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MiniAGI

MiniAGI AI Agent
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Category:Programming Agents

Overview

A compact Python agent experiment with basic tools, short-term memory and an optional critic.

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MiniAGI is Bernhard Mueller's small Python autonomous-agent experiment. It combines an objective-driven prompt, basic tools, short-term memory summarization and an optional critic using the OpenAI API. The README names GPT-3.5-Turbo and GPT-4 as its original model targets. These are historical compatibility statements, not a verification against current models. The repository is accessible but its latest recorded push was June 2023. Examples illustrate experiments rather than guaranteed outcomes or completed commercial integrations.

Product overview

Capabilities, pricing and fit

MiniAGI is Bernhard Mueller's small Python autonomous-agent experiment. It combines an objective-driven prompt, basic tools, short-term memory summarization and an optional critic using the OpenAI API.

Information as of September 29, 2026

What it can do

  • Objective-driven CLI

    Start the Python program with a task objective.[1]

  • Memory and critic

    The README documents memory summarization and an optional critic.[1]

  • Local setup

    Python 3.10+ and optional Docker setup are documented.[2]

Where it fits best

  • Study a compact autonomous-agent implementation.[1]
  • Experiment with Python and shell tools under supervision.[1]
  • Explore optional critic-based task checking.[1]

Pricing and availability

MIT-licensed source; model API usage and local compute are separate costs.[1]

Platforms: Python 3.10+, Self-hosted[1]

Sources (2)
  1. MiniAGI repositoryGitHub · checked 2026-09-29
  2. MiniAGI Docker instructionsDocumentation · checked 2026-09-29

Autonomy level

72/100

Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.

Reasoning: MiniAGI follows a supplied objective using basic tools, summarized short-term memory and an optional critic. Developers configure model access and the local environment, supervise tool use and inspect results. Its original implementation is a legacy experiment.

Comparisons

Custom Comparisons

Some of the use cases of MiniAGI:

  • Study a compact autonomous-agent implementation.
  • Experiment with Python and shell tools under supervision.
  • Explore optional critic-based task checking.

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Pricing model:

free

Code access:

open-source

Popularity level: 37%

Industries:

Software DevelopmentResearchAutomation

Tags:

lightweightautonomous agenttask automationgpt-4developers
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