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.

Retrieve Stats

On this page

POST /brain/retrieve/graph-qa Request body Response Code examples Graph QA vs. regular retrieve Example — building a Q&A agent Guardrails

ATLAS.

API documentation

Graph QA.

Answer complex relational questions using multi-hop traversal over your Neo4j knowledge graph.

Cost: 10 operations per call. Available on Starter plan and above.

POST/brain/retrieve/graph-qa

Graph QA is the highest-intelligence retrieval mode. Instead of simple vector similarity, it:

Finds seed entities — uses vector search to locate the starting nodes most relevant to your question
Traverses the graph — BFS expansion up to max_hops hops from each seed
Grounds the answer — passes only the traversed facts to the LLM, forcing a fact-grounded response with zero hallucination

This is ideal for questions that require connecting multiple pieces of information stored across different ingestion sessions.

Request body

{

"query": "Which engineers are connected to the PostgreSQL migration and what were their roles?",

"max_hops": 3,

"k": 10

}

Field	Type	Required	Default	Description
`query`	string	✓	—	Natural language question to answer from the knowledge graph.
`max_hops`	int		3	Graph traversal depth (1–5). Higher = more connected context.
`k`	int		10	Max facts to retrieve (1–50).
`persona`	string		null	Restrict graph traversal to a persona.

Response

{

"answer": "Based on the knowledge graph [1][3], Sarah Jenkins led the PostgreSQL migration as Lead Engineer. She worked alongside Alex Chen (Database Administrator) [2] and the migration was approved by Mark Liu (CTO) [4]. The migration involved moving from MySQL to PostgreSQL for improved JSONB support.",

"facts_used": 12

}

Field	Type	Description
`answer`	string	LLM-generated answer grounded in retrieved graph facts. Cites fact numbers.
`facts_used`	int	Number of graph facts passed to the LLM as context.

Code examples

from atlas_mem import AtlasMem

brain = AtlasMem(api_key="atlas_...", base_url="https://api.bsyncs.com", user_id="user-123")

# Ask a multi-hop question

answer = brain.ask("Which engineers worked on the PostgreSQL migration?")

print(answer)

# With deeper traversal

answer = brain.ask(

"What is the relationship between Sarah Jenkins and the payment infrastructure?",

max_hops=4,

)

print(answer)

Graph QA vs. regular retrieve

Feature	retrieve	graph-qa
Memory sources	`Episodic + Semantic + Working`	`Semantic graph only`
Answer type	Ranked facts list	Natural language answer
Multi-hop	Optional	Always
LLM call	Optional (compression)	Always
Best for	Injecting context into your own LLM call	Direct factual Q&A from the graph
Cost	2 ops	10 ops

Use retrieve when you want to control the LLM call yourself. Use graph-qa when you want Atlas to answer the question directly.

Example — building a Q&A agent

from atlas_mem import AtlasMem

brain = AtlasMem(

api_key="atlas_...",

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

user_id="user-123",

)

# First, build up knowledge

brain.add("Sarah Jenkins is the Lead Engineer at Acme Corp.")

brain.add("Sarah led the PostgreSQL migration in Q3 2024.")

brain.add("The PostgreSQL migration improved query performance by 60%.")

brain.add("Alex Chen is the Database Administrator who executed the migration.")

brain.add("The migration was approved by Mark Liu, CTO of Acme Corp.")

# Now ask relational questions — no LLM key needed on your side

print(brain.ask("Who approved the database migration?"))

# → "The database migration was approved by Mark Liu, the CTO of Acme Corp. [4]"

print(brain.ask("What was the outcome of the PostgreSQL migration?"))

# → "The PostgreSQL migration improved query performance by 60%. [3]"

print(brain.ask("How is Sarah Jenkins connected to the CTO?"))

# → "Sarah Jenkins, as Lead Engineer, led the PostgreSQL migration which was approved by Mark Liu (CTO). [1][4][5]"

Guardrails

Graph QA has built-in prompt injection protection:

Facts are numbered and sandboxed — the LLM is instructed to cite by number, not quote
Instructions embedded in stored facts (e.g. “ignore previous instructions”) are stripped before being passed to the LLM
Results are scoped to your user_id — the traversal query includes user_id filters at every hop, so cross-tenant leakage is impossible at the database level

Graph QA requires OPENAI_API_KEY to be set on the server. If it is unavailable, the endpoint returns {"answer": "", "error": "LLM not available"}.

