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feat(server): let the fake engine answer chat with scripted tool calls, and run the agent-loop check in CI - #739
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…s, and run the agent-loop check in CI The weightless fake had no chat template, so /v1/chat/completions answered 501 for it and the OpenCode example's conformance check could only run against a GPU-backed model. The fake adapter now implements the generation adapter's chat methods with a deterministic script: with tools declared it calls the named or first tool, passing the last user message as every required argument; after tool results it answers with them; otherwise it answers hash-derived tokens. When the profile enables thinking, one sentence of reasoning comes first and is returned only when asked. A prompt token is a word and an output token is a streamed piece, output stops at the token limit with finish_reason length, and a prompt that leaves no room in the window is refused. The single-node chat route sends fake profiles through the same adapter-owned chat path as remote profiles, so the fake's output passes the route's real validation, tool-name, usage and reasoning checks. A new sie-fake:chat profile declares thinking, a 131,072-token window and a 4,096-token output cap. The example gains a test that starts the repository's sie-server with the fake bundle on the CPU and asserts each conformance case: 9 pass, parallel_tool_calls fails because the fake makes one call per turn, and tool_choice_required is recorded as supported.
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Review comments at @packages/sie_server/src/sie_server/adapters/fake/adapter.py:
- Around line 727-749: Update the output-step construction so a tool call marker
and all argument pieces from `call_name` and `arguments` are treated as an
atomic minimum output; emit them before optional reasoning, and omit reasoning
when the remaining budget cannot fit it. If the complete call exceeds `budget`,
reject the request with the appropriate invalid-request error rather than
truncating the call.
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examples/coding-agent-opencode/README.mdexamples/coding-agent-opencode/tests/test_fake_engine.pypackages/sie_server/models/sie-fake.yamlpackages/sie_server/src/sie_server/adapters/fake/adapter.pypackages/sie_server/src/sie_server/api/openai_local.pypackages/sie_server/tests/adapters/test_fake_chat.pypackages/sie_server/tests/api/test_fake_chat_route.py
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…tead of cutting the call
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Why
The weightless
sie-fakemodel answered/v1/chat/completionswith501 unsupported_operation: it has no chat template and no managed generation child. So the OpenCode example's agent-loop check (examples/coding-agent-opencode/conformance.py) could only run against a GPU-backed or hosted model, and CI covered it through scripted stubs only, never through a realsie-server.What changes
The fake answers chat from a script.
FakeAdapterimplements the generation adapter's existingchat_completionandchat_completion_streammethods, the hooks a remote adapter already uses when it owns chat. No new abstraction. The script is deterministic, takes no weights, and is documented as test-only in the adapter docstring:tool_choicenot"none", a turn whose last message is not a tool result calls one tool: the named one, else the first declared. Every required parameter gets the last user message's text.tool_choice: "required"or a named function forces the call after a tool result too.role: toolmessages answersTool result: <content>for each.default_completion_tokenshash-derived tokens, as/v1/generatedoes.chat_template_kwargsenables thinking, one sentence of reasoning that names the rule applied comes first. It is returned only withkeep_reasoning, which the route sets frominclude_reasoning, and its tokens count in usage either way.max_tokens(ormax_completion_tokens) withfinish_reason: "length", and also at the room left inmax_seq_length. A tool call is never cut: a call turn without room for its reasoning and the whole call is refused with400 invalid_request, the way the local engine path refuses an unterminated tool block. A prompt that leaves no room is refused with400 INPUT_TOO_LONG.nrepeats the turn. Usage is exact.The single-node route sends fake profiles down the adapter-owned chat path. In
api/openai_local.py,_adapter_answers_chat(config)is true for remote profiles (as before) and for profiles whose adapter is the fake. Such a request gets:tool_choice: "required";_remote_chat_response, with its usage, tool-name, reasoning and strict-output checks.So the fake's output passes the route's real checks. MLX, SGLang and remote dispatch are unchanged.
A
sie-fake:chatprofile declaresenable_thinking: true, a 131,072-token window (max_seq_length) and a 4,096-token output cap. That is the shapeconformance.pyasks for:max_tokens: 4096on every turn, and a 68,000-word prompt.The example runs the check in CI.
examples/coding-agent-opencode/tests/test_fake_engine.pystarts the repository'ssie-serverwith-b fake --device cpu, the same way the existing stub-upstream test starts it. It then runsconformance.runagainstsie-fake:chatand asserts each case. The example's existing CI step (uv run --frozen pytest -qaftermise run sync) picks the test up, so no workflow change is needed. WithCIset, a missing server binary fails instead of skipping. The README's "Checked without a GPU" section now shows how to run the check against the fake, and what a pass there does and does not mean.Conformance against
sie-fake:chatWhat the fake cannot honestly pass is recorded rather than faked:
parallel_tool_callsfails. The fake makes one call per turn, and the test asserts that failure.needle_foundisfalse. The case does not require the needle.finish_reason_stoppasses on hash tokens, not on the word "ready". The case checks only the finish reason.Each pass checks SIE's single-node chat wire, not a model.
Behaviour changes
sie-fakeprofile now answers single-node/v1/chat/completionsinstead of501. The baresie-fakehas a 512-token chat window (itsmax_sequence_length), a 1,024-token output cap and no reasoning./v1/modelsstill advertisestools: falseforsie-fake. Capability flags are model-wide, and the gateway path still has no fake chat.Validation
mise run lint,mise run typecheck: pass.packages/sie_server/tests/adapters/test_fake_chat.pycovers the script's properties.packages/sie_server/tests/api/test_fake_chat_route.pydrives the real route, with the catalog config and adapter and only the registry stubbed.packages/sie_server/testsrun on macOS: 11,783 passed and 5 failed. All 5 are serving-artifact materialization tests (config/test_serving_artifacts.py,core/test_model_loader_cache.py), and they fail the same way againstorigin/mainsource on this machine. The Linux CI run is the reference for them.CI=1:uv lock --check,ruff format --check,ruff check, andpytestall pass.tests/test_fake_engine.pypasses 8 tests in about 11 s.Mutation check
Each mutant ran against the two new server test files and the example's
tests/test_fake_engine.py(CI=1, realsie-server). The tests ran from a shadow copy of the source, so the checkout stayed untouched. Every mutant is caught.requiredon a CPU device)tool_choice_required)keep_reasoningroom < 0)stop/tool_calls, notlengthnignoredmax_tokensignoredSummary by CodeRabbit
chatprofile is available with reasoning enabled, a 131,072-token context window, and a 4,096-token output limit. Requests that exceed token limits are rejected.