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.

ATLAS.

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API documentation

Endpoint examples

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ATLAS.

Theme

Blog

API documentation

Endpoint examples

API documentation

Health.

Liveness probe — check Neo4j, Redis, Qdrant, and model status.

GET/brain/health

This endpoint requires no authentication. Use it for uptime monitoring, load balancer health checks, and cold start detection.

Response

readyboolean

true when all downstream services are connected and models are loaded.

Use this as your single "is Atlas usable?" signal.

models_loadedboolean

true when Qwen 0.5B (fact extraction) and MiniLM (embedder) are loaded.

false for 60–120 seconds after cold start. All brain endpoints return 503 while this is false.

neo4jstring

"connected" or "error: <reason>".

Neo4j stores the semantic knowledge graph.

redisstring

"connected" or "error: <reason>".

Redis stores working memory and the entity canonicalisation cache.

qdrantstring

"connected" or "error: <reason>".

Qdrant stores episodic memory vector embeddings.

Examples

curl --request GET \

--url https://api.bsyncs.com/brain/health

{

"ready": true,

"models_loaded": true,

"neo4j": "connected",

"redis": "connected",

"qdrant": "connected"

}

Stats

API documentation

Health.

Liveness probe — check Neo4j, Redis, Qdrant, and model status.

GET/brain/health

This endpoint requires no authentication. Use it for uptime monitoring, load balancer health checks, and cold start detection.

Response

readyboolean

true when all downstream services are connected and models are loaded.

Use this as your single "is Atlas usable?" signal.

models_loadedboolean

true when Qwen 0.5B (fact extraction) and MiniLM (embedder) are loaded.

false for 60–120 seconds after cold start. All brain endpoints return 503 while this is false.

neo4jstring

"connected" or "error: <reason>".

Neo4j stores the semantic knowledge graph.

redisstring

"connected" or "error: <reason>".

Redis stores working memory and the entity canonicalisation cache.

qdrantstring

"connected" or "error: <reason>".

Qdrant stores episodic memory vector embeddings.

Examples

curl --request GET \

--url https://api.bsyncs.com/brain/health

{

"ready": true,

"models_loaded": true,

"neo4j": "connected",

"redis": "connected",

"qdrant": "connected"

}