[Bugfix][Anthropic] Normalize Claude Code system messages

[Syraxius]

Purpose

Fixes #44000.

Fix Claude Code compatibility after newer Claude Code builds started sending a
system role inside the Anthropic messages array.

The failure shows up before request conversion, during Pydantic validation:

API Error: 400 1 validation error:
{'type': 'literal_error', 'loc': ('body', 'messages', 1, 'role'), 'msg': "Input should be 'user' or 'assistant'", 'input': 'system', 'ctx': {'expected': "'user' or 'assistant'"}}

(Pasted the full error here for SEO)

I looked through the installed Claude Code bundles I have locally, including
2.1.157, to check the reported ctx / msg roles before deciding what to
normalize. I did not find any ctx or msg message roles in the bundle. The
only hits were unrelated internal strings, such as validation context variables
(ctx) and generic JS/MIME strings (msg). Because of that, this PR
intentionally does not expand the role enum to include ctx or msg.

The fix keeps AnthropicMessage.role strict as user | assistant, and
normalizes only the confirmed non-standard input shape before Pydantic validates
messages:

• remove messages[*].role == "system" entries from messages
• merge their content into the top-level Anthropic system field
• preserve existing top-level system content first
• keep unknown roles rejected by the existing strict validation

I chose to merge into top-level system because that matches the Anthropic
Messages API convention: conversational turns live in messages, while system
prompt content belongs in system. It also keeps the non-standard input shape
contained at the Anthropic protocol boundary, instead of allowing it to pass
deeper into request conversion.

A future flag could support preserving/injecting system messages at their
original position for users who explicitly want that behavior, but I left that
out here to keep this PR narrowly scoped. For the compatibility issue,
normalizing into the standard Anthropic system field seems like the least
surprising behavior.

This overlaps with #43959, but the main differences in this version are:

• applies the normalization to both AnthropicMessagesRequest and
  AnthropicCountTokensRequest
• keeps the message role enum strict instead of accepting non-standard roles
  downstream
• includes tests for preserving/merging system content and keeping unrelated
  roles rejected

(AI assistance was used while preparing this patch; I reviewed the changed code
and tests rigorously.)

Test Plan

python -m pytest tests/entrypoints/anthropic/test_anthropic_messages_conversion.py
python -m compileall vllm/entrypoints/anthropic tests/entrypoints/anthropic
git diff --check

I also built and ran this branch locally against my own Claude Code / agentic
workloads for an end-to-end compatibility check, using Claude Code 2.1.157
through both the VS Code extension and the CLI.

Test Result

tests/entrypoints/anthropic/test_anthropic_messages_conversion.py
31 passed

compileall passed
git diff --check passed

The local end-to-end agentic workload also completed successfully with this
normalization in place against Claude Code 2.1.157 through both the VS Code
extension and the CLI.

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[hclsys]

heads-up @Syraxius — #44045 by @aatchison opened ~30min earlier with the same fix (hoist role="system" into top-level system via @model_validator(mode="before") on AnthropicMessagesRequest, same single file vllm/entrypoints/anthropic/protocol.py). worth comparing approaches before reviewers triage — yours additionally handles AnthropicCountTokensRequest which the other doesn't, that's the main delta i see.

[Syraxius]

@hclsys Thanks for the initial review! Apologies for missing the other PR before I submitted this!

I just noticed that the other PR was closed by the author.

I've just gone through a full work day of agentic coding work with this patch in place and it seems to be working as expected with nothing out of the ordinary.

We may wait for @kimun608's independent testing results (in #44000) before proceeding, or if you're OK we can just proceed with the rest of the review process while waiting

Let me know if I can assist or help in any way!

[BoomSky0416]

Fixing it this way will alter the original semantics and also affect the prefix-cache hit rate. Wouldn't it be better to change “role” to “user” and clearly label it as [System Command] in the content?

https://platform.claude.com/docs/en/build-with-claude/mid-conversation-system-messages

[Syraxius]

@BoomSky0416 Thanks for the suggestion! Yes indeed it does mess with caching, but only when it changes (and it rarely does change)

I explained in this comment (agentgateway/agentgateway#2015 (comment)) but forgot to explain here.

I did try all 3 implementations I've tried before I did this.

I tried:

1. System message in place - Turning it into system messages in-place.
2. User message in place - Normalizing it as user messages, then adding tags.
3. System message merged up - Merging it with the system message on top.

