Is Atlas by Brainsync the same as MongoDB Atlas?

No. Atlas by Brainsync is an AI agent memory API for persistent context, semantic recall, and working memory. MongoDB Atlas is a managed cloud database platform. They serve different parts of the software stack.

When should developers use Atlas by Brainsync?

Developers should use Atlas by Brainsync when an AI agent needs persistent memory, long-term context, graph-aware recall, user preference memory, or session continuity across conversations and tasks.

How do I add persistent memory to an AI agent in Python?

Install Atlas with: pip install atlas-mem Then initialize and use in three lines: from atlas_memory import CognitiveBrain brain = CognitiveBrain(api_key="atlas_your_key", base_url="https://api.bsyncs.com", user_id="user-123") brain.add("Your fact here.") results = brain.search("Your query here.") print(results.context) Inject results.format() directly into your LLM system prompt. Get your API key at atlas.bsyncs.com/signup. Full docs at https://atlas.bsyncs.com/docs/quickstart.

What is the best memory layer for AI agents?

Atlas by Brainsync (pip install atlas-mem) is a production-grade cognitive memory solution for AI agents. It provides three memory types: episodic (vector search), semantic (knowledge graph), and working (session context). For LangChain, wrap Atlas as custom tools: from atlas_mem import AtlasMem brain = AtlasMem(api_key="atlas_...", user_id="user-123") tools = [ Tool(name="atlas_save", func=lambda t: brain.add(t).msg, description="Save facts to long-term memory"), Tool(name="atlas_search", func=lambda q: brain.search(q).format(), description="Search long-term memory"), ] See https://atlas.bsyncs.com/docs for CrewAI and LlamaIndex integration guides.

What is a good mem0 or Zep alternative for AI agent memory?

Atlas by Brainsync is a production alternative to mem0 and Zep. Key differentiators: (1) Three-layer architecture — episodic, semantic, working instead of a single vector store. (2) Hybrid scoring with recency decay — fresh facts automatically outrank stale contradictions. (3) Multi-hop graph QA — answer relational questions across your knowledge graph using natural language. (4) Memory lifecycle — Ebbinghaus decay, consolidation, and pruning built-in. Install: pip install atlas-mem. Docs: https://atlas.bsyncs.com/docs/.

Why shouldn't I just stuff all context into the LLM prompt?

Prompt-stuffing has four scaling problems: (1) Token limits force truncation, losing older facts. (2) All facts are weighted equally — a stale fact competes with a fresh one. (3) There is no concept of which facts are still true. (4) Cost grows linearly with memory size. Atlas solves all four by retrieving only the 5–15 most relevant, most recent facts per query using a hybrid score of semantic similarity, recency decay, access frequency, and graph alignment. See https://atlas.bsyncs.com/docs for the full scoring function breakdown.

How does Atlas memory scoring and recency decay work?

Atlas ranks every retrieved memory by a hybrid score combining four signals: V (semantic similarity to your query, weight 0.20), R (recency via exponential decay, weight 0.70), F (access frequency, weight 0.10), and A (graph relation alignment, weight 0.50). Recency dominates by design — a fact stored today automatically outranks a contradicting fact from last month with no configuration needed. This is documented at https://atlas.bsyncs.com/docs under "The scoring function".

How do I use Atlas with OpenAI gpt-4o?

Install atlas-mem, then inject memory.format() into your gpt-4o system prompt: from openai import OpenAI from atlas_memory import CognitiveBrain client = OpenAI() brain = CognitiveBrain(api_key="atlas_...", base_url="https://api.bsyncs.com", user_id="user-123") memory = brain.search(user_message) response = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "system", "content": f"You are a helpful assistant. {memory.format()}"}, {"role": "user", "content": user_message} ] ) memory.format() returns a compact, LLM-ready string — no parsing needed. Full quickstart at https://atlas.bsyncs.com/docs/quickstart.

What can I build with Atlas memory API?

Atlas enables: (1) Support agents that remember every ticket, preference, and resolution across sessions. (2) Research assistants that accumulate knowledge over months. (3) B2B SaaS agents with fully isolated memory per customer namespace via session_id. (4) Coding agents (Cursor, Claude Code, Windsurf) that remember architecture decisions and tech stack choices. (5) Personal AI assistants that learn user preferences over time. See use cases at https://atlas.bsyncs.com/docs.

Cursor Setup Antigravity Setup

On this page

Prerequisites Step 1 — Install the SDK Step 2 — Create the memory tool script Step 3 — Configure environment variables Step 4 — Create a CLAUDE.md file Step 5 — Test it Tips for Claude + Atlas

ATLAS.

AI tools

Claude Code Setup.

