Closes#11616.
The agent's API retry loop hardcoded max_retries = 3, so users with
fallback providers on flaky primaries burned through ~3 × provider
timeout (e.g. 3 × 180s = 9 minutes) before their fallback chain got a
chance to kick in.
Expose a new config key:
agent:
api_max_retries: 3 # default unchanged
Set it to 1 for fast failover when you have fallback providers, or
raise it if you prefer longer tolerance on a single provider. Values
< 1 are clamped to 1 (single attempt, no retry); non-integer values
fall back to the default.
This wraps the Hermes-level retry loop only — the OpenAI SDK's own
low-level retries (max_retries=2 default) still run beneath this for
transient network errors.
Changes:
- hermes_cli/config.py: add agent.api_max_retries default 3 with comment.
- run_agent.py: read self._api_max_retries in AIAgent.__init__; replace
hardcoded max_retries = 3 in the retry loop with self._api_max_retries.
- cli-config.yaml.example: documented example entry.
- hermes_cli/tips.py: discoverable tip line.
- tests/run_agent/test_api_max_retries_config.py: 4 tests covering
default, override, clamp-to-one, and invalid-value fallback.
NormalizedResponse and ToolCall now have backward-compat properties
so the agent loop can read them directly without the shim:
ToolCall: .type, .function (returns self), .call_id, .response_item_id
NormalizedResponse: .reasoning_content, .reasoning_details,
.codex_reasoning_items
This eliminates the 35-line shim and its 4 call sites in run_agent.py.
Also changes flush_memories guard from hasattr(response, 'choices')
to self.api_mode in ('chat_completions', 'bedrock_converse') so it
works with raw boto3 dicts too.
WS1 items 3+4 of Cycle 2 (#14418).
Follow-up for #13862 — the post-init api_mode upgrade at __init__ (direct OpenAI /
gpt-5-requires-responses path) runs AFTER the eager transport warm. Clear the cache
so the stale chat_completions entry is evicted.
Cosmetic: correctness was already fine since _get_transport() keys by current
api_mode, but this avoids leaving unused cache state behind.
Consolidate 4 per-transport lazy singleton helpers (_get_anthropic_transport,
_get_codex_transport, _get_chat_completions_transport, _get_bedrock_transport)
into one generic _get_transport(api_mode) with a shared dict cache.
Collapse the 65-line main normalize block (3 api_mode branches, each with
its own SimpleNamespace shim) into 7 lines: one _get_transport() call +
one _nr_to_assistant_message() shared shim. The shim extracts provider_data
fields (codex_reasoning_items, reasoning_details, call_id, response_item_id)
into the SimpleNamespace shape downstream code expects.
Wire chat_completions and bedrock_converse normalize through their transports
for the first time — these were previously falling into the raw
response.choices[0].message else branch.
Remove 8 dead codex adapter imports that have zero callers after PRs 1-6.
Transport lifecycle improvements:
- Eagerly warm transport cache at __init__ (surfaces import errors early)
- Invalidate transport cache on api_mode change (switch_model, fallback
activation, fallback restore, transport recovery) — prevents stale
transport after mid-session provider switch
run_agent.py: -32 net lines (11,988 -> 11,956).
PR 7 of the provider transport refactor.
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
When the streaming connection dropped AFTER user-visible text was
delivered but a tool call was in flight, we stubbed the turn with a
'⚠ Stream stalled mid tool-call; Ask me to retry' warning — costing
an iteration and breaking the flow. Users report this happening
increasingly often on long SSE streams through flaky provider routes.
Fix: in the existing inner stream-retry loop, relax the
deltas_were_sent short-circuit. If a tool call was in flight
(partial_tool_names populated) AND the error is a transient connection
error (timeout, RemoteProtocolError, SSE 'connection lost', etc.),
silently retry instead of bailing out. Fire a brief 'Connection
dropped mid tool-call; reconnecting…' marker so the user understands
the preamble is about to be re-streamed.
Researched how Claude Code (tombstone + non-streaming fallback),
OpenCode (blind Effect.retry wrapping whole stream), and Clawdbot
(4-way gate: stopReason==error + output==0 + !hadPotentialSideEffects)
handle this. Chose the narrow Clawdbot-style gate: retry only when
(a) a tool call was actually in flight (otherwise the existing
stub-with-recovered-text is correct for pure-text stalls) and
(b) the error is transient. Side-effect safety is automatic — no
tool has been dispatched within this single API call yet.
UX trade-off: user sees preamble text twice on retry (OpenCode-style).
Strictly better than a lost action with a 'retry manually' message.
If retries exhaust, falls through to the existing stub-with-warning
path so the user isn't left with zero signal.
Tests: 3 new tests in TestSilentRetryMidToolCall covering
(1) silent retry recovers tool call; (2) exhausted retries fall back
to stub; (3) text-only stalls don't trigger retry. 30/30 pass.
* fix(plugins): auto-coerce user-installed memory plugins to kind=exclusive
User-installed memory provider plugins at $HERMES_HOME/plugins/<name>/
were being dispatched to the general PluginManager, which has no
register_memory_provider method on PluginContext. Every startup logged:
Failed to load plugin 'mempalace': 'PluginContext' object has no
attribute 'register_memory_provider'
Bundled memory providers were already skipped via skip_names={memory,
context_engine} in discover_and_load, but user-installed ones weren't.
Fix: _parse_manifest now scans the plugin's __init__.py source for
'register_memory_provider' or 'MemoryProvider' (same heuristic as
plugins/memory/__init__.py:_is_memory_provider_dir) and auto-coerces
kind to 'exclusive' when the manifest didn't declare one explicitly.
