# Agentops Stacks > Scaffold a new AgentOps Stacks project — a multi-agent LangGraph bundle (DAB) with shared components, per-agent Databricks Apps, evaluation, and CI/CD. Use when the user wants to start a new AI agent project on Databricks. Triggers on "scaffold a new agentops project", "new DAB with CI/CD", "start a new Databricks AI project", "create agentops-stacks project". - Skill: `databricks-solutions/agentops-stacks` (Agent Skill, multi-file: 11 files) - Install (CLI): `npx skillmds@latest add databricks-solutions/agentops-stacks` - Raw SKILL.md: https://api.skillmd.com/api/skills/databricks-solutions/agentops-stacks/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: databricks-solutions (https://skillmd.com/u/databricks-solutions) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/databricks-solutions/agentops-stacks --- # agentops-stacks — Agent Project Scaffold Generates a production-ready multi-agent project on Databricks: shared components (retriever, memory, tools), per-agent LangGraph graphs served as Databricks Apps via MLflow AgentServer, evaluation with ConversationSimulator, and CI/CD workflows. The first scaffold creates the bundle with one agent. Additional agents are added later via `/add-agent` or manually under `src/agents/`. ## Prerequisites 1. **Databricks CLI** installed and on PATH. Verify with `databricks --version` (skip in Genie Code). 2. **uv** for Python dependency management. 3. **Node.js >=20.19** for the chat UI frontend. If any are missing, surface install instructions and stop. ## Scaffold workflow ``` Scaffold Progress: - [ ] Phase 0: Infer intent - [ ] Phase 1: Infrastructure inputs - [ ] Phase 2: Data sources - [ ] Phase 3: Tools - [ ] Phase 4: Evaluation - [ ] Phase 5: Validate, confirm, scaffold - [ ] Phase 6: Surface next steps ``` ### Phase 0: Infer intent from what the user said Before asking anything, read the user's description and pre-fill as many inputs as you can. Only ask about inputs that are genuinely unknown or ambiguous. Show what you've inferred so the user can correct it. **Keyword → input mapping:** | If the user mentions… | Infer | |---|---| | "RAG", "retrieval", "search documents", "knowledge base", "unstructured data" | `use_vector_search: yes` | | "already chunked", "existing Delta table", "I have a chunked table" | `has_chunked_table: yes` | | "memory", "remember conversations", "conversation history", "persistent context" | `use_lakebase: yes` — still ask `memory_type` unless they specified it | | "short-term memory", "session memory", "within-session" | `use_lakebase: yes`, `memory_type: short_term` | | "long-term memory", "cross-session memory", "persistent memory" | `use_lakebase: yes`, `memory_type: long_term` | | "UC functions", "Unity Catalog tools", "catalog functions", "SQL tools" | `use_uc_functions: yes` | | "functions already exist", "existing UC functions" | `use_uc_functions: yes`, `uc_functions_exist: yes` | | "GitHub", "GitHub Actions" | `cicd_platform: github_actions` | | "GitHub Enterprise", "GHES" | `cicd_platform: github_actions_for_github_enterprise_servers` | | "GitLab" | `cicd_platform: gitlab` | | "Azure DevOps", "ADO" | `cicd_platform: azure_devops` | | "AWS", "Amazon" | `cloud: aws` | | "Azure" (non-DevOps context) | `cloud: azure` | | "GCP", "Google Cloud" | `cloud: gcp` | | "synthetic", "generate test data", "LLM simulator" | `eval_dataset_source: synthetic` | | "production traces", "from prod", "from logs" | `eval_dataset_source: production_traces` | | "existing dataset", "I already have eval data" | `eval_dataset_source: existing` | After inference, present a summary like: > Based on what you described, I'll configure: > - Vector Search (RAG): **yes** > - Lakebase memory: **no** ← _ask if unsure_ > - UC functions: **no** > - Eval dataset: **synthetic** > > Does that look right, or would you like to change anything? Then only ask for inputs still missing. ### Phase 1: Infrastructure Collect any of these not already known: 1. **project_name** (string) — Bundle name. Must match `^[a-z][a-z0-9_]{2,}$`. 