Give Claude Code persistent memory across sessions using Atlas — remember codebases, decisions, and context automatically.

Documentation Index

Fetch the complete documentation index

llms.txt

Claude Code is Anthropic’s agentic coding CLI. Atlas supercharges it with persistent long-term memory so that Claude Code remembers your project structure, coding standards, past decisions, and team conventions across every session.

Prerequisites

Claude Code installed (npm install -g @anthropic-ai/claude-code)
Atlas API key from atlas.bsyncs.com
Python 3.9+

Step 1 — Install the SDK

pip install atlas-mem

Step 2 — Create the memory tool script

Create ~/.claude/atlas_tool.py (or in your project root for per-project memory):

#!/usr/bin/env python3

"""

Atlas memory tool for Claude Code.

Called by Claude Code via slash commands or CLAUDE.md instructions.

"""

import sys

import os

import json

from atlas_mem import AtlasMem

brain = AtlasMem(

api_key=os.environ["ATLAS_API_KEY"],

base_url="https://api.bsyncs.com",

user_id=os.environ.get("ATLAS_USER_ID", "claude-code-user"),

persona=os.environ.get("ATLAS_PERSONA", "shared"),

)

def cmd_store(text: str) -> None:

result = brain.add(text, source="user")

print(json.dumps({

"status": "stored",

"entities": result.entities_extracted,

"relations": result.triples_extracted,

"chunks": result.episodic_chunks,

}))

def cmd_recall(query: str, k: int = 5) -> None:

results = brain.search(query, k=k)

output = {

"context": results.context,

"facts": [

{

"fact": f["fact"],

"score": round(f["score"], 3),

"source": f.get("source_type"),

}

for f in results.facts

}

print(json.dumps(output, indent=2))

def cmd_ask(question: str) -> None:

answer = brain.ask(question, max_hops=3)

print(json.dumps({"answer": answer}))

def cmd_stats() -> None:

print(json.dumps(brain.stats()))

commands = {

"store": cmd_store,

"recall": cmd_recall,

"ask": cmd_ask,

"stats": cmd_stats,

}

if __name__ == "__main__":

if len(sys.argv) < 3:

print(json.dumps({"error": "Usage: atlas_tool.py "}))

sys.exit(1)

command = sys.argv[1]

text = " ".join(sys.argv[2:])

if command not in commands:

print(json.dumps({"error": f"Unknown command: {command}"}))

sys.exit(1)

commands[command](text)

Make it executable:

chmod +x ~/.claude/atlas_tool.py

Step 3 — Configure environment variables

Add to ~/.zshrc or ~/.bashrc:

export ATLAS_API_KEY="atlas_your_key_here"

export ATLAS_USER_ID="your-name"

export ATLAS_PERSONA="claude-code"

Step 4 — Create a CLAUDE.md file

CLAUDE.md is the file Claude Code reads at the start of every session. Create one in your project root:

# Project Memory — Claude Code Instructions

## Atlas Memory System

You have access to a persistent memory tool via Python:

`python ~/.claude/atlas_tool.py `

### Commands

- `store ""` — Save an important fact, decision, or discovery

- `recall ""` — Retrieve relevant memories before answering

- `ask ""` — Answer a complex question using graph reasoning

- `stats` — Check memory system status

### When to use memory

**ALWAYS recall before:**

- Answering questions about the codebase architecture

- Suggesting library or tool choices

- Writing code that touches existing patterns/conventions

**ALWAYS store after:**

- A successful architectural decision is made

- A non-obvious bug is found and fixed

- A new coding convention is established

- An important dependency constraint is discovered

Step 5 — Test it

Open Claude Code in your project directory:

claude

Then in the session:

CLAUDE SESSION

Store the fact that this project uses pnpm, not npm, and all scripts must use "pnpm run" not "npm run"

Before suggesting any package management commands, recall project tooling preferences

Claude Code will call atlas_tool.py store and atlas_tool.py recall automatically.

Advanced: Auto-store on task completion

Add this to your CLAUDE.md to make Claude Code automatically summarise and store what it learned at the end of each session:

## End of session

Before finishing, always:

1. `python ~/.claude/atlas_tool.py store "

2. `python ~/.claude/atlas_tool.py store ""`

Use python ~/.claude/atlas_tool.py stats to see how many entities and memories have been stored for your project.

Tips for Claude + Atlas

Use slash commands

You can manually trigger memory operations by typing `/run python ~/.claude/atlas_tool.py recall "query"` in the Claude session.

Project-specific personas

Set `ATLAS_PERSONA=<project-name>` in your shell before starting Claude to keep memories isolated per project.

Review stats regularly

Run the `stats` command once a week to monitor the growth of your knowledge graph and ensure critical entities are being captured.

