# {{ name }}

{{ description }}

This project was scaffolded with `a2a init`. It starts as a DeepAgents-backed
A2A agent that uses caller-provided LLM credentials from `ctx.llm`.

## Durable Files

A2A Cloud workspaces are grant-scoped and backed by MinIO. Files become durable
when your agent uses one of the platform-owned file paths:

- `ctx.workspace` for direct workspace reads and writes
- `ctx.write_artifact(...)` for explicit output artifacts
- `ctx.sandbox` for commands that read or write files in a sandbox
- `ctx.workspace_backend()` for framework file tools such as DeepAgents

DeepAgents has its own built-in file tools (`write_file`, `read_file`,
`edit_file`). Without an A2A backend, those tools write into LangGraph state
only, so files can appear to the agent but never reach MinIO or `/workspace`.

Keep this line when building DeepAgents graphs:

```python
backend = ctx.workspace_backend()
return create_deep_agent(model=model, backend=backend, tools=[...])
```

Invoke DeepAgents graphs with the starter recursion budget:

```python
state = await graph.ainvoke(
    {"messages": [{"role": "user", "content": prompt}]},
    config={"recursion_limit": 500},
)
```

The backend respects the caller's grant. In handoffs, generated files should go
through `ctx.workspace_backend()`, `ctx.write_artifact(...)`, or a sandbox helper
so they are mirrored to the caller workspace instead of becoming private virtual
files.

For stable, human-readable paths, write intentional outputs to
`/workspace/outputs/...` or `ctx.write_artifact(...)`. If sandboxed code writes
to process-local paths such as `/tmp/result.csv`, `/root`, or `/app`, the
platform captures changed rootfs files under `outputs/rootfs-captures/...` so
the caller can still download and inspect them.

When a skill needs to run real code, render media, convert files, or call a
CLI that writes outputs, use the workspace-mounted sandbox helpers:

```python
result = await ctx.workspace_shell(
    "python script.py --out /tmp/result.txt",
    image="python:3.11-slim",
    timeout_seconds=120,
)
```

Do not rely on `asyncio.create_subprocess_exec(...)` for durable outputs. A
plain subprocess runs in the agent container, which is not mounted to the
caller workspace; files it creates in `/tmp` or the image filesystem can vanish
after the request. Use the sandbox helpers for any file-producing toolchain.

## DeepAgents Skills

If this project grows reusable workflow knowledge, add source-controlled skill
folders under `skills/<skill-name>/SKILL.md`. The starter's `_seed_runtime_skills`
helper copies those packaged skills into the invocation workspace and passes the
resulting source path to `create_deep_agent(..., skills=[...])`, which is how
DeepAgents discovers skills with progressive disclosure.

## Run Locally

```bash
python -m pip install -r requirements.txt
a2a dev
a2a test
a2a test --invoke --skill summarize --args-json '{"text":"hello"}'
a2a card
```

`a2a dev` loads `.env.local`, creates `.a2a/workspace/{inputs,outputs}`, serves
the same HTTP invoke/card/MCP endpoints as production, and hot reloads local
code. Files written through `ctx.workspace_backend()` land under
`.a2a/workspace/outputs` before you deploy.

## Auth

The template is public by default (`auth_model = NoAuth`). To require the
caller's app login, declare a typed auth model and resolver in `agent.py`.
Resolvers receive the inbound bearer token and return the principal exposed as
`ctx.auth`.

```python
from a2a_pack import JWTAuth, OIDCUserInfoAuthResolver

auth_model = JWTAuth
auth_resolver = OIDCUserInfoAuthResolver(
    "https://auth.example.com/oauth2/userinfo",
    auth_model=JWTAuth,
)
```

For homegrown auth or SAML-backed apps, expose a bearer-token `/me` or
`/introspect` endpoint and use the same resolver contract.

## Deploy

```bash
a2a deploy
```