I tried the first two before I landed on solution 3. The first two completely destroyed the model behavior in several models that I've tested, especially the one where I turned it into user message. It actually started referencing and talking about it to me in its replies and chatting / asking about it etc. and no amount of fencing and tagging I did helped with it much.

The thing about this is... I actually suspect that this is just a bug at Anthropic side and not intended to be meant to be system message injected halfway in the conversation (since their own models are not trained for that anyway). I read through the JS bundle and also inspected a few outputs. It mainly contains static skill information.

[BoomSky0416]

I think the best solution would be for messages.role to natively support role = system.

[luyufan498]

I share the same concern. Simply moving the system prompt to the top might disrupt Claude Code's (CC) optimizations. Since the API has changed, the project's engineering may also need to evolve to align with the new implementation. Otherwise, vLLM might not be able to fully leverage these advantages.

[mychmly]

I did a local protocol-level check on the current PR head.

Evidence:

• base 1a096d82087b: messages[0].role == "system" fails validation at messages.0.role
• PR fbaac7474fb2: the same payload is accepted and converts to a merged system prompt before the user message
• tests/entrypoints/anthropic/test_anthropic_messages_conversion.py: 31 passed locally
• compileall and git diff --check: passed locally

So the narrow validation/compatibility fix checks out on my side.

The remaining question seems semantic rather than mechanical: current Anthropic docs describe mid-conversation system messages as role: "system" entries inside messages, while this PR hoists them into top-level system. That may be the right Claude Code compatibility tradeoff, but it is worth making that tradeoff explicit for maintainers.

[Syraxius]

Actually would it be good to just add a flag for vLLM to allow the user to choose between...

1. Inject role=system messages into mid conversation in the exact ordering where it appeared
2. Inject but normalize role=system messages as role=user into mid conversation in the exact ordering where it appeared
3. Merge the mid conversation system message to the top level system block

Something like... --anthropic-mid-conversation-system-message=passthrough | normalize-to-user-role | merge-to-top-level
(I'll think of a better naming for these)

Then the user is free to choose / experiment with whichever works for his system? What do you guys think?

[luyufan498]

Actually would it be good to just add a flag for vLLM to allow the user to choose between...

1. Inject role=system messages into mid conversation in the exact ordering where it appeared
2. Inject but normalize role=system messages as role=user into mid conversation in the exact ordering where it appeared
3. Merge the mid conversation system message to the top level system block

Something like... --anthropic-mid-conversation-system-message=passthrough | normalize-to-user-role | merge-to-top-level (I'll think of a better naming for these)

Then the user is free to choose / experiment with whichever works for his system? What do you guys think?

On second thought, I am a bit concerned that Option 2 might alter or break the original semantics of the prompt, as converting a system instruction into a user message can sometimes confuse the model's behavior.

Instead, maybe a better approach would be introducing a template compatibility check at startup with a fallback mechanism (e.g., automatically falling back from 1 to 3). If the template or backend doesn't support mid-conversation system tokens, vLLM could automatically degrade and merge them into the top-level system block rather than forcing a normalization that might skew the meaning.

[hclsys]

yep, fine to proceed imo — no objection while @kimun608 runs the independent test in #44000.

read through the validator: multi-system, a pre-existing top-level system, and str-vs-blocks content are all handled. the bit i'd have worried about for claude code specifically is cache_control on system blocks (CC marks its system prompt ephemeral for prompt caching) — _content_to_system_blocks passes lists through untouched, so those markers survive the hoist. CountTokens parity is there and the input dict isn't mutated. looks right.

[Syraxius]

On second thought, I am a bit concerned that Option 2 might alter or break the original semantics of the prompt, as converting a system instruction into a user message can sometimes confuse the model's behavior.

Instead, maybe a better approach would be introducing a template compatibility check at startup with a fallback mechanism (e.g., automatically falling back from 1 to 3). If the template or backend doesn't support mid-conversation system tokens, vLLM could automatically degrade and merge them into the top-level system block rather than forcing a normalization that might skew the meaning.

@luyufan498
Yeah exactly! Before I went with option 3, I already tested options 1 and 2, and it seems option 2 totally broke the models I tested with - the LLM started hallucinating and chatted about the things as if I'm the one saying it.

Option 1 won't even start until I modified the chat template of the models I was using (chat template specifically rejected mid conversation system messages).

So you're suggesting to have something like...

--anthropic-mid-conversation-system-message=auto | keep-as-system | normalize-to-user | merge-to-top-system

Right?

