Menu-driven list/add/delete/connectivity-test for config.json models
section - no hand-written JSON needed. Key convention preserved: env
var name only, never plaintext (or empty = keyless local endpoint).
Delete auto-cleans dangling fallback refs; default profile via
api.default_model; save failure reported. 17 unit tests, 441 passed.
- root README: aibox section notes four audit rounds (Batch E/F/G/H/I)
- aibox README: v3.5 changelog (Batch G/H/I), test suite count 424,
test file listing and aibox.py line count refreshed
Observability: FailoverClient.get_stats() exposes aggregate counters
(requests / failovers / cooldown_skips / exhausted, snapshot copy);
new on_failover(from_name, to_name, reason) callback fires on every
failover decision — to_name=None marks chain exhaustion — for future
admin-panel/frontend wiring, wrapped so callback exceptions never
affect the request path.
Timeout budget: profile field "failover_timeout_budget" caps the total
seconds spent walking the chain (default unlimited; values <= 0 are
rejected to None with a warning). Once elapsed, remaining profiles are
skipped — prevents worst-case N x timeout stacking on longer chains.
Exhaustion is counted once per call via a single end-of-loop increment
(an initial double count at the budget branch was caught by the new
budget test and folded into the unified counter).
Retry-After: TransientLLMError now carries retry_after parsed from the
numeric Retry-After response header (non-numeric values like HTTP-dates
are ignored); FailoverClient._mark_fail uses it to extend that
profile's cooldown beyond the default 60s, respecting server-side
throttling on 429s. _fail_at entries became (fail_time, cooldown)
tuples; three existing tests that poke the internal map were updated.
Config: create_llm_client passes profile.failover_timeout_budget into
the FailoverClient constructor.
Tests: new TestL4cObservabilityAndBudget (13 cases: stats on
success/exhaustion/cooldown-skip, snapshot copy, hook called with
chain-tail None, hook exception safety, budget stops chain via mocked
monotonic, invalid budget ignored, config field plumbing, default
no-budget regression, Retry-After parse/ignore/extend-cooldown). Full
suite 355 passed.
FailoverClient.stream_chat now walks the chain but only fails over
while no event has been produced yet — replay before the first
reasoning/content/tool_call event is invisible to the user. Once any
event is out, failures propagate directly (replay would duplicate
frontend output and corrupt tool-call accumulation).
DeepSeekClient.stream_chat classifies non-200 responses: 429/5xx raise
TransientLLMError (failover-eligible), auth/param 4xx keep raising
RuntimeError. Chain exhaustion re-raises the last exception so the
existing rollback/error handling in think_and_respond catches it; an
all-cooling chain raises RuntimeError.
Cancel-flag guard: LLMClient.cancel() sets _cancelled because the
RequestException raised by force-closing the connection is
indistinguishable from a real network fault. FailoverClient.cancel()
is now defined explicitly (overriding __getattr__ delegation),
broadcasts to every client in the chain and sets the wrapper flag;
stream_chat/chat_completion reset it per call and suppress failover
when it is set — otherwise interrupting would make the system hop to
another vendor and keep generating against the user's intent.
Tests: new TestL4bStreamFailover (12 cases: failover before first
event on TransientLLMError/ConnectionError, no failover after first
event, cancel never fails over on both paths, cancel broadcast, chain
exhaustion, all-cooling, cooldown skip, success isolation, transient/
auth status classification). Full suite 342 passed.
When a profile declares "fallback": ["kimi", "local"], create_llm_client
now returns a FailoverClient wrapper holding the ordered chain. For the
idempotent non-streaming path (chat_completion — conversation
compression, light subagents):
- Fail over on transient errors only: HTTP 429/5xx (now raised as
TransientLLMError by DeepSeekClient) and connection errors/timeouts
(previously swallowed into None by chat_completion's catch-all, now
re-raised). Auth/param 4xx failures keep returning None with no
failover — config problems stay loud, consistent with the H1/H2
error-early philosophy.
- Failed profiles enter a 60s in-process cooldown and are skipped
until it expires; chain exhaustion returns None per the existing
failure contract.
- Config structure errors fail at construction (non-list fallback,
unknown profile name, self-reference — H2-style). Environmental
problems (e.g. fallback profile missing its API key) skip that
fallback with a warning instead of blocking the primary.
- Unspecified attributes delegate to the primary client via
__getattr__ (name/model/api_key/cancel), so DeepSeekChat and
set_client keep working unchanged. stream_chat still delegates to
the primary directly; first-token failover is L4b.
create_llm_client refactored: single-profile construction extracted
into _construct_profile_client, shared by the main path and the
fallback chain (flat-api path untouched).
Tests: new TestL4aChatCompletionFailover (15 cases: chain build,
validation, 429/5xx/connection failover, 401 no-failover, exhaustion,
cooldown skip/expiry, missing-key skip with warning, transient raise,
4xx None regression, stream delegation); full suite 330 passed.
L1 display name: client.name now resolves to the profile's
display_name field, falling back to the profile key — the dialogue
context no longer labels Kimi/Qwen sessions "DeepSeek". The legacy
flat api section keeps "DeepSeek" (zero breakage).
_resolve_model_profile now returns (profile, model, profile_name).
L2 prefix escape: "model:NAME" forces bare-model semantics even when
a profile happens to be named NAME; "profile:NAME" forces profile
semantics and raises a clear error (listing available profiles) when
missing, instead of silently falling back to bare-name behavior.
Empty prefixes are rejected. Wired through --model / create_llm_client
/ subagent model params.
L3 profile temperature: new profile field "temperature" becomes the
client's default_temperature. chat_completion/stream_chat treat
temperature=None as "use profile default, else the old hardcoded
0.3/0.7", so explicit caller values still win. DeepSeekChat and
run_light_subagent resolve CLI > profile > constants.DEFAULT_TEMPERATURE.
L5 reasoning field aliases: _parse_delta recognizes
reasoning_content / reasoning / thinking / thinking_content (first
non-empty wins), so non-DeepSeek vendors' thinking streams are no
longer silently dropped.
L4 (cross-profile failover) intentionally deferred: streaming
failover needs request-replay semantics and is feature-level work.
Tests: new TestLBatch (16 cases); 7 existing unpack sites updated to
the 3-tuple. Full suite 315 passed.
Profile-level reasoning_enabled was attached as an instance attribute
after construction only when a profile matched; the base class never
declared it and consumers probed with double getattr. Any code that
accidentally set client.reasoning_enabled would silently override the
global switch.
The flag is now an explicit LLMClient constructor parameter with
three-state semantics: True/False (profile-level explicit, or the
global api.reasoning_enabled value concretized when the profile omits
the field) and None (no profile -> consumer falls back to global).
create_llm_client passes it at construction and no longer mutates the
client afterwards; DeepSeekChat._reasoning_enabled simplifies to a
single getattr probe that still tolerates clients without the attr.
Tests: TestM3ReasoningConstructor (4 cases) covering profile True,
profile-omitted -> global concretized, base default None, and the
None -> global fallback at the consumer; one legacy assertion updated
to the new contract (attribute exists, value None). Full suite 299
passed.
run_full_subagent replaced sub_chat.client by direct assignment after
construction, leaving ConversationCompressor (bound to the original
client in DeepSeekChat.__init__) and the api_key/name snapshots
pointing at the default model — compression requests silently kept
using the default endpoint/model while the dialogue itself used the
requested one.
DeepSeekChat gains set_client(): swaps self.client and refreshes
api_key, name, and compressor.llm_client in one place. run_full_subagent
now routes model overrides through it.
Tests: new TestM1SetClient (2 cases: full reference refresh on a real
DeepSeekChat instance, and run_full_subagent calling set_client with
the new client); full suite 295 passed.
The LLM had no way to know which profile names exist in the models
config section; a hallucinated name fell through to bare-name semantics
(default endpoint + bogus model) and failed at request time with API
400.
SubAgentTool now builds the model hint dynamically from config via
_model_names_hint(): description and the model parameter's JSON Schema
description both list the profile names, with the default profile
(api.default_model) marked. Empty or malformed models sections keep
the original wording untouched (zero breakage); exceptions degrade to
no hint.
README: tool table note, test count 293, new v3.4 changelog entry
summarizing the H1/H2/H3/M2 profile-hardening batch.
Tests: new TestM2SubAgentModelHint (5 cases); full suite 293 passed.
--model was accepted by parse_args but only consumed inside
handle_once_mode, so `python aibox.py --model kimi` (server mode)
silently ignored the override.
The model now flows as a process-wide override:
main() -> EnhancedAIManager(model=...) -> DeepSeekChat(model=...) in
all three AI creation paths: add_ai (main dialogue),
WebSocketHandler._create_session_ai (per-connection sessions), and
TaskScheduler._create_task_ai (scheduled tasks). Manager logs
"CLI 模型覆盖已启用" at startup for observability. --once mode
plumbing is unchanged; --model=None keeps config-driven behavior.
Tests: new TestH3ModelPlumbing (6 cases) mocking DeepSeekChat to
assert the constructor kwarg across manager/session/task paths plus
parse_args; full suite 288 passed.
Profiles previously had no validation, so config errors surfaced far
from their origin and cryptically:
- explicit profile missing `model` fell back to the profile NAME as the
model (a profile named "local" would send "model": "local")
- default profile missing `model` with no model param produced
"model": null -> API 400
- missing `api_url` surfaced as requests InvalidURL
_resolve_model_profile now runs _validate_profile (dict type + api_url)
before reading fields and _require_model on the effective model,
raising ValueErrors that name the profile, the missing field, and the
config.json path to fix. Bare model names still legally substitute for
a default profile's missing model field.
Tests: new TestH2SchemaValidation (6 cases: missing model/api_url,
null-model via default profile, bare-name substitution, non-dict
profile, e2e create_llm_client); full suite 282 passed.
