Qredence Documentation

Fleet is delivered as a backend service plus a terminal client. The maintained public surface is the HTTP + SSE API and the CLI. This page documents the small Python surface exposed for embedding and scripting. There is no maintained public runtime library. Modules under src/fleet_rlm/ (api, chat, rlm, daytona, sessions, persistence, composition) are internal ownership boundaries and change without notice. When the docs disagree with the code, trust the code.

Application factory

Fleet exposes one FastAPI factory. The default composition inventory comes from the profile selected in config/fleet.toml.

from fleet_rlm.api.app import create_app

app = create_app()

create_app():

Builds the FastAPI application.
Installs handlers and routers.
Eagerly constructs the immutable bundled Skill catalog.

FastAPI lifespan validates settings and installs one complete Daytona (or explicitly injected private-test) runtime inventory. Lifespan owns startup rollback and shutdown.

Console-script entry points

These are the packaged commands. Each is thin glue over Click or the FastAPI factory.

Command	Module	Purpose
`fleet`	`fleet_rlm.cli.entry:cli`	Operator CLI. `cli`, `web`, `doctor`, `settings`, `skills` subcommands.
`fleet-rlm`	`fleet_rlm.cli.serve:serve_api`	Standalone backend launcher, plus `daytona-snapshot`.
`fleet-doctor`	`fleet_rlm.cli.doctor:main`	Bounded diagnostics.
`fleet-rlm-daytona-snapshot`	`fleet_rlm.cli.serve:daytona_snapshot`	Build or refresh the Daytona base snapshot.

See the CLI reference for their flags and semantics.

Programmatic backend startup

To embed the backend inside another Python process, call the FastAPI factory and hand it to your ASGI server. Loopback binding stays enforced through the CLI layer; when embedding, enforce network exposure at your ASGI server or reverse proxy.

import uvicorn
from fleet_rlm.api.app import create_app

if __name__ == "__main__":
    uvicorn.run(create_app(), host="127.0.0.1", port=8000, log_config=None)

The lifespan probe still requires config/fleet.toml with an explicit [config] default_profile and every environment variable referenced by that profile. The database must already be at the Alembic head; startup does not run migrations.

Sandbox-serializable inputs

dspy.RLM receives large inputs through DSPy’s own SandboxSerializable contract (DSPy 3.3.0b1). Fleet-owned host code builds these values on the Fleet Root’s behalf. They are host-constructed under strict local validation and are not part of a public Python-integration surface. Custom Skill Signatures see only JSON-compatible common input fields. If you are authoring a Skill, define your Signature against the shipped common input contract; do not import Fleet-internal capsule classes.

Import verification

Verify the small maintained entry points import cleanly against the installed distribution:

uv run python -c "from fleet_rlm.api.app import create_app"
uv run python -c "from fleet_rlm.cli.entry import cli"
uv run python -c "from fleet_rlm.cli.serve import serve_api"

Fleet-RLM

Fleet Pi

GEPA Omni

Qredence Skills

Fleet Reasoner

Qredence Plugins

fleet-rlm Python entry points

Application factory

Console-script entry points

Programmatic backend startup

Sandbox-serializable inputs

Import verification

See also

​Application factory

​Console-script entry points

​Programmatic backend startup

​Sandbox-serializable inputs

​Import verification

​See also

Application factory

Console-script entry points

Programmatic backend startup

Sandbox-serializable inputs

Import verification

See also