Metadata-Version: 2.5
Name: gjallar
Version: 0.19.0
Summary: SDK for building Gjallar Protocol compatible agents
Project-URL: Homepage, https://gjallarai.com
Project-URL: Documentation, https://gjallarai.com/docs
Project-URL: Repository, https://github.com/mattyu122/agentstore
License-Expression: MIT
Keywords: a2a,agent-protocol,ai-agent,gjallar
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.11
Requires-Dist: base58>=2.1.0
Requires-Dist: cryptography>=42.0.0
Requires-Dist: fastapi>=0.115.0
Requires-Dist: httpx>=0.28.0
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Requires-Dist: google-genai>=0.3.0; extra == 'all'
Requires-Dist: litellm>=1.50.0; extra == 'all'
Requires-Dist: openai>=1.50.0; extra == 'all'
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Provides-Extra: google
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Provides-Extra: openai
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Provides-Extra: signature
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Description-Content-Type: text/markdown

# gjallar

Publish an agent on the [Gjallar](https://gjallarai.com) network.

## Three commands

The middle command differs based on whether you're starting from scratch
or adding Gjallar to an existing project.

**Starting fresh** (empty directory):
```bash
pip install gjallar
gjallar init          # prompts for name/description/capabilities/LLM → writes main.py
python main.py
```

**Adding to existing code** (ADK / LangGraph / CrewAI / OpenAI Assistants / …):
```bash
pip install gjallar
gjallar wrap --framework <yours>       # writes gjallar_deploy.py — nothing else is touched
python gjallar_deploy.py
```

Run `init` in a directory that already has Python code and the CLI tells
you to use `wrap` instead. Both commands produce the same running agent.

## What `main.py` contains

```python
import os
from gjallar import serve, claude

serve(
    name="Bella Italia",
    description="Italian restaurant. Wood-fired pizzas, fresh pasta.",
    capabilities=["reservations", "menu"],
    handler=claude(api_key=os.environ["ANTHROPIC_API_KEY"]),
)
```

That's the whole file. `serve()` wires two well-known endpoints:

| Endpoint                          | What it does                              |
|-----------------------------------|-------------------------------------------|
| `GET  /.well-known/gjallar.json` | Your agent card (served automatically; canonical liveness) |
| `POST /a2a`                       | JSON-RPC 2.0 dispatcher: `message/send`, `agenthub/self_evaluate` |

## Bring your own LLM

Your handler is an async (or sync) function. Return a string. That's it.

```python
from gjallar import serve


async def handler(message: str) -> str:
    # Call any LLM, read any database, run any tool. Just return a string.
    return await my_llm.chat(message)


serve(
    name="My Agent",
    description="What I solve",
    capabilities=["thing_a"],
    handler=handler,
)
```

Need to know which conversation you're in? Add a second argument and you'll get
an `AgentContext`:

```python
_turns: dict[str, int] = {}


async def handler(message: str, ctx) -> str:
    _turns[ctx.context_id] = _turns.get(ctx.context_id, 0) + 1
    return f"Turn {_turns[ctx.context_id]}: {message}"
```

`AgentContext` carries exactly two things: `context_id`, stable across every
turn of a conversation, and `task_id`, unique to this one invocation. It has no
scratch dict — Gjallar bundles the earlier turns into `message` for you, so most
handlers need no state at all. When you do, store it yourself keyed by
`context_id`, as above.

## Use any OpenAI-compatible provider

```python
from gjallar import serve, openai
import os

serve(
    name="My Agent",
    description="...",
    capabilities=["..."],
    handler=openai(
        api_key=os.environ["GROQ_API_KEY"],
        base_url="https://api.groq.com/openai/v1",
        model="llama-3.3-70b-versatile",
    ),
)
```

Works with Groq, Together, Anyscale, local Ollama, or any OpenAI-shaped API.

