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.

Antigravity Setup Consolidate

On this page

Base URL Authentication Rate limits Response format Common status codes Health check

ATLAS.

API documentation

Introduction.

Atlas REST API reference — all endpoints, authentication, rate limits, and error codes.

Base URL

https://api.bsyncs.com

All endpoints are prefixed with /brain. If you are self-hosting, replace the base URL with your own domain or IP.

Authentication

Atlas uses API key authentication. Pass your key in the x-api-key request header on every call.

curl -X POST https://api.bsyncs.com/brain/retrieve \

-H "x-api-key: atlas_your_key_here" \

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

-d '{"query": "What database does Acme use?", "k": 5}'

The user_id in every request body is ignored by the server. Atlas derives the user namespace server-side from your API key. This prevents clients from reading other tenants' data.

Rate limits

Rate limits are enforced per API key using a per-minute sliding window.

Tier	Requests / minute
Free	10 RPM
Starter	60 RPM
Pro	300 RPM
Scale	1,000 RPM
Enterprise	Unlimited

When a rate limit is hit, the API returns HTTP 429 with a Retry-After header:

{

"error": "rate_limit_exceeded",

"limit": 10,

"tier": "free",

"retry_after_seconds": 43,

"upgrade": "https://atlas.bsyncs.com/pricing"

}

Monthly operation limits

In addition to per-minute rate limits, each tier has a monthly operations budget. Operations are consumed based on endpoint cost:

Endpoint	Cost per call
`/ingest`	5 ops
`/retrieve`	2 ops
`/graph-qa`	10 ops
`/consolidate`	3 ops
`/prune`	1 op
`/stats`	0 ops (free)
`/health`	0 ops (free)

When the monthly budget is exceeded:

{

"error": "monthly_limit_exceeded",

"tier": "free",

"upgrade": "https://atlas.bsyncs.com/pricing"

}

Response format

All endpoints return JSON. Successful responses follow the schema documented per endpoint. Error responses always have a detail field:

{

"detail": "Service still loading models."

}

Common status codes

Code	Meaning
`200`	Success
`400`	Validation error — check request body
`401`	Invalid or revoked API key
`403`	Feature not available on your plan
`503`	Service still starting up (models loading) — retry in 30s

Health check

Before making any memory calls, you can verify the service is fully online:

curl https://api.bsyncs.com/brain/health

{

"ready": true,

"models_loaded": true,

"neo4j": "connected",

"redis": "connected",

"qdrant": "connected",

"version": "1.0.0"

}

The service takes up to 120 seconds to load models on cold start. If models_loaded is false, wait and retry. All memory endpoints return 503 until models are ready.

SDK vs. direct API

The atlas-mem Python SDK wraps these endpoints automatically. You only need to call the REST API directly if you are using a language other than Python or building a custom integration.

Next steps

Ingest

Store text into episodic and semantic memory.

Retrieve

Hybrid search across all memory stores.

Consolidate

Decay, prune, and compress memories.

Graph QA

Natural language multi-hop graph queries.

Antigravity Setup Consolidate

On this page

Base URL Authentication Rate limits Response format Common status codes Health check

API documentation

Introduction.

Atlas REST API reference — all endpoints, authentication, rate limits, and error codes.

Base URL

https://api.bsyncs.com

All endpoints are prefixed with /brain. If you are self-hosting, replace the base URL with your own domain or IP.

Authentication

Atlas uses API key authentication. Pass your key in the x-api-key request header on every call.

curl -X POST https://api.bsyncs.com/brain/retrieve \

-H "x-api-key: atlas_your_key_here" \

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

-d '{"query": "What database does Acme use?", "k": 5}'

The user_id in every request body is ignored by the server. Atlas derives the user namespace server-side from your API key. This prevents clients from reading other tenants' data.

Rate limits

Rate limits are enforced per API key using a per-minute sliding window.

Tier	Requests / minute
Free	10 RPM
Starter	60 RPM
Pro	300 RPM
Scale	1,000 RPM
Enterprise	Unlimited

When a rate limit is hit, the API returns HTTP 429 with a Retry-After header:

{

"error": "rate_limit_exceeded",

"limit": 10,

"tier": "free",

"retry_after_seconds": 43,

"upgrade": "https://atlas.bsyncs.com/pricing"

}

Monthly operation limits

In addition to per-minute rate limits, each tier has a monthly operations budget. Operations are consumed based on endpoint cost:

Endpoint	Cost per call
`/ingest`	5 ops
`/retrieve`	2 ops
`/graph-qa`	10 ops
`/consolidate`	3 ops
`/prune`	1 op
`/stats`	0 ops (free)
`/health`	0 ops (free)

When the monthly budget is exceeded:

{

"error": "monthly_limit_exceeded",

"tier": "free",

"upgrade": "https://atlas.bsyncs.com/pricing"

}

Response format

All endpoints return JSON. Successful responses follow the schema documented per endpoint. Error responses always have a detail field:

{

"detail": "Service still loading models."

}

Common status codes

Code	Meaning
`200`	Success
`400`	Validation error — check request body
`401`	Invalid or revoked API key
`403`	Feature not available on your plan
`503`	Service still starting up (models loading) — retry in 30s

Health check

Before making any memory calls, you can verify the service is fully online:

curl https://api.bsyncs.com/brain/health

{

"ready": true,

"models_loaded": true,

"neo4j": "connected",

"redis": "connected",

"qdrant": "connected",

"version": "1.0.0"

}

The service takes up to 120 seconds to load models on cold start. If models_loaded is false, wait and retry. All memory endpoints return 503 until models are ready.

SDK vs. direct API

The atlas-mem Python SDK wraps these endpoints automatically. You only need to call the REST API directly if you are using a language other than Python or building a custom integration.

Next steps

Ingest

Store text into episodic and semantic memory.

Retrieve

Hybrid search across all memory stores.

Consolidate

Decay, prune, and compress memories.

Graph QA

Natural language multi-hop graph queries.