roboshed

Reusable agent factories, capabilities, sandbox policies, tools, and skills built on Roboz. Version 0.1.1.dev1 is a development snapshot; APIs are unstable. Dependencies are Roboz and Pydantic only.

Includes guarded Unix file commands, Python patch editing, CLI/file/email instructions, and provider-neutral email contracts and tools. It does not install any model SDK, Proton, document SDK, web service, or backend framework.

from pathlib import Path
from roboz import stop
from roboz.deployment import DeployableAgent, Capability
from roboz.llm import MockLLMEndpoint
from roboshed.capabilities import FileCommands, FileEditing
from roboshed.sandbox import PermissionPolicy, Sandbox

permissions = PermissionPolicy.local(Path("./sandbox"))
agent = DeployableAgent(
    name="file_worker",
    system_prompt="Complete the user's task, then call stop.",
    default_capabilities=(
        Capability(tools=(stop,)),
        FileCommands(),
        FileEditing(),
    ),
)
agent.set_agent_endpoint(MockLLMEndpoint([
    {"action": "stop", "rationale": "done", "value": "Ready."}
]))
agent.set_attributes(permissions=permissions)
agent, background_agents = agent.build()
result, messages = agent.invoke()

roboshed.capabilities provides FileCommands, FileEditing, Compactification, ConversationSnapshots, MemoryConsolidation, ArtifactRetention, and MaintenanceCadence, alongside the tools and skills modules. Applications choose fixed capabilities through default_capabilities and append application extensions with add_capabilities(). A capability owns its tools and skills. Generic DeployableAgent definitions live in roboz.deployment. Reusable orchestrator and librarian constructors live in roboshed.agents. The orchestrator owns stop and guarded file work, and the Librarian owns snapshots, consolidation, retention, and cadence. Root-only application capabilities append after the agent's protected defaults, without unpacking the role definitions.

Each capability declares the typed attributes it reads from its owning DeployableAgent. Runtime controls remain separate. Use set_attributes() to supply standalone permission policies and sandbox inputs before building.

See the factory and migration guide.

RoboSprawl deployment recipe

roboshed.deployments.robosprawl.RoboSprawl is the concrete lazy persistent orchestrator and Librarian recipe. Construct RoboSprawl() without inputs; supply the required scoped sandbox, endpoint getter, and memory endpoint with set_sandbox(), set_endpoint_getter(), and set_memory_endpoint(). set_additional_capabilities(), set_specialists(), set_interaction_mode(), and set_event_sinks() supply optional inputs (empty sequences and interaction_mode=None by default). A None mode inherits core's current output setting, falling back to CLI when none is bound. Argument-free build() returns a fresh root/background-agent tuple or reports missing required inputs. robosprawl is an alias for this class.

The recipe loads project memory and supplies project locations through initial messages. Its root follows the selected model getter; the Librarian uses its separate memory endpoint. Configuration and building start no agents or provider clients and create no project directories. Use a fresh recipe per new run; setters snapshot the sandbox and sequence containers, while supplied endpoints and capability/child objects remain caller-owned. The application owns scope selection and runtime lifecycle. See the migration example.

Conversation compaction

The following fragment belongs inside an agent or tool builder. endpoint is the selected compaction model, and agent_pipe is the owning agent's event pipe. These are independent inputs to the tool.

from roboshed.tools import get_compactify_messages_when_needed_tool

compact = get_compactify_messages_when_needed_tool(
    endpoint=endpoint, threshold_percent=60, pipe=agent_pipe, timeout_s=60,
)

Include this tool in an agent's default_tools and pass that agent's owning EventPipe. The standalone factory defaults to an 80% threshold and no timeout; system_prompt and skill_message override the full continuation instructions. The tool preserves the contiguous bootstrap prefix and folds the remaining history, including previous summaries, into a new continuation message. Its status also carries the summary for event persistence. Each constructed tool owns its compaction count; constructing one per agent keeps counters independent.

Successful status reports describe the compacted history's current usage and headroom. Summaries are budgeted below the configured threshold and endpoint capacity, including the preserved prefix and continuation payload. If there is no room for a summary, or the returned replacement still exceeds the budget after summarization retries, the tool returns blocked without changing history or the counter. The continuation payload retains percent_used_before.

Cancellation and interruption propagate through the existing Roboz summarizer. An optional positive, finite timeout_s bounds each provider attempt, not the whole compaction. Failed attempts leave history and the counter unchanged; late provider results are ignored without forcibly killing worker threads. Summarization messages and model-call events use the supplied pipe.

The public tool name and persisted caller are compactify_messages_when_needed. This tool owns the shared continuation prompts and retains RoboSprawl's caller name. The old robosprawl.compaction import is replaced by roboshed.tools. Shed also owns the shared summarizer and Librarian memory pipeline; core provides the mechanisms they use.

Context API migration

Low-level tool factories now use roboz.Ctx(**values) directly, with service, endpoint, and executable objects supplied without wrappers. The specialized context classes have been removed. Existing tool builders retain their keyword arguments, defaults, permission checks, cancellation, and timeout behavior. See the migration guide for low-level context fields and state ownership.

Deployable agent graphs

The root DeployableAgent owns recursive subagents and background_agents; both slots contain the same definition type. Configure each object explicitly before building:

sandbox.configure_scope(folder)
definition.set_attributes(permissions=sandbox.permissions())
agent, background_agents = definition.build(event_sinks=(dispatch,))

Construct each application sandbox directly:

sandbox = Sandbox(root=application_root, shared="workspace")
sandbox.configure_scope(folder)

The host supplies folder at runtime. For now it is a direct child of the sandbox's existing projects_dir, with unchanged tiered permission behavior. Default persistence paths follow that scope. Startup memory and endpoints are agent configuration. Each build creates fresh runtime state; applications can supply an agent-specific sink factory for persistence.

The Librarian constructor declares its standard maintenance sequence: snapshots, consolidation, retention, then cadence. Pass its sandbox and the recursive foreground names directly to the constructor before attaching it as a background agent. The orchestrator also takes the configured sandbox and captures its permission policy when constructed.

Invoke the returned agent directly and retain the background agents for control. Repeated builds create fresh runtimes and bindings, but supplied endpoints, capabilities, and sinks remain caller-owned. No execution state is retained on the definition. The old deployment wrapper, factories, host protocols, and result bundles are removed. Use core's roboz.llm.ModelSelector for lazy model selection. roboz.dependencies supplies exact dependency registration and binding; roboshed.dependency_health supplies isolated inspect_dependencies, probes, and monitoring without a web framework or provider SDK. Permission policies treat configured folder names literally. Health scheduling retries observation failures; timed-out workers retain their concurrency slots until completion. See agent factories for the contracts and examples.

The robosprawl skill from roboshed.skills covers sandbox orientation and the HUD file-link/markdown contract. The external RoboSprawl application may select it through Capability(auto_loaded_skills=(robosprawl,)). Concrete paths, endpoints, extra capabilities, and specialist definitions remain application choices. The robosprawl recipe assembles and builds a fresh agent graph.