2. **initial_agent_name** (string) — Name of the first agent. Same pattern. Default: `default`. 3. **cloud** — `aws`, `azure`, or `gcp`. 4. **cicd_platform** — `github_actions`, `github_actions_for_github_enterprise_servers`, `azure_devops`, or `gitlab`. 5. **destination** (path) — Must be a Git folder. Default: cwd if it's a Git folder. ### Phase 2: Data sources Confirm or ask only for inputs not inferred: 6. **use_vector_search** — `yes`/`no` - If yes → **has_chunked_table** — "Do you already have a chunked Delta table to sync from? If no, the scaffold includes ingestion and preparation notebooks." → `yes`/`no` 7. **use_lakebase** — `yes`/`no` - If yes and `memory_type` not inferred → **memory_type** — `short_term`, `long_term`, or `both` ### Phase 3: Tools 8. **use_uc_functions** — `yes`/`no` - If yes and `uc_functions_exist` not inferred → **uc_functions_exist** — "Are those UC functions already defined in your catalog?" → `yes`/`no` ### Phase 4: Evaluation 9. **eval_dataset_source** — only ask if not inferred from context: - `synthetic` — generate a golden dataset using an LLM simulator (default) - `manual` — scaffold a notebook with example rows to fill in manually - `production_traces` — build from production traces filtered by tag - `existing` — skip dataset creation (you already have one) ### Phase 5: Validate, confirm, scaffold Write all collected inputs to `/tmp/agentops-stacks-inputs.json` using these exact keys: ```json { "input_project_name": "", "input_initial_agent_name": "", "input_cloud": "", "input_cicd_platform": "", "input_use_vector_search": "yes|no", "input_has_chunked_table": "yes|no", "input_use_lakebase": "yes|no", "input_memory_type": "short_term|long_term|both", "input_use_uc_functions": "yes|no", "input_uc_functions_exist": "yes|no", "input_eval_dataset_source": "synthetic|manual|production_traces|existing" } ``` Omit conditional keys whose parent is `no` (e.g. omit `input_has_chunked_table` if `input_use_vector_search` is `no`). The schema defaults handle those. Validate: ```bash python scripts/validate_inputs.py --config /tmp/agentops-stacks-inputs.json ``` Show the user a summary of which components are enabled, then confirm before running: ```bash bash scripts/scaffold.sh --config /tmp/agentops-stacks-inputs.json --destination ``` ### Phase 6: Next steps See [reference/post-scaffold.md](reference/post-scaffold.md) for the full next-steps checklist. After scaffolding: 1. `cd /` and run `uv sync` 2. Set workspace hosts in `databricks.yml` and create Unity Catalog catalogs — see `docs/setup.md` 3. `databricks bundle validate -t dev` 4. `databricks bundle deploy -t dev` to deploy Point the user at the reference example closest to what they just scaffolded: | If they enabled... | Best reference example | |---|---| | Vector Search (RAG) | [`examples/simple-rag/`](../../../examples/simple-rag/) — full RAG agent: VS ingestion, retrieval, Foundation Model API, Databricks App, eval gate | | Lakebase memory or multiple agents | [`examples/multi-agent/`](../../../examples/multi-agent/) — three agents sharing tools, each an independent App, per-agent eval datasets | | Neither (minimal start) | [`examples/hello-agent/`](../../../examples/hello-agent/) — minimal pyfunc agent: register → evaluate → gate loop | Each example is a complete reference with agent code, DAB resources, setup notebooks, and an eval gate. Encourage the user to copy patterns from the nearest example rather than starting from scratch. ## Adding more agents 1. Create `src/agents//` with `agent.py`, `tools.py`, `app/`, `eval/` 2. Add a new `resources.apps.` entry in `databricks.yml` 3. Add to `.agentops-stacks/manifest.yml` under `agents:` 4. Add entry points in `pyproject.toml` (or use `AGENT_MODULE` env var) A future `/add-agent` skill will automate this. ## Additional references - **Genie Code flow**: See [reference/genie-code.md](reference/genie-code.md) - **Troubleshooting**: See [reference/common-issues.md](reference/common-issues.md) - **Template repo**: - **Official app template**: