# Introducing Neocortex ðŸ§
Every AI memory system you've used does the same thing: store everything, retrieve by similarity, hope for the best.
The outcome: Your agent drowns in stale context. Responses degrade. Costs inflate. You end up writing custom pruning logic at 2am.
Neocortex takes a fundamentally different approach. Inspired by how the human brain actually works, it **intelligently forgets noise** so your AI only reasons over what matters. Low-value memories decay naturally over time. Knowledge your users interact with gets reinforced and rises to the top. It doesn't require manual cleanup and there is no context window anxiety.
The result: an AI memory system that processes over **1 billion tokens**, stays lean and focused, and gets smarter with every interaction.
For a deployment serving 100,000 users with ongoing conversation history:
| Approach | Cost per user per year |
| ---------------------------------------------- | ---------------------- |
| Frontier model context (everything in-context) | \~$90,000 |
| Standard RAG pipeline | \~$2.40 |
| **Neocortex** | **\~$0.72** |
The 1,000:1 compression ratio means storage and compute costs grow slowly relative to conversation volume. Neocortex indexes a conversation for $0.0004, compared to $0.0112 for Mem0 (28x lower).
## Core Features
### Intelligent Noise Filtering
Memories that aren't accessed naturally decay over time. Frequently recalled knowledge becomes more durable. The system stays lean on its own without manual cleanup and intervention.
### Interaction-Aware
Not all memories are equal. Views, reactions, replies, and content creation all signal what matters. Knowledge people engage with rises to the top; ignored information fades away.
### Low Latency, Low Cost, High Quality
No compromise on speed and quality when processing data with Neocortex. Everything is processed at low cost and low latency while maintaining high benchmark scores
| Metric | Neocortex | Nearest Competitor |
| --------------- | ---------- | ------------------------- |
| Average latency | \~1.1s | \~3.6s (Mem0) |
| Query cost | \~$0.00095 | \~$0.00085 (Mem0) |
| Index cost | \~$0.0005 | \~$0.014 (Mem0, 28x more) |
## Quick Start
```bash
pip install tinyhumansai
```
```python
import tinyhumansai as api
client = api.TinyHumanMemoryClient("YOUR_APIKEY_HERE")
# Store a memory
client.ingest_memory({
"key": "user-preference-theme",
"content": "User prefers dark mode",
"namespace": "preferences",
"metadata": {"source": "onboarding"},
})
# Ask a question using stored memory
response = client.recall_with_llm(
prompt="What is the user's preference for theme?",
api_key="OPENAI_API_KEY"
)
print(response.text) # The user prefers dark mode
```
That's it. Ingest memories, recall them with any LLM. Neocortex handles the hard parts: deduplication, decay, graph-based retrieval, and noise pruning.
{% hint style="info" %}
Neocortex is currently in **closed alpha**. To get access, [reach out to us](mailto:founders@tinyhumans.ai).
{% endhint %}
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://tinyhumans.gitbook.io/neocortex/readme.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.