zai-org / zai-org/feedback

Feature request: automatic thinking level selection (adaptive reasoning budget)

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priority: P2
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Description

Problem

Right now the thinking mode/level in ZCode is a manual choice. Most requests don't need deep reasoning, but I can't safely leave it on because it burns quota and adds latency; and for genuinely hard tasks I forget to raise it.

Proposal

An "auto" thinking level, selectable alongside the existing levels:

  • the router/model estimates task complexity first (tool count, codebase spread, ambiguity of the request) and picks the reasoning budget per turn;
  • cheap/obvious turns run with minimal or no thinking;
  • multi-step planning, debugging and architecture work automatically get a deeper budget;
  • a visible indicator of the chosen level per turn, so behavior is observable and debuggable.
Why

Long-horizon agentic sessions mix trivial tool calls with genuinely hard decisions. A fixed thinking level is either wasteful (always high) or fragile (always low). Adaptive selection would cut cost/latency without losing quality on the hard steps — this is exactly where an agent CLI differs from a chat UI.

Concrete example from real usage

In one production session I alternated between: a one-line fix in a shell script (needed zero thinking), and planning a failover procedure across 6 servers with DNS/API orchestration (needed deep reasoning). With a fixed level, one of the two suffers: either every trivial edit pays the thinking tax, or the hard planning runs shallow because I forgot to switch the level back.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by tracing the existing thinking mode/level controls and the router/model entry point. Define how task complexity, per-turn budget selection, and the visible chosen-level indicator should be represented, then verify behavior across trivial tool calls and multi-step planning sessions.

Written by the indexing model from the issue text.

Assessment

Domain
ai, cli
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Active
Clarity
Needs clarification
Newbie friendliness
35/100

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