Selectools
Open-source agent development and engineering for tool calling, human in the loop, agent evaluation.
What does this agent do, and when should you use it?
The repository describes Selectools as: Production-ready Python framework for AI agents with built-in guardrails, audit logging, cost tracking, and hybrid RAG. Supports OpenAI, Anthropic, Gemini, Ollama. By NichevLabs. This profile is a source-based catalog entry; an independent FARS review is still pending.
Production-ready Python framework for AI agents with built-in guardrails, audit logging, cost tracking, and hybrid RAG. Supports OpenAI, Anthropic, Gemini, Ollama. By NichevLabs.
- Evaluate it for an agent application or engineering workflow.
- Prototype integrations around its documented tools or APIs.
- Inspect the source before adapting it to an existing stack.
What are this agent's strengths and limitations?
- Public source and README are available for inspection.
- Focused on tool calling, human in the loop, agent evaluation.
- Setup, model-provider support, and maturity must be confirmed against the current release.
- No independent FARS score has been assigned yet.
How do you install or deploy this agent?
Follow the current installation instructions in the [repository README](https://github.com/johnnichev/selectools#readme). Requirements and provider setup vary by release.
How do you use this agent?
Start with the examples and quickstart in the [repository documentation](https://github.com/johnnichev/selectools#readme), then test the workflow with limited permissions and non-sensitive data.