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.

Introduction Ingest

On this page

POST /brain/consolidate Request body Response Code examples Prune only — POST /brain/prune Memory decay explained When to consolidate

ATLAS.

API documentation

Consolidate.

Run the memory lifecycle pipeline — decay, prune, and compress memories to keep your knowledge graph sharp.

Documentation Index

Fetch the complete documentation index at: https://docs.bsyncs.com/llms.txt

Use this file to discover all available pages before exploring further.

Cost: 3 operations per call

POST/brain/consolidate

Runs the full memory lifecycle pipeline for your account:

Temporal decay — applies the Ebbinghaus forgetting curve to all relationship confidences. Memories that haven’t been accessed recently lose confidence exponentially.
Pruning — removes relationships whose confidence has fallen below the survival threshold (PRUNE_THRESHOLD, default 0.05). Orphaned nodes are also cleaned up.
Cluster compression (requires OpenAI) — identifies densely connected entity clusters and uses an LLM to compress related facts into higher-level abstractions.

Consolidation runs automatically every hour in the background. Call this endpoint manually only when you want to force an immediate cleanup — for example, after a large batch ingestion.

Request body

{

"persona": "shared",

"force": false

}

Field	Type	Required	Default	Description
`persona`	string		null	Restrict consolidation to a specific persona.
`force`	bool		false	Force consolidation even if it ran recently.

Response

{

"memories_reinforced": 0,

"memories_decayed": 142,

"memories_pruned": 17,

"memories_compressed": 43,

"abstractions_created": 8,

"latency_ms": 3241.0

}

Field	Type	Description
`memories_decayed`	int	Relationships whose confidence was reduced by the forgetting curve.
`memories_pruned`	int	Relationships and episodic chunks deleted for falling below threshold.
`memories_compressed`	int	Individual facts merged into cluster abstractions.
`abstractions_created`	int	New higher-level summary nodes created in the knowledge graph.
`latency_ms`	float	Total time in milliseconds.

Code examples

from atlas_mem import AtlasMem

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

# Standard consolidation

result = brain.consolidate()

print(f"Pruned {result['memories_pruned']} stale memories")

print(f"Created {result['abstractions_created']} abstractions")

# Force immediate consolidation

result = brain.consolidate(force=True)

Prune only — `POST /brain/prune`

If you only want to remove low-confidence memories without running decay or compression:

Cost: 1 operation per call

{

"threshold": 0.05,

"dry_run": true,

"persona": "shared"

}

Field	Type	Default	Description
`threshold`	float	0.05	Confidence below which memories are pruned.
`dry_run`	bool	true	If true, returns the count without deleting anything.
`persona`	string	null	Restrict pruning to a persona.

Always run `dry_run: true` first:

# Preview — how many memories are below threshold?

preview = brain.prune(threshold=0.1, dry_run=True)

print(f"{preview['candidates']} memories eligible for pruning")

# Execute if satisfied

result = brain.prune(threshold=0.1, dry_run=False)

print(f"{result['pruned']} memories deleted")

Memory decay explained

Atlas uses the Ebbinghaus forgetting curve: R = e^(-t/S), where:

R = retention (applied to relationship confidence)
t = time since last access (days)
S = memory stability = DECAY_HALF_LIFE_DAYS / ln(2)

With the default DECAY_HALF_LIFE_DAYS=7, a memory not accessed for:

Days elapsed	Confidence multiplier
1 day	~0.91×
7 days	~0.50×
14 days	~0.25×
30 days	~0.05× (prunable)

Memories that are frequently retrieved resist decay — each access resets the clock and reinforces the access_count frequency score.

When to consolidate

After large batch ingestion

Run immediately after importing bulk historical data to prune noise and build abstractions.

Weekly maintenance

Keep your knowledge graph clean and high-confidence for production agents.

Before a new project phase

Clear out stale context before starting a new development sprint with your agent.

Introduction Ingest

On this page

POST /brain/consolidate Request body Response Code examples Prune only — POST /brain/prune Memory decay explained When to consolidate

API documentation

Consolidate.

Run the memory lifecycle pipeline — decay, prune, and compress memories to keep your knowledge graph sharp.

