banditdb-python 0.1.4

Official Python SDK for BanditDB

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).

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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

Method	Description
health()	Returns True if the server is reachable and healthy.
list_campaigns()	Returns a list of all live campaigns with their alpha and arm_count.
campaign_info(campaign_id)	Returns the full diagnostic state for one campaign: per-arm theta, theta_norm, prediction_count, reward_count, and totals. Raises APIError (404) if not found.
create_campaign(campaign_id, arms, feature_dim, alpha=1.0, algorithm="linucb")	Register a new campaign. algorithm is "linucb" (default) or "thompson_sampling". alpha controls exploration for both — for TS it sets the posterior width; 1.0 is the principled default.
delete_campaign(campaign_id)	Delete a campaign. Returns False if not found.
predict(campaign_id, context)	Returns (arm_id, interaction_id).
reward(interaction_id, reward)	Close the feedback loop. Reward must be in [0, 1].
checkpoint()	Flush the WAL, snapshot models, write Parquet files, rotate the WAL. Returns a summary string.
export()	List per-campaign Parquet files created by checkpoint(). Returns a formatted string.

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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 five tools:

Tool	What it does
create_campaign	Create a new decision campaign. Accepts algorithm ("linucb" or "thompson_sampling") and alpha. Use Thompson Sampling for natural Bayesian exploration with no tuning needed.
list_campaigns	List all active campaigns (shows algorithm and alpha) — useful to check what exists before calling get_intuition.
campaign_diagnostics	Inspect per-arm learning state: theta_norm, prediction counts, reward rates. Use this when a campaign doesn't seem to be learning.
get_intuition	Ask BanditDB which arm to pick for a given context. Returns the arm and an interaction_id to save.
record_outcome	Report whether the chosen action succeeded (1.0) or failed (0.0). Updates the shared model.

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

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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]"

Estimator	Function	How it works	When to use
Replay	replay(df)	Accepts each interaction with probability (1/K) / propensity (Li et al. 2010). Unbiased sample of the uniform random policy.	Sanity check baseline. Low coverage is expected — ~1/K of interactions are used.
IPS / SNIPS	ips(df, clip=10.0)	Uses every interaction with importance weight (1/K) / propensity. Self-normalised to reduce variance. Weight clipping (default 10×) controls the bias-variance tradeoff.	Primary estimator. Use when you have enough data but want full coverage.
Doubly Robust	doubly_robust(df, clip=10.0)	Fits a linear reward model, then applies an IPS correction on residuals. Consistent if either the reward model or the propensities are correct.	Best statistical efficiency. Use when comparing multiple policies or sweeping alpha.

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")

# How much reward would a uniform random policy have earned?
print(replay(df))
# OPEResult(method='replay', estimate=0.4821, std_error=0.0312, coverage=22.1% [33/149])

print(ips(df))
# OPEResult(method='ips', estimate=0.5103, std_error=0.0187, coverage=100.0% [149/149])

print(doubly_robust(df))
# OPEResult(method='doubly_robust', estimate=0.5219, std_error=0.0141, coverage=100.0% [149/149])

# Compare against the observed reward of the logging policy:
print("Observed (logging policy):", df["reward"].mean())
# If observed >> estimate, the campaign has learned something real — it outperforms random.

Practical use: sweep alpha offline before deploying. Train a campaign on real traffic, checkpoint to Parquet, then replay different alpha values through doubly_robust() to find the best exploration level — no live experiment needed.

Note: OPE requires the propensity column, which is only written for LinUCB campaigns. Thompson Sampling campaigns log null propensities because TS arm selection is stochastic and propensity scoring requires a deterministic logging policy.

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Choosing an Algorithm

BanditDB supports two contextual bandit algorithms, selected at campaign creation time.

Algorithm	algorithm value	Exploration style	When to use
LinUCB	"linucb" (default)	Deterministic UCB bonus: θ·x + α√(x·A⁻¹·x)	Predictable, tunable. Sweep alpha offline to calibrate.
Linear Thompson Sampling	"thompson_sampling"	Samples θ̃ ~ N(θ, α²·A⁻¹), scores by θ̃·x	Bayesian posterior — no alpha-sweep needed. alpha=1.0 is the natural posterior width. Concurrent users automatically diversify choices.

# LinUCB (default) — tune alpha to control how long it keeps exploring
db.create_campaign("routing", ["fast", "cheap"], feature_dim=4, alpha=1.5)

# Thompson Sampling — natural Bayesian exploration, alpha=1.0 is ideal
db.create_campaign("routing_ts", ["fast", "cheap"], feature_dim=4,
                   algorithm="thompson_sampling")

Both algorithms share identical state (A⁻¹, b, θ per arm), so the predict → reward loop is the same regardless of which you choose.

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Error Handling

Exception	When raised
BanditDBError	Base exception — catch this to handle all SDK errors.
ConnectionError	Server is offline or unreachable.
TimeoutError	Request exceeded the configured timeout.
APIError	Server returned an error (e.g., campaign not found, unauthorized).

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License

Apache-2.0 — Copyright (C) 2026 Simeon Lukov and Dynamic Pricing Ltd. See the main repository for details.