recipes

Reference recipes

Recipes live in [google/adk-recipes](https://github.com/google/adk-recipes). **`core/python/`** is the curated tier — canonical ADK patterns maintained by the agents-cli team.

**Reading this page is not studying a recipe.** Every `core/` recipe ships an **`AGENTS.md`** — intent, a ranked "study in this order" file tour, what to copy as-is versus what is recipe-specific, and the gotchas. Clone the recipe and read its `AGENTS.md` before you implement anything it covers. Until you have opened it you are answering from memory. Hand-writing a Docker or E2B sandbox wrapper, a skill loader, a moderation callback or a memory store — for a capability the index lists — means you stopped at the name.

**Study and adapt — don't scaffold from a recipe.**

[ -d /tmp/adk-recipes ] || git clone --filter=blob:none --depth 1 --sparse \
  https://github.com/google/adk-recipes /tmp/adk-recipes
cd /tmp/adk-recipes
git sparse-checkout add core/python/<recipe>
cat core/python/<recipe>/AGENTS.md

Topic → recipe

Capabilities below are **not** scaffold flags — they come from studying a recipe and adapting it.

| You need | Study | |---|---| | Retrieval / search over your own documents (RAG) | `rag-agent-search` (managed ingestion) · `rag-vector-search` (custom chunking + embeddings) | | Running shell commands or Python on a user's behalf; a sandboxed, isolated or per-user environment or workspace | `long-horizon-harness` | | Agent-loadable skills — `SKILL.md` folders discovered at runtime, rebound mid-session, promoted and demoted from memory | `long-horizon-harness` | | Long-running autonomy — works across days, resumes, unattended, compacts context | `long-horizon-harness` | | Approval gate, escalation or human sign-off before a risky, high-value or irreversible action (human-in-the-loop) | `long-horizon-harness` (durable, mid-turn) · `ambient-expense-agent` (workflow pause) · `deep-search` (plan approval) | | Memory across conversations | `cross-session-memory` (the primitive) · `long-horizon-harness` (self-improvement loop built on it) | | Blocking harmful content or risky calls — moderation in one place, covering a coordinator and every sub-agent without editing them | `safety-plugins` (runner-wide plugins) · `long-horizon-harness` (per-tool guard chain + exfil detection) | | Per-user credentials the model must never see | `long-horizon-harness` | | OAuth user consent to act on a user's data | `oauth-user-consent-flow` | | Sub-agent delegation with isolated context windows | `long-horizon-harness` | | No chat interface — records or messages land on a queue and are processed automatically; event-driven, scheduled, batch or headless worker | `ambient-expense-agent` (Pub/Sub queue consumer) · `long-horizon-harness` (routines + scheduler) | | Iterative research with cited sources | `deep-search` | | Generating images or video — product photography, a model wearing the item (virtual try-on), 360° spins, background replacement — and MCP toolsets | `genmedia-for-commerce` | | A2A interop, incl. Gemini app client quirks | `long-horizon-harness` | | Monitoring a deployed agent's quality from its production traces — scheduled evaluation, recurring-failure tracking, root-cause analysis | `ambient-quality-agent` |

In Phase 1, clone the recipes named above and read `/tmp/adk-recipes/core/python/<recipe>/AGENTS.md` before you write any code. During Phase 0, naming them in the spec is enough — the clone waits for approval. A bare how-question has no spec to wait for: clone before you answer it.

The recipes

These ten are the **complete** set of `core/` python recipes. If a capability isn't listed here, there is no core recipe for it — don't guess at a plausible name (`core/python/code-execution` and `core/python/human-in-the-loop` do not exist). Check `contrib/` or build it yourself.

• **`long-horizon-harness`** — a complete agent *harness*: per-user sandbox, runtime-discovered `SKILL.md`

skills, cross-session memory with a self-improvement loop, layered tool guardrails, sub-agent delegation with durable HITL, and per-user secrets. Its `AGENTS.md` maps each interface to the real function that implements it, so lift one pattern without adopting the whole harness.

• Key files: `AGENTS.md`, `horizon/agent.py`, `horizon/fast_api_app.py`, `docs/architecture.md`, `docs/quickstart.md`
• Keywords: harness, sandbox, shell execution, code execution, isolated environment, long-horizon, long-running, multi-day, autonomous, resumable, compaction, guardrails, exfil, egress, approval gate, human-in-the-loop, HITL, per-user secrets, credentials, sub-agents, delegation, self-improving, memory bank, routines, scheduler, a2a, skills, model routing
• **`rag-agent-search`** — managed document search via Search in Gemini platform (Discovery Engine) with a

fully-managed GCS Data Connector: drop files in a bucket, no ingestion code to maintain.

• Key files: `AGENTS.md`, `app/agent.py`, `infra/terraform/agent_platform_search.tf`, `infra/terraform/scripts/setup_data_connector.py`
• Keywords: RAG, document search, Discovery Engine, Search in Gemini platform, Agent Platform Search, managed ingestion, GCS data connector, PDF, HTML, grounding
• **`rag-vector-search`** — RAG with Vector Search 2.0 in Gemini platform and a KFP ingestion pipeline (chunking +

BigQuery staging; embeddings auto-generated server-side).

• Key files: `AGENTS.md`, `app/agent.py`, `data_ingestion/data_ingestion_pipeline/pipeline.py`, `infra/terraform/scripts/setup_vector_search_collection.py`
• Keywords: RAG, retrieval, vector search, embeddings, similarity search, ScaNN, semantic search, document Q&A, ingestion pipeline, chunking
• **`cross-session-memory`** — remembers user preferences and facts across sessions via Memory Bank in

Gemini platform: written after each turn, recalled at the start of a later one.

• Key files: `AGENTS.md`, `app/app_utils/memory_config.py`, `app/agent.py`, `app/fast_api_app.py`

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