sus-adk 0.1.0

A modular sus-adk for LLM interaction via cookies, inspired by LangChain and Google-ADK.

sus-adk

A modular, extensible framework for interacting with LLM providers using cookies for authentication/session management. Inspired by LangChain and Google-ADK.

Features

• Modular agent, provider, session, and tool abstractions
• Integrates with any LLM provider using a single GenericProvider
• Cookie/session-based authentication (manual or automatic via browser, with headless and browser selection)
• Chaining and workflow support for multi-step agentic tasks
• Tool support: register, validate, and invoke custom tools/functions
• LLM-driven tool selection: agent can parse LLM output to invoke tools
• Multi-step agentic loop: alternate between LLM and tool calls, inject tool results
• OpenAI function-calling compatibility: generate OpenAI-compatible function specs from tools
• Memory support: store and retrieve conversational/workflow state
• Context window management: limit context passed to LLMs
• Error recovery: handle and log tool/LLM errors
• Vector store memory: semantic retrieval of relevant knowledge
• Streaming: stream LLM/tool responses to the user
• Integration hooks: connect to external systems (webhooks, APIs, databases)
• Async support: async run/stream methods for scalable applications
• Distributed API: FastAPI REST API for multi-agent and remote use
• UI integration: Streamlit web UI for interactive demos
• Cloud deployment: Docker/cloud-ready
• Automatic browser-based cookie fetching (headless, Chrome/Firefox/Edge)
• Extensible for plugins and custom providers
• Fully tested and production-ready

Installation

Install via pip:

pip install agentic-framework

Or, for local development:

pip install .

Automatic Cookie Fetching via Browser (Headless & Browser Selection)

If you don't have cookies, the agent can fetch them for you using a browser. You can choose the browser and headless mode:

from agentic_framework import Agent
from agentic_framework.providers import GenericProvider

provider = GenericProvider("https://llm-provider.com/api/generate")
url = "https://llm-provider.com/login"  # Replace with your provider's login URL

# Use headless Firefox for cookie fetching
agent = Agent(provider, url_for_cookies=url, cookie_wait_time=60, browser='firefox', headless=True)

result = agent.run("Hello, world!")
print("LLM response:", result)

Supported browsers: 'chrome', 'firefox', 'edge'. Set headless=False to see the browser window.

Usage

Basic Example

from agentic_framework import Agent, Session
from agentic_framework.providers import GenericProvider

session = Session({"sessionid": "your-session-id"})
api_url = "https://llm-provider.com/api/generate"  # Replace with your provider's endpoint
provider = GenericProvider(api_url)
agent = Agent(provider, session)
result = agent.run("Hello, world!")
print(result)

Chaining

from agentic_framework import Chain

chain = Chain([
    lambda x: agent.run(f"Summarize: {x}"),
    lambda x: agent.run(f"Translate to French: {x}")
])

output = chain.run("The quick brown fox jumps over the lazy dog.")
print(output)

Session Management

from agentic_framework import Session

session = Session()
session.set_cookie('sessionid', 'abc123')
print(session.get_cookie('sessionid'))
print(session.as_dict())

Tool Support & LLM-Driven Tool Selection

Define, register, and use tools (custom Python functions) with argument schema validation. The agent can also parse LLM output to invoke tools automatically.

from agentic_framework import Agent, Session, Tool
from agentic_framework.providers import GenericProvider

def add(a, b):
    return a + b

add_tool = Tool(
    name="add",
    description="Add two numbers",
    func=add,
    arg_schema={"a": int, "b": int}
)

provider = GenericProvider("https://fake-llm.com/api/generate")
session = Session()
agent = Agent(provider, session)
agent.register_tool(add_tool)

# Direct tool call
result = agent.call_tool("add", a=2, b=3)
print(result)  # Output: 5

# LLM-driven tool selection (simulate LLM output)
class DummyProvider(GenericProvider):
    def generate(self, prompt, session, **kwargs):
        return 'TOOL: add {"a": 2, "b": 3}'

agent.provider = DummyProvider("")
result = agent.run_with_tools("What is 2 + 3?")
print(result)  # Output: 5

Tool Listing

You can list all registered tools and their descriptions:

for tool in agent.tools.values():
    print(f"{tool.name}: {tool.description}")

Multi-Step Agentic Loop (LLM/Tool Alternation)

from agentic_framework import Agent, Session, Tool
from agentic_framework.providers import GenericProvider

add_tool = Tool(
    name="add",
    description="Add two numbers",
    func=lambda a, b: a + b,
    arg_schema={"a": int, "b": int}
)

class DummyProvider(GenericProvider):
    def __init__(self, responses):
        super().__init__("")
        self.responses = responses
        self.call_count = 0
    def generate(self, prompt, session, **kwargs):
        resp = self.responses[self.call_count]
        self.call_count += 1
        return resp

responses = [
    'TOOL: add {"a": 2, "b": 3}',
    'The answer is 5.'
]

provider = DummyProvider(responses)
session = Session()
agent = Agent(provider, session)
agent.register_tool(add_tool)

result = agent.run_agentic_loop("What is 2 + 3?")
print("Final result:", result)  # Output: The answer is 5.

OpenAI Function-Calling Compatibility

You can generate OpenAI-compatible function specs from tools for use with OpenAI's function-calling API:

from agentic_framework import Tool

def add(a: int, b: int) -> int:
    return a + b

tool = Tool(
    name="add",
    description="Add two numbers",
    func=add,
    arg_schema={"a": int, "b": int}
)

spec = tool.openai_function_spec()
print(spec)

Memory, Context Window, and Error Recovery

from agentic_framework import Agent, Session, Memory
from agentic_framework.providers import GenericProvider

# Dummy provider that echoes context and can raise errors
def error_provider_generate(prompt, session, context=None, **kwargs):
    if "fail" in prompt:
        raise RuntimeError("Simulated LLM failure")
    return f"Prompt: {prompt} | Context: {context}"

class ErrorProvider(GenericProvider):
    def generate(self, prompt, session, context=None, **kwargs):
        return error_provider_generate(prompt, session, context, **kwargs)

memory = Memory()
session = Session()
provider = ErrorProvider("")
agent = Agent(provider, session, memory=memory, context_window=2)

# Add some messages to memory
agent.run("First message")
agent.run("Second message")
agent.run("Third message")

# The context window should only include the last 2 messages
print("Context window:", memory.get_messages(2))

# Simulate LLM error
result = agent.run("fail now")
print("Error recovery result:", result)
print("Memory after error:", memory.get_messages())

Vector Memory & Semantic Retrieval

from agentic_framework import Agent, Session, VectorMemory
from agentic_framework.providers import GenericProvider

class PrintContextProvider(GenericProvider):
    def generate(self, prompt, session, context=None, **kwargs):
        return f"Prompt: {prompt} | Context: {context}"

vector_memory = VectorMemory()
session = Session()
provider = PrintContextProvider("")
agent = Agent(provider, session, vector_memory=vector_memory, context_window=2)

agent.add_to_vector_memory("The Eiffel Tower is in Paris.")
agent.add_to_vector_memory("The capital of France is Paris.")
agent.add_to_vector_memory("Mount Everest is the tallest mountain.")

result = agent.run("Where is the Eiffel Tower?", use_semantic_context=True)
print("Result with semantic context:", result)

Streaming

from agentic_framework import Agent, Session
from agentic_framework.providers import GenericProvider

class StreamingProvider(GenericProvider):
    def stream_generate(self, prompt, session, context=None, **kwargs):
        for word in (prompt + " streamed!").split():
            yield word

provider = StreamingProvider("")
session = Session()
agent = Agent(provider, session)

for chunk in agent.stream_run("Hello world"):
    print(chunk)

Integration Hooks

from agentic_framework import Agent, Session
from agentic_framework.providers import GenericProvider

def fake_webhook(data):
    return f"Webhook received: {data}"

provider = GenericProvider("")
session = Session()
agent = Agent(provider, session)

agent.register_integration("webhook", fake_webhook)
result = agent.call_integration("webhook", {"event": "test", "value": 42})
print("Integration result:", result)

Async Usage

import asyncio
from agentic_framework import Agent, Session
from agentic_framework.providers import GenericProvider

class AsyncProvider(GenericProvider):
    async def async_generate(self, prompt, session, context=None, **kwargs):
        await asyncio.sleep(0.1)
        return f"Async response: {prompt} | Context: {context}"

async def main():
    provider = AsyncProvider("")
    session = Session()
    agent = Agent(provider, session)
    result = await agent.async_run("Hello async!")
    print("Async run result:", result)

if __name__ == "__main__":
    asyncio.run(main())

Distributed API (FastAPI)

Run the REST API:

uvicorn backend.api:app --reload

• POST /run — Run agent on a prompt
• POST /stream — Stream agent response
• POST /tool — Call a tool
• POST /integration — Call an integration
• GET /memory — Get memory state

UI Integration (Streamlit)

Run the web UI:

streamlit run backend/ui_app.py

Cloud Deployment

You can deploy with Docker or any cloud service that supports FastAPI/Streamlit. Example Dockerfile:

FROM python:3.9
WORKDIR /app
COPY backend/ .
RUN pip install -r requirements.txt
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

Testing

All tests are in backend/tests/. To run:

python -m unittest discover backend/tests

Extending

To support custom request/response handling, subclass BaseProvider and implement generate:

from agentic_framework.provider import BaseProvider
from agentic_framework.session import Session

class MyCustomProvider(BaseProvider):
    def __init__(self, api_url):
        self.api_url = api_url
    def generate(self, prompt: str, session: Session, **kwargs):
        # Custom logic here
        pass

Examples

See backend/examples/ for example scripts.

License

MIT