Metadata-Version: 2.3
Name: a2a-pack
Version: 0.1.43
Summary: Developer SDK + CLI for building, packaging, and deploying A2A agents.
Project-URL: Homepage, https://a2acloud.io
Project-URL: Documentation, https://docs.a2acloud.io
Project-URL: Repository, https://gitea.a2acloud.io/gitea_admin/a2a-pack
Project-URL: Issues, https://gitea.a2acloud.io/gitea_admin/a2a-pack/issues
Author-email: a2a cloud <hello@a2acloud.io>
License: MIT License
        
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Keywords: a2a,agent,agents,ai,llm,marketplace,mcp,microvm,model-context-protocol,sandbox
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Framework :: FastAPI
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: >=3.11
Requires-Dist: boto3>=1.34
Requires-Dist: cryptography>=42
Requires-Dist: fastapi>=0.110
Requires-Dist: httpx>=0.27
Requires-Dist: jinja2>=3
Requires-Dist: pydantic>=2.6
Requires-Dist: pyjwt>=2.8
Requires-Dist: python-multipart>=0.0.9
Requires-Dist: pyyaml>=6
Requires-Dist: rich>=13
Requires-Dist: typer>=0.12
Requires-Dist: uvicorn[standard]>=0.27
Provides-Extra: dev
Requires-Dist: build>=1.2; extra == 'dev'
Requires-Dist: httpx>=0.27; extra == 'dev'
Requires-Dist: playwright>=1.45; extra == 'dev'
Requires-Dist: pytest-asyncio>=0.23; extra == 'dev'
Requires-Dist: pytest>=8; extra == 'dev'
Requires-Dist: twine>=5; extra == 'dev'
Description-Content-Type: text/markdown

# a2a-pack

**Developer SDK + CLI for building, packaging, and deploying full A2A compliant agents.**

One Python class becomes a sandboxed, discoverable, MCP-compatible,
full A2A compliant AI agent on the [a2a cloud](https://a2acloud.io)
platform. Other agents reach yours via HMAC-signed grants. The platform
owns deployment, execution, permissions, and (when you're ready) billing.

a2a-pack makes [Google's Agent2Agent protocol](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)
easy: Agent Cards, tasks, messages, artifacts, file/data exchange, streaming,
auth, JSON-RPC, REST, and protocol errors are handled by the SDK runtime.

```bash
pip install a2a-pack
a2a login
a2a init research-agent
cd research-agent
a2a dev
a2a test --invoke
a2a deploy
# -> https://research-agent.a2acloud.io   (A2A, MCP, TLS, all wired)
```

## What an agent looks like

```python
from pydantic import BaseModel
from a2a_pack import (
    A2AAgent, LLMProvisioning, NoAuth, Pricing, RunContext, skill,
)


class GreeterConfig(BaseModel):
    suffix: str = "!"


class Greeter(A2AAgent[GreeterConfig, NoAuth]):
    name = "greeter"
    description = "Say hi."
    version = "0.1.0"
    config_model = GreeterConfig
    auth_model = NoAuth

    # Hosted generated agents usually use platform LLM grants. The scoped
    # token is exposed through ctx.llm at invoke time.
    llm_provisioning = LLMProvisioning.PLATFORM
    pricing = Pricing(price_per_call_usd=0.01, caller_pays_llm=False)

    @skill(description="Greet someone.")
    async def greet(self, ctx: RunContext[NoAuth], who: str) -> str:
        await ctx.emit_progress(f"greeting {who}")
        return f"hello {who}{self.config.suffix}"
```

That's it. `a2a deploy` packages the source, the control plane builds
the image, ArgoCD reconciles, you get a full A2A compliant public URL.

## Local development

Use the same agent card, invoke path, secret names, and workspace contract
before uploading anything:

```bash
a2a dev
```

That starts the agent at `http://127.0.0.1:8000`, loads `.env.local`, creates
`.a2a/workspace/{inputs,outputs}`, and enables hot reload. Skills are callable at
`POST /invoke/{skill}` and the card is visible at
`/.well-known/agent-card`.

It also serves a bundled local console at `http://127.0.0.1:8000/_dev` with
drag-and-drop file upload into `inputs/`, workspace file previews/downloads,
schema-derived skill inputs, streaming progress, and final results.

## Packed frontends

Agents can ship with a static frontend app mounted by the same runtime:

```bash
a2a init chart-agent --frontend static
a2a init chart-agent --frontend react
a2a init private-app --frontend react --auth platform
a2a frontend build
a2a dev
```

The manifest lives in `a2a.yaml`:

```yaml
frontend:
  path: frontend
  build: npm run build   # optional for prebuilt static bundles
  dist: dist
  mount: /app
  auth: inherit
```

Use `--auth platform` to scaffold a packed app that requires an A2A Cloud
session. The generated agent uses `PlatformUserAuth`, so skill code can read
`ctx.auth.user_id`, `ctx.auth.email`, `ctx.auth.org_slug`, and `ctx.auth.scopes`
without wiring OAuth itself. Hosted deployments verify the HttpOnly platform
session cookie against the control plane; trusted identity headers are opt-in
for gateway deployments.