Documentation Index

Fetch the complete documentation index at: https://docs.bsyncs.com/llms.txt

Use this file to discover all available pages before exploring further.

Cost: 3 operations per call

POST/brain/consolidate

Runs the full memory lifecycle pipeline for your account:

Temporal decay — applies the Ebbinghaus forgetting curve to all relationship confidences. Memories that haven’t been accessed recently lose confidence exponentially.
Pruning — removes relationships whose confidence has fallen below the survival threshold (PRUNE_THRESHOLD, default 0.05). Orphaned nodes are also cleaned up.
Cluster compression (requires OpenAI) — identifies densely connected entity clusters and uses an LLM to compress related facts into higher-level abstractions.

Consolidation runs automatically every hour in the background. Call this endpoint manually only when you want to force an immediate cleanup — for example, after a large batch ingestion.

Request body

{

"persona": "shared",

"force": false

}

Field	Type	Required	Default	Description
`persona`	string		null	Restrict consolidation to a specific persona.
`force`	bool		false	Force consolidation even if it ran recently.

Response

{

"memories_reinforced": 0,

"memories_decayed": 142,

"memories_pruned": 17,

"memories_compressed": 43,

"abstractions_created": 8,

"latency_ms": 3241.0

}

Field	Type	Description
`memories_decayed`	int	Relationships whose confidence was reduced by the forgetting curve.
`memories_pruned`	int	Relationships and episodic chunks deleted for falling below threshold.
`memories_compressed`	int	Individual facts merged into cluster abstractions.
`abstractions_created`	int	New higher-level summary nodes created in the knowledge graph.
`latency_ms`	float	Total time in milliseconds.

Code examples

from atlas_mem import AtlasMem

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

# Standard consolidation

result = brain.consolidate()

print(f"Pruned {result['memories_pruned']} stale memories")

print(f"Created {result['abstractions_created']} abstractions")

# Force immediate consolidation

result = brain.consolidate(force=True)

Prune only — `POST /brain/prune`

If you only want to remove low-confidence memories without running decay or compression:

Cost: 1 operation per call

{

"threshold": 0.05,

"dry_run": true,

"persona": "shared"

}

Field	Type	Default	Description
`threshold`	float	0.05	Confidence below which memories are pruned.
`dry_run`	bool	true	If true, returns the count without deleting anything.
`persona`	string	null	Restrict pruning to a persona.

Always run `dry_run: true` first:

# Preview — how many memories are below threshold?

preview = brain.prune(threshold=0.1, dry_run=True)

print(f"{preview['candidates']} memories eligible for pruning")

# Execute if satisfied

result = brain.prune(threshold=0.1, dry_run=False)

print(f"{result['pruned']} memories deleted")

Memory decay explained

Atlas uses the Ebbinghaus forgetting curve: R = e^(-t/S), where:

R = retention (applied to relationship confidence)
t = time since last access (days)
S = memory stability = DECAY_HALF_LIFE_DAYS / ln(2)

With the default DECAY_HALF_LIFE_DAYS=7, a memory not accessed for:

Days elapsed	Confidence multiplier
1 day	~0.91×
7 days	~0.50×
14 days	~0.25×
30 days	~0.05× (prunable)

Memories that are frequently retrieved resist decay — each access resets the clock and reinforces the access_count frequency score.

When to consolidate

After large batch ingestion

Run immediately after importing bulk historical data to prune noise and build abstractions.

Weekly maintenance

Keep your knowledge graph clean and high-confidence for production agents.

Before a new project phase

Clear out stale context before starting a new development sprint with your agent.

ATLAS.

Consolidate.

Request body

Response

Code examples

Prune only — POST /brain/prune

Always run dry_run: true first:

Memory decay explained

When to consolidate

After large batch ingestion

Weekly maintenance

Before a new project phase

Consolidate.

Request body

Response

Code examples

Prune only — POST /brain/prune

Always run dry_run: true first:

Memory decay explained

When to consolidate

After large batch ingestion

Weekly maintenance

Before a new project phase

Prune only — `POST /brain/prune`

Always run `dry_run: true` first:

Prune only — `POST /brain/prune`

Always run `dry_run: true` first: