dataact

(data + ReAct — a ReAct agent harness built for data workflows)

A minimal, transparent, data-native agent harness for Python — built without bash.

Most agent frameworks hand the model a shell and call it a day. dataact takes a different approach: the model operates entirely through a sandboxed Python interpreter, with data stored in a session cache and exposed as named handles. No bash. No framework magic. Just a loop you can read in an afternoon.

Built as an installable reference implementation for engineers who want to understand how a production-style harness actually works. It is not a polished SDK surface; the convenience API exists to remove setup noise while keeping the harness boundaries visible.

The design is covered in a three-part series:

• Designing a ReAct Harness for Data Workflows Without Bash
• How a Bash-Free Data Agent Remembers Its Work
• The Bugs Hidden Inside a Data Agent Harness

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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. dataact deliberately constrains the model to Python only — which turns out to be enough for most data workloads and forces cleaner tool design.

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Core design decisions

Each decision here is intentional. Understanding them is the point.

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. This keeps context lean without hiding data from the model.

Prefix-stable system prompt The system prompt never changes between turns. Reminders, state, and nags are appended to the conversation suffix. This is a KV-cache discipline: 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. Connectors are only visible when relevant.

Subagent isolation Spawned subagents get a fresh adapter and a fresh cache. State is transferred explicitly via input_handles. No implicit shared state. This makes subagent behaviour reproducible and debuggable.

Suffix-only nag reminders The planner escalates reminders at 4 / 8 / 12 turns without progress. These are always appended to the suffix, never inserted into the prefix, so the KV cache is never busted by reminder text.

JSONL turn logging Every turn is logged to a .jsonl file from the start. Not bolted on later. Each line is a complete turn record including latency, token counts, and cache hit/miss. Reproducibility is a first-class concern.

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Install

# requires Python 3.10+ and uv
uv sync

Quick start

Agent needs a provider adapter. The adapter is the boundary between the provider SDK and the harness: it turns Anthropic/OpenAI responses into dataact's normalised Message, ToolUseBlock, and token-count types. It is explicit on purpose so the harness is not tied to one model provider, and tests can swap in FakeAdapter without touching the loop.

For Anthropic:

from dataact import Agent
from dataact.providers.anthropic import AnthropicAdapter

adapter = AnthropicAdapter(model="claude-sonnet-4-6")
agent = Agent(adapter=adapter, system="You are a data analyst.")

result = agent.run("Compute the mean of [1, 2, 3, 4, 5] and print it.")
print(result)

For OpenAI, install the optional extra and change only the adapter:

pip install "dataact[openai]"

from dataact.providers.openai import OpenAIAdapter

adapter = OpenAIAdapter(model="gpt-4o-mini")

Run the minimal Anthropic example:

uv run python examples/quickstart.py

examples/quickstart.py requires ANTHROPIC_API_KEY when run as a script. Tests import build_agent() and drive it with FakeAdapter, so the example stays covered without token spend.

Connector example

Connector helpers keep the quick path small while preserving progressive disclosure. Connector tools start hidden; the model must call load_connectors before it can use them.

from dataact import Agent
from dataact.providers.anthropic import AnthropicAdapter

adapter = AnthropicAdapter(model="claude-sonnet-4-6")
agent = Agent(adapter=adapter, system="You are a data analyst.")

market_data = agent.connector(
    "market_data",
    description="Market data tools.",
)


def fetch_ohlcv(symbol: str) -> list[dict]:
    return [{"symbol": symbol, "close": 101.2}]


market_data.tool(
    fetch_ohlcv,
    description="Fetch OHLCV data for a ticker.",
)

result = agent.run("Load market_data and inspect AAPL.")
print(result)

What Agent composes

Agent is a thin composition layer over the lower-level primitives:

• A provider adapter translates model-provider SDK objects into the harness's normalised response types.
• Harness owns the ReAct loop, messages, dispatch, reminders, and JSONL logging.
• SessionCache stores large values as handles plus compact snapshots.
• python_interpreter is the controlled execution surface; there is no bash tool.
• list_variables exposes cache handles without dumping raw payloads.
• ConnectorRegistry keeps connector tools hidden until loaded.
• Planner reminders and subagents are opt-in helpers, not a second runtime.

For the explicit wiring, read examples/advanced_wiring.py. It deliberately shows the moving parts that Agent composes.

Run the advanced example - it loads a checked-in FRED unemployment-rate sample, runs analysis, uses subagents and the planner (requires ANTHROPIC_API_KEY):

uv run python examples/advanced_wiring.py

Run tests:

uv run pytest tests/ -v
uv run pytest tests/smoke_tests.py -m live -v  # requires OPENAI_API_KEY

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Project structure

dataact/
  loop.py          # Harness: the core ReAct loop
  cache.py         # SessionCache: handle/snapshot storage
  providers/       # Normalised adapter interface (Anthropic and OpenAI)
  tools/
    interpreter.py # Sandboxed Python executor
    connectors.py  # Progressive connector registry
    planner.py     # Plan/nag tool
    subagent.py    # Isolated subagent spawning
    variables.py   # list_variables tool
  types.py         # Shared types: Message, ToolSpec, ContentBlock
  logger.py        # JSONL turn logging
  observe.py       # Latency measurement
examples/
  quickstart.py        # Minimal Agent path
  advanced_wiring.py   # Explicit Harness wiring
  data/                # Small public sample data for the advanced demo

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Sandbox disclaimer

The Python interpreter uses AST checks and restricted globals to reduce accidental misuse. It is not a container sandbox and should not be treated as safe for untrusted input.

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License

MIT