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Quick Start

This guide walks through knowledge ingest, code registration, provenance, and explicit memory using the mock project in tests/smoke_test/. The examples use claude; substitute another agent (goose, codex, opencode, cline) if you prefer — the prompts are agent-agnostic.

DSAgt is BYOA: your agent talks to its own LLM provider directly, and the trace store is a serverless SQLite file per project.

Setup

# Install (any Python 3.12/3.13 environment)
pip install "git+https://github.com/AI-ModCon/dsagt.git"

# Set a convenience variable for the smoke test directory (not a normal dsagt step)
export SMOKE_DIR="$(pwd)/tests/smoke_test"

# 1. Create a project.  `dsagt init` is interactive — follow the menu to name it
#    `quickstart`, pick your agent, and choose knowledge collections + skill sources.
#    It sets up the knowledge base on first run (a ~130 MB local embedder downloads once).
dsagt init

# 2. Launch the agent in the project:
dsagt start quickstart     # …or: cd ~/dsagt-projects/quickstart && <your agent>

Agent Prompts

Inside the agent, paste these prompts one at a time. Replace $SMOKE_DIR with the absolute path you exported — the chat does not expand shell variables.

  1. Ingest the docs in $SMOKE_DIR/knowledge/ into a collection named knowledge.

  2. Register the CLI utility at $SMOKE_DIR/csv_summary.py as a code named csv-summary so we can reuse it.

  3. Use the scan-directory code from the registry to scan $SMOKE_DIR/data/.

  4. Run the csv-summary code on $SMOKE_DIR/data/samples.csv and tell me the columns, row count, and any columns with null values.

  5. Put this in explicit memory: samples.csv has null values in the status and timestamp columns.

  6. Tell me what you remember about the samples dataset.

csv_summary.py is stdlib-only, so there's no dependency to install — registration and execution work out of the box. Step 4's null-column finding is the fact you store and recall in 5–6.

Capabilities Covered

Prompt Capability
1 dsagt-server (kb_ingest) — chunks and indexes docs into ChromaDB
2 dsagt-server (save_code_spec) — writes codes/csv-summary/SKILL.md (a skill-standard dir), wrapping the executable with dsagt-run
3–4 dsagt-run provenance wrapper — records each execution to trace_archive/
5–6 Explicit memory (kb_remember → .dsagt/explicit_memories.yaml) + KB recall (kb_get_memories)

Verify the Artifacts

Exit the agent (Ctrl+C or /exit), then:

dsagt info quickstart                       # config + a session/trace summary
ls ~/dsagt-projects/quickstart/{codes,trace_archive}
cat ~/dsagt-projects/quickstart/.dsagt/explicit_memories.yaml

# Traces land in a serverless SQLite store.  Browse them with:
mlflow ui --backend-store-uri sqlite:///$HOME/dsagt-projects/quickstart/mlflow.db

Non-Interactive Smoke Test

The same flow runs non-interactively and asserts each artifact is present:

dsagt smoke-test --agent claude

Knowledge Base Setup

dsagt init sets up the project's knowledge base with three kinds of collection:

  • Code Specs — DSAgt's built-in code specs, always set up so the agent finds them via search_registry from the first session.
  • Skill Corpus — the skill sources you chose at init (default genesis), cloned and indexed so search_skills returns installable skills.
  • Knowledge Collections — optional reference document sets you chose at init (nemo_curator, aidrin).

--include / --exclude (asset names, or all) select the set non-interactively. The default embedder is a local sentence-transformers model (~130 MB, CPU-side, no API key).

Optional: Episodic Memory

Answer yes to "Enable episodic memory?" in the dsagt init prompts to have the MCP server capture each session turn into a searchable session_memory collection. Capture is mechanical (chunk + embed) and reuses the local embedder, so there's nothing extra to download. See Memory → Episodic Memory.