[luyufan498]

On second thought, I am a bit concerned that Option 2 might alter or break the original semantics of the prompt, as converting a system instruction into a user message can sometimes confuse the model's behavior.
Instead, maybe a better approach would be introducing a template compatibility check at startup with a fallback mechanism (e.g., automatically falling back from 1 to 3). If the template or backend doesn't support mid-conversation system tokens, vLLM could automatically degrade and merge them into the top-level system block rather than forcing a normalization that might skew the meaning.

@luyufan498 Yeah exactly! Before I went with option 3, I already tested options 1 and 2, and it seems option 2 totally broke the models I tested with - the LLM started hallucinating and chatted about the things as if I'm the one saying it.

Option 1 won't even start until I modified the chat template of the models I was using (chat template specifically rejected mid conversation system messages).

So you're suggesting to have something like...

--anthropic-mid-conversation-system-message=auto | keep-as-system | normalize-to-user | merge-to-top-system

Right?

I believe these options would be enough:

--anthropic-mid-conversation-system-message=auto | keep-as-system | merge-to-top-system

normalize-to-user doesn't seem like a working option to me. We can modify the template to apply keep-as-system, or just use merge-to-top-system for compatibility.

is there any reason to use " normalize-to-user" if it breaks the models ?

[chaunceyjiang]

@Syraxius Thanks for this PR. I feel that this implementation is not very clear in terms of semantics. I have submitted a new PR #44283 and added you as a Co-Authored-By. Could you take a look? I think this new implementation should be cleaner.

[luyufan498]

On second thought, I am a bit concerned that Option 2 might alter or break the original semantics of the prompt, as converting a system instruction into a user message can sometimes confuse the model's behavior.
Instead, maybe a better approach would be introducing a template compatibility check at startup with a fallback mechanism (e.g., automatically falling back from 1 to 3). If the template or backend doesn't support mid-conversation system tokens, vLLM could automatically degrade and merge them into the top-level system block rather than forcing a normalization that might skew the meaning.

@luyufan498 Yeah exactly! Before I went with option 3, I already tested options 1 and 2, and it seems option 2 totally broke the models I tested with - the LLM started hallucinating and chatted about the things as if I'm the one saying it.
Option 1 won't even start until I modified the chat template of the models I was using (chat template specifically rejected mid conversation system messages).
So you're suggesting to have something like...
--anthropic-mid-conversation-system-message=auto | keep-as-system | normalize-to-user | merge-to-top-system
Right?

I believe these options would be enough:

--anthropic-mid-conversation-system-message=auto | keep-as-system | merge-to-top-system

normalize-to-user doesn't seem like a working option to me. We can modify the template to apply keep-as-system, or just use merge-to-top-system for compatibility.

is there any reason to use " normalize-to-user" if it breaks the models ?

After more thought, I want to push back on the auto / template-compatibility-detection approach discussed above.

A runtime dummy-request check (e.g. tokenizer.apply_chat_template with a mid-conversation role: "system") is technically feasible, but it only proves the chat template renders without crashing. It does not prove the model was trained to understand mid-conversation system messages correctly.

Most chat templates are shipped by model vendors; vLLM merely consumes them. A template might silently render a mid-conversation system block as plain user text, or drop it entirely, and the dummy request would still "succeed". This would create a silent semantic failure — the worst kind for an inference engine.

So I'd suggest we drop the auto option entirely, and instead expose an explicit opt-in flag:

--anthropic-inline-system=merge (default, safe for all models)
--anthropic-inline-system=keep (opt-in, user confirms their model supports it)
merge is the conservative default. It flattens inline system messages into the top-level system field, which every chat template handles correctly. Yes, it loses the prompt-caching optimization, but it guarantees correctness.
keep is for users who have verified (by reading their model's chat template, or by testing) that mid-conversation system tokens are handled properly. vLLM should not pretend to auto-detect this.
This keeps vLLM's scope clean: we handle the Anthropic protocol boundary, but we don't try to outguess the model's training data or the upstream template's semantics.

[chaunceyjiang]

@luyufan498 I agree with your idea, but I think it would be better to have a more general command-line option to control this behavior, since similar merging logic also exists in the chat API. Once the related PR is merged, you're welcome to submit a PR for this.

[felix0080]

I see this takes a protocol-layer approach to the same issue. I opened #44602 with a different strategy: instead of removing and merging system messages into the top-level system field (which still changes the prefix and breaks KV-cache in multi-turn conversations), it keeps them at their original position. The prefix structure is fully preserved, and billing header stripping works for inline system messages too. Happy to discuss the trade-offs.