This routes the plugin to plugins/memory discovery instead of the
general loader.
The escape hatch: if a manifest explicitly declares kind: standalone,
the heuristic doesn't override it.
Reported by Uncle HODL on Discord.
* fix(nous): actionable CLI message when Nous 401 refresh fails
Mirrors the Anthropic 401 diagnostic pattern. When Nous returns 401
and the credential refresh (_try_refresh_nous_client_credentials)
also fails, the user used to see only the raw APIError. Now prints:
🔐 Nous 401 — Portal authentication failed.
Response: <truncated body>
Most likely: Portal OAuth expired, account out of credits, or
agent key revoked.
Troubleshooting:
• Re-authenticate: hermes login --provider nous
• Check credits / billing: https://portal.nousresearch.com
• Verify stored credentials: $HERMES_HOME/auth.json
• Switch providers temporarily: /model <model> --provider openrouter
Addresses the common 'my hermes model hangs' pattern where the user's
Portal OAuth expired and the CLI gave no hint about the next step.
Adds schema v7 'api_call_count' column. run_agent.py increments it by 1
per LLM API call, web_server analytics SQL aggregates it, frontend uses
the real counter instead of summing sessions.
The 'API Calls' card on the analytics dashboard previously displayed
COUNT(*) from the sessions table — the number of conversations, not
LLM requests. Each session makes 10-90 API calls through the tool loop,
so the reported number was ~30x lower than real.
Salvaged from PR #10140 (@kshitijk4poor). The cache-token accuracy
portions of the original PR were deferred — per-provider analytics is
the better path there, since cache_write_tokens and actual_cost_usd
are only reliably available from a subset of providers (Anthropic
native, Codex Responses, OpenRouter with usage.include).
Tests:
- schema_version v7 assertion
- migration v2 -> v7 adds api_call_count column with default 0
- update_token_counts increments api_call_count by provided delta
- absolute=True sets api_call_count directly
- /api/analytics/usage exposes total_api_calls in totals
- Add configurable retain_tags / retain_source / retain_user_prefix /
retain_assistant_prefix knobs for native Hindsight.
- Thread gateway session identity (user_name, chat_id, chat_name,
chat_type, thread_id) through AIAgent and MemoryManager into
MemoryProvider.initialize kwargs so providers can scope and tag
retained memories.
- Hindsight attaches the new identity fields as retain metadata,
merges per-call tool tags with configured default tags, and uses
the configurable transcript labels for auto-retained turns.
Co-authored-by: Abner <abner.the.foreman@agentmail.to>
Fourth and final transport — completes the transport layer with all four
api_modes covered. Wraps agent/bedrock_adapter.py behind the ProviderTransport
ABC, handles both raw boto3 dicts and already-normalized SimpleNamespace.
Wires all transport methods to production paths in run_agent.py:
- build_kwargs: _build_api_kwargs bedrock branch
- validate_response: response validation, new bedrock_converse branch
- finish_reason: new bedrock_converse branch in finish_reason extraction
Based on PR #13467 by @kshitijk4poor, with one adjustment: the main normalize
loop does NOT add a bedrock_converse branch to invoke normalize_response on
the already-normalized response. Bedrock's normalize_converse_response runs
at the dispatch site (run_agent.py:5189), so the response already has the
OpenAI-compatible .choices[0].message shape by the time the main loop sees
it. Falling through to the chat_completions else branch is correct and
sidesteps a redundant NormalizedResponse rebuild.
Transport coverage — complete:
| api_mode | Transport | build_kwargs | normalize | validate |
|--------------------|--------------------------|:------------:|:---------:|:--------:|
| anthropic_messages | AnthropicTransport | ✅ | ✅ | ✅ |
| codex_responses | ResponsesApiTransport | ✅ | ✅ | ✅ |
| chat_completions | ChatCompletionsTransport | ✅ | ✅ | ✅ |
| bedrock_converse | BedrockTransport | ✅ | ✅ | ✅ |
17 new BedrockTransport tests pass. 117 transport tests total pass.
160 bedrock/converse tests across tests/agent/ pass. Full tests/run_agent/
targeted suite passes (885/885 + 15 skipped; the 1 remaining failure is the
pre-existing test_concurrent_interrupt flake on origin/main).
Third concrete transport — handles the default 'chat_completions' api_mode used
by ~16 OpenAI-compatible providers (OpenRouter, Nous, NVIDIA, Qwen, Ollama,
DeepSeek, xAI, Kimi, custom, etc.). Wires build_kwargs + validate_response to
production paths.
Based on PR #13447 by @kshitijk4poor, with fixes:
- Preserve tool_call.extra_content (Gemini thought_signature) via
ToolCall.provider_data — the original shim stripped it, causing 400 errors
on multi-turn Gemini 3 thinking requests.
- Preserve reasoning_content distinctly from reasoning (DeepSeek/Moonshot) so
the thinking-prefill retry check (_has_structured) still triggers.
- Port Kimi/Moonshot quirks (32000 max_tokens, top-level reasoning_effort,
extra_body.thinking) that landed on main after the original PR was opened.
- Keep _qwen_prepare_chat_messages_inplace alive and call it through the
transport when sanitization already deepcopied (avoids a second deepcopy).
- Skip the back-compat SimpleNamespace shim in the main normalize loop — for
chat_completions, response.choices[0].message is already the right shape
with .content/.tool_calls/.reasoning/.reasoning_content/.reasoning_details
and per-tool-call .extra_content from the OpenAI SDK.
run_agent.py: -239 lines in _build_api_kwargs default branch extracted to the
transport. build_kwargs now owns: codex-field sanitization, Qwen portal prep,
developer role swap, provider preferences, max_tokens resolution (ephemeral >
user > NVIDIA 16384 > Qwen 65536 > Kimi 32000 > anthropic_max_output), Kimi
reasoning_effort + extra_body.thinking, OpenRouter/Nous/GitHub reasoning,
Nous product attribution tags, Ollama num_ctx, custom-provider think=false,
Qwen vl_high_resolution_images, request_overrides.
39 new transport tests (8 build_kwargs, 5 Kimi, 4 validate, 4 normalize
including extra_content regression, 3 cache stats, 3 basic). Tests/run_agent/
targeted suite passes (885/885 + 15 skipped; the 1 remaining failure is the
test_concurrent_interrupt flake present on origin/main).
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.
* feat(models): hide OpenRouter models that don't advertise tool support
Port from Kilo-Org/kilocode#9068.
hermes-agent is tool-calling-first — every provider path assumes the
model can invoke tools. Models whose OpenRouter supported_parameters
doesn't include 'tools' (e.g. image-only or completion-only models)
cannot be driven by the agent loop and fail at the first tool call.
Filter them out of fetch_openrouter_models() so they never appear in
the model picker (`hermes model`, setup wizard, /model slash command).
Permissive when the field is missing — OpenRouter-compatible gateways
(Nous Portal, private mirrors, older snapshots) don't always populate
supported_parameters. Treat missing as 'unknown → allow' rather than
silently emptying the picker on those gateways. Only hide models
whose supported_parameters is an explicit list that omits tools.
Tests cover: tools present → kept, tools absent → dropped, field
missing → kept, malformed non-list → kept, non-dict item → kept,
empty list → dropped.
* feat(delegate): cross-agent file state coordination for concurrent subagents
Prevents mangled edits when concurrent subagents touch the same file
(same process, same filesystem — the mangle scenario from #11215).
Three layers, all opt-out via HERMES_DISABLE_FILE_STATE_GUARD=1:
1. FileStateRegistry (tools/file_state.py) — process-wide singleton
tracking per-agent read stamps and the last writer globally.
check_stale() names the sibling subagent in the warning when a
non-owning agent wrote after this agent's last read.
2. Per-path threading.Lock wrapped around the read-modify-write
region in write_file_tool and patch_tool. Concurrent siblings on
the same path serialize; different paths stay fully parallel.
V4A multi-file patches lock in sorted path order (deadlock-free).
3. Delegate-completion reminder in tools/delegate_tool.py: after a
subagent returns, writes_since(parent, child_start, parent_reads)
appends '[NOTE: subagent modified files the parent previously
read — re-read before editing: ...]' to entry.summary when the
child touched anything the parent had already seen.
Complements (does not replace) the existing path-overlap check in
run_agent._should_parallelize_tool_batch — batch check prevents
same-file parallel dispatch within one agent's turn (cheap prevention,
zero API cost), registry catches cross-subagent and cross-turn
staleness at write time (detection).
Behavior is warning-only, not hard-failing — matches existing project
style. Errors surface naturally: sibling writes often invalidate the
old_string in patch operations, which already errors cleanly.
Tests: tests/tools/test_file_state_registry.py — 16 tests covering
registry state transitions, per-path locking, per-path-not-global
locking, writes_since filtering, kill switch, and end-to-end
integration through the real read_file/write_file/patch handlers.
Adds role='leaf'|'orchestrator' to delegate_task. With max_spawn_depth>=2,
an orchestrator child retains the 'delegation' toolset and can spawn its
own workers; leaf children cannot delegate further (identical to today).
Default posture is flat — max_spawn_depth=1 means a depth-0 parent's
children land at the depth-1 floor and orchestrator role silently
degrades to leaf. Users opt into nested delegation by raising
max_spawn_depth to 2 or 3 in config.yaml.
Also threads acp_command/acp_args through the main agent loop's delegate
dispatch (previously silently dropped in the schema) via a new
_dispatch_delegate_task helper, and adds a DelegateEvent enum with
legacy-string back-compat for gateway/ACP/CLI progress consumers.
Config (hermes_cli/config.py defaults):
delegation.max_concurrent_children: 3 # floor-only, no upper cap
delegation.max_spawn_depth: 1 # 1=flat (default), 2-3 unlock nested
delegation.orchestrator_enabled: true # global kill switch
Salvaged from @pefontana's PR #11215. Overrides vs. the original PR:
concurrency stays at 3 (PR bumped to 5 + cap 8 — we keep the floor only,
no hard ceiling); max_spawn_depth defaults to 1 (PR defaulted to 2 which
silently enabled one level of orchestration for every user).
Co-authored-by: pefontana <fontana.pedro93@gmail.com>
Reported during the TUI v2 blitz test: switching from openrouter to
anthropic via `/model <name> --provider anthropic` appeared to succeed,
but the next turn kept hitting openrouter — the provider the user was
deliberately moving away from.
Two gaps caused this:
1. `Agent.switch_model` reset `_fallback_activated` / `_fallback_index`
but left `_fallback_chain` intact. The chain was seeded from
`fallback_providers:` at agent init for the *original* primary, so
when the new primary returned 401 (invalid/expired Anthropic key),
`_try_activate_fallback()` picked the old provider back up without
informing the user. Prune entries matching either the old primary
(user is moving away) or the new primary (redundant) whenever the
primary provider actually changes.
2. `_apply_model_switch` persisted `HERMES_MODEL` but never updated
`HERMES_INFERENCE_PROVIDER`. Any ambient re-resolution of the runtime
(credential pool refresh, compressor rebuild, aux clients) falls
through to that env var in `resolve_requested_provider`, so it kept
reporting the original provider even after an in-memory switch.
Adds three regression tests: fallback-chain prune on primary change,
no-op on same-provider model swap, and env-var sync on explicit switch.
The 💾 Cache footer was gated on `self._use_prompt_caching`, which is
only True for Anthropic marker injection (native Anthropic, OpenRouter
Claude, Anthropic-wire gateways, Qwen on OpenCode/Alibaba). Providers
with automatic server-side prefix caching — OpenAI, Kimi, DeepSeek,
Qwen on OpenRouter — return `prompt_tokens_details.cached_tokens` too,
but users couldn't see their cache % because the display path never
fired for them. Result: people couldn't tell their cache was working or
broken without grepping agent.log.
`canonical_usage` from `normalize_usage()` already unifies all three
API shapes (Anthropic / Codex Responses / OpenAI chat completions) into
`cache_read_tokens` and `cache_write_tokens`. Drop the gate and read
from there — now the footer fires whenever the provider reported any
cached or written tokens, regardless of whether hermes injected markers.
Also removes duplicated branch-per-API-shape extraction code.
Qwen models on OpenCode, OpenCode Go, and direct DashScope accept
Anthropic-style cache_control markers on OpenAI-wire chat completions,
but hermes only injected markers for Claude-named models. Result: zero
cache hits on every turn, full prompt re-billed — a community user
reported burning through their OpenCode Go subscription on Qwen3.6.
Extend _anthropic_prompt_cache_policy to return (True, False) — envelope
layout, not native — for the Alibaba provider family when the model name
contains 'qwen'. Envelope layout places markers on inner content blocks
(matching pi-mono's 'alibaba' cacheControlFormat) and correctly skips
top-level markers on tool-role messages (which OpenCode rejects).
Non-Qwen models on these providers (GLM, Kimi) keep their existing
behaviour — they have automatic server-side caching and don't need
client markers.
Upstream reference: pi-mono #3392 / #3393 documented this contract for
opencode-go Qwen models.
Adds 7 regression tests covering Qwen3.5/3.6/coder on each affected
provider plus negative cases for GLM/Kimi/OpenRouter-Qwen.
Two call sites still used a raw substring check to identify ollama.com:
hermes_cli/runtime_provider.py:496:
_is_ollama_url = "ollama.com" in base_url.lower()
run_agent.py:6127:
if fb_base_url_hint and "ollama.com" in fb_base_url_hint.lower() ...
Same bug class as GHSA-xf8p-v2cg-h7h5 (OpenRouter substring leak), which
was fixed in commit dbb7e00e via base_url_host_matches() across the
codebase. The earlier sweep missed these two Ollama sites. Self-discovered
during April 2026 security-advisory triage; filed as GHSA-76xc-57q6-vm5m.
Impact is narrow — requires a user with OLLAMA_API_KEY configured AND a
custom base_url whose path or look-alike host contains 'ollama.com'.
Users on default provider flows are unaffected. Filed as a draft advisory
to use the private-fork flow; not CVE-worthy on its own.
Fix is mechanical: replace substring check with base_url_host_matches
at both sites. Same helper the rest of the codebase uses.
Tests: 67 -> 71 passing. 7 new host-matcher cases in
tests/test_base_url_hostname.py (path injection, lookalike host,
localtest.me subdomain, ollama.ai TLD confusion, localhost, genuine
ollama.com, api.ollama.com subdomain) + 4 call-site tests in
tests/hermes_cli/test_runtime_provider_resolution.py verifying
OLLAMA_API_KEY is selected only when base_url actually targets
ollama.com.
Fixes GHSA-76xc-57q6-vm5m
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.
Full AST-based scan of all .py files to find every case where a module
or name is imported locally inside a function body but is already
available at module level. This is the second pass — the first commit
handled the known cases from the lint report; this one catches
everything else.
Files changed (19):
cli.py — 16 removals: time as _time/_t/_tmod (×10),
re / re as _re (×2), os as _os, sys,
partial os from combo import,
from model_tools import get_tool_definitions
gateway/run.py — 8 removals: MessageEvent as _ME /
MessageType as _MT (×3), os as _os2,
MessageEvent+MessageType (×2), Platform,
BasePlatformAdapter as _BaseAdapter
run_agent.py — 6 removals: get_hermes_home as _ghh,
partial (contextlib, os as _os),
cleanup_vm, cleanup_browser,
set_interrupt as _sif (×2),
partial get_toolset_for_tool
hermes_cli/main.py — 4 removals: get_hermes_home, time as _time,
logging as _log, shutil
hermes_cli/config.py — 1 removal: get_hermes_home as _ghome
hermes_cli/runtime_provider.py
— 1 removal: load_config as _load_bedrock_config
hermes_cli/setup.py — 2 removals: importlib.util (×2)
hermes_cli/nous_subscription.py
— 1 removal: from hermes_cli.config import load_config
hermes_cli/tools_config.py
— 1 removal: from hermes_cli.config import load_config, save_config
cron/scheduler.py — 3 removals: concurrent.futures, json as _json,
from hermes_cli.config import load_config
batch_runner.py — 1 removal: list_distributions as get_all_dists
(kept print_distribution_info, not at top level)
tools/send_message_tool.py
— 2 removals: import os (×2)
tools/skills_tool.py — 1 removal: logging as _logging
tools/browser_camofox.py
— 1 removal: from hermes_cli.config import load_config
tools/image_generation_tool.py
— 1 removal: import fal_client
environments/tool_context.py
— 1 removal: concurrent.futures
gateway/platforms/bluebubbles.py
— 1 removal: httpx as _httpx
gateway/platforms/whatsapp.py
— 1 removal: import asyncio
tui_gateway/server.py — 2 removals: from datetime import datetime,
import time
All alias references (_time, _t, _tmod, _re, _os, _os2, _json, _ghh,
_ghome, _sif, _ME, _MT, _BaseAdapter, _load_bedrock_config, _httpx,
_logging, _log, get_all_dists) updated to use the top-level names.
