agent-data-toolkit 0.2.0

Connect agents to data

Agent Data Toolkit

An open source toolkit that provides modular connectors for AI agents to interact with diverse data systems. It standardizes connection and query patterns, handles configuration and errors, and delivers results in a consistent format. Designed to be reused by other projects, it enables frameworks like MCP servers or custom agent implementations to define tools on top of a common abstraction layer for seamless data access.

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Current Data Connectors

Connector	Status	Extras name	Notes
PostgreSQL	✅ Ready	postgresql	Supports queries, schema introspection, DataFrame helpers
Elasticsearch	🚧 Planned	elasticsearch	Query and aggregation support (future)
MongoDB	🚧 Planned	mongodb	Basic CRUD and aggregation pipelines
Neo4j	🚧 Planned	neo4j	Cypher query support (future)
Splunk	🚧 Planned	splunk	Search/query API support (future)

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Installation

From PyPI

The easiest way is to install the package directly from PyPI:

# With uv (recommended)
uv pip install agent-data-toolkit

# Or with pip
pip install agent-data-toolkit

To enable a specific connector, install with extras. For example, PostgreSQL:

uv pip install "agent-data-toolkit[postgresql]"
# or
pip install "agent-data-toolkit[postgresql]"

From Source

If you want to work with the latest code from GitHub:

git clone https://github.com/Cyb3rWard0g/agent-data-toolkit.git
cd agent-data-toolkit

# Create a virtual environment
uv venv .venv
source .venv/bin/activate

# Install in editable/dev mode
uv pip install -e ".[dev]"
# or
pip install -e ".[dev]"

You can still add extras here, e.g.:

uv pip install -e ".[dev,postgresql]"

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Quick Example

Using the PostgreSQL connector:

from agent_data_toolkit.postgresql import PostgresClient

# Create client from environment variables (PG_DSN / POSTGRES_DSN / DATABASE_URL)
pg = PostgresClient.from_env()

rows = pg.query_rows("SELECT 1 AS ok")
print(rows)

pg.close()

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Release Process

To publish a new release to PyPI:

0. Install dev dependencies

   uv pip install -e ".[dev]"
   

1. Ensure all changes are committed and tests pass:

   uv run pytest tests/
   

2. Create and push an annotated tag for the release:

   git tag -a v0.1.0 -m "Release 0.1.0"
   git push origin v0.1.0
   

3. Checkout the tag to ensure you are building exactly from it:

   git checkout v0.1.0
   

4. Clean old build artifacts:

   rm -rf dist
   rm -rf build
   rm -rf src/*.egg-info
   

5. Upgrade build and upload tools:

   uv pip install --upgrade build twine packaging setuptools wheel setuptools_scm
   

6. Build the package:

   uv run python -m build
   

7. (Optional) Check metadata:

   uv run twine check dist/*
   

8. Upload to PyPI:

   uv run twine upload dist/*
   

Notes:

• Twine ≥ 6 and packaging ≥ 24.2 are required for modern metadata support.
• Always build from the tag (git checkout vX.Y.Z) so setuptools_scm resolves the exact version.
• git checkout v0.1.0 puts you in detached HEAD mode; that’s normal. When done, return to your branch with:

  git switch -
  

• If you’re building in CI, make sure tags are fetched:

  git fetch --tags --force --prune
  git fetch --unshallow || true
  

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