https://github.com/the-swarm-corporation/swarms-client
A production-grade Python client for the Swarms API, providing a simple and intuitive interface for creating and managing AI swarms.
https://github.com/the-swarm-corporation/swarms-client
agent-collaboration agentic-ai agentic-systems agentic-workflow agents ai anthropic artificial-learning cohere docs machine-learning ml multi-agent openai swarns
Last synced: about 2 months ago
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A production-grade Python client for the Swarms API, providing a simple and intuitive interface for creating and managing AI swarms.
- Host: GitHub
- URL: https://github.com/the-swarm-corporation/swarms-client
- Owner: The-Swarm-Corporation
- License: apache-2.0
- Created: 2025-05-13T01:27:19.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-08-26T03:38:06.000Z (about 2 months ago)
- Last Synced: 2025-08-26T04:22:03.899Z (about 2 months ago)
- Topics: agent-collaboration, agentic-ai, agentic-systems, agentic-workflow, agents, ai, anthropic, artificial-learning, cohere, docs, machine-learning, ml, multi-agent, openai, swarns
- Language: Python
- Homepage: https://docs.swarms.ai
- Size: 2.56 MB
- Stars: 5
- Watchers: 2
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
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README
# Swarms Client Python API library
[)](https://pypi.org/project/swarms-client/)
The Swarms Client Python library provides convenient access to the Swarms Client REST API from any Python 3.8+
application. The library includes type definitions for all request params and response fields,
and offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx). The full comprehensive documentation can be found on [docs.swarms.ai](https://docs.swarms.ai)
and there is also simple documentation available in the [api.md](api.md) in this repository.
## Installation
```sh
# install from PyPI
pip install swarms-client
```
## Quick Example
```python
import os
from swarms_client import SwarmsClient
from dotenv import load_dotenv
load_dotenv()
client = SwarmsClient(
api_key=os.getenv("SWARMS_API_KEY"),
)
patient_symptoms = """
Patient: 45-year-old female
Chief Complaint: Chest pain and shortness of breath for 2 days
Symptoms:
- Sharp chest pain that worsens with deep breathing
- Shortness of breath, especially when lying down
- Mild fever (100.2°F)
- Dry cough
- Fatigue
"""
out = client.swarms.run(
name="ICD Analysis Swarm",
description="A swarm that analyzes ICD codes",
swarm_type="ConcurrentWorkflow",
task=patient_symptoms,
agents=[
{
"agent_name": "ICD Analyzer",
"description": "An agent that analyzes ICD codes",
"system_prompt": "You are an expert ICD code analyzer. Your task is to analyze the ICD codes and provide a detailed explanation of the codes.",
"model_name": "groq/openai/gpt-oss-120b",
"role": "worker",
"max_loops": 1,
"max_tokens": 8192,
"temperature": 0.5,
},
{
"agent_name": "ICD Code Explanation",
"description": "An agent that explains the ICD codes",
"system_prompt": "You are an expert ICD code explainer. Your task is to explain the ICD codes to the user.",
"model_name": "groq/openai/gpt-oss-120b",
"role": "worker",
"max_loops": 1,
"max_tokens": 8192,
"temperature": 0.5,
},
{
"agent_name": "ICD Code Explanation",
"description": "An agent that explains the ICD codes",
"system_prompt": "You are an expert ICD code explainer. Your task is to explain the ICD codes to the user.",
"model_name": "groq/openai/gpt-oss-120b",
"role": "worker",
"max_loops": 1,
"max_tokens": 8192,
"temperature": 0.5,
},
],
)
print(out)
```
## Helper Methods
```python
import os
import json
from dotenv import load_dotenv
from swarms_client import SwarmsClient
load_dotenv()
client = SwarmsClient(api_key=os.getenv("SWARMS_API_KEY"))
print(json.dumps(client.models.list_available(), indent=4))
print(json.dumps(client.health.check(), indent=4))
print(json.dumps(client.swarms.get_logs(), indent=4))
print(json.dumps(client.client.rate.get_limits(), indent=4))
print(json.dumps(client.swarms.check_available(), indent=4))
```
## API Features
## Usage
The full API of this library can be found in [api.md](api.md).
