# TransformerOptimus/SuperAGI

<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.

Repository: https://github.com/TransformerOptimus/SuperAGI
Canonical: https://ross.abutalabs.com/products/superagi
Homepage: https://superagi.com/
Language: Python
License: MIT
License Family: permissive
Topics: agi, ai, autonomous-agents, python, agents, artificial-intelligence, gpt-4, openai, artificial-general-intelligence, superagi, llm, llmops, nextjs, pinecone, hacktoberfest
Last push: 2025-01-22T22:14:07+00:00

## Health v2 (maintenance only)
Score: 21/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 2, release rhythm 8, longevity 86
- inputs: {"age_days": 1208, "days_push": 588, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 17660, forks 2231 (observed 2026-08-28T04:11:20.143902+00:00)

## What it is
SuperAGI is a dev-first open source framework for building, managing, and running autonomous AI agents powered by large language models. It provides tooling for agent provisioning, tool integration, memory via vector stores, and a UI for monitoring agent runs.

## Use cases
- build autonomous ai agents that complete multi-step tasks
- run an auto-gpt style agent loop reliably
- give llm agents custom tools and integrations
- monitor and manage multiple ai agents from a dashboard
- add long-term memory to agents with a vector database
- automate workflows with self-directed ai agents

## When to choose
- you want a batteries-included framework for autonomous LLM agents with a UI
- you need agent tooling, memory, and scheduling out of the box in Python
- you prefer a self-hosted, MIT-licensed alternative to closed agent platforms

## When to avoid
- you need a lightweight minimal agent loop you fully control
- you require actively maintained releases and current model support
- you only need simple chatbot or RAG pipelines without autonomy

## Facets
- artifact type: framework
- maturity: maintenance
- function: agent-framework, llm-inference, rag, workflow-automation, developer-tools
- domain: artificial-intelligence, large-language-models, developer-tools
- platform: python, self-hosted, cross-platform
- tags: autonomous-agents, agi, llmops, openai, gpt-4, vector-database, nextjs-dashboard, ai-agents, automation, docker

## Member repositories
- TransformerOptimus/SuperAGI (main) score 21

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:20.143902+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:02:31.939397+00:00, confidence not recorded.
  - homepage: https://superagi.com/ (fetched 2026-08-29T08:00:46.773745+00:00, sha a463b91163b5)
- Data as of 2026-08-30T08:39:29.467469+00:00.