Qwen3.x and DeepSeek-V3.x default to chatty/hallucinatory tool use without
enforcement steering — agents narrate "calling tool X" without actually
emitting a tool call, or run partial loops. Both model families fit the
same failure pattern TOOL_USE_ENFORCEMENT_GUIDANCE was already injected
for (gpt, codex, gemini, gemma, grok, glm).
Co-authored-by: briandevans <252620095+briandevans@users.noreply.github.com>
Squashed salvage of:
- 403e567ce fix(agent): add qwen and deepseek to TOOL_USE_ENFORCEMENT_MODELS
- 9433eabe7 test(agent): use realistic qwen-plus identifier in enforcement test
Fixes#28079.
The system prompt's 'Conversation started:' line carried minute precision
(%I:%M %p), making it byte-unstable across every rebuild path. Within a
CLI session the in-memory cache held, but on the gateway path (fresh
AIAgent per turn → restore from session DB), any silent failure in the
read or write path dropped the cache stem and forced a full re-prefill
on every subsequent turn. Local prefix-caching backends (llama.cpp /
vLLM) saw this as KV-cache invalidation; remote prefix-caching providers
saw it as an Anthropic-style cache miss.
Three changes:
1. Date-only timestamp ('Sunday, May 17, 2026' instead of '... 03:42 PM').
System prompt now byte-stable for the full day. The model can still
query exact time via tools when it actually needs it. Credit:
@iamfoz (PR #20451).
2. Loud logging on session DB write failures. The update_system_prompt
call used to log at DEBUG, hiding disk-full / locked-database / schema
drift behind a silent fall-through that forced fresh rebuilds on
every subsequent turn. Now WARN with the session id and exception so
persistent issues show up in agent.log without verbose mode.
3. Three-way stored-state distinction on read. The previous
'session_row.get("system_prompt") or None' collapsed three states
into one (missing row / null column / empty string). Now we tell them
apart and WARN when a continuing session lands on null/empty (which
means the previous turn's write never persisted — every subsequent
turn rebuilds and the prefix cache misses every time).
The restore block is extracted into _restore_or_build_system_prompt()
so the prefix-cache path can be unit-tested in isolation.
E2E proof: fresh AIAgent constructed for turn 2 across a minute-boundary
sleep restores byte-identical bytes from the session DB. NULL stored
prompt fires the new warning. Date-only timestamp survives the rebuild
path. All on real SessionDB, no mocks.
Tests:
- tests/agent/test_system_prompt_restore.py (10 new tests)
- tests/run_agent/test_run_agent.py::TestBuildSystemPrompt::
test_datetime_is_date_only_not_minute_precision
Closes#20451 (date-only), #18547 (prefix stabilization),
#8689 (stabilize timestamp across compression), #15866 (timestamp
caching question), #8687 (compression timestamp), #27339
(claim #3: live timestamp in cached system prompt).
Co-authored-by: Martyn Forryan <9133432+iamfoz@users.noreply.github.com>
Grok models hit the same failure modes that OPENAI_MODEL_EXECUTION_GUIDANCE
addresses for GPT/Codex: claiming completion without tool calls
('to be honest, I didn't create the file yet'), suggesting workarounds
instead of using existing tools (proposing a folder-based memory system
when the memory tool exists), replying with plans instead of executing.
TOOL_USE_ENFORCEMENT_GUIDANCE was already injected for any model whose
name contains 'grok' (TOOL_USE_ENFORCEMENT_MODELS). This extends the
follow-on family-specific block — OPENAI_MODEL_EXECUTION_GUIDANCE
(tool_persistence / mandatory_tool_use / act_dont_ask / prerequisite_checks
/ verification / missing_context) — to grok-named models too.
The OPENAI_ prefix is retained for backwards compat with imports/tests;
docstring + inline comment now note that the body is family-agnostic and
the prefix reflects origin, not exclusivity.
Tests cover the OpenRouter slug (x-ai/grok-4.3) and the xai-oauth bare
name (grok-4.3), plus a negative control on claude.
E2E verified against a real AIAgent build of the system prompt for both
xai-oauth and openrouter grok models.
run_agent.py taken from HEAD (the extracted forwarder structure). The 25
run_agent.py fixes that landed on main during the PR's life need to be
ported into the agent/* extracted modules in follow-up commits.
Four fixes from PR #27248 review:
1. **__init__ forwarder is now keyword-forwarded** (daimon-nous review).
Previously the run_agent.AIAgent.__init__ wrapper forwarded all 64
params positionally to agent.agent_init.init_agent, so adding a
65th param on main would require three lockstep edits (signature,
init_agent signature, forwarder call) or silently shift every value.
Keyword forwarding makes this trivially safe — adding a param now
only needs the two signatures and one extra keyword line.
2. **Drop dead _ra() in agent/codex_runtime.py** (daimon-nous + Copilot).
The lazy run_agent reference was defined but never called inside
this module — the codex paths use agent.* accessors only.
3. **Drop unused imports in agent/codex_runtime.py** (Copilot):
contextvars, threading, time, uuid, Optional. Carried over from
run_agent.py during the original extraction.
4. **Tighten three source-introspection test guards** (Copilot):
- test_memory_nudge_counter_hydration.py — was scanning the
concatenated source of run_agent.py + agent/conversation_loop.py
and matching self.X or agent.X form. Now asserts the
hydration block lives in agent/conversation_loop.py specifically
with the agent.X form — the body never moves back, so if it
ever drifts a future re-introduction fails the guard.
- test_run_agent.py::TestMemoryNudgeCounterPersistence — anchor on
agent.iteration_budget = IterationBudget exactly (was just
iteration_budget = IterationBudget) so an unrelated identifier
ending in iteration_budget can't match.
- test_run_agent.py::TestMemoryProviderTurnStart — assert the
agent._user_turn_count form directly (the extracted body uses
agent.X, not self.X — accepting either was a transitional fudge).
- test_jsondecodeerror_retryable.py — scan agent/conversation_loop.py
only, not the concatenation.
Not addressed in this commit:
* Pre-existing bugs in agent/tool_executor.py (heartbeat index
mismatch when calls are blocked, _current_tool clobber in result
loop, blocked-counted-as-completed in spinner summary, dead
result_preview computation). These were preserved byte-for-byte from
the original _execute_tool_calls_concurrent — worth a separate
follow-up PR with proper tests.
* _OpenAIProxy.__instancecheck__ concern — pre-existing, not flagged
by any of the original test patches (nothing actually does
isinstance(x, OpenAI) against the proxy instance).
* agent_init.py:949 mem_config potential NameError — pre-existing;
only triggers if _agent_cfg.get('memory', {}) itself raises, which
it can't with a stock dict.
tests/run_agent/ + tests/agent/: 4313 passed, 1 pre-existing
test_auxiliary_client failure (unchanged).
run_agent.py: 3821 -> 3937 lines (+116 from the keyword-forwarded
init call's verbosity). Final: 16083 -> 3937 (-12146, 75% reduction).
The 3,877-line run_conversation body — the agent loop itself — moves out
of run_agent.py into a dedicated module. AIAgent.run_conversation is
now a thin forwarder that delegates to agent.conversation_loop.run_conversation
with the AIAgent instance as the first argument.
This is the largest single extraction in the run_agent.py refactor.
The body keeps all 163 self.X references intact (rewritten as agent.X),
all nested closures, all retry/backoff/compression machinery. Symbols
that tests or callers patch on run_agent (_set_interrupt,
handle_function_call, AIAgent class attrs) are resolved through _ra()
inside the extracted module so the patch surface is preserved.
Five tests doing inspect.getsource(AIAgent.run_conversation) updated to
scan agent.conversation_loop.run_conversation. Two source-introspection
tests (TestMemoryNudgeCounterPersistence, TestMemoryProviderTurnStart)
updated to accept either self.X (legacy) or agent.X (extracted
form) in the matched assertions.
Live E2E verified on three model paths:
* openai/gpt-5.4 (OpenAI chat completions via OpenRouter)
* anthropic/claude-sonnet-4.6 (Anthropic Messages via OpenRouter)
* moonshotai/kimi-k2-thinking (reasoning model, reasoning_content path)
Plus read_file tool execution, terminal tool, web_search.
tests/run_agent/ + tests/agent/: 4313 passed, 1 pre-existing failure
(test_auxiliary_client::test_custom_endpoint... — same as on main).
run_agent.py: 9800 -> 5944 lines (-3856).
Total reduction since baseline: 16083 -> 5944 (-10139, 63%).
Move _interruptible_streaming_api_call out of run_agent.py — the biggest
single method in the file. Body lives next to interruptible_api_call
in agent/chat_completion_helpers.py so streaming + non-streaming code
share one home.
Nested closures (_call_chat_completions, _call_anthropic, the codex
stream branch) all come along with the body and still capture the
parent function's locals as expected.
AIAgent keeps a thin forwarder method. is_local_endpoint added to
the import block (used by the stream stale-timeout disable logic).
One source-introspection test in TestAnthropicInterruptHandler is
updated to scan agent.chat_completion_helpers.interruptible_streaming_api_call
instead of AIAgent._interruptible_streaming_api_call.
tests/run_agent/ + tests/agent/: 4312 passed (same pre-existing
test_auxiliary_client failure).
run_agent.py: 12277 -> 11385 lines (-892).
Port from openai/codex#17667: MCP servers can now opt-in to parallel
tool execution by setting supports_parallel_tool_calls: true in their
config. This allows tools from the same server to run concurrently
within a single tool-call batch, matching the behavior already available
for built-in tools like web_search and read_file.
Previously all MCP tools were forced sequential because they weren't in
the _PARALLEL_SAFE_TOOLS set. Now _should_parallelize_tool_batch checks
is_mcp_tool_parallel_safe() which looks up the server's config flag.
Config example:
mcp_servers:
docs:
command: "docs-server"
supports_parallel_tool_calls: true
Changes:
- tools/mcp_tool.py: Track parallel-safe servers in _parallel_safe_servers
set, populated during register_mcp_servers(). Add is_mcp_tool_parallel_safe()
public API.
- run_agent.py: Add _is_mcp_tool_parallel_safe() lazy-import wrapper. Update
_should_parallelize_tool_batch() to check MCP tools against server config.
- 11 new tests covering the feature end-to-end.
- Updated MCP docs and config reference.
* fix(langfuse): reject placeholder credentials with one-shot warning
When operators leave HERMES_LANGFUSE_PUBLIC_KEY / HERMES_LANGFUSE_SECRET_KEY
at a template value like 'placeholder', 'test-key', or 'your-langfuse-key',
the Langfuse SDK silently accepts the credentials at construction time and
drops every trace at flush time. No warning, no error — just an empty
Langfuse dashboard the operator only notices hours later.
Add prefix-based validation in _get_langfuse() against the documented
'pk-lf-' / 'sk-lf-' prefixes that Langfuse always issues server-side.
Anything else fires a single warning naming the offending env var(s)
with a log-safe value preview (full string for short placeholders so the
operator knows which template they left in place; truncated for long
values so a real secret pasted into the wrong field never hits the log),
then short-circuits via the existing _INIT_FAILED cache so the warning
fires once per process, not once per hook invocation.
The check sits after the 'Langfuse is None' SDK-installed guard so hosts
without the optional langfuse SDK don't see misleading 'set real keys'
hints when the actionable fix is 'pip install langfuse'. Missing
credentials remains the documented opt-out path and stays silent — no
log noise for unconfigured installs.
Fixes#22763Fixes#23823
* fix(langfuse): use actual API request messages for generation input
on_pre_llm_request previously used the messages kwarg alone, which
could be None when Hermes passes the payload via request_messages,
conversation_history, or user_message instead. Add _coerce_request_messages
to pick the first available list across all variants, falling back to a
synthetic user message. Generations now show the real outbound payload
rather than an empty input.
* fix(langfuse): record tool call outputs in traces
Tool observations showed input (arguments) but output was always
undefined. Root cause: when tool_call_id is empty, pre_tool_call stored
observations under a unique time-based key that post_tool_call could
never reconstruct, so every tool span was closed without output by the
_finish_trace sweep.
Fix pre/post matching by routing empty-tool_call_id tools through a
per-name FIFO queue (pending_tools_by_name) instead of the time-based
key. Tools with a tool_call_id continue to use the id-keyed dict.
Also:
- Preserve OpenAI-style nested function shape in serialized tool calls
so Langfuse renders name/arguments correctly
- Keep name + tool_call_id on role:tool messages for proper pairing
- Backfill tool results onto the matching turn_tool_calls entry so the
generation's tool-call record carries the result alongside arguments
- Coerce request messages from whichever field the runtime provides
(request_messages, messages, conversation_history, user_message)
* fix(langfuse): salvage-review polish — drop dead is_first_turn, shallow-copy request_messages, real threaded FIFO test
Self-review of the combined #22345 + #23831 salvage surfaced three issues
worth fixing in the same PR rather than as follow-ups:
1. Drop is_first_turn from the pre_api_request hook. The boolean expression
`not bool(conversation_history)` was wrong: conversation_history is
reassigned to None mid-run after compression (5 sites in run_agent.py),
so the value flips False -> True mid-conversation on every post-compression
API call. The langfuse plugin never consumed it, so the kwarg was both
misleading AND dead.
2. Replace copy.deepcopy(request_messages) with shallow list() copy. The
pre_api_request hook contract discards return values (invoke_hook never
writes back to api_kwargs), and the langfuse plugin's _serialize_messages
already builds its own snapshot dicts via _safe_value. A deepcopy on every
API call would walk every tool result and base64 image — significant
overhead for no real isolation benefit. Shallow copy of the outer list
protects against later mutations of api_messages without paying for the
inner-dict walk.
3. Rename test_empty_tool_call_id_concurrent_fifo_order ->
test_empty_tool_call_id_observations_are_fifo_within_tool_name and add a
real test_threaded_post_calls_preserve_fifo_under_lock that spawns 8
threads behind a barrier to actually exercise _STATE_LOCK on the
pending_tools_by_name queue. The original test was sequential and only
validated Python list semantics; this one validates the lock discipline.
4. Fix stale 'Cleared by reset_cache_for_tests()' comment on _INIT_FAILED —
that function does not exist. Tests reload the module via sys.modules.pop
+ importlib.import_module instead.
Tests: 37 langfuse plugin tests pass, 658 plugin tests overall pass.
---------
Co-authored-by: xxxigm <tuancanhnguyen706@gmail.com>
Co-authored-by: Brian Conklin <brian@dralth.com>
When a kanban worker subprocess hits the iteration budget, the agent
loop strips tools and asks the model for a summary. The model cannot
call kanban_block itself at that point, so the process exits rc=0
without calling kanban_complete or kanban_block — a protocol violation
that the dispatcher detects as a fatal error, giving up after 1 failure
and stranding downstream tasks.
Fix: after _handle_max_iterations() returns, check HERMES_KANBAN_TASK
and call kanban_block with a reason describing the exhaustion. The
dispatcher then sees a clean block transition instead of a protocol
violation, and the task can be retried or escalated by a human.
Fixes [Bug] kanban-worker exits cleanly (rc=0) on iteration-budget
exhaustion without calling kanban_complete or kanban_block #23216
DeepSeek V4 Pro returns thinking content as typed blocks inside the
content array rather than as a top-level reasoning_content field:
[{"type": "thinking", "thinking": "..."}, {"type": "output", ...}]
_extract_reasoning only handled content as a plain string, so the
thinking text was silently dropped. On the next turn the session was
replayed without the thinking block, causing:
HTTP 400: The content[].thinking in the thinking mode must be
passed back to the API.
Fix: when content is a list and no structured reasoning field was
found, scan for items with type=='thinking' and accumulate their
'thinking' (or 'text') value into reasoning_parts. Structured fields
(reasoning, reasoning_content, reasoning_details) still take priority
so existing provider behaviour is unchanged.
Closes#21944
Introduces providers/ package — single source of truth for every
inference provider. Adding a simple api-key provider now requires one
providers/<name>.py file with zero edits anywhere else.
What this PR ships:
- providers/ package (ProviderProfile ABC + 33 profiles across 4 api_modes)
- ProviderProfile declarative fields: name, api_mode, aliases, display_name,
env_vars, base_url, models_url, auth_type, fallback_models, hostname,
default_headers, fixed_temperature, default_max_tokens, default_aux_model
- 4 overridable hooks: prepare_messages, build_extra_body,
build_api_kwargs_extras, fetch_models
- chat_completions.build_kwargs: profile path via _build_kwargs_from_profile,
legacy flag path retained for lmstudio/tencent-tokenhub (which have
session-aware reasoning probing that doesn't map cleanly to hooks yet)
- run_agent.py: profile path for all registered providers; legacy path
variable scoping fixed (all flags defined before branching)
- Auto-wires: auth.PROVIDER_REGISTRY, models.CANONICAL_PROVIDERS,
doctor health checks, config.OPTIONAL_ENV_VARS, model_metadata._URL_TO_PROVIDER
- GeminiProfile: thinking_config translation (native + openai-compat nested)
- New tests/providers/ (79 tests covering profile declarations, transport
parity, hook overrides, e2e kwargs assembly)
Deltas vs original PR (salvaged onto current main):
- Added profiles: alibaba-coding-plan, azure-foundry, minimax-oauth
(were added to main since original PR)
- Skipped profiles: lmstudio, tencent-tokenhub stay on legacy path (their
reasoning_effort probing has no clean hook equivalent yet)
- Removed lmstudio alias from custom profile (it's a separate provider now)
- Skipped openrouter/custom from PROVIDER_REGISTRY auto-extension
(resolve_provider special-cases them; adding breaks runtime resolution)
- runtime_provider: profile.api_mode only as fallback when URL detection
finds nothing (was breaking minimax /v1 override)
- Preserved main's legacy-path improvements: deepseek reasoning_content
preserve, gemini Gemma skip, OpenRouter response caching, Anthropic 1M
beta recovery, etc.
- Kept agent/copilot_acp_client.py in place (rejected PR's relocation —
main has 7 fixes landed since; relocation would revert them)
- _API_KEY_PROVIDER_AUX_MODELS alias kept for backward compat with existing
test imports
Co-authored-by: kshitijk4poor <82637225+kshitijk4poor@users.noreply.github.com>
Closes#14418
DeepSeek V4 Pro tightened thinking-mode validation and rejects empty-string
reasoning_content with HTTP 400:
The reasoning content in the thinking mode must be passed back to the API.
run_agent.py injected "" at three fallback sites — the tool-call pad in
_build_assistant_message and both injection branches of
_copy_reasoning_content_for_api (cross-provider poison guard + unconditional
thinking pad). All three now emit " " (single space), which satisfies the
non-empty check on V4 Pro without leaking fabricated reasoning.
Also upgrades stale empty-string placeholders on replay: sessions persisted
before this change have reasoning_content="" pinned at creation time; when
the active provider enforces thinking-mode echo, the replay path now rewrites
"" -> " " so existing users don't 400 on their first V4 Pro turn after
updating. Non-thinking providers still round-trip "" verbatim.
Updates 9 existing assertions + adds 2 regression tests (stale-placeholder
upgrade, non-thinking verbatim preservation).
Refs #15250, #17400.
Closes#17341.
feat(gateway): refine Platform._missing_ and platform-connected dispatch
Restricts plugin-name acceptance to bundled plugin scan + registry
(no arbitrary string -> enum-pollution), pulls per-platform connectivity
checks into a _PLATFORM_CONNECTED_CHECKERS lambda map with a clean
_is_platform_connected method, and adds tests covering the checker map,
plugin platform interface, and IRC setup wizard.
Streaming-only providers (glm, MiniMax, gpt-5.x via aigw, Anthropic via
openai-compat shims) emit reasoning through delta.reasoning_content
chunks that get accumulated into the local reasoning_text string — but
never land on the assistant message object as a top-level attribute. The
prior guard at _build_assistant_message only wrote reasoning_content
when the SDK exposed hasattr(msg, 'reasoning_content'), so these
providers persisted the chain-of-thought under the internal 'reasoning'
key and omitted the protocol-standard field.
The poison was silent until the user later switched to a DeepSeek-v4 or
Kimi thinking model, at which point replay failed with HTTP 400:
'The reasoning_content in the thinking mode must be passed back to the
API.' One reported session store accumulated 4,031 poisoned messages
across 1,101 files (#16844).
Fix: add an additive fallback that promotes the already-sanitized
reasoning_text to reasoning_content when no earlier branch wrote it AND
reasoning text was actually captured. Layered on top of the existing
SDK-attr branch and DeepSeek ''-pad (#15250) rather than replacing them,
so every existing behavior is preserved:
- SDK-exposed reasoning_content (OpenAI/Moonshot/DeepSeek SDK) still
wins.
- DeepSeek tool-call ''-pad still fires when the SDK exposes the attr
but the value is None.
- Non-thinking turns with no reasoning leave the field absent, so
_copy_reasoning_content_for_api's cross-provider leak guard (#15748),
promote-from-'reasoning' tier, and thinking-pad tier remain live at
replay time.
- No empty '' gets eagerly written on every assistant turn (which would
have bypassed the read-side ladder and triggered empty thinking-block
insertion in the Anthropic adapter).
Tests: three new TestBuildAssistantMessage cases covering the streaming
promotion path, SDK precedence, and field-absent-when-no-reasoning
invariant.
Credit @Sanjays2402 for the original diagnosis and patch in #16884;
this is a scoped rework that preserves the existing read-side
compensation code as defense in depth.
Refs #16844, #16884, #15250, #15353, #15748.
Reviewer pushback on the original boundary-hardening commits — three
overreach points pulled plugin-specific policy into shared core paths:
1. gateway/run.py hardcoded a '## Honcho Context' literal split for
vision-LLM output. Plugin-format heading in framework code; could
truncate legitimate output naturally containing that header.
Drop the literal split; keep generic sanitize_context (the wrapper
strip is plugin-agnostic). Plugin-specific cleanup belongs at the
provider boundary, not the shared gateway path.
2. run_agent.run_conversation scrubbed user_message and
persist_user_message before the conversation loop. User text is
sacred — if a user types a literal <memory-context> tag we must
not silently delete it. The producer (build_memory_context_block)
is the only legitimate emitter; user input should never need the
reverse op.
3. _build_assistant_message scrubbed model output before persistence.
Same hazard: would silently mutate legitimate documentation/code
the model emits containing the literal markers. The streaming
scrubber catches real leaks delta-by-delta before content is
concatenated; persist-time scrub was redundant belt-and-suspenders.
4. _fire_stream_delta stripped leading newlines from every delta unless
a paragraph break flag was set. Mid-stream '\n' is legitimate
markdown — lists, code fences, paragraph breaks — and chunk
boundaries are arbitrary. Narrow lstrip to the very first delta
of the stream only (so stale provider preamble still gets cleaned
on turn start, but mid-stream formatting survives).
Plus: build_memory_context_block now logs a warning when its defensive
sanitize_context strips something — surfaces buggy providers returning
pre-wrapped text instead of silently double-fencing.
Net architectural change: scrub surface collapses from 8 sites to 3
(StreamingContextScrubber on output deltas, plugin→backend send,
build_memory_context_block input-validation). Plugin-specific strings
stay out of shared runtime paths. User input and persisted assistant
output are no longer mutated.
Tests: rescoped TestMemoryContextSanitization (helper-correctness only,
no source-inspection of removed call sites), updated vision tests to
drop '## Honcho Context' literal-split assertions, updated
_build_assistant_message persistence test to assert preservation.
Added: cross-turn scrubber reset, build_memory_context_block warn-on-
violation, mid-stream newline preservation (plain + code fence).
Azure OpenAI exposes an OpenAI-compatible endpoint at
`{resource}.openai.azure.com/openai/v1` that accepts the standard
`openai` Python client. Two issues prevented gpt-5.x models from working:
1. `_max_tokens_param()` only sent `max_completion_tokens` for
`api.openai.com` URLs. Azure also requires `max_completion_tokens`
for gpt-5.x models.
2. The `codex_responses` upgrade gate unconditionally upgraded gpt-5.x
to Responses API. Azure does NOT support the Responses API — it serves
gpt-5.x on the regular `/chat/completions` path, causing a 404.
Fix: add `_is_azure_openai_url()` that matches `openai.azure.com` URLs.
- `_max_tokens_param()` now returns `max_completion_tokens` for Azure.
- The `codex_responses` upgrade gate skips Azure so gpt-5.x stays on
`chat_completions` where Azure actually serves it.
- The fallback-provider api_mode picker also recognises Azure and stays
on chat_completions.
- Tests cover max_tokens routing, api_mode behaviour, and URL detection.
gpt-4.x models on Azure are unaffected (already used chat_completions +
max_tokens, which Azure accepts for those models).
Salvage of PR #10086 — rewritten against current main where the
codex_responses upgrade gate gained copilot-acp / explicit-api_mode
exclusions.
The AIAgent.flush_memories pre-compression save, the gateway
_flush_memories_for_session, and everything feeding them are
obsolete now that the background memory/skill review handles
persistent memory extraction.
Problems with flush_memories:
- Pre-dates the background review loop. It was the only memory-save
path when introduced; the background review now fires every 10 user
turns on CLI and gateway alike, which is far more frequent than
compression or session reset ever triggered flush.
- Blocking and synchronous. Pre-compression flush ran on the live agent
before compression, blocking the user-visible response.
- Cache-breaking. Flush built a temporary conversation prefix
(system prompt + memory-only tool list) that diverged from the live
conversation's cached prefix, invalidating prompt caching. The
gateway variant spawned a fresh AIAgent with its own clean prompt
for each finalized session — still cache-breaking, just in a
different process.
- Redundant. Background review runs in the live conversation's
session context, gets the same content, writes to the same memory
store, and doesn't break the cache. Everything flush_memories
claimed to preserve is already covered.
What this removes:
- AIAgent.flush_memories() method (~248 LOC in run_agent.py)
- Pre-compression flush call in _compress_context
- flush_memories call sites in cli.py (/new + exit)
- GatewayRunner._flush_memories_for_session + _async_flush_memories
(and the 3 call sites: session expiry watcher, /new, /resume)
- 'flush_memories' entry from DEFAULT_CONFIG auxiliary tasks,
hermes tools UI task list, auxiliary_client docstrings
- _memory_flush_min_turns config + init
- #15631's headroom-deduction math in
_check_compression_model_feasibility (headroom was only needed
because flush dragged the full main-agent system prompt along;
the compression summariser sends a single user-role prompt so
new_threshold = aux_context is safe again)
- The dedicated test files and assertions that exercised
flush-specific paths
What this renames (with read-time backcompat on sessions.json):
- SessionEntry.memory_flushed -> SessionEntry.expiry_finalized.
The session-expiry watcher still uses the flag to avoid re-running
finalize/eviction on the same expired session; the new name
reflects what it now actually gates. from_dict() reads
'expiry_finalized' first, falls back to the legacy 'memory_flushed'
key so existing sessions.json files upgrade seamlessly.
Supersedes #15631 and #15638.
Tested: 383 targeted tests pass across run_agent/, agent/, cli/,
and gateway/ session-boundary suites. No behavior regressions —
background memory review continues to handle persistent memory
extraction on both CLI and gateway.
- Load prompt_caching.cache_ttl in AIAgent (5m default, 1h opt-in)
- Document DEFAULT_CONFIG and developer guide example
- Add unit tests for default, 1h, and invalid TTL fallback
Made-with: Cursor
Port from openclaw/openclaw#67318. Some open models (notably Gemma
variants served via OpenRouter) emit tool calls as XML blocks inside
assistant content instead of via the structured tool_calls field:
<function name="read_file"><parameter name="path">/tmp/x</parameter></function>
<tool_call>{"name":"x"}</tool_call>
<function_calls>[{...}]</function_calls>
Left unstripped, this raw XML leaked to gateway users (Discord, Telegram,
Matrix, Feishu, Signal, WhatsApp, etc.) and the CLI, since hermes-agent's
existing reasoning-tag stripper handled only <think>/<thinking>/<thought>
variants.
Extend _strip_think_blocks (run_agent.py) and _strip_reasoning_tags
(cli.py) to cover:
* <tool_call>, <tool_calls>, <tool_result>
* <function_call>, <function_calls>
* <function name="..."> ... </function> (Gemma-style)
The <function> variant is boundary-gated (only strips when the tag sits
at start-of-line or after sentence punctuation AND carries a name="..."
attribute) so prose mentions like 'Use <function> declarations in JS'
are preserved. Dangling <function name="..."> with no close is
intentionally left visible — matches OpenClaw's asymmetry so a truncated
streaming tail still reaches the user.
Tests: 9 new cases in TestStripThinkBlocks (run_agent) + 9 in new file
tests/run_agent/test_strip_reasoning_tags_cli.py. Covers Qwen-style
<tool_call>, Gemma-style <function name="...">, multi-line payloads,
prose preservation, stray close tags, dangling open tags, and mixed
reasoning+tool_call content.
Note: this port covers the post-streaming final-text path, which is what
gateway adapters and CLI display consume. Extending the per-delta stream
filter in gateway/stream_consumer.py to hide these tags live as they
stream is a separate follow-up; for now users may see raw XML briefly
during a stream before the final cleaned text replaces it.
Refs: openclaw/openclaw#67318
Add ResponsesApiTransport wrapping codex_responses_adapter.py behind the
ProviderTransport ABC. Auto-registered via _discover_transports().
Wire ALL Codex transport methods to production paths in run_agent.py:
- build_kwargs: main _build_api_kwargs codex branch (50 lines extracted)
- normalize_response: main loop + flush + summary + retry (4 sites)
- convert_tools: memory flush tool override
- convert_messages: called internally via build_kwargs
- validate_response: response validation gate
- preflight_kwargs: request sanitization (2 sites)
Remove 7 dead legacy wrappers from AIAgent (_responses_tools,
_chat_messages_to_responses_input, _normalize_codex_response,
_preflight_codex_api_kwargs, _preflight_codex_input_items,
_extract_responses_message_text, _extract_responses_reasoning_text).
Keep 3 ID manipulation methods still used by _build_assistant_message.
Update 18 test call sites across 3 test files to call adapter functions
directly instead of through deleted AIAgent wrappers.
24 new tests. 343 codex/responses/transport tests pass (0 failures).
PR 4 of the provider transport refactor.
Kimi/Moonshot endpoints require explicit parameters that Hermes was not
sending, causing 'Response truncated due to output length limit' errors
and inconsistent reasoning behavior.
Root cause analysis against Kimi CLI source (MoonshotAI/kimi-cli,
packages/kosong/src/kosong/chat_provider/kimi.py):
1. max_tokens: Kimi's API defaults to a very low value when omitted.
Reasoning tokens share the output budget — the model exhausts it on
thinking alone. Send 32000, matching Kimi CLI's generate() default.
2. reasoning_effort: Kimi CLI sends this as a top-level parameter (not
inside extra_body). Hermes was not sending it at all because
_supports_reasoning_extra_body() returns False for non-OpenRouter
endpoints.
3. extra_body.thinking: Kimi CLI uses with_thinking() which sets
extra_body.thinking={"type":"enabled"} alongside reasoning_effort.
This is a separate control from the OpenAI-style reasoning extra_body
that Hermes sends for OpenRouter/GitHub. Without it, the Kimi gateway
may not activate reasoning mode correctly.
Covers api.kimi.com (Kimi Code) and api.moonshot.ai/cn (Moonshot).
Tests: 6 new test cases for max_tokens, reasoning_effort, and
extra_body.thinking under various configs.
Kimi's gateway selects the correct temperature server-side based on the
active mode (thinking -> 1.0, non-thinking -> 0.6). Sending any
temperature value — even the previously "correct" one — conflicts with
gateway-managed defaults.
Replaces the old approach of forcing specific temperature values (0.6
for non-thinking, 1.0 for thinking) with an OMIT_TEMPERATURE sentinel
that tells all call sites to strip the temperature key from API kwargs
entirely.
Changes:
- agent/auxiliary_client.py: OMIT_TEMPERATURE sentinel, _is_kimi_model()
prefix check (covers all kimi-* models), _fixed_temperature_for_model()
returns sentinel for kimi models. _build_call_kwargs() strips temp.
- run_agent.py: _build_api_kwargs, flush_memories, and summary generation
paths all handle the sentinel by popping/omitting temperature.
- trajectory_compressor.py: _effective_temperature_for_model returns None
for kimi (sentinel mapped), direct client calls use kwargs dict to
conditionally include temperature.
- mini_swe_runner.py: same sentinel handling via wrapper function.
- 6 test files updated: all 'forces temperature X' assertions replaced
with 'temperature not in kwargs' assertions.
Net: -76 lines (171 added, 247 removed).
Inspired by PR #13137 (@kshitijk4poor).
Follow up salvaged PR #12668 by threading base_url through the
remaining direct-call sites so kimi-k2.5 uses temperature=1.0 on
api.moonshot.ai and keeps 0.6 on api.kimi.com/coding. Add focused
regression tests for run_agent, trajectory_compressor, and
mini_swe_runner.
One source fix (web_server category merge) + five test updates that
didn't travel with their feature PRs. All 13 failures on the 04-19
CI run on main are now accounted for (5 already self-healed on main;
8 fixed here).
Changes
- web_server.py: add code_execution → agent to _CATEGORY_MERGE (new
singleton section from #11971 broke no-single-field-category invariant).
- test_browser_camofox_state: bump hardcoded _config_version 18 → 19
(also from #11971).
- test_registry: add browser_cdp_tool (#12369) and discord_tool (#4753)
to the expected built-in tool set.
- test_run_agent::test_tool_call_accumulation: rewrite fragment chunks
— #0f778f77 switched streaming name-accumulation from += to = to
fix MiniMax/NIM duplication; the test still encoded the old
fragment-per-chunk premise.
- test_concurrent_interrupt::_Stub: no-op
_apply_pending_steer_to_tool_results — #12116 added this call after
concurrent tool batches; the hand-rolled stub was missing it.
- test_codex_cli_model_picker: drop the two obsolete tests that
asserted auto-import from ~/.codex/auth.json into the Hermes auth
store. #12360 explicitly removed that behavior (refresh-token reuse
races with Codex CLI / VS Code); adoption is now explicit via
`hermes auth openai-codex`. Remaining 3 tests in the file (normal
path, Claude Code fallback, negative case) still cover the picker.
Validation
- scripts/run_tests.sh across all 6 affected files + surrounding tests
(54 tests total) all green locally.
Follow-up on top of mvanhorn's cherry-picked commit. Original PR only
wired request_timeout_seconds into the explicit-creds OpenAI branch at
run_agent.py init; router-based implicit auth, native Anthropic, and the
fallback chain were still hardcoded to SDK defaults.
- agent/anthropic_adapter.py: build_anthropic_client() accepts an optional
timeout kwarg (default 900s preserved when unset/invalid).
- run_agent.py: resolve per-provider/per-model timeout once at init; apply
to Anthropic native init + post-refresh rebuild + stale/interrupt
rebuilds + switch_model + _restore_primary_runtime + the OpenAI
implicit-auth path + _try_activate_fallback (with immediate client
rebuild so the first fallback request carries the configured timeout).
- tests: cover anthropic adapter kwarg honoring; widen mock signatures
to accept the new timeout kwarg.
- docs/example: clarify that the knob now applies to every transport,
the fallback chain, and rebuilds after credential rotation.
Inline reasoning tags in an assistant message's content field leak to every downstream consumer: messaging platforms (#8878, #9568), API replay of prior turns, session transcript, CLI recap, generated session titles, and context compression. _extract_reasoning() already captures the reasoning text into msg['reasoning'] separately, so the raw tags in content are redundant.
Stripping once at the storage boundary in _build_assistant_message() cleans the content for every downstream path in one place — no per-platform or per-path stripper needed. Measured impact on a real MiniMax M2.7-highspeed session (per @luoyejiaoe-source, #9306): 55% of assistant messages started with <think> blocks, 51/100 session titles were polluted, 16% content-size reduction.
3 new regression tests in TestBuildAssistantMessage: closed-pair strip with reasoning capture, no-think-tag passthrough, and unterminated-block strip.
Resolves#8878 and #9568.
Originally proposed as PR #9250.
Providers served via NIM (MiniMax M2.7, some Moonshot/DeepSeek proxies) sometimes drop the closing </think> tag, leaving raw reasoning in the assistant's content field. _strip_think_blocks()'s closed-pair regex is non-greedy so it only matches complete blocks — any orphan <think>...EOF survived the stripper and leaked to users (#8878, #9568, #10408).
Adds an unterminated-tag pass that fires when an open reasoning tag sits at a block boundary (start of text or after a newline) with no matching close. Everything from that tag to end of string is stripped. The block-boundary check mirrors gateway/stream_consumer.py's filter so models that mention <think> in prose are not over-stripped.
Also makes the closed-pair regexes consistently case-insensitive so <THINK>...</THINK> and <Thinking>...</Thinking> are handled uniformly — previously the mixed-case open tag would bypass the closed-pair pass and be caught by the unterminated-tag pass, taking trailing visible content with it.
6 new regression tests in TestStripThinkBlocks covering: unterminated <think>, unterminated <thought>, multi-line unterminated, line-start orphan with preserved prefix, prose-mention non-regression, mixed-case closed pairs.
The implementation is inspired by @luinbytes's PR #10408 report of the NIM/MiniMax symptom. This commit does not include the 💭/🧠 emoji regexes from that PR — those glyphs are Hermes CLI display decorations, not model content markers.
* fix(tests): make AIAgent constructor calls self-contained (no env leakage)
Tests in tests/run_agent/ were constructing AIAgent() without passing
both api_key and base_url, then relying on leaked state from other
tests in the same xdist worker (or process-level env vars) to keep
provider resolution happy. Under hermetic conftest + pytest-split,
that state is gone and the tests fail with 'No LLM provider configured'.
Fix: pass both api_key and base_url explicitly on 47 AIAgent()
construction sites across 13 files. AIAgent.__init__ with both set
takes the direct-construction path (line 960 in run_agent.py) and
skips the resolver entirely.
One call site (test_none_base_url_passed_as_none) left alone — that
test asserts behavior for base_url=None specifically.
This is a prerequisite for any future matrix-split or stricter
isolation work, and lands cleanly on its own.
Validation:
- tests/run_agent/ full: 760 passed, 0 failed (local)
- Previously relied on cross-test pollution; now self-contained
* fix(tests): update opencode-go model order assertion to match kimi-k2.5-first
commit 78a74bb promoted kimi-k2.5 to first position in model suggestion
lists but didn't update this test, which has been failing on main since.
Reorder expected list to match the new canonical order.
Group A (3 tests): 'No LLM provider configured' RuntimeError
- test_user_message_surrogates_sanitized, test_counters_initialized_in_init,
test_openai_prompt_tokens_unchanged
- Root cause: AIAgent.__init__ now requires base_url alongside api_key to
skip resolve_provider_client() (which returns None when API keys are
blanked in CI). Added base_url='http://localhost:1234/v1' to test
agent construction.
Group B (5 tests): Discord slash command auto-registration
- test_auto_registers_missing_gateway_commands, test_auto_registered_command_*,
test_register_skill_group_*
- Root cause: xdist workers that loaded a discord mock WITHOUT
app_commands.Command/Group caused _register_slash_commands() to fail
silently. Added comprehensive shared discord mock in
tests/gateway/conftest.py (same pattern as existing telegram mock).
Group C (5 errors): Discord reply mode 'NoneType has no DMChannel'
- All TestReplyToText tests
- Root cause: FakeDMChannel was not a subclass of real discord.DMChannel,
so isinstance() checks in _handle_message failed when running in full
suite (real discord installed). Made FakeDMChannel inherit from
discord.DMChannel when available. Removed fragile monkeypatch approach.
Group D (2 tests): detect_provider_for_model wrong provider
- test_openrouter_slug_match (got 'ai-gateway'), test_bare_name_gets_
openrouter_slug (got 'copilot')
- Root cause: ai-gateway, copilot, and kilocode are multi-vendor
aggregators that list other providers' models (OpenRouter-style slugs).
They were being matched in Step 1 before OpenRouter. Added all three
to _AGGREGATORS set so they're skipped like nous/openrouter.
Group E (1 test): model_flow_custom StopIteration
- test_model_flow_custom_saves_verified_v1_base_url
- Root cause: 'Display name' prompt was added after the test was written.
The input iterator had 5 answers but the flow now asks 6 questions.
Added 6th empty string answer.
Group F (1 test): Telegram proxy env assertion
- test_uses_proxy_env_for_primary_and_fallback_transports
- Root cause: _resolve_proxy_url() now checks TELEGRAM_PROXY first
(via resolve_proxy_url('TELEGRAM_PROXY')). Test didn't clear this
env var, allowing potential leakage from other tests in xdist workers.
Added TELEGRAM_PROXY to the cleanup list.
