CrewAI
Multi-agent automation framework & studio to build and run AI crews
SuperAGI
Easily build, manage, and run autonomous AI agents
A developer‑first framework to provision, extend, and operate autonomous AI agents with GUI, toolkits, and multi‑vector DB support, deployable via cloud, Docker, or DigitalOcean.
Overview
Overview
SuperAGI is a developer‑first framework that lets you provision, spawn, and run autonomous AI agents at scale. With a web‑based GUI and an Action Console, teams can monitor, interact with, and fine‑tune agents in real time. It supports concurrent execution, allowing multiple agents to operate simultaneously without interference.
Agents can be extended through a marketplace of toolkits, connect to multiple vector databases, store memory, and operate with optimized token usage to control costs. You can configure token limits per step and enable persistent memory to let agents learn from past interactions. Built‑in performance telemetry provides insights for continuous improvement.
Deployments are flexible: use SuperAGI Cloud for instant access, run the Docker‑compose stack locally (including GPU‑enabled variants), or launch a one‑click DigitalOcean droplet. The framework supports custom fine‑tuned models and ReAct‑style workflows for complex automation. Extensive documentation, a YouTube channel, and an active Discord community help accelerate onboarding and troubleshooting.
Highlights
Provision, spawn, and deploy production‑ready autonomous agents
Marketplace of extensible toolkits for external system integration
Web‑based GUI and Action Console for real‑time interaction
Support for multiple vector databases, memory storage, and token optimization
Pros
- Developer‑first design simplifies agent creation
- Scalable concurrent execution of multiple agents
- Extensible via marketplace toolkits
- Built‑in telemetry and token control for cost management
Considerations
- Active development stage may cause occasional instability
- Full functionality requires Docker or cloud setup
- Limited out‑of‑the‑box models; custom fine‑tuning often needed
- Community support can vary in response time
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Fit guide
Great for
- AI engineers building custom autonomous workflows
- Startups prototyping AI‑driven automation
- Teams needing a GUI to monitor agent performance
- Projects that require integration with multiple vector stores
Not ideal when
- Non‑technical users without development resources
- Production environments demanding certified enterprise support
- Use cases needing real‑time low‑latency inference on edge devices
- Organizations requiring strict compliance certifications not yet provided
How teams use it
Customer support ticket triage
Agents automatically classify, prioritize, and route tickets, reducing manual handling time.
Data enrichment pipeline
Agents fetch, summarize, and store external data into vector DBs for downstream analytics.
Automated report generation
Agents gather metrics, apply ReAct workflows, and produce formatted reports on schedule.
DevOps incident response
Agents monitor alerts, retrieve logs, and suggest remediation steps, accelerating resolution.
Tech snapshot
Python71%
JavaScript24%
CSS4%
Shell1%
Dockerfile1%
Batchfile1%
Frequently asked questions
What programming languages are required?
SuperAGI is written in Python and runs via Docker, so Python knowledge is sufficient for extending agents.
Can I use my own LLM models?
Yes, you can connect custom or fine‑tuned models through the configuration.
Is there a hosted version?
SuperAGI Cloud provides a quick‑start hosted environment; you can also self‑host locally or on DigitalOcean.
How does token usage get optimized?
The framework includes controls to limit token consumption per step, helping manage API costs.
What databases are supported for vector storage?
Multiple vector DBs are supported; you can configure the one that fits your stack.
Project at a glance
Stable- Stars
- 17,090
- Watchers
- 17,090
- Forks
- 2,154
LicenseMIT
Repo age2 years old
Last commit12 months ago
Primary languagePython
Last synced 12 hours ago