At runtime the agent serves the app and generated contract data:

| Path | Purpose |
|---|---|
| `/app/` | Packed frontend app |
| `/app/config.json` | Agent name, endpoints, auth mode, docs URL, skill schemas |
| `/app/a2a-client.js` | Browser client for `agent.call(...)` and `agent.skills.<name>(...)` |
| `/.well-known/a2a-skills.json` | Skill names, scopes, input schemas, output schemas |
| `/auth/session` | Local dev session, or platform-provided user/session metadata |

Deploys either copy a prebuilt `frontend/dist` bundle or run `frontend.build`
inside a Node build stage before the Python A2A runtime starts.

The React scaffold creates a Vite app with a generated skill runner. Start
`a2a dev` in one terminal and `npm run dev` inside `frontend/` in another;
Vite proxies `/app/config.json`, `/invoke`, `/auth`, `/mcp`, and
`/.well-known` to the local agent runtime.

Run preflight checks before deploy:

```bash
a2a test
a2a test --invoke --skill summarize --args-json '{"text":"hello"}'
```

Secrets stay local in `.env.local`. Workspace-backed framework tools, including
DeepAgents via `ctx.workspace_backend()`, write durable local outputs under
`.a2a/workspace/outputs` so you can inspect what will become downloadable files
in A2A Cloud.

If your skill runs code that creates files, use `ctx.workspace_shell(...)` or
`ctx.workspace_python(...)`. Those commands run in the platform sandbox with
the caller's workspace mounted, so `/workspace/...` writes persist directly and
changed files elsewhere in the sandbox root filesystem are mirrored under
`outputs/rootfs-captures/...`. Plain in-process `subprocess` calls inside the
agent container are not workspace-mounted or rootfs-captured.

## Bring your own auth

Agents can make caller identity explicit by declaring an `auth_model` and an
`auth_resolver`. The resolver receives the inbound bearer token, validates it
against your auth system, and returns the typed principal skills read from
`ctx.auth`.

```python
from a2a_pack import A2AAgent, JWTAuth, OIDCUserInfoAuthResolver, RunContext, skill


class CustomerAgent(A2AAgent):
    name = "customer-agent"
    description = "Uses the caller's app login"
    auth_model = JWTAuth
    auth_resolver = OIDCUserInfoAuthResolver(
        "https://auth.example.com/oauth2/userinfo",
        auth_model=JWTAuth,
    )

    @skill(scopes=["profile:read"])
    async def profile(self, ctx: RunContext[JWTAuth]) -> dict[str, str | None]:
        return {"sub": ctx.auth.sub, "email": ctx.auth.email}
```

For custom APIs, subclass `AuthResolver` and call your own `/me`,
`/introspect`, or session-exchange endpoint. SAML-backed apps use the same
contract by exposing a bearer-token bridge endpoint that returns JSON.

## Public surface

| Concept | Where |
|---|---|
| `A2AAgent` base class + `@skill` decorator | `a2a_pack.agent` |
| `RunContext`, `ctx.llm`, `ctx.ask`, `ctx.request_scope`, `ctx.ensure_read`, `ctx.ensure_write` | `a2a_pack.context` |
| Grant mint/verify (HMAC, audience-bound, glob-filtered, time-limited) | `a2a_pack.grants` |
| Workspace negotiation surface | `a2a_pack.workspace` |
| Sandbox client (microVM via libkrun) | `a2a_pack.sandbox` |
| Agent-to-agent client (HTTP, in-memory, custom) | `a2a_pack.a2a_client` |
| MCP server (skills → tools, mountable into your FastAPI app) | `a2a_pack.mcp` |
| Lifecycle / Resources / Pricing / LLMProvisioning declarations | `a2a_pack.runtime` |
| Card schema (auto-derived from your class) | `a2a_pack.card` |

## A2A made easy

- Subclass `A2AAgent`.
- Decorate async methods with `@skill`.
- Use `RunContext` for progress, auth/input requests, artifacts, scoped files,
  sandbox execution, and agent-to-agent calls.
- Run `a2a deploy`; the runtime exposes full A2A plus MCP and HTTPS.

Full reference + auto-generated docs at **https://docs.a2acloud.io**.

## Self-hosting

The platform pieces (control plane, sandbox runtime, gitea, ArgoCD,
MinIO, LiteLLM) live at
[gitea.a2acloud.io](https://gitea.a2acloud.io) — the SDK is the only
piece you need on PyPI. If you want to run the whole stack locally
or in your own cluster, the bootstrap recipe is in the platform
[README](https://gitea.a2acloud.io/gitea_admin/a2a-pack).

## License

MIT — see [LICENSE](LICENSE).