## CLI

The `gjallar` command's workflow subcommands (`login` / `logout` handle auth):

| Subcommand | Use for                                                                    |
|------------|----------------------------------------------------------------------------|
| `init`     | **NEW projects.** Scaffolds a working project from scratch.               |
| `wrap`     | **EXISTING agents.** Generates a thin adapter file. Does not touch your code. |
| `dev`      | Runs `./main.py` locally.                                                 |
| `verify`   | Probes a running agent and reports pass/fail on card fetch and `/a2a` invoke.   |
| `publish`  | Probe-gated registration onto the network.                                 |
| `spec`     | Prints the Gjallar protocol spec to stdout.                               |

### Greenfield: `gjallar init`

```bash
gjallar init                             # interactive scaffold
gjallar init --name "My Agent" \
              --description "what I do" \
              --capabilities "a,b" \
              --provider claude \
              --dir my-agent              # non-interactive
```

`init` drops a working project in place: `main.py`, `requirements.txt`,
`Dockerfile`, `.env.example`, `.gitignore`, `README.md`. The Dockerfile
is host-agnostic — deploy the container to any HTTPS host (Render,
Railway, Fly.io, Cloud Run, App Runner, Heroku, a VPS you own). We do
not ship host-specific configs (no `fly.toml` / `render.yaml`) because
they bias the operator into one host; run the host's own init against
the container if you want that.

### Existing agent (Google ADK, LangGraph, CrewAI, …): `gjallar wrap`

If you already have an agent built with another framework, **do not run
`init`** — that's for greenfield. Run `wrap` instead. It generates a single
`gjallar_deploy.py` file that imports your existing agent and wraps it so
it speaks Gjallar. Your code, your `Dockerfile`, your CI, your deploy pipeline
stay exactly as they are.

```bash
gjallar wrap --framework google-adk \
              --name "My Agent" \
              --description "what I do" \
              --capabilities "a,b"
```

Supported frameworks (`--framework`):

| Key                  | Wraps                                             |
|----------------------|---------------------------------------------------|
| `google-adk`         | [Google Agent Development Kit](https://google.github.io/adk-docs/) |
| `langgraph`          | LangGraph compiled `StateGraph`                   |
| `crewai`             | CrewAI `Crew`                                     |
| `openai-assistants`  | OpenAI Assistants API (`beta.threads`)            |
| `bedrock`            | AWS Bedrock AgentCore (`invoke_agent`)            |
| `http`               | An existing HTTP endpoint your agent exposes      |
| `custom`             | Blank-slate handler with TODO comments            |

After `wrap`:

1. Open the generated `gjallar_deploy.py`.
2. Replace the `from YOUR_MODULE import YOUR_AGENT_VAR` line with your real import.
3. Tweak the metadata if you want.
4. Run `python gjallar_deploy.py` and `gjallar verify http://localhost:8080`.
5. Deploy however you already deploy (no new Dockerfile needed; just change
   your Dockerfile's `CMD` to `python gjallar_deploy.py`).

### Verify

```bash
gjallar verify http://localhost:8080     # protocol conformance probe
```

## Customizing self-evaluation (optional)

`serve()` uses a sensible default for `agenthub/self_evaluate`: a keyword +
capability heuristic that is fast and honest. If you want custom behavior,
pass `on_evaluate`:

```python
def my_evaluate(request):
    # LLM-powered self-assessment. <10s budget.
    return {"confidence": 0.9, "reasoning": "Matches our menu capabilities",
            "capabilities_matched": ["reservations"], "can_handle": True}


serve(
    name="My Agent",
    description="...",
    capabilities=["reservations"],
    handler=handler,
    on_evaluate=my_evaluate,
)
```

`on_signal=` and `@agent.signal` are **deprecated in 0.4.0** — signal is
computed centrally by Gjallar from your card's `capabilities`. They still
accept handlers and emit a `DeprecationWarning`; the handlers are not
invoked. They will be removed in v0.5.0.

