MongoDB Knowledge

Store agent knowledge in MongoDB Atlas with per-tenant user_id / agent_id / run_id scope on every read and write.

Quick Start

Agent with MongoDB knowledge

from praisonaiagents import Agent

agent = Agent(
    name="SupportBot",
    instructions="Answer using the customer's session history.",
    knowledge={
        "vector_store": {
            "provider": "mongodb",
            "config": {
                "connection_string": "mongodb+srv://...",
                "database": "praison",
                "collection": "support_knowledge",
            },
        },
    },
    user_id="customer_42",
    agent_id="support_bot_v1",
    run_id="session_2026_08_09",
)

agent.start("What did we agree on for the refund?")

Direct Knowledge API

from praisonaiagents import Knowledge

kb = Knowledge(config={
    "vector_store": {
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb+srv://...",
            "database": "praison",
            "collection": "support_knowledge",
        },
    }
})

kb.add("Refund promised: $49 by Aug 12.",
       user_id="customer_42", agent_id="support_bot_v1", run_id="session_2026_08_09")

kb.search("refund promise",
          user_id="customer_42", agent_id="support_bot_v1", run_id="session_2026_08_09")

How It Works

The adapter persists user_id / agent_id / run_id on each document and applies any provided scope as a pre-filter on search. On Atlas $vectorSearch, the scope becomes the stage-level filter. Without Atlas vector search, the adapter falls back to MongoDB text search and merges the scope into the find() query. None values are dropped — omit an identifier to broaden the search on that dimension.

Configuration Options

Config keys read from MongoDBKnowledgeAdapter.__init__ under vector_store.config.

Option	Type	Default	Description
`connection_string`	`str`	`"mongodb://localhost:27017/"`	MongoDB connection URI (use `mongodb+srv://...` for Atlas)
`database`	`str`	`"praisonai"`	Database name
`collection`	`str`	`"knowledge_base"`	Collection name
`use_vector_search`	`bool`	`True`	Enable Atlas `$vectorSearch`; falls back to text search when unavailable

Embeddings are configured via the top-level embedder key:

config = {
    "vector_store": {
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb+srv://...",
            "database": "praison",
            "collection": "support_knowledge",
        },
    },
    "embedder": {
        "provider": "openai",
        "config": {"model": "text-embedding-3-small"},
    },
}

Common Patterns

Isolate a single customer’s session by combining all three scopes:

kb.search("refund promise",
          user_id="customer_42", agent_id="support_bot_v1", run_id="session_2026_08_09")

Broaden across all sessions for one customer by omitting run_id:

kb.search("refund history", user_id="customer_42")

Best Practices

Use Atlas for vector search

use_vector_search=True needs a MongoDB Atlas cluster with a vector index on the embedding path (similarity cosine). Without Atlas, the adapter transparently falls back to text search.

Always pass scope for multi-tenant apps

Provide user_id (and optionally agent_id / run_id) on both add() and search() to keep tenants isolated. Omitted scopes broaden the search on that dimension.

Prefer the Agent API

Passing knowledge={...} and user_id=... on the Agent handles scoping and context injection automatically. Use the direct Knowledge class only for custom indexing or search control.

Use mongodb+srv:// for Atlas connections

Atlas connection strings start with mongodb+srv:// or contain mongodb.net — the adapter detects these to enable vector search.

As of PR #3810, the MongoDB adapter honors user_id / agent_id / run_id on both add() and search(). On earlier releases these kwargs were silently ignored, producing cross-tenant results. Upgrade to a release built after commit 6b6707c1.

Knowledge Backends

Compare Chroma, mem0, and MongoDB storage backends

MongoDB Memory

Use MongoDB as the agent memory store

Getting Started

Install

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Telemetry & Analytics

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

Quick Start

How It Works

Configuration Options

Common Patterns

Best Practices

Knowledge Backends

MongoDB Memory

​Quick Start

​How It Works

​Configuration Options

​Common Patterns

​Best Practices

​Related

Knowledge Backends

MongoDB Memory

Quick Start

How It Works

Configuration Options

Common Patterns

Best Practices

Related