~/.cert/deepseek.key is a DeepSeek-only credential; when a models profile
specified its own api_key_env (e.g. MOONSHOT_API_KEY) but the env var was
unset, resolve_api_key silently read that file and sent the DeepSeek key
as Bearer to a third-party endpoint (cross-vendor credential leak).
resolve_api_key gains allow_key_file (default True for the legacy flat
api section); create_llm_client passes allow_key_file=profile is None so
any profile path raises a clear RuntimeError instead, pointing users to
set the env var or use "" for local keyless endpoints.
Tests: new TestH1KeyIsolation (4 cases) with a mocked readable
deepseek.key proving the profile path errors out while the flat api
section still reads the file; full suite 276 passed.
Batch F bullet now split into F1/F2 entries; F2 covers unified
timeout-path cleanup of tool streaming attributes and reader-thread
finalization with the CPython stream-lock correction; test count 272.
N6: both streaming-forward timeout paths (deadline hit, join(5)
expiry) previously returned early and skipped the
tool.streaming_output / tool.output_queue cleanup, leaving the daemon
thread writing through the instance attribute — zombie output could
leak into the next session's queue once the attribute got reassigned.
Now unified through a timed_out flag with cleanup at the single exit;
stream_tool_timeout becomes an instance attribute (3600s default,
test-tunable).
N4: reader-thread finalization after timeout/grace break. First
implementation followed the audit suggestion (close stdout + join),
but PoC regression proved close() blocks: CPython stream objects hold
an internal lock, so closing while readline is blocked parks until
readline returns (PoC-1 regressed 3s -> 15.2s, exactly the
Start-Sleep duration). Corrected strategy: when reader already got
EOF, close stdout to release the pipe fd; when reader is still
blocked, only join(timeout=2) then give up (daemon-bounded, thread
ends naturally when the grandchild exits and EOF arrives), keeping
the main flow prompt. Audit report updated with this correction.
Tests: new tests/test_f2_fixes.py (5 cases), PoC regression 5/5
(PoC-1/4 back to ~5s), full suite 272 passed.
- feature list & tool table: set_proxy password semantics (memory-only,
password_env for persistence)
- config example: fix stale flat proxy section to actual proxy.socks5
structure with password_env; add plaintext-not-persisted note
- changelog: new v3.3 section covering Batch E (command exec protection
chain) and Batch F (credential protection), test count 267
- config.set_proxy gains password_env (aligned with api_key_env
convention): config file persists only the env var name; plaintext
password lives in memory for the current session only
- _save_config deep-copies and nulls proxy.socks5.password before
json.dump, so every save path is sanitized (defense in depth)
- get_proxy resolves password as in-memory plaintext first, then via
password_env from os.environ; downstream consumers (llm_client,
web tool curl args) unchanged
- legacy migration on load: plaintext found on disk is kept in memory
for the session, file is immediately rewritten sanitized
- SetProxyTool exposes password_env and reports password source
- registry debug logs mask password/api_key/token/secret/authorization
kwargs (N11)
Tests: new tests/test_config.py (3 cases: no plaintext on save,
password_env resolution, legacy migration); full suite 267 passed.
Audit report updated with Batch F progress.
- H1: CommandLineTool.execute_streaming now purely delegates to injected
executor; removes 150-line duplicate Popen loop that bypassed 1MB cap,
cancel() and timeout protections
- H2: thread the blocking readline into a daemon reader thread; main loop
polls queue with 0.1s ticks so timeout/1MB checks fire even for silent
commands (verified: 3s timeout on 15s sleep, was 15s+ before)
- H3: stdin=DEVNULL on both Popen sites; stdin-waiting commands now hit
EOF instead of deadlocking the tool thread forever
- M1: heredoc delimiter uniquification in write_file guidance (content
line equal to AIBOX_EOF no longer truncates into shell injection)
- M2: 3600s total deadline on registry streaming forward loop
Verification: PoC regression 5/5 PASS (timeout/1MB/cancel/delegation),
full unit suite 264 passed. Audit report updated with Batch E results.
- Move all default prompt content from PromptManager._ensure_* methods into aibox/prompts/ template files
- Add 8 new template files: 00_IDENTITY, 100_STATUS, 1_memory_search, 2_memory_save, 5_memo_management, 8_identity_management, 9_language_instruction, 11_emotion_management
- Refactor _ensure_* methods to copy from template directory via _ensure_from_template()
- _ensure_skill_files now auto-discovers N_*.md files instead of hardcoded list
- Update README.md with complete file manifest
- Single source of truth: aibox/prompts/ is the only prompt template source
- Rewrite ORCHESTRATOR_TURN_GUIDANCE: remove conservative '宁缺毋滥' principle,
add 5 trigger scenarios each for ask_user and express_emotion
- Rewrite conversation start guidance: split into 3 steps with concrete examples
- Improve AskUserTool description: from passive 'encountered a problem' to
proactive 'actively care about user, clarify needs, confirm intent'
- Improve ExpressEmotionTool description: add 'like caring for a friend' tone
and 4 usage scenarios
- Update 99_SOUL.md: add behavior rules 9 (proactive questioning) and 10
(emotional expression), expand workflow from 7 to 9 steps adding emotion
interaction and follow-up questioning
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