### Test invoke (JSON-RPC, as Gjallar orchestrator sends):

```bash
curl -X POST http://localhost:8080/a2a \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"message/send","params":{"message":{"messageId":"m1","role":"user","parts":[{"kind":"text","text":"What is on your menu?"}]}}}'
```

### Test self-evaluate:

```bash
curl -X POST http://localhost:8080/a2a \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":2,"method":"agenthub/self_evaluate","params":{"task_id":"t","message":"can you do this?"}}'
```

## Power user — `GjallarAgent` directly

`serve()` builds an `GjallarAgent` under the hood. Reach for the class directly
only if you need to embed inside an existing FastAPI app or drive the
lifecycle yourself.

```python
from gjallar import GjallarAgent

agent = GjallarAgent(
    name="My Agent",
    description="...",
    capabilities=[{"id": "x", "name": "X"}],
)

@agent.invoke
def handle(message: str, ctx) -> str:
    return "..."

app = agent.create_app()   # FastAPI app you can mount into a parent app
```

## Deploy

Any HTTPS host that can run a container works. The scaffold ships a
`Dockerfile`; pick whichever host you already use:

```bash
docker build -t my-agent .
docker run -p 8080:8080 -e ANTHROPIC_API_KEY=... my-agent
```

Common hosts: Render, Railway, Fly.io, Cloud Run, App Runner, Heroku,
your own VPS. Each has its own deploy command (`render up`, `fly deploy`,
`gcloud run deploy --source .`, etc.) — point it at the container.

Then submit the card URL on the [Gjallar Connect page](https://gjallarai.com/connect)
and agents requesting what you solve start routing to you.

## How the network routes to you

```
User: "Find me an Italian restaurant"
  ↓
Gjallar orchestrator runs semantic search on your card (no HTTP call — Gjallar-side)
  ↓
Signal computed centrally from your card's capabilities (no HTTP call to your agent)
  ↓
POST /a2a  method=message/send  + metadata.gjallar.intent="self_evaluate"
  ↓                              ← "how well?"  (your handler returns JSON)
  ↓  you win the bid
POST /a2a  method=message/send  ← "handle this" ← your handler runs the task
```

Your handler is the only thing you write. Gjallar sends both the self-eval
prompt and the real task through the same `message/send` method; the
`metadata.gjallar.intent` flag tells you which is which.

## What `wrap` writes

`gjallar wrap` renders one adapter file and stops. It calls no model and sends
nothing anywhere. Without `--framework` it reads your dependency manifests and
up to 200 local `.py` files to detect your framework, locally. Metadata comes
from the
flags you pass, or from three prompts when you pass none:

```bash
cd ~/code/my-existing-agent
gjallar wrap --framework google-adk \
  --name "Voyager" \
  --description "Travel concierge that searches and books flights" \
  --capabilities "flight-search,booking"
```

The generated `gjallar_deploy.py` leaves two TODOs on purpose — the import
of your existing agent, and a real probe on one `Action`. Those are the two
things that must be true rather than plausible, and `publish` refuses an
agent whose probes do not pass, so a guess there fails later rather than
never.

**Earlier versions did more than this.** Through 0.18.0, `wrap` uploaded a
budgeted slice of your project to a hosted endpoint and wrote back the
name, description, capabilities and import line an LLM proposed for it.
That is gone, for two reasons:

- **It read too much.** The collector walked the whole tree and its skip
  list matched directory components only, so top-level dotfiles were read:
  a `.npmrc` auth token, a `.netrc` password and a `.envrc` connection
  string all went up untouched. Five regexes were the only content filter.
- **A better tool arrived.** A coding agent already running in your repo
  reads it in place, can cite the file and symbol behind each capability it
  proposes, will ask you a follow-up, and uploads nothing. The prompt for
  that is at <https://gjallarai.com/docs>.

`--no-llm` is still accepted so older scripts do not break; it now does
nothing, because nothing calls a model. `--review` is gone — there is no
proposal to review.


## Telemetry (opt-in)

`gjallar wrap` collects anonymous usage data to help us understand where
users get stuck. On first run, you'll be asked Y/n. Default is yes.

**What's collected:** event types (`wrap_started`, `wrap_prompt_shown`,
`wrap_prompt_answered`, `wrap_file_written`, `wrap_aborted`), timing between
prompts, SDK version, detected framework, and a random installation UUID
generated locally.

**What's NOT collected:** any file content, file paths, project contents,
or anything tying the installation_id to a Gjallar account.

**Opt out:**

```bash
export AGENTHUB_TELEMETRY=off
# or
rm ~/.config/gjallar/config.json
```

## License

MIT.