Sweep ~74 redundant local imports across 21 files where the same module
was already imported at the top level. Also includes type fixes and lint
cleanups on the same branch.
Add agent/transports/types.py with three shared dataclasses:
- NormalizedResponse: content, tool_calls, finish_reason, reasoning, usage, provider_data
- ToolCall: id, name, arguments, provider_data (per-tool-call protocol metadata)
- Usage: prompt_tokens, completion_tokens, total_tokens, cached_tokens
Add normalize_anthropic_response_v2() to anthropic_adapter.py — wraps the
existing v1 function and maps its output to NormalizedResponse. One call site
in run_agent.py (the main normalize branch) uses v2 with a back-compat shim
to SimpleNamespace for downstream code.
No ABC, no registry, no streaming, no client lifecycle. Those land in PR 3
with the first concrete transport (AnthropicTransport).
46 new tests:
- test_types.py: dataclass construction, build_tool_call, map_finish_reason
- test_anthropic_normalize_v2.py: v1-vs-v2 regression tests (text, tools,
thinking, mixed, stop reasons, mcp prefix stripping, edge cases)
Part of the provider transport refactor (PR 2 of 9).
The mid-run steer marker was '[USER STEER (injected mid-run, not tool
output): <text>]'. Replaced with a plain two-newline-prefixed
'User guidance: <text>' suffix.
Rationale: the marker lives inside the tool result's content string
regardless of whether the tool returned JSON, plain text, an MCP
result, or a plugin result. The bracketed tag read like structured
metadata that some tools (terminal, execute_code) could confuse with
their own output formatting. A plain labelled suffix works uniformly
across every content shape we produce.
Behavior unchanged:
- Still injected into the last tool-role message's content.
- Still preserves multimodal (Anthropic) content-block lists by
appending a text block.
- Still drained at both sites added in #12959 and #13205 — per-tool
drain between individual calls, and pre-API-call drain at the top
of each main-loop iteration.
Checked Codex's equivalent (pending_input / inject_user_message_without_turn
in codex-rs/core): they record mid-turn user input as a real role:user
message via record_user_prompt_and_emit_turn_item(). That's cleaner for
their Responses-API model but not portable to Chat Completions where
role alternation after tool_calls is strict. Embedding the guidance in
the last tool result remains the correct placement for us.
Validation: all 21 tests in tests/run_agent/test_steer.py pass.
Aslaaen's fix in the original PR covered _detect_api_mode_for_url and the
two openai/xai sites in run_agent.py. This finishes the sweep: the same
substring-match false-positive class (e.g. https://api.openai.com.evil/v1,
https://proxy/api.openai.com/v1, https://api.anthropic.com.example/v1)
existed in eight more call sites, and the hostname helper was duplicated
in two modules.
- utils: add shared base_url_hostname() (single source of truth).
- hermes_cli/runtime_provider, run_agent: drop local duplicates, import
from utils. Reuse the cached AIAgent._base_url_hostname attribute
everywhere it's already populated.
- agent/auxiliary_client: switch codex-wrap auto-detect, max_completion_tokens
gate (auxiliary_max_tokens_param), and custom-endpoint max_tokens kwarg
selection to hostname equality.
- run_agent: native-anthropic check in the Claude-style model branch
and in the AIAgent init provider-auto-detect branch.
- agent/model_metadata: Anthropic /v1/models context-length lookup.
- hermes_cli/providers.determine_api_mode: anthropic / openai URL
heuristics for custom/unknown providers (the /anthropic path-suffix
convention for third-party gateways is preserved).
- tools/delegate_tool: anthropic detection for delegated subagent
runtimes.
- hermes_cli/setup, hermes_cli/tools_config: setup-wizard vision-endpoint
native-OpenAI detection (paired with deduping the repeated check into
a single is_native_openai boolean per branch).
Tests:
- tests/test_base_url_hostname.py covers the helper directly
(path-containing-host, host-suffix, trailing dot, port, case).
- tests/hermes_cli/test_determine_api_mode_hostname.py adds the same
regression class for determine_api_mode, plus a test that the
/anthropic third-party gateway convention still wins.
Also: add asslaenn5@gmail.com → Aslaaen to scripts/release.py AUTHOR_MAP.
Requests through Vercel AI Gateway now carry referrerUrl / appName /
User-Agent attribution so traffic shows up in the gateway's analytics.
Adds _AI_GATEWAY_HEADERS in auxiliary_client and a new
ai-gateway.vercel.sh branch in _apply_client_headers_for_base_url.
Follow-up for salvaged PR #3185:
- run_agent.py: pass self.api_key to query_ollama_num_ctx() so Ollama
behind an auth proxy (same issue class as the LM Studio fix) can be
probed successfully.
- scripts/release.py AUTHOR_MAP: map @tannerfokkens-maker's local-hostname
commit email.
When /steer is sent during an API call (model thinking), the steer text
sits in _pending_steer until after the next tool batch — which may never
come if the model returns a final response. In that case the steer is
only delivered as a post-run follow-up, defeating the purpose.
Add a pre-API-call drain at the top of the main loop: before building
api_messages, check _pending_steer and inject into the last tool result
in the messages list. This ensures steers sent during model thinking are
visible on the very next API call.
If no tool result exists yet (first iteration), the steer is restashed
for the post-tool drain to pick up — injecting into a user message would
break role alternation.
Three new tests cover the pre-API-call drain: injection into last tool
result, restash when no tool message exists, and backward scan past
non-tool messages.
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).
Extract 12 Codex Responses API format-conversion and normalization functions
from run_agent.py into agent/codex_responses_adapter.py, following the
existing pattern of anthropic_adapter.py and bedrock_adapter.py.
run_agent.py: 12,550 → 11,865 lines (-685 lines)
Functions moved:
- _chat_content_to_responses_parts (multimodal content conversion)
- _summarize_user_message_for_log (multimodal message logging)
- _deterministic_call_id (cache-safe fallback IDs)
- _split_responses_tool_id (composite ID splitting)
- _derive_responses_function_call_id (fc_ prefix conversion)
- _responses_tools (schema format conversion)
- _chat_messages_to_responses_input (message format conversion)
- _preflight_codex_input_items (input validation)
- _preflight_codex_api_kwargs (API kwargs validation)
- _extract_responses_message_text (response text extraction)
- _extract_responses_reasoning_text (reasoning extraction)
- _normalize_codex_response (full response normalization)
All functions are stateless module-level functions. AIAgent methods remain
as thin one-line wrappers. Both module-level helpers are re-exported from
run_agent.py for backward compatibility with existing test imports.
Includes multimodal inline image support (PR #12969) that the original PR
was missing.
Based on PR #12975 by @kshitijk4poor.
Follow-up for PR #12252 salvage:
- Extract 75-line inline repair block to _repair_tool_call_arguments()
module-level helper for testability and readability
- Remove redundant 'import re as _re' (re already imported at line 33)
- Bound the while-True excess-delimiter removal loop to 50 iterations
- Add 17 tests covering all 6 repair stages
- Add sirEven to AUTHOR_MAP in release.py
Cherry-picked from PR #12252 by @sirEven.
Models like GLM-5.1 via Ollama can produce malformed tool_call arguments
(truncated JSON, trailing commas, Python None). The existing except
Exception: pass silently passes broken args to the API, which rejects
them with HTTP 400, crashing the session.
Adds a multi-stage repair pipeline at the pre-send normalization point:
1. Empty/whitespace-only → {}
2. Python None literal → {}
3. Strip trailing commas
4. Auto-close unclosed brackets
5. Remove excess closing delimiters
6. Last resort: replace with {} (logged at WARNING)
Cherry-picked from PR #12481 by @Sanjays2402.
Reasoning models (GLM-5.1, QwQ, DeepSeek R1) inflate completion_tokens
with internal thinking tokens. The compression trigger summed
prompt_tokens + completion_tokens, causing premature compression at ~42%
actual context usage instead of the configured 50% threshold.
Now uses only prompt_tokens — completion tokens don't consume context
window space for the next API call.
- 3 new regression tests
- Added AUTHOR_MAP entry for @Sanjays2402
Closes#12026
OpenAI-compatible clients (Open WebUI, LobeChat, etc.) can now send vision
requests to the API server. Both endpoints accept the canonical OpenAI
multimodal shape:
Chat Completions: {type: text|image_url, image_url: {url, detail?}}
Responses: {type: input_text|input_image, image_url: <str>, detail?}
The server validates and converts both into a single internal shape that the
existing agent pipeline already handles (Anthropic adapter converts,
OpenAI-wire providers pass through). Remote http(s) URLs and data:image/*
URLs are supported.
Uploaded files (file, input_file, file_id) and non-image data: URLs are
rejected with 400 unsupported_content_type.
Changes:
- gateway/platforms/api_server.py
- _normalize_multimodal_content(): validates + normalizes both Chat and
Responses content shapes. Returns a plain string for text-only content
(preserves prompt-cache behavior on existing callers) or a canonical
[{type:text|image_url,...}] list when images are present.
- _content_has_visible_payload(): replaces the bare truthy check so a
user turn with only an image no longer rejects as 'No user message'.
- _handle_chat_completions and _handle_responses both call the new helper
for user/assistant content; system messages continue to flatten to text.
- Codex conversation_history, input[], and inline history paths all share
the same validator. No duplicated normalizers.
- run_agent.py
- _summarize_user_message_for_log(): produces a short string summary
('[1 image] describe this') from list content for logging, spinner
previews, and trajectory writes. Fixes AttributeError when list
user_message hit user_message[:80] + '...' / .replace().
- _chat_content_to_responses_parts(): module-level helper that converts
chat-style multimodal content to Responses 'input_text'/'input_image'
parts. Used in _chat_messages_to_responses_input for Codex routing.
- _preflight_codex_input_items() now validates and passes through list
content parts for user/assistant messages instead of stringifying.
- tests/gateway/test_api_server_multimodal.py (new, 38 tests)
- Unit coverage for _normalize_multimodal_content, including both part
formats, data URL gating, and all reject paths.
- Real aiohttp HTTP integration on /v1/chat/completions and /v1/responses
verifying multimodal payloads reach _run_agent intact.
- 400 coverage for file / input_file / non-image data URL.
- tests/run_agent/test_run_agent_multimodal_prologue.py (new)
- Regression coverage for the prologue no-crash contract.
- _chat_content_to_responses_parts round-trip coverage.
- website/docs/user-guide/features/api-server.md
- Inline image examples for both endpoints.
- Updated Limitations: files still unsupported, images now supported.
