeoHive opens no new path out of your network.

All posts →

[

Engineering

Why MD Files Are Not Enough

CLAUDE.md and AGENTS.md are a good place to start and a poor place to stay. What the evidence says, the five ways they break, and what a context layer does differently.

Dean McGregorChief Technology Officer

](https://www.neohive.ai/blog/why-md-files-are-not-enough/)[

Insights

What Is a Context Layer for AI? Definition, History, and Why It Matters

A context layer is the persistent, governed retrieval tier that decides what your AI tools see. Here is what it means, where the idea came from, and how NeoHive fits.

Ralf HuuckFounder

](https://www.neohive.ai/blog/what-is-a-context-layer-for-ai/)[

Engineering

1.6x More Tasks Solved, 4x Cheaper Than Frontier Models: Agents With a Context Layer

With NeoHive the same model solved 1.6x more unseen tasks, at 1.2x lower cost per correct answer than GPT-5.6 Sol and up to 6.8x lower than Claude Opus 5.

Nader AwadLead AI Engineer

](https://www.neohive.ai/blog/tau-context-layer-agent-bench/)

Start with one repository.The memory grows from there.

Free to get started · No credit card required · Enterprise pilots available