Agently 4.1.3.1 Release Notes

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Agently 4.1.3.1 is a foundation release for explicit multi-turn task information management. It adds a durable Workspace substrate, a Recall context skeleton, and Action Runtime defaults that let application code store and recover task information across repeated execution steps.

This release does not introduce autonomous WorkLoop planning. Application code, TriggerFlow definitions, or ordinary Python loops still decide when to observe, ingest, search, checkpoint, and build context.

Highlights

• agent.use_workspace(...) configures one Workspace with structured records, SQLite metadata/FTS, managed content storage, and an editable files_root.
• agent.workspace exposes ingest(...), put(...), get(...), get_data(...), search(...), link(...), links(...), checkpoint(...), latest_checkpoint(...), history(...), capabilities(), and build_context(...).
• Recall is plugin-shaped through RecallPlanner, Retriever, and ContextBuilder; default auto and software_dev profiles are available.
• agent.enable_workspace_file_actions(...) exposes the Workspace file working tree as list/search/read/write actions without creating a second Workspace.
• Shell and Node.js helpers inherit agent.workspace.files_root when a Foundation Workspace is configured.
• agent.enable_workspace(...) remains as a compatibility alias and warns in favor of enable_workspace_file_actions(...).
• OpenAI-compatible requesters now preserve an explicit Authorization header even when an api_key is also configured.

Recommended Usage

agent = Agently.create_agent("issue-run").use_workspace("./.agently/runs/issue-123")

record = await agent.workspace.ingest(
    content={"route": None, "status": "failed"},
    collection="observations",
    kind="route_attempt",
    summary="Provider returned no route candidate",
    scope={"task_id": "issue-123", "area": "routing"},
    source={"type": "workflow", "step": "attempt-1"},
)

await agent.workspace.checkpoint(
    "issue-123",
    {"status": "failed", "evidence": record["id"]},
    step_id="attempt-1",
)

context = await agent.workspace.build_context(
    goal="Prepare the second routing attempt",
    scope={"task_id": "issue-123", "area": "routing"},
    budget={"max_items": 4},
)

Expose file actions separately when the model or action layer needs to read or write files in the Workspace file area:

agent.use_workspace("./.agently/runs/issue-123")
agent.enable_workspace_file_actions(write=True)
agent.enable_shell(commands=["cat", "python"])

Action outputs are not automatically memory. Store them explicitly:

result = agent.action.execute_action("inspect_workspace_files", {"cmd": "cat notes/runtime.txt"})
await agent.workspace.ingest(
    content={"stdout": result["data"]["stdout"]},
    collection="observations",
    kind="action_output",
    summary="Shell inspection output",
    scope={"task_id": "issue-123"},
    source={"type": "action", "name": "inspect_workspace_files"},
)

Examples

• examples/workspace/workspace_loop_foundation.py demonstrates an explicit TriggerFlow loop that stores observations and decisions, links evidence, writes checkpoints, and builds Recall context.
• examples/workspace/workspace_with_action_output.py demonstrates writing a file through Workspace file actions, reading it through a shell action, then explicitly ingesting the action output into Workspace before building context.

Compatibility

• Package version: 4.1.3.1.
• Release manifest: compatibility/releases/4.1.3.1.json.
• Recommended agently-devtools: >=0.1.5,<0.2.0.
• Workspace advanced Recall hardening, vector retrieval, model-assisted recall planning, and WorkLoop self-planning remain planned follow-up work.