Infrastructure for Agent Memory

The Neural Memory Cloud
for AI Agents.

RAG is broken. Vector DBs fetch text by keyword similarity, not reasoning utility. Eichen replaces them with a Neural Compiler that compresses your docs into Memory Tokens -- 16x smaller, full reasoning fidelity.

16xToken Compression
6%Cost vs. Raw Text
ZeroReasoning Loss
MCPNative Protocol
eichen.config.ts
import { connect } from '@eichen/sdk'

const memory = await connect({
  repo: 'github.com/acme/docs',
  compile: 'neural',
  serve: 'mcp',
  optimize: true
})

// Agent gets full knowledge at 6% token cost
The RAG Problem

Vector DBs Are Broken for Agents

Standard RAG pipelines fetch text based on keyword similarity, not reasoning utility. This forces massive raw text into the context window -- expensive, noisy, and prone to hallucination.

Vector DB

Keyword Similarity

query: "transfer API"
returns: 50 vaguely related chunks

Fetches text that looks similar to the query, not text that is actually useful for reasoning. High recall, low precision.

$$$

Context Stuffing

Expensive & Noisy

tokens: 128,000
useful: ~8,000

Developers stuff raw text into the context window to compensate. 94% of tokens are noise -- burning money and confusing the model.

Result

Hallucinations

Agent hallucinates API parameters
Task fails silently

The model cannot distinguish signal from noise in a bloated context. It invents parameters, misinterprets schemas, and fails tasks.

Eichen replaces raw text retrieval with neural compilation -- giving agents dense, reasoning-optimized memory at a fraction of the cost.

How It Works

Four Steps to Neural Memory

From GitHub repo to production-ready MCP server. Eichen compiles your documentation into dense Memory Tokens that give agents full knowledge at a fraction of the cost.

Connect

Link Your GitHub Repository

Point Eichen at your GitHub repo. We ingest your Markdown documentation, READMEs, API specs, and any structured text -- automatically staying in sync as you push changes.

  • One-click GitHub integration
  • Auto-sync on push
  • Markdown, MDX, and PDF support
  • Monorepo-aware ingestion
terminal
$ eichen connect acme/docs

  Scanning repository...
  Found 47 Markdown files
  Found 12 API specifications
  Watching for changes...

  Connected: acme/docs
Compile

Markdown to Neural Memory

The Neural Compiler uses the SCP pipeline: Synthesize QA pairs from your text, then Compress them into K Memory Tokens that maximize reasoning fidelity. The result is a Neural Index -- not embeddings, but optimized memory slots.

  • QA pair synthesis from docs
  • Encoder-optimized Memory Tokens
  • 16x smaller than raw text
  • Full reasoning fidelity preserved
terminal
$ eichen compile --repo acme/docs

  Synthesizing QA pairs...
  Training encoder on 2,847 pairs
  Compressing to 256 Memory Tokens
  
  Neural Index: ei_idx_8f3k2a9
  Compression: 16.2x
  Fidelity: 99.4%
Serve

MCP Server for Any Agent

Eichen provides a dedicated MCP Server URL for your compiled memory. When an agent requests a resource, it does not get raw text -- it gets dense Memory Tokens via the Model Context Protocol, at 6% of the token cost.

  • Dedicated MCP Server URL
  • Serves Memory Tokens, not text
  • 6% of traditional token cost
  • Compatible with any MCP client
terminal
server: mcp.eichen.ai/acme/docs

  Agent requests resource://api/transfer
  
  Traditional: 12,400 tokens (raw text)
  Eichen:         744 tokens (memory)
  
  Savings: 93.8%
  Fidelity: preserved
Optimize

Self-Healing Memory

When an agent fails a task, Eichen observes the failure signal, identifies which Memory Tokens were active, and applies a negative gradient to fix retrieval for that query type. Your memory gets smarter with every interaction.

  • Automatic failure detection
  • Gradient-based token adjustment
  • Per-query-type optimization
  • Memory improves over time
terminal
signal: tool_call failed (400)

  Identifying active Memory Tokens...
  Tokens [14, 27, 103] were active
  Applying negative gradient...
  
  Updated: ei_idx_8f3k2a9
  Next retrieval: corrected
Technical Architecture

Built on Apple's CLaRa Research

Eichen's Neural Compiler is based on the CLaRa architecture (Feb 2026), which demonstrated that documentation can be compressed into dense vector representations that retain full reasoning fidelity for downstream models.

16x

smaller than raw text while retaining full reasoning fidelity for the model

94%

reduction in token costs compared to traditional context-stuffing approaches

Ingestion Engine (The Compiler)

Ingests Markdown and PDFs from GitHub. Uses the SCP pipeline: a small LLM Synthesizes (Question, Answer) pairs, then a specialized encoder Compresses them into K Memory Tokens that maximize the likelihood of correct answers. The output is a Neural Index -- optimized memory slots, not just embeddings.

Runtime (The MCP Server)

Serves compiled memory via the Model Context Protocol. When an agent requests a resource, Eichen returns dense Memory Tokens instead of raw text -- either via prompt injection or high-density summaries. The agent gets the same knowledge at 6% of the original token cost.

Feedback Loop (Self-Healing)

The Eichen proxy observes tool_call failures in real time. When a failure occurs, it identifies which Memory Tokens were active and applies a negative gradient to those tokens for that query type. The memory automatically improves with every interaction.

neural compile
$ eichen compile --repo acme/api-docs

  [SCP Pipeline]
  
  1. Synthesize
     Generating QA pairs from 47 files...
     Created 2,847 (question, answer) pairs
  
  2. Compress
     Training encoder on QA corpus...
     Mapping to 256 Memory Tokens
     Compression ratio: 16.2x
  
  3. Index
     Neural Index: ei_idx_k8m2n4
     Serving at: mcp.eichen.ai/acme/api-docs
     
  Ready.

Why Eichen?

Whether you are a solo developer building an agent or an enterprise running thousands -- Eichen gives your agents the memory they actually need.

For Developers

Ship faster with smarter agents

  • Connect your docs repo and get an MCP endpoint in minutes
  • Agents get full knowledge at 6% of traditional token costs
  • Self-healing memory means fewer failed tool calls to debug
  • Integrates with any MCP-compatible agent framework

For AI Teams

Scale agent infrastructure

  • Centralized memory compilation for all team repositories
  • Shared Neural Indices across agents and environments
  • Automatic optimization from real-world agent interactions
  • Observable memory performance with built-in analytics

For Enterprises

Reduce cost, increase reliability

  • Cut token costs by 94% across all agent workloads
  • Eliminate hallucinations caused by context-stuffing
  • Self-hosted deployment options for sensitive documentation
  • SOC 2 compliant infrastructure with audit trails
Core Principles

Memory That Thinks, Not Just Matches

Eichen is not another vector database. It is a neural compiler that turns your documentation into the densest, most useful form an AI agent can consume.

Reasoning-First Retrieval

Unlike vector DBs that match on keyword similarity, Eichen compiles memory optimized for reasoning utility. Your agent gets the knowledge it needs to reason correctly, not just text that looks related.

16x Compression, Zero Loss

Memory Tokens are dense vector representations that retain full reasoning fidelity while being 16x smaller than raw text. Agents perform just as well with 94% fewer tokens.

MCP Native, Protocol Open

Built for the Model Context Protocol from day one. Eichen serves compiled memory as MCP resources, compatible with Claude, GPT, and any MCP-enabled agent framework.

Self-Healing by Design

Every agent failure is a training signal. Eichen mathematically adjusts Memory Token weights to fix retrieval for that query type, so your memory gets smarter over time without manual intervention.

Give your agents
real memory.

Connect your repo, compile to Neural Memory, and serve it via MCP. Your agents get full knowledge at 6% of the cost -- and the memory heals itself over time.

Free during early access. No credit card required.