data-harness

data + ReAct — a controlled data-agent SDK for Python workflows.

A data-native agent SDK for Python — built around controlled execution, handle-based state, provider adapters, sessions, subagents, and reconstructable runs.

Most agent frameworks hand the model a shell and call it a day. data-harness takes a different approach: the model operates through a constrained Python interpreter, with data stored in a session cache and exposed as named handles. No bash. Explicit state. Logs that can reconstruct what happened.

Why no bash?

Giving an agent shell access is the path of least resistance, but it creates real problems in production: unpredictable side effects, security exposure, and behaviour that's hard to reproduce. data-harness deliberately constrains the model to Python only — which turns out to be enough for most data workloads and forces cleaner tool design.

Core design decisions

Handle/snapshot pattern — Large objects (DataFrames, arrays, query results) live in a SessionCache, not in message history. The model only sees a compact snapshot — shape, columns, a few sample rows. It accesses the data by writing Python against the handle name.

Prefix-stable system prompt — The system prompt never changes between turns. Reminders, state, and nags are appended to the conversation suffix. A stable prefix means the provider can cache it, which reduces latency and cost on long runs.

Progressive connector disclosure — Data connectors (databases, APIs, warehouses) are registered but hidden from the tool list until explicitly loaded. A shorter tool list means the model makes better routing decisions.

Subagent isolation — Spawned subagents get a fresh adapter and a fresh cache. State is transferred explicitly via input_handles. No implicit shared state.

JSONL turn logging — Every turn is logged to a .jsonl file from the start. Each line is a complete turn record including latency, token counts, and cache hit/miss.

Install

pip install data-harness

For OpenAI support:

pip install "data-harness[openai]"

Quick example

from data_harness import Agent
from data_harness.providers.anthropic import AnthropicAdapter

agent = Agent(
    adapter=AnthropicAdapter(model="claude-sonnet-4-6"),
    system="You are a data analyst.",
)
print(agent.run("Compute the mean of [1, 2, 3, 4, 5]."))

See the Quickstart guide to go further.

Design series

The design behind data-harness is covered in a three-part series:

License

MIT