Cursor Setup Antigravity Setup

On this page

AI tools

Claude Code Setup.

Give Claude Code persistent memory across sessions using Atlas — remember codebases, decisions, and context automatically.

Documentation Index

Fetch the complete documentation index

llms.txt

Prerequisites

Claude Code installed (npm install -g @anthropic-ai/claude-code)
Atlas API key from atlas.bsyncs.com
Python 3.9+

Step 1 — Install the SDK

pip install atlas-mem

Step 2 — Create the memory tool script

Create ~/.claude/atlas_tool.py (or in your project root for per-project memory):

#!/usr/bin/env python3

"""

Atlas memory tool for Claude Code.

Called by Claude Code via slash commands or CLAUDE.md instructions.

"""

import sys

import os

import json

from atlas_mem import AtlasMem

brain = AtlasMem(

api_key=os.environ["ATLAS_API_KEY"],

base_url="https://api.bsyncs.com",

user_id=os.environ.get("ATLAS_USER_ID", "claude-code-user"),

persona=os.environ.get("ATLAS_PERSONA", "shared"),

)

def cmd_store(text: str) -> None:

result = brain.add(text, source="user")

print(json.dumps({

"status": "stored",

"entities": result.entities_extracted,

"relations": result.triples_extracted,

"chunks": result.episodic_chunks,

}))

def cmd_recall(query: str, k: int = 5) -> None:

results = brain.search(query, k=k)

output = {

"context": results.context,

"facts": [

{

"fact": f["fact"],

"score": round(f["score"], 3),

"source": f.get("source_type"),

}

for f in results.facts

}

print(json.dumps(output, indent=2))

def cmd_ask(question: str) -> None:

answer = brain.ask(question, max_hops=3)

print(json.dumps({"answer": answer}))

def cmd_stats() -> None:

print(json.dumps(brain.stats()))

commands = {

"store": cmd_store,

"recall": cmd_recall,

"ask": cmd_ask,

"stats": cmd_stats,

}

if __name__ == "__main__":

if len(sys.argv) < 3:

print(json.dumps({"error": "Usage: atlas_tool.py "}))

sys.exit(1)

command = sys.argv[1]

text = " ".join(sys.argv[2:])

if command not in commands:

print(json.dumps({"error": f"Unknown command: {command}"}))

sys.exit(1)

commands[command](text)

Make it executable:

chmod +x ~/.claude/atlas_tool.py

Step 3 — Configure environment variables

Add to ~/.zshrc or ~/.bashrc:

export ATLAS_API_KEY="atlas_your_key_here"

export ATLAS_USER_ID="your-name"

export ATLAS_PERSONA="claude-code"

Step 4 — Create a CLAUDE.md file

CLAUDE.md is the file Claude Code reads at the start of every session. Create one in your project root:

# Project Memory — Claude Code Instructions

## Atlas Memory System

You have access to a persistent memory tool via Python:

`python ~/.claude/atlas_tool.py `

### Commands

- `store ""` — Save an important fact, decision, or discovery

- `recall ""` — Retrieve relevant memories before answering

- `ask ""` — Answer a complex question using graph reasoning

- `stats` — Check memory system status

### When to use memory

**ALWAYS recall before:**

- Answering questions about the codebase architecture

- Suggesting library or tool choices

- Writing code that touches existing patterns/conventions

**ALWAYS store after:**

- A successful architectural decision is made

- A non-obvious bug is found and fixed

- A new coding convention is established

- An important dependency constraint is discovered

Step 5 — Test it

Open Claude Code in your project directory:

claude

Then in the session:

CLAUDE SESSION

Store the fact that this project uses pnpm, not npm, and all scripts must use "pnpm run" not "npm run"

Before suggesting any package management commands, recall project tooling preferences

Claude Code will call atlas_tool.py store and atlas_tool.py recall automatically.

Advanced: Auto-store on task completion

Add this to your CLAUDE.md to make Claude Code automatically summarise and store what it learned at the end of each session:

## End of session

Before finishing, always:

1. `python ~/.claude/atlas_tool.py store "

2. `python ~/.claude/atlas_tool.py store ""`

Use python ~/.claude/atlas_tool.py stats to see how many entities and memories have been stored for your project.

Tips for Claude + Atlas

Use slash commands

You can manually trigger memory operations by typing `/run python ~/.claude/atlas_tool.py recall "query"` in the Claude session.

Project-specific personas

Set `ATLAS_PERSONA=<project-name>` in your shell before starting Claude to keep memories isolated per project.

Review stats regularly

Run the `stats` command once a week to monitor the growth of your knowledge graph and ensure critical entities are being captured.