Practical overview
When it fits
Use Agentsilex when implementation transparency and direct modification matter more than a large prebuilt abstraction layer, especially for educational projects, prototypes and custom Python agent systems.
Who it is for
- Python developers building custom AI agents
- Educators teaching agent concepts
- Researchers prototyping agent architectures
- Companies seeking a modifiable foundation for agent systems
Documented uses
- Define Python functions as agent tools.
- Run single-agent conversations with session history.
- Route requests between specialist agents through handoffs.
- Switch between supported language-model providers through LiteLLM.
- Create validated structured outputs with Pydantic.
Requirements & limits
The repository is public and displays an MIT license. Installation is documented through pip or uv. The supplied material does not establish pricing or account requirements for model providers.
- The core implementation is described as approximately 300 lines and intended to be readable and hackable.
- LiteLLM integration is documented for switching among many model ecosystems, including OpenAI, Anthropic, Google Gemini and DeepSeek.
- The framework includes both tool calling and multi-agent handoffs.
- Pydantic structured output and type-based tool schema extraction support typed Python workflows.
- OpenTelemetry tracing can be connected to Phoenix or other OTLP backends.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Agentsilex and create a basic tool-using agent
Set up the package and reproduce the repository’s documented Python agent structure.
Before you start
- Python 3.12 or newer.
- A model provider configured for the selected LiteLLM model.
- A Python environment with package installation available.
- The Agentsilex package installed with pip or uv.
Steps
Install the package with pip install agentsilex, or add it with uv add agentsilex.
Import Agent, Runner, Session and tool from agentsilex.
Define a Python function with the @tool decorator.
Create an Agent with a model, instructions and the tool, then create a Session and Runner.
Call runner.run with the agent and a user query, and read result.final_output.
Expected outcome
A Python agent structure that can receive a query, use a defined tool and return a final natural-language output.
Guide limitations
- The example’s weather function returns a fixed value and notes that a production implementation would call a real weather API.
- This is a documented setup and usage recipe, not an independent execution test.
Add Phoenix tracing for an Agentsilex workflow
Configure the repository’s documented OpenTelemetry and Phoenix observability path.
Before you start
- Agentsilex installed in a Python environment.
- Phoenix installed with pip.
- A local environment able to run the Phoenix server.
- An OTLP endpoint configuration supported by the local setup.
Steps
Install Phoenix with pip install arize-phoenix.
Start Phoenix with python -m phoenix.server.main serve.
Set OTEL_EXPORTER_OTLP_ENDPOINT to http://localhost:4317.
Run the agent workflow and inspect traces at http://localhost:6006.
Expected outcome
Phoenix is configured to display a trace tree for agent execution, tool calls and handoffs, according to the repository instructions.
Guide limitations
- The recipe describes the documented configuration path and is not an independent observability verification.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 3 October 2026
- Original website title
- howl-anderson/agentsilex · Official homepage title · retained in its source language