```python
import os
from swarms_client import SwarmsClient
client = SwarmsClient(
api_key=os.environ.get("SWARMS_API_KEY"), # This is the default and can be omitted
)
response = client.get_root()
```
While you can provide an `api_key` keyword argument,
we recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)
to add `SWARMS_API_KEY="My API Key"` to your `.env` file
so that your API Key is not stored in source control.
## Async usage
Simply import `AsyncSwarmsClient` instead of `SwarmsClient` and use `await` with each API call:
```python
import os
import asyncio
from swarms_client import AsyncSwarmsClient
client = AsyncSwarmsClient(
api_key=os.environ.get("SWARMS_API_KEY"), # This is the default and can be omitted
)
async def main() -> None:
response = await client.get_root()
asyncio.run(main())
```
Functionality between the synchronous and asynchronous clients is otherwise identical.
### With aiohttp
By default, the async client uses `httpx` for HTTP requests. However, for improved concurrency performance you may also use `aiohttp` as the HTTP backend.
You can enable this by installing `aiohttp`:
```sh
# install from PyPI
pip install swarms-client[aiohttp]
```
Then you can enable it by instantiating the client with `http_client=DefaultAioHttpClient()`:
```python
import asyncio
from swarms_client import DefaultAioHttpClient
from swarms_client import AsyncSwarmsClient
async def main() -> None:
async with AsyncSwarmsClient(
api_key="My API Key",
http_client=DefaultAioHttpClient(),
) as client:
response = await client.get_root()
asyncio.run(main())
```
## Using types
Nested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev) which also provide helper methods for things like:
- Serializing back into JSON, `model.to_json()`
- Converting to a dictionary, `model.to_dict()`
Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set `python.analysis.typeCheckingMode` to `basic`.
## Nested params
Nested parameters are dictionaries, typed using `TypedDict`, for example:
```python
from swarms_client import SwarmsClient
client = SwarmsClient()
response = client.agent.run(
agent_config={"agent_name": "agent_name"},
)
print(response.agent_config)
```
## Handling errors
When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of `swarms_client.APIConnectionError` is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of `swarms_client.APIStatusError` is raised, containing `status_code` and `response` properties.
All errors inherit from `swarms_client.APIError`.
```python
import swarms_client
from swarms_client import SwarmsClient
client = SwarmsClient()
try:
client.get_root()
except swarms_client.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
except swarms_client.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
except swarms_client.APIStatusError as e:
print("Another non-200-range status code was received")
print(e.status_code)
print(e.response)
```
Error codes are as follows:
| Status Code | Error Type |
| ----------- | -------------------------- |
| 400 | `BadRequestError` |
| 401 | `AuthenticationError` |
| 403 | `PermissionDeniedError` |
| 404 | `NotFoundError` |
| 422 | `UnprocessableEntityError` |
| 429 | `RateLimitError` |
| >=500 | `InternalServerError` |
| N/A | `APIConnectionError` |
### Retries
Certain errors are automatically retried 2 times by default, with a short exponential backoff.
Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,
429 Rate Limit, and >=500 Internal errors are all retried by default.
You can use the `max_retries` option to configure or disable retry settings:
```python
from swarms_client import SwarmsClient
# Configure the default for all requests:
client = SwarmsClient(
# default is 2
max_retries=0,
)
# Or, configure per-request:
client.with_options(max_retries=5).get_root()
```
### Timeouts
By default requests time out after 1 minute. You can configure this with a `timeout` option,
which accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/timeouts/#fine-tuning-the-configuration) object:
```python
from swarms_client import SwarmsClient
# Configure the default for all requests:
client = SwarmsClient(
# 20 seconds (default is 1 minute)
timeout=20.0,
)
# More granular control:
client = SwarmsClient(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
client.with_options(timeout=5.0).get_root()
```
On timeout, an `APITimeoutError` is thrown.
Note that requests that time out are [retried twice by default](#retries).
## Advanced
### Logging
We use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.
You can enable logging by setting the environment variable `SWARMS_CLIENT_LOG` to `info`.
```shell
$ export SWARMS_CLIENT_LOG=info
```
Or to `debug` for more verbose logging.
