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Error: 400 Missing required parameter: 'messages[2].reasoning_content'. on any Beam request #1073

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Description

@ThePlenkov

Summary

any request returns this on Beam
❯ bro?

❯ Error: 400 Missing required parameter: 'messages[2].reasoning_content'. This model needs the reasoning of
every earlier assistant message: send back the 'reasoning_content' returned with that message. If 're...

Type "continue" to try again. If the issue persists, contact support: https://commandcode.ai/discord
Trace ID: c4feb3d694a896a0b8f8d7d16ae4b68d

Expected Behavior

it should work

Actual Behavior

it doesn't work

Steps to reproduce the issue

just select beam model

Command Code Version

1.88.0

Operating System

macOS

Terminal/IDE

No response

Shell

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Session file (optional)

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Fix prompt (optional)

No response

Additional context

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Activity

  1. added theissue type on Oct 25, 2026
  2. huizixin commented on Nov 4, 2026

    @huizixin

    Adding another data point — this is reproducible on the hosted reflection/beam:free model (via vercel-ai-gateway), not only BYOK/Beam.

    Environment:

    • Command Code Desktop 0.1.60 (latest release), macOS (darwin)
    • Model: reflection/beam:free (provider: vercel-ai-gateway), reasoningEffort: max
    • Error: 400 Missing required parameter: 'messages[47].reasoning_content'. This model needs the reasoning of every earlier assistant message: send back the 'reasoning_content' returned with that message.

    Reproduction: multi-turn session on this model. The failure appears on a follow-up turn after an earlier assistant turn did substantial reasoning/generation (short trivial turns sometimes pass). Trace IDs available on request.

    This matches the root cause analyzed in #877 (reasoning_content dropped when replaying prior assistant turns as history). The upstream only enforces the requirement once the prior turn produced real reasoning, which explains the intermittent nature.

    Workaround in the meantime: switch to a model that does not return reasoning_content (e.g. a non-thinking model) for multi-turn sessions.

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