MongoDB Memory

Configure MongoDB and Atlas Vector Search for agent memory

Use MongoDB as a document-backed memory store with optional Atlas Vector Search for semantic retrieval.

python

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    instructions="Remember facts across sessions.",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "use_vector_search": True,
            "embedder": {
                "provider": "ollama",
                "config": {"model": "nomic-embed-text"},
            },
        },
    },
)
agent.start("Remember that my favourite colour is teal")

The user chats with the agent; short- and long-term memory persist in MongoDB, embedded fully local through Ollama.

Prefer the shortest, flat form? Use embedding_model directly:

python

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "use_vector_search": True,
            "embedding_model": "ollama/nomic-embed-text",
        },
    },
)

Diagram

How It Works

Diagram

Quick Start

Simple Usage

python

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
        },
    },
)

With Configuration

python

import os
from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": os.getenv("MONGODB_URI", "mongodb://localhost:27017/"),
            "database": "praisonai",
            "use_vector_search": True,
        },
    },
)

Configuration

Option	Type	Default	Description
`connection_string`	`str`	`mongodb://localhost:27017/`	MongoDB URI
`database`	`str`	`praisonai`	Database name
`use_vector_search`	`bool`	`False`	Enable Atlas Vector Search on short-term and long-term memory
`embedding_model`	`str`	`"text-embedding-3-small"`	Embedding model as a litellm string (e.g. `"ollama/nomic-embed-text"`). Wins over `embedder`.
`embedder`	`dict`	`None`	Alternative shape: `{"provider": "...", "config": {"model": "..."}}`. Recombined into `"<provider>/<model>"` for litellm; `openai` stays bare.
`max_pool_size`	`int`	`50`	Connection pool maximum
`min_pool_size`	`int`	`10`	Connection pool minimum
`max_idle_time`	`int`	`30000`	Max idle time in ms
`server_selection_timeout`	`int`	`5000`	Server selection timeout in ms

Vector Search

When use_vector_search: True:

Both short-term and long-term writes include an embedding — using text-embedding-3-small by default, or whatever you configure (see below).
Searches use MongoDB $vectorSearch against index vector_index on field embedding — the same field in both collections.
If vector search fails or is unavailable, either tier falls back to MongoDB text search.

When use_vector_search: False (default), only MongoDB text indexes are used.

Configuring the embedding model

The adapter resolves the embedding model at construction, in this precedence order: embedding_model → config["embedder"] → falls back to text-embedding-3-small.

Use the embedder block (preferred — the same shape used across memory, knowledge, and the MongoDB knowledge adapter):

python

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "use_vector_search": True,
            "embedder": {
                "provider": "ollama",
                "config": {"model": "mxbai-embed-large"},
            },
        },
    },
)

Or the flat embedding_model string (litellm-style, shortest):

python

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "use_vector_search": True,
            "embedding_model": "ollama/nomic-embed-text",
        },
    },
)

Atlas Setup

Create a vector search index named vector_index on both the short_term_memory and long_term_memory collections.
Set the indexed path to embedding on each.
Pass use_vector_search: True in agent config.

Best Practices

Use environment variables for credentials

Store connection strings in MONGODB_URI rather than hard-coding credentials in source files.

Enable vector search for semantic recall

Set use_vector_search: True on Atlas when you need similarity search; text indexes suffice for keyword lookup.

Size the connection pool for concurrency

Tune max_pool_size and min_pool_size when running many agents against the same cluster.

Create indexes before production traffic

Configure the vector_index Atlas index and text indexes before scaling agent workloads.

Custom Memory Adapters

Registry pattern and custom backend registration.

Memory Advanced Search

Reranking, relevance cutoffs, and quality filtering.

Dakera Memory

Self-hosted, decay-weighted vector recall via the Dakera server.

Local Memory & Knowledge

Run memory and knowledge fully local with Ollama embeddings.

Ollama Embeddings

Local embedding models and their auto-detected dimensions.

MongoDB Memory

How It Works

Quick Start

Configuration

Vector Search

Configuring the embedding model

Atlas Setup

Best Practices

Related