Aurelion Agent — base agent loop, tool calling, tracing, and LLM provider interface
Project description
aurelion-agent
Part of the AURELION Ecosystem
A modular base agent loop with tool calling, multi-provider LLM support, and built-in trace observability. Drop it into any project to get a production-ready agent in minutes.
Install
pip install aurelion-agent
Quickstart
from aurelion_agent import BaseAgent, LLMProvider
class MyAgent(BaseAgent):
name = "my-agent"
system_prompt = "You are a helpful assistant."
agent = MyAgent(llm=LLMProvider(provider="groq", model="llama-3.3-70b-versatile"))
trace = agent.respond("What is data minimisation?")
print(trace.last_response)
Providers
Supports: groq, openai, anthropic (claude), ollama, gemini, mistral
LLMProvider(provider="openai", model="gpt-4o")
LLMProvider(provider="ollama", model="llama3")
LLMProvider(provider="anthropic", model="claude-3-5-sonnet-20241022")
Trace Sinks
Wire observability into any agent with built-in sinks:
from aurelion_agent import FileSink, AgentOpsSink, stdout_sink
agent = MyAgent(
llm=LLMProvider(provider="groq"),
trace_sinks=[
stdout_sink,
FileSink("traces.jsonl"),
AgentOpsSink("https://your-agentops-instance.fly.dev", api_key="..."),
]
)
Tool Calling
from aurelion_agent import BaseAgent, LLMProvider, tool
class ResearchAgent(BaseAgent):
name = "researcher"
system_prompt = "You research topics thoroughly."
@tool
def search_web(self, query: str) -> str:
"""Search the web for information."""
# your implementation
return results
Architecture
aurelion-agent is the Agent module of the AURELION modular cognitive architecture:
KERNEL → Knowledge schema and organizational structure
MEMORY → Persistent storage and retrieval
NEXUS → Agent orchestration and routing
ADVISOR → Strategic planning and knowledge management
AGENT → This package — AI collaboration and prompt protocols
Links
- GitHub: aurelion-labs/aurelion-eco
- Ecosystem: AURELION Ecosystem
Project details
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