Agent tasks for Python
Eight registered tasks cover planning, single decisions, loops, reflection,
routing, history compression, named tools, and injected KV memory.
Import the tasks and app from render_lab_tasks_agent.tasks.
Composition and environment
LLM calls inherit the render-lab-tasks-llm provider/model/ledger environment
contract. Credentials remain lazy. Raw decision implementations accept
CompletionDeps(complete=...); loops take AgentDeps(step=..., tools=...).
The default loop dispatches wrapped agent.step through ctx.run so every
model decision is a durable child run. Register ToolBinding values with
register_agent_tool; each binding's run(ctx, args) must dispatch wrapped
Render tasks via ctx.run and return a string observation.
memory_impl requires an injected MemoryDeps(kv=...); there is no implicit
in-memory fallback or default KV account. Re-register a wrapper under your own
namespace to supply the store. No live agent/provider run verified this port.
Installation
pip install render-lab-tasks-agent==0.1.0
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