explaingit

askexort/exort

0PythonAudience · developerComplexity · 3/5ActiveLicenseSetup · easy

TLDR

Python CLI AI agent that picks tools, runs them, and loops until it answers. Works free via Groq or local Ollama, or with paid OpenAI/Anthropic keys.

Mindmap

mindmap
  root((exort))
    Inputs
      Natural language prompts
      API keys or Ollama
      YAML config
    Outputs
      Tool-driven answers
      SQLite chat history
      Telegram replies
    Use Cases
      Free local AI agent
      Web research with citations
      Shell and file automation
    Tech Stack
      Python
      Groq
      Ollama
      SQLite

Things people build with this

USE CASE 1

Run a free AI agent locally with Ollama and no API key

USE CASE 2

Automate web research that fetches pages and writes a comparison

USE CASE 3

Build a Telegram bot backed by an agent with shell and file tools

USE CASE 4

Plug in a custom OpenAI-compatible provider like OpenRouter or DeepSeek

Tech stack

PythonGroqOllamaSQLiteTelegram

Getting it running

Difficulty · easy Time to first run · 5min

Needs either a free Groq key or a local Ollama install before the first query works.

MIT license, free to use, modify, and ship in commercial projects as long as the copyright notice stays.

In plain English

Exort is a Python command-line AI agent that can decide to use tools rather than only chat. The author frames it as something built for hobbyists and small projects rather than enterprises, with the headline pitch that it costs zero dollars to start because it works out of the box with Groq's free API tier or with a fully local Ollama model. What separates an agent from a chatbot, in the README's framing, is that Exort can reason about a question, pick a tool, run it, read the result, and loop until it has an actual answer. The example given is asking for the top three Python web frameworks and their GitHub star counts: Exort runs a web search, then fetches each project's GitHub page, then writes a comparison table. The built-in tool set covers web search through DuckDuckGo, fetching any URL, reading and writing files, listing and searching directories, running shell commands, executing Python snippets, and loading images for vision models. Installation is two commands: clone the repo and run pip install. Three quick-start paths follow. Groq is the recommended starter because it is free and fast. Ollama is the offline option, picking up any local model the user has pulled. OpenAI or Anthropic keys plug in the same way for paid use. Custom OpenAI-compatible providers like Together, OpenRouter, DeepSeek, Mistral, LM Studio, and vLLM can be added by editing a YAML config file. Inside, the project is split into an engine that runs a perceive, reason, act, reflect loop; a CLI shell; a YAML config loader; per-provider backends; a tool registry the author calls the GearBox; a SQLite store for conversation history; a playbook system for markdown knowledge files; and a Telegram bot frontend. The MIT-licensed code is presented as deliberately simple and easy to extend.

Copy-paste prompts

Prompt 1
Walk me through installing exort with Groq's free tier and running my first agent query in under 5 minutes
Prompt 2
Show me how to add a new tool to exort's GearBox, with a minimal example I can paste into tools/
Prompt 3
Configure exort to use a local Ollama llama3.1 model and explain how the engine loop calls it
Prompt 4
Wire exort's Telegram bot to a private chat and explain how conversation history is stored in SQLite
Prompt 5
Add OpenRouter as a custom provider in ~/.exort/config.yaml and switch to it at runtime
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Generated 2026-05-22 · Model: sonnet-4-6 · Verify against the repo before relying on details.