In this case, use the retrieve endpoint instead and pass the facts to your own LLM.

Retrieve Stats

On this page

POST /brain/retrieve/graph-qa Request body Response Code examples Graph QA vs. regular retrieve Example — building a Q&A agent Guardrails

API documentation

Graph QA.

Answer complex relational questions using multi-hop traversal over your Neo4j knowledge graph.

Cost: 10 operations per call. Available on Starter plan and above.

POST/brain/retrieve/graph-qa

Graph QA is the highest-intelligence retrieval mode. Instead of simple vector similarity, it:

Finds seed entities — uses vector search to locate the starting nodes most relevant to your question
Traverses the graph — BFS expansion up to max_hops hops from each seed
Grounds the answer — passes only the traversed facts to the LLM, forcing a fact-grounded response with zero hallucination

This is ideal for questions that require connecting multiple pieces of information stored across different ingestion sessions.

Request body

{

"query": "Which engineers are connected to the PostgreSQL migration and what were their roles?",

"max_hops": 3,

"k": 10

}

Field	Type	Required	Default	Description
`query`	string	✓	—	Natural language question to answer from the knowledge graph.
`max_hops`	int		3	Graph traversal depth (1–5). Higher = more connected context.
`k`	int		10	Max facts to retrieve (1–50).
`persona`	string		null	Restrict graph traversal to a persona.

Response

{

"facts_used": 12

}

Field	Type	Description
`answer`	string	LLM-generated answer grounded in retrieved graph facts. Cites fact numbers.
`facts_used`	int	Number of graph facts passed to the LLM as context.

Code examples

from atlas_mem import AtlasMem

brain = AtlasMem(api_key="atlas_...", base_url="https://api.bsyncs.com", user_id="user-123")

# Ask a multi-hop question

answer = brain.ask("Which engineers worked on the PostgreSQL migration?")

print(answer)

# With deeper traversal

answer = brain.ask(

"What is the relationship between Sarah Jenkins and the payment infrastructure?",

max_hops=4,

)

print(answer)

Graph QA vs. regular retrieve

Feature	retrieve	graph-qa
Memory sources	`Episodic + Semantic + Working`	`Semantic graph only`
Answer type	Ranked facts list	Natural language answer
Multi-hop	Optional	Always
LLM call	Optional (compression)	Always
Best for	Injecting context into your own LLM call	Direct factual Q&A from the graph
Cost	2 ops	10 ops

Use retrieve when you want to control the LLM call yourself. Use graph-qa when you want Atlas to answer the question directly.

Example — building a Q&A agent

from atlas_mem import AtlasMem

brain = AtlasMem(

api_key="atlas_...",

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

user_id="user-123",

)

# First, build up knowledge

brain.add("Sarah Jenkins is the Lead Engineer at Acme Corp.")

brain.add("Sarah led the PostgreSQL migration in Q3 2024.")

brain.add("The PostgreSQL migration improved query performance by 60%.")

brain.add("Alex Chen is the Database Administrator who executed the migration.")

brain.add("The migration was approved by Mark Liu, CTO of Acme Corp.")

# Now ask relational questions — no LLM key needed on your side

print(brain.ask("Who approved the database migration?"))

# → "The database migration was approved by Mark Liu, the CTO of Acme Corp. [4]"

print(brain.ask("What was the outcome of the PostgreSQL migration?"))

# → "The PostgreSQL migration improved query performance by 60%. [3]"

print(brain.ask("How is Sarah Jenkins connected to the CTO?"))

# → "Sarah Jenkins, as Lead Engineer, led the PostgreSQL migration which was approved by Mark Liu (CTO). [1][4][5]"

Guardrails

Graph QA has built-in prompt injection protection:

Facts are numbered and sandboxed — the LLM is instructed to cite by number, not quote
Instructions embedded in stored facts (e.g. “ignore previous instructions”) are stripped before being passed to the LLM
Results are scoped to your user_id — the traversal query includes user_id filters at every hop, so cross-tenant leakage is impossible at the database level

Graph QA requires OPENAI_API_KEY to be set on the server. If it is unavailable, the endpoint returns {"answer": "", "error": "LLM not available"}.

In this case, use the retrieve endpoint instead and pass the facts to your own LLM.