Qredence Documentation

Get fleet-rlm running against a Daytona Sandbox in a few minutes.

Prerequisites

Python 3.13 and uv.
Daytona API key for Sandbox execution.
A provider credential. The default daytona-recursive profile uses OpenCode Go (FLEET_OPENCODE_GO_API_KEY, FLEET_OPENCODE_GO_BASE_URL); the managed and benchmark profiles use the Databricks AI Gateway.
A Postgres URL (FLEET_DATABASE_URL) for durable deployments. Local SQLite is only suitable for development.
Node 22.19+ and pnpm if you want to run the supervised backend plus pi-tui via fleet cli.

1. Install fleet-rlm

Clone the repository and sync the extras:

git clone https://github.com/qredence/fleet-rlm.git
cd fleet-rlm
uv sync --all-extras --dev

2. Choose a profile

Non-secret runtime policy lives in config/fleet.toml. Confirm the desired profile in [config] default_profile before starting the backend:

[config]
default_profile = "daytona-recursive"

Shipped profiles:

Profile	Provider	Recursion	Tracing
`daytona`	OpenCode Go	disabled	local MLflow
`daytona-recursive` (default)	OpenCode Go	one child level	local MLflow
`daytona-managed`	Databricks AI Gateway	disabled	managed MLflow
`daytona-bench` / `daytona-bench-40`	Databricks AI Gateway	disabled	disabled

The pi-tui /profiles command rewrites default_profile for the next restart. See the generated profile matrix for the exact environment names and token caps each profile expects.

3. Configure environment variables

Only variables named by the selected profile are read; process exports win over .env values.

.env

# Required for every Daytona profile
FLEET_DAYTONA_API_KEY=...
FLEET_DATABASE_URL=postgresql+asyncpg://user:pass@host/db

# Interactive profiles (daytona, daytona-recursive)
FLEET_OPENCODE_GO_API_KEY=...
FLEET_OPENCODE_GO_BASE_URL=https://<gateway>/v1

# Managed and benchmark profiles instead of the two above
# DATABRICKS_TOKEN=...
# FLEET_DATABRICKS_AI_GATEWAY_BASE_URL=https://<gateway>/serving-endpoints

The managed profile additionally requires FLEET_MLFLOW_EXPERIMENT_NAME, FLEET_MLFLOW_TRACE_CATALOG, FLEET_MLFLOW_TRACE_SCHEMA, FLEET_MLFLOW_TRACE_TABLE_PREFIX, and FLEET_MLFLOW_TRACING_SQL_WAREHOUSE_ID.

4. Initialize the database

Fleet never applies migrations at startup. Bring the configured database to the canonical Alembic head before running the backend:

uv run python scripts/db_init.py

Confirm the head with uv run alembic check.

5. Prepare the Daytona snapshot

Verify the required Daytona base snapshot exists so recursive delegations skip the per-run image pull:

make daytona-snapshot-check

Use uv run fleet doctor daytona for a bounded probe of settings, database head, provider auth, Volume visibility, mount scoping, and interpreter execution before you diagnose a real Turn.

6. Start Fleet

Pick one runtime surface:

uv run fleet cli

uv run fleet web --port 8000

uv run fleet-rlm serve-api --port 8000

Launchers default to 127.0.0.1 and reject non-loopback binds unless --allow-non-loopback-bind is passed. Backend and MLflow output stream to .fleet_rlm/logs/; latest.log and mlflow-latest.log point to the active files. Forward arguments to pi-tui after --:

uv run fleet cli -- --session <session-uuid>

7. Stream a Turn from the API

Every Turn requires an Idempotency-Key. The stream begins immediately with a transient data-status chunk, then emits Runtime Events, and closes with finish + [DONE].

# Create a Session
SESSION=$(curl -s -X POST http://127.0.0.1:8000/api/sessions \
  -H "Content-Type: application/json" -d '{"title": "hello"}' | jq -r .id)

# Stream one Turn
curl -N -X POST "http://127.0.0.1:8000/api/sessions/$SESSION/turns" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: $(uuidgen)" \
  -d '{"text": "Summarize the workspace README."}'

To cancel a live Run, PUT /api/runs/{run_id}/cancellation. Cancelled attempts persist a bounded tombstone in committed history.

Next steps

Installation

Install from source and pin the pi-tui workspace.

Configuration reference

Full config/fleet.toml and environment matrix.

HTTP API

Turn streaming contract and error envelope.

Troubleshooting

Common startup, Daytona, and database errors.

Fleet-RLM

Fleet Pi

GEPA Omni

Qredence Skills

Fleet Reasoner

Qredence Plugins

fleet-rlm quickstart

Prerequisites

1. Install fleet-rlm

2. Choose a profile

3. Configure environment variables

4. Initialize the database

5. Prepare the Daytona snapshot

6. Start Fleet

7. Stream a Turn from the API

Next steps

Installation

Configuration reference

HTTP API

Troubleshooting

​Prerequisites

​1. Install fleet-rlm

​2. Choose a profile

​3. Configure environment variables

​4. Initialize the database

​5. Prepare the Daytona snapshot

​6. Start Fleet

​7. Stream a Turn from the API

​Next steps

Installation

Configuration reference

HTTP API

Troubleshooting

Prerequisites

1. Install fleet-rlm

2. Choose a profile

3. Configure environment variables

4. Initialize the database

5. Prepare the Daytona snapshot

6. Start Fleet

7. Stream a Turn from the API

Next steps