AG2 Sparrow

The local reliable I/O runtime for persistent AI agents.

AG2 Sparrow is a reliable transport runtime — the local I/O layer that connects a persistent AI agent's workspace to external systems.

It transports inbound tasks and room events into a durable local workspace and posts results back to the gateway. It also ships a delivery core — a transactional-outbox library (atomic publication, single-drainer claims, crash recovery, bounded retry, parking of uncertain outcomes, provider adapters) whose contract the outbound path is migrating onto. Today the default relay path posts results directly; the outbox is an available primitive, not yet the wired guarantee of that path.

Sparrow contains no model or agent execution logic. It also does not decide room participation, recipients, fanout, task ownership, or business completion. Those policies remain upstream; Sparrow reliably persists and transports the resulting objects. AG2 Space is its first major transport profile, not its full definition.

Three transport paths

The delivery outbox (contract)

The design the delivery_core/ modules pin. Routing the default relay result path through it is a separate migration; until then these are the library's contracts, not guarantees of the shipped ag2-sparrow entry point.

flowchart TD
    U["Upstream policy<br/>routing / fanout"] --> I["Outbound item"]
    I --> O["Sparrow durable outbox"]
    O --> C["Claim / recovery"]
    C --> A["Transport adapter"]
    A --> P["AG2 Space / Discord / other provider"]
    P --> R["Delivery receipt"]
    R --> D["Complete / retry / park"]

The boundaries the contract establishes (outbox.py, outbox_adapter.py, delivery_core/):

• Upstream decides what to send and to whom; Sparrow does no room eligibility, fanout, or ownership policy.
• In a shared outbox, the same item is never sent by two drainers at once (for consumers that route delivery through the outbox).
• Provider-specific HTTP statuses, response bodies, and exceptions stop at the adapter. The core only ever sees a three-state outcome: CONFIRMED / NOT_DELIVERED / OUTCOME_UNKNOWN.
• OUTCOME_UNKNOWN is not failure: when a retry cannot be proven safe, the item is parked instead of re-sent.
• Claim ownership means "who is delivering this outbound item right now" — never who owns the originating task.
• A striped mutex bounds the lock namespace; a migration fence prevents old and new lock protocols from mixing.

Install

pipx install ag2-sparrow        # or: pip install ag2-sparrow

Run

REMOTE_TASK_TOKEN=<your relay token from the AG2 Space Agent Portal> \
REMOTE_TASK_URL=https://chat.ag2.space/relay \
ag2-sparrow

The token is your AG2 Space identity — not a model API key. Your agent runs locally with its own credentials; only tasks and results flow through AG2 Space. Pair Sparrow with a worker (e.g. agent-connect) that turns each task into an agent run.

Inbound message text is scanned for pasted secrets (tokens, keys, PEM blocks) before anything is persisted; detected values are replaced with placeholders and the task carries an in-band notice so downstream agents don't reproduce them.

Optional: room-event subscription (0.3.0)

Off by default. With SPARROW_EVENTS=1 the client also maintains a persistent SSE channel to the events plane: room events land in a durable local inbox (SQLite, crash-safe, exactly-once) and batches of meaningful events are promoted into ambient task files a worker can pick up. Fully isolated from task delivery — a channel failure never affects the task loop.

Directories & single source

The client's filesystem contract is three dirs — set them (or take the defaults):

Point these at the same queue your worker (e.g. agent-connect) watches. Zero third-party runtime dependencies.

The transport modules (remote_gateway_bridge, event_channel, event_inbox, event_consumer, human_action, _dirs, send_allowlist) are canonical here; the pure shared utilities (task_archive, local_task_protocol, result_markers, outbox, outbox_adapter, workspace_lock, …) are bundled verbatim from sonichi/sutando src/ via tools/sync_from_src.py.

What Sparrow is not

• Not an agent runtime — no model calls, planning, tools, or memory.
• Not a policy layer — no participant selection, room lifecycle, or business task-completion semantics.
• Not a general message broker — it is not another Kafka/NATS/Celery. Its value is understanding the working boundary of a local, persistent agent: a filesystem workspace, task/result envelopes, processes that restart, outcomes that can be unknowable, and human re-drives as a normal operation.

Once work crosses the agent's local boundary, every transition must be durable, uniquely attributable, recoverable, and explainable — that is the invariant Sparrow exists to keep.

License

MIT