DSAgt¶

DataSmith Agent — AI-assisted data pipeline builder.

DSAgt connects an MCP-compatible AI coding agent to code registration, a semantic knowledge base, execution provenance, and observability infrastructure. It provides data-pipeline scaffolding around your existing agent CLI or VS Code extension (Claude Code, Goose, Codex, and others).

Supported Agents¶

Agent	Install	Verify
Claude Code	`npm i -g @anthropic-ai/claude-code`	`claude --version`
Goose	See Goose docs	`goose --version`
Codex	`npm i -g @openai/codex` (or `brew install --cask codex`)	`codex --version`
opencode	See opencode docs	`opencode --version`
Cline	`npm i -g cline`	`cline --version`

Prerequisites¶

Python 3.12 or 3.13
One of the supported agent platforms above, installed and authenticated against your LLM provider
uv — only for the development install

Installation¶

For use (no development)¶

python3.12 -m venv ~/.venvs/dsagt          # or: conda create -n dsagt python=3.12 && conda activate dsagt
source ~/.venvs/dsagt/bin/activate         # (Windows venv: ~\.venvs\dsagt\Scripts\activate)
pip install "git+https://github.com/AI-ModCon/dsagt.git"
dsagt --version                            # 0.2.0

This puts the dsagt CLI on your PATH. Create your first project — dsagt init is interactive (it walks you through the agent platform, project location, packaged knowledge collections, and skill sources) and sets up the knowledge base on first run:

dsagt init                      # interactive; pick agent, collections, sources

Then start dsagt (shorthand for starting your agent with the dsagt MCP server enabled), or open the project in VS Code:

dsagt start <my-project>   # ≈ cd ~/dsagt-projects/my-project && claude   (or your preferred agent)

Or, if you use a VS Code agent extension, just open the folder as a project in VS Code and start the agent — dsagt init already made the dsagt MCP server available via the native interface (e.g. for Claude, exposed in the project's .mcp.json).

To upgrade later, reinstall — re-running dsagt init reconfigures an existing project in place:

pip install --upgrade "git+https://github.com/AI-ModCon/dsagt.git"

Pin to a specific release: e.g. pip install "git+https://github.com/AI-ModCon/dsagt.git@0.2.0".

For development¶

Clone the repo and use uv (editable install; add --all-groups for the test suite):

git clone https://github.com/AI-ModCon/dsagt.git
cd dsagt && uv sync --all-groups
source .venv/bin/activate

Key Capabilities¶

Capability	What it does
Code Registry	Register CLI codes as markdown specs; the agent discovers and runs them via `search_registry`
Knowledge Base	Hybrid semantic + keyword (BM25) search over indexed ChromaDB collections
Skills Discovery	Search the external skill corpus and install workflow skills on demand via `search_skills` / `install_skill`, without flooding the agent's context
Provenance	`dsagt-run` wrapper records every code execution to `trace_archive/`; `reconstruct_pipeline` renders it as a runnable script
Explicit Memory	User-confirmed facts persisted to YAML and the knowledge base
Episodic Memory	Opt-in: the MCP server mechanically chunks and embeds each session turn into a searchable `session_memory` collection (recency-weighted retrieval)
Observability	Serverless MLflow tracing (a per-project SQLite file) — DSAgt's own spans plus agent traces recovered from the on-disk transcript

See the Quick Start to try all of these in a single session.