BanditDB Python SDK

The official Python client and Model Context Protocol (MCP) server for BanditDB — the ultra-fast, lock-free Contextual Bandit database written in Rust.

BanditDB abstracts away the complex linear algebra of Reinforcement Learning (LinUCB, Thompson Sampling) behind a dead-simple API. Build real-time personalizers, dynamic A/B tests, and give LLM agents mathematically rigorous persistent memory.

Installation

pip install banditdb-python

Requires the BanditDB Rust server running (default: http://localhost:8080).

1. Standard SDK Usage

The client features automatic connection pooling, exponential backoff retries, and strict timeouts.

from banditdb import Client, BanditDBError

# Connect to the BanditDB server.
# Pass api_key if BANDITDB_API_KEY is set on the server.
db = Client(
    url="http://localhost:8080",
    timeout=2.0,
    api_key="your-secret-key",   # omit if server runs without auth
)

try:
    # 1. Create a campaign (run once at startup)
    # algorithm defaults to "linucb"; use "thompson_sampling" for Bayesian exploration
    db.create_campaign(
        campaign_id="checkout_upsell",
        arms=["offer_discount", "offer_free_shipping"],
        feature_dim=3,
    )
    # or: db.create_campaign(..., algorithm="thompson_sampling")

    # 2. A user arrives — ask the database what to show them
    # Context: [is_mobile, cart_value_normalized, is_returning_user]
    arm_id, interaction_id = db.predict("checkout_upsell", [1.0, 0.8, 0.0])
    print(f"Showing: {arm_id}")  # e.g., "offer_free_shipping"

    # 3. The user clicked — send the reward
    db.reward(interaction_id, reward=1.0)

except BanditDBError as e:
    print(f"Database error: {e}")

All Client methods

Health

Campaigns

Predict & Reward

Data & Export

2. The AI "Hive Mind" (Model Context Protocol)

Standard LLM agents are stateless — if they route a task to the wrong model and fail, they repeat the same mistake tomorrow. BanditDB's built-in MCP server gives the entire agent swarm shared persistent memory.

Starting the MCP server

# Set environment variables before starting
export BANDITDB_URL=http://localhost:8080
export BANDITDB_API_KEY=your-secret-key   # omit if server runs without auth

banditdb-mcp

Connecting to Claude Desktop

Add to your Claude configuration file:

• Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "banditdb": {
      "command": "banditdb-mcp",
      "args": [],
      "env": {
        "BANDITDB_URL": "http://localhost:8080",
        "BANDITDB_API_KEY": "your-secret-key"
      }
    }
  }
}

The agent swarm now has nine tools:

Every decision made by any agent in the network improves the routing for all future agents.

3. Data Science & Offline Evaluation

BanditDB event-sources every prediction and reward to a Write-Ahead Log (WAL). Calling checkpoint() compiles completed prediction→reward pairs into Snappy-compressed Parquet files — one per campaign — for offline analysis with Polars or Pandas.

Every prediction is guaranteed to appear in the Parquet file even if its reward arrives hours later: BanditDB re-emits in-flight interactions at each checkpoint so delayed rewards are always captured in a future cycle.

# Checkpoint: snapshot models, write Parquet, rotate the WAL.
# Call this on a schedule or after significant traffic.
summary = db.checkpoint()
print(summary)
# "Checkpoint written and WAL rotated: 2 campaigns, offset 4821 bytes,
#  150 interactions exported, 3 in-flight re-emitted"

# List which Parquet files are available
print(db.export())
# 'Parquet files in /data/exports: ["llm_routing.parquet"]'

# Load directly from the mounted volume into Polars.
# Flat schema: interaction_id | arm_id | reward | predicted_at | rewarded_at | propensity | feature_0 | ...
import polars as pl
df = pl.read_parquet("/data/exports/llm_routing.parquet")
print(df.head())
print(df.columns)

Offline Policy Evaluation (OPE)

The SDK ships three OPE estimators in banditdb.eval. They answer the question: "what would my average reward have been under a different policy — without running a live experiment?"

Install the eval dependencies:

pip install "banditdb-python[eval]"

All three estimators:

• Accept a Polars or pandas DataFrame loaded from a BanditDB Parquet export
• Evaluate the uniform random policy as the target (the unbiased baseline to beat)
• Raise ValueError for Thompson Sampling campaigns (propensity column is null — TS does not log propensities)
• Return an OPEResult with estimate, std_error, n_used, n_total, and method

import polars as pl
from banditdb.eval import replay, ips, doubly_robust

df = pl.read_parquet("/data/exports/llm_routing.parquet")

Read the full README on GitHub →