Validated live against openrouter/anthropic/claude-opus-4.6:
POST /v1/chat/completions → 200, vision-accurate description
POST /v1/responses → 200, same image, clean output_text
POST /v1/chat/completions [file] → 400 unsupported_content_type
POST /v1/responses [input_file] → 400 unsupported_content_type
POST /v1/responses [non-image data URL] → 400 unsupported_content_type
Closes#5621, #8253, #4046, #6632.
Co-authored-by: Paul Bergeron <paul@gamma.app>
Co-authored-by: zhangxicen <zhangxicen@example.com>
Co-authored-by: Manuel Schipper <manuelschipper@users.noreply.github.com>
Co-authored-by: pradeep7127 <pradeep7127@users.noreply.github.com>
Previously, /steer text was only injected after an entire tool batch
completed (_execute_tool_calls_sequential/concurrent returned). If the
batch had a long-running tool (delegate_task, terminal build), the
steer waited for ALL tools to finish before landing — functionally
identical to /queue from the user's perspective.
Now _apply_pending_steer_to_tool_results() is called after EACH
individual tool result is appended to messages, in both the sequential
and concurrent paths. A steer arriving during Tool 1 lands in Tool 1's
result before Tool 2 starts executing.
Also handles leftover steers in the gateway: if a steer arrives during
the final API call (no tool batch to drain into), it's now delivered as
the next user turn instead of being silently dropped.
Fixes user report from Utku.
Context compression silently failed when the auxiliary compression model's
context window was smaller than the main model's compression threshold
(e.g. GLM-4.5-air at 131k paired with a 150k threshold). The feasibility
check warned but the session kept running and compression attempts errored
out mid-conversation.
Two changes in _check_compression_model_feasibility():
1. Hard floor: if detected aux context < MINIMUM_CONTEXT_LENGTH (64k),
raise ValueError so the session refuses to start. Mirrors the existing
main-model rejection at AIAgent.__init__ line 1600. A compression model
below 64k cannot summarise a full threshold-sized window.
2. Auto-correct: when aux context is >= 64k but below the computed
threshold, lower the live compressor's threshold_tokens to aux_context
(and update threshold_percent to match so later update_model() calls
stay in sync). Warning reworded to say what was done and how to
persist the fix in config.yaml.
Only ValueError re-raises; other exceptions in the check remain swallowed
as non-fatal.
Third-party gateways that speak the native Anthropic protocol (MiniMax,
Zhipu GLM, Alibaba DashScope, Kimi, LiteLLM proxies) now work end-to-end
with the same feature set as direct api.anthropic.com callers. Synthesizes
eight stale community PRs into one consolidated change.
Five fixes:
- URL detection: consolidate three inline `endswith("/anthropic")`
checks in runtime_provider.py into the shared _detect_api_mode_for_url
helper. Third-party /anthropic endpoints now auto-resolve to
api_mode=anthropic_messages via one code path instead of three.
- OAuth leak-guard: all five sites that assign `_is_anthropic_oauth`
(__init__, switch_model, _try_refresh_anthropic_client_credentials,
_swap_credential, _try_activate_fallback) now gate on
`provider == "anthropic"` so a stale ANTHROPIC_TOKEN never trips
Claude-Code identity injection on third-party endpoints. Previously
only 2 of 5 sites were guarded.
- Prompt caching: new method `_anthropic_prompt_cache_policy()` returns
`(should_cache, use_native_layout)` per endpoint. Replaces three
inline conditions and the `native_anthropic=(api_mode=='anthropic_messages')`
call-site flag. Native Anthropic and third-party Anthropic gateways
both get the native cache_control layout; OpenRouter gets envelope
layout. Layout is persisted in `_primary_runtime` so fallback
restoration preserves the per-endpoint choice.
- Auxiliary client: `_try_custom_endpoint` honors
`api_mode=anthropic_messages` and builds `AnthropicAuxiliaryClient`
instead of silently downgrading to an OpenAI-wire client. Degrades
gracefully to OpenAI-wire when the anthropic SDK isn't installed.
- Config hygiene: `_update_config_for_provider` (hermes_cli/auth.py)
clears stale `api_key`/`api_mode` when switching to a built-in
provider, so a previous MiniMax custom endpoint's credentials can't
leak into a later OpenRouter session.
- Truncation continuation: length-continuation and tool-call-truncation
retry now cover `anthropic_messages` in addition to `chat_completions`
and `bedrock_converse`. Reuses the existing `_build_assistant_message`
path via `normalize_anthropic_response()` so the interim message
shape is byte-identical to the non-truncated path.
Tests: 6 new files, 42 test cases. Targeted run + tests/run_agent,
tests/agent, tests/hermes_cli all pass (4554 passed).
Synthesized from (credits preserved via Co-authored-by trailers):
#7410 @nocoo — URL detection helper
#7393 @keyuyuan — OAuth 5-site guard
#7367 @n-WN — OAuth guard (narrower cousin, kept comment)
#8636 @sgaofen — caching helper + native-vs-proxy layout split
#10954 @Only-Code-A — caching on anthropic_messages+Claude
#7648 @zhongyueming1121 — aux client anthropic_messages branch
#6096 @hansnow — /model switch clears stale api_mode
#9691 @TroyMitchell911 — anthropic_messages truncation continuation
Closes: #7366, #8294 (third-party Anthropic identity + caching).
Supersedes: #7410, #7367, #7393, #8636, #10954, #7648, #6096, #9691.
Rejects: #9621 (OpenAI-wire caching with incomplete blocklist — risky),
#7242 (superseded by #9691, stale branch),
#8321 (targets smart_model_routing which was removed in #12732).
