---

name: super-agi description: "Routes SuperAGI autonomous-agent framework tasks across deployment, FastAPI service, agent workflows, toolkits, model providers, resources, and vector-store operations." disable-model-invocation: true metadata: disco-role: operating license: MIT

SuperAGI Repo Skill

Use this skill when a task involves SuperAGI, the open-source autonomous-agent framework for building, managing, and running tool-using AI agents. It is a router for future Researcher sessions; read the focused sub-skill before taking action.

First Checks

1. If a checkout is available, confirm it is a SuperAGI checkout by looking for a top-level superagi/ package, main.py, config_template.yaml, and one or more docker-compose*.yaml files.
2. Read references/repo-provenance.md before deciding whether this skill matches the checkout. If the commit, dirty state, or major source layout differs, refresh the repo skill.
3. Read references/overview.md for the architecture map, service names, and core terminology.
4. Use scripts/summarize_superagi_checkout.py when you need a safe static summary of a provided checkout.
5. Use scripts/check_superagi_config.py to validate a config.yaml-style file without contacting external providers.

Route by Task

• Install, Docker, local runtime, configuration, migrations, GUI, or GPU/local LLM deployment: read sub-skills/deployment-configuration/SKILL.md.
• FastAPI routes, public API usage, /v1/agent API-key endpoints, auth, webhooks, migrations, or data model questions: read sub-skills/api-service/SKILL.md.
• Agent creation, workflow selection, prompt/output parsing, task queues, scheduling, wait-for-permission, or Celery execution loops: read sub-skills/agents-workflows/SKILL.md.
• Built-in tools, custom toolkits, marketplace/external tools, tool config keys, secret handling, or tool execution errors: read sub-skills/toolkits-integrations/SKILL.md.
• LLM providers, model API keys, local LLM settings, resource uploads, knowledge bases, FILE/S3 storage, vector DBs, or embedding stores: read sub-skills/models-resources-vector/SKILL.md.

Minimal Operating Context

SuperAGI is not a small import-only package. Typical local operation uses a multi-service stack:

• Python FastAPI backend served from main:app.
• Celery worker/beat tasks from superagi.worker.
• PostgreSQL for application state and Alembic migrations.
• Redis for Celery and task queue state.
• Next.js GUI behind an nginx proxy.
• Optional GPU/local LLM deployment via a separate CUDA Dockerfile/compose path.

The Python source root is superagi. The checkout has no standard pyproject.toml, setup.py, or setup.cfg; prefer Docker or explicit checkout imports over assuming pip install superagi works.

Minimal Setup and Verification

For a user's own SuperAGI checkout, the public local path is Docker-first:

cp config_template.yaml config.yaml
# edit config.yaml for DB/Redis/provider/storage settings before startup
docker compose -f docker-compose.yaml config

Use docker compose ... config as a safe parse/topology check. Start the stack only when the downstream user authorizes the build, migrations, volumes, and long-running services. For source-only inspection, check that python -c "import superagi" works from an environment where the checkout root is on PYTHONPATH; do not claim a packaged pip install superagi workflow for this snapshot.

Safe Defaults for Future Agents

• Prefer static inspection, config validation, and helper --help checks before starting services.
• Do not run full docker compose up, run.sh, entrypoint.sh, marketplace downloads, or provider key validation unless the downstream user explicitly wants those side effects and has supplied the required services/credentials.
• Treat API keys, OAuth credentials, S3 keys, vector DB credentials, and provider tokens as user secrets. Never hard-code values from examples.
• For Docker operation, create config.yaml from the checkout's template and adjust DB/Redis hosts to match the deployment target before starting services.
• For local Python operation outside Docker, expect extra setup work: Python 3.10, requirements, PostgreSQL, Redis, NLTK data, and frontend dependencies.

Verification and Troubleshooting

• For cross-cutting failures, read references/troubleshooting.md.
• For service and config failures, read the deployment sub-skill's references/troubleshooting.md.
• For route/auth/database failures, read the API sub-skill's troubleshooting reference.
• For parser/tool/workflow failures, read the agents-workflows and toolkits-integrations troubleshooting references.
• For provider/vector/resource failures, read the models-resources-vector troubleshooting reference.

Repository Skill Metadata

Structured router metadata lives in references/repo-routing-metadata.json. It is consumed by the managed repo-skill importer when import is approved in a separate workflow.