### How to tell whether `None` means `null` or missing
In an API response, a field may be explicitly `null`, or missing entirely; in either case, its value is `None` in this library. You can differentiate the two cases with `.model_fields_set`:
```py
if response.my_field is None:
if 'my_field' not in response.model_fields_set:
print('Got json like {}, without a "my_field" key present at all.')
else:
print('Got json like {"my_field": null}.')
```
### Accessing raw response data (e.g. headers)
The "raw" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,
```py
from swarms_client import SwarmsClient
client = SwarmsClient()
response = client.with_raw_response.get_root()
print(response.headers.get('X-My-Header'))
client = response.parse() # get the object that `get_root()` would have returned
print(client)
```
These methods return an [`APIResponse`](https://github.com/The-Swarm-Corporation/swarms-sdk/tree/main/src/swarms_client/_response.py) object.
The async client returns an [`AsyncAPIResponse`](https://github.com/The-Swarm-Corporation/swarms-sdk/tree/main/src/swarms_client/_response.py) with the same structure, the only difference being `await`able methods for reading the response content.
#### `.with_streaming_response`
The above interface eagerly reads the full response body when you make the request, which may not always be what you want.
To stream the response body, use `.with_streaming_response` instead, which requires a context manager and only reads the response body once you call `.read()`, `.text()`, `.json()`, `.iter_bytes()`, `.iter_text()`, `.iter_lines()` or `.parse()`. In the async client, these are async methods.
```python
with client.with_streaming_response.get_root() as response:
print(response.headers.get("X-My-Header"))
for line in response.iter_lines():
print(line)
```
The context manager is required so that the response will reliably be closed.
### Making custom/undocumented requests
This library is typed for convenient access to the documented API.
If you need to access undocumented endpoints, params, or response properties, the library can still be used.
#### Undocumented endpoints
To make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other
http verbs. Options on the client will be respected (such as retries) when making this request.
```py
import httpx
response = client.post(
"/foo",
cast_to=httpx.Response,
body={"my_param": True},
)
print(response.headers.get("x-foo"))
```
#### Undocumented request params
If you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request
options.
#### Undocumented response properties
To access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You
can also get all the extra fields on the Pydantic model as a dict with
[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).
### Configuring the HTTP client
You can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:
- Support for [proxies](https://www.python-httpx.org/advanced/proxies/)
- Custom [transports](https://www.python-httpx.org/advanced/transports/)
- Additional [advanced](https://www.python-httpx.org/advanced/clients/) functionality
```python
import httpx
from swarms_client import SwarmsClient, DefaultHttpxClient
client = SwarmsClient(
# Or use the `SWARMS_CLIENT_BASE_URL` env var
base_url="http://my.test.server.example.com:8083",
http_client=DefaultHttpxClient(
proxy="http://my.test.proxy.example.com",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
)
```
You can also customize the client on a per-request basis by using `with_options()`:
```python
client.with_options(http_client=DefaultHttpxClient(...))
```
### Managing HTTP resources
By default the library closes underlying HTTP connections whenever the client is [garbage collected](https://docs.python.org/3/reference/datamodel.html#object.__del__). You can manually close the client using the `.close()` method if desired, or with a context manager that closes when exiting.
```py
from swarms_client import SwarmsClient
with SwarmsClient() as client:
# make requests here
...
# HTTP client is now closed
```
## Versioning
This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:
1. Changes that only affect static types, without breaking runtime behavior.
2. Changes to library internals which are technically public but not intended or documented for external use. _(Please open a GitHub issue to let us know if you are relying on such internals.)_
3. Changes that we do not expect to impact the vast majority of users in practice.
We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.
We are keen for your feedback; please open an [issue](https://www.github.com/The-Swarm-Corporation/swarms-sdk/issues) with questions, bugs, or suggestions.
### Determining the installed version
If you've upgraded to the latest version but aren't seeing any new features you were expecting then your python environment is likely still using an older version.
You can determine the version that is being used at runtime with:
```py
import swarms_client
print(swarms_client.__version__)
```
## Requirements
Python 3.8 or higher.
## Contributing
See [the contributing documentation](./CONTRIBUTING.md).