Co-authored-by: nocoo <nocoo@users.noreply.github.com>
Co-authored-by: Keyu Yuan <leoyuan0099@gmail.com>
Co-authored-by: Zoee <30841158+n-WN@users.noreply.github.com>
Co-authored-by: sgaofen <135070653+sgaofen@users.noreply.github.com>
Co-authored-by: Only-Code-A <bxzt2006@163.com>
Co-authored-by: zhongyueming <mygamez@163.com>
Co-authored-by: Xiaohan Li <hansnow@users.noreply.github.com>
Co-authored-by: Troy Mitchell <i@troy-y.org>
Bedrock rejects ``global-anthropic-claude-opus-4-7`` with ``HTTP 400:
The provided model identifier is invalid`` because its inference
profile IDs embed structural dots
(``global.anthropic.claude-opus-4-7``) that ``normalize_model_name``
was converting to hyphens. ``AIAgent._anthropic_preserve_dots`` did
not include ``bedrock`` in its provider allowlist, so every Claude-on-
Bedrock request through the AnthropicBedrock SDK path shipped with
the mangled model ID and failed.
Root cause
----------
``run_agent.py:_anthropic_preserve_dots`` (previously line 6589)
controls whether ``agent.anthropic_adapter.normalize_model_name``
converts dots to hyphens. The function listed Alibaba, MiniMax,
OpenCode Go/Zen and ZAI but not Bedrock, so when a user set
``provider: bedrock`` with a dotted inference-profile model the flag
returned False and ``normalize_model_name`` mangled every dot in the
ID. All four call sites in run_agent.py
(``build_anthropic_kwargs`` + three fallback / review / summary paths
at lines 6707, 7343, 8408, 8440) read from this same helper.
The bug shape matches #5211 for opencode-go, which was fixed in commit
f77be22c by extending this same allowlist.
Fix
---
* Add ``"bedrock"`` to the provider allowlist.
* Add ``"bedrock-runtime."`` to the base-URL heuristic as
defense-in-depth, so a custom-provider-shaped config with
``base_url: https://bedrock-runtime.<region>.amazonaws.com`` also
takes the preserve-dots path even if ``provider`` isn't explicitly
set to ``"bedrock"``. This mirrors how the code downstream at
run_agent.py:759 already treats either signal as "this is Bedrock".
Bedrock model ID shapes covered
-------------------------------
| Shape | Preserved |
| --- | --- |
| ``global.anthropic.claude-opus-4-7`` (reporter's exact ID) | ✓ |
| ``us.anthropic.claude-sonnet-4-5-20250929-v1:0`` | ✓ |
| ``apac.anthropic.claude-haiku-4-5`` | ✓ |
| ``anthropic.claude-3-5-sonnet-20241022-v2:0`` (foundation) | ✓ |
| ``eu.anthropic.claude-3-5-sonnet`` (regional inference profile) | ✓ |
Non-Claude Bedrock models (Nova, Llama, DeepSeek) take the
``bedrock_converse`` / boto3 path which does not call
``normalize_model_name``, so they were never affected by this bug
and remain unaffected by the fix.
Narrow scope — explicitly not changed
-------------------------------------
* ``bedrock_converse`` path (non-Claude Bedrock models) — already
correct; no ``normalize_model_name`` in that pipeline.
* Provider aliases (``aws``, ``aws-bedrock``, ``amazon``,
``amazon-bedrock``) — if a user bypasses the alias-normalization
pipeline and passes ``provider="aws"`` directly, the base-URL
heuristic still catches it because Bedrock always uses a
``bedrock-runtime.`` endpoint. Adding the aliases themselves to the
provider set is cheap but would be scope creep for this fix.
* No other places in ``agent/anthropic_adapter.py`` mangle dots, so
the fix is confined to ``_anthropic_preserve_dots``.
Regression coverage
-------------------
``tests/agent/test_bedrock_integration.py`` gains three new classes:
* ``TestBedrockPreserveDotsFlag`` (5 tests): flag returns True for
``provider="bedrock"`` and for Bedrock runtime URLs (us-east-1 and
ap-northeast-2 — the reporter's region); returns False for non-
Bedrock AWS URLs like ``s3.us-east-1.amazonaws.com``; canary that
Anthropic-native still returns False.
* ``TestBedrockModelNameNormalization`` (5 tests): every documented
Bedrock model-ID shape survives ``normalize_model_name`` with the
flag on; inverse canary pins that ``preserve_dots=False`` still
mangles (so a future refactor can't decouple the flag from its
effect).
* ``TestBedrockBuildAnthropicKwargsEndToEnd`` (2 tests): integration
through ``build_anthropic_kwargs`` shows the reporter's exact model
ID ends up unmangled in the outgoing kwargs.
Three of the new flag tests fail on unpatched ``origin/main`` with
``assert False is True`` (preserve-dots returning False for Bedrock),
confirming the regression is caught.
Validation
----------
``source venv/bin/activate && python -m pytest
tests/agent/test_bedrock_integration.py tests/agent/test_minimax_provider.py
-q`` -> 84 passed (40 new bedrock tests + 44 pre-existing, including
the minimax canaries that pin the pattern this fix mirrors).
CI-aligned broad suite: 12827 passed, 39 skipped, 19 pre-existing
baseline failures (all reproduce on clean ``origin/main``; none in
the touched code path).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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.
- only use the native adapter for the canonical Gemini native endpoint
- keep custom and /openai base URLs on the OpenAI-compatible path
- preserve Hermes keepalive transport injection for native Gemini clients
- stabilize streaming tool-call replay across repeated SSE events
- add follow-up tests for base_url precedence, async streaming, and duplicate tool-call chunks
