tools/approval.py no longer re-exports sibling names (approval_context/prompt/floors/detection/
human_wait/smart/gateway_wait); it imports only what it uses. Siblings reference sibling-defined
names directly (module-attribute reads on tools.approval_context so patching the defining module
still works); only facade-owned state (_lock, _gateway_queues, _permanent_approved, _denied,
_denial_breaker_addendum, _gateway_notify_cb) is still read back through tools.approval.
approval_detection calls its own _command_detection_variants instead of late-binding through the facade.
For each issue anchor present in BASE 63279301bc non-test .py and absent on HEAD, the BASE comment/docstring block was re-attached at the HEAD location of the code it explained (matched by the distinctive code line / enclosing def). Sentences already covered by an existing HEAD comment were deduped; the issue number always survives. Insert-only: no code lines changed.
The generic tool_call(name, arguments: object) bridge hides a deferred tool's
real parameter schema from provider-native validation. Before this, only
top-level required-key absence was checked, so invalid enums, wrong types,
nested required fields and forbidden extra properties reached the handler
or MCP server. Now the call is coerced (same coerce_tool_args path normal
dispatch uses) and validated with the schema's declared JSON Schema draft;
failures return the path, constraint and parameters schema so the model
repairs the call in one round-trip. Fails open on missing/malformed schemas,
external $ref, or missing jsonschema.
Fixes#73175
Salvaged from #73179 onto current main (post core-tool deferral #97979).
Co-authored-by: teknium1 <teknium@nousresearch.com>
Adds a `modify` response type to pre_tool_call hooks so a hook can
transform tool arguments before the tool executes, instead of repairing
results afterwards via post_tool_call.
- hermes_cli/plugins.py: _dispatch_pre_tool_call_hooks() fires hooks once
and returns (block_message, modified_args); modify directives
shallow-merge into an accumulated dict built from the original args.
- agent/shell_hooks.py: _parse_response() accepts both the canonical
{"action": "modify", "args": {...}} and Claude Code-compatible
{"decision": "modify", "tool_input": {...}} wire formats.
- model_tools.py, agent/tool_executor.py, agent/agent_runtime_helpers.py:
dispatch sites migrated; modified args applied before execution.
- Docs + 10 new tests (merge semantics, precedence, block interplay).
Salvaged from PR #28953. Best fix for #18988.
Promote _split_model_config_default to hermes_cli/config.py as the single
shared helper for flattening dict-valued model.default/model.model config.
All 8 defense-in-depth sites now route through it instead of inlining
their own isinstance checks with inconsistent key orders.
Changes:
- Add split_model_config_default() to hermes_cli/config.py (public)
- cli.py: _split_model_config_default delegates to shared helper
- Fix key extraction order: agent_runtime_helpers.py was reversed
(default->model); now consistent (model->default) across all sites
- Remove provider-as-model-name fallback from main.py, oneshot.py,
model_tools.py, cli.py — provider is a routing key, not a model ID
- Add 'name' to _normalize_root_model_keys flattening loop and
_has_nested_default detection to cover the deprecated model.name alias
Tests: 118 passed + 1 skipped (cli_init, managed_scope, config).
E2E: 31/31 passed (config chokepoint, managed scope, crash site,
negative cases, edge cases).
A dict-valued model.default (e.g. {provider:..., model:...}) in config.yaml
was leaking into agent.model and crashing the agent at init:
AttributeError: 'dict' object has no attribute 'lower'
agent/agent_runtime_helpers.py: anthropic_prompt_cache_policy
This manifested on the Telegram gateway as an infinite reset loop: every
turn built an agent with model=dict, crashed during init, the gateway
treated the failed turn as a session needing reset, and /reset rebuilt the
agent and crashed again.
Coerce dict -> string at every model-resolution entry point so the value
is normalized once and never reaches a .lower() call as a dict:
- agent/agent_runtime_helpers.py: anthropic_prompt_cache_policy (the crash site)
- agent/agent_init.py: configured default model resolution
- cli.py: CLI config model + _normalize_model_for_provider
- hermes_cli/main.py: _has_any_provider_configured
- hermes_cli/oneshot.py: _run_agent model resolution
- hermes_cli/runtime_provider.py: _get_model_config default handling
- model_tools.py: _resolve_active_context_length
Approval marks were emitted under a synthetic 'default' relay session:
the hook payload carried only turn_id/tool_call_id, so the observability
plugin's _session_id() fell back to 'default', parenting approval marks
to a session scope that never closes — and close-time exporters never
shipped them. The audit board's approval tables stayed empty while
approvals were demonstrably firing (staging 2026-08-10).
Bind session_id in set_current_observability_context at both dispatch
sites (model_tools tool dispatch, plugins pre-tool-call approval gate)
and forward it on every approval hook. Explicit session_id in a hook
payload still wins; unbound contexts omit it (legacy behavior).
Follow-ups on the salvaged Browser Use CLI integration (PR #66476):
- browser_exec runs model-written Python on the host. Strip it at
tool-definition time for sessions whose resolved toolsets exclude
'terminal' so terminal-less surfaces (locked-down messaging configs)
don't silently regain host code execution through the browser toolset.
Session-level gate in model_tools, not a check_fn (check_fn results are
TTL-cached process-wide across sessions).
- Replace the live 'browser-use skill' schema fetch with a pinned helpers
digest: no third-party version-drifting text in the prompt, byte-stable
schema across machines. A/B benchmarked (108 runs, opus-4.8 + kimi-k3,
6 multi-step web tasks x 3 arms x 3 reps): pinned digest matches the
full skill dump 36/36 vs 36/36 at ~equal tokens; both cut total task
tokens ~60% vs the legacy browser_* toolset.
- Docs note for the terminal gate; contributor mapping for salvage.
Cold CLI time-to-banner was ~1.8s (hermes) / ~2.8s (hermes -w). The banner
path was paying for work the session doesn't need before first input:
- aux availability probes built REAL OpenAI/httpx clients (openai import
~0.3s + SSL context) just to answer check_fns. New aux_probe_mode()
returns a cache-excluded stub; resolution policy unchanged.
- tools/mcp_tool imported the mcp SDK (~260ms, mcp.types pydantic model
construction) at module import even with zero MCP servers configured.
SDK import is now lazy behind _ensure_mcp_sdk(); _MCP_AVAILABLE is a
find_spec probe so every existing gate/test keeps its semantics.
- banner blocked 500ms on the update-check prefetch; now waits 50ms and
defers the warning line to a daemon thread (prints above the prompt).
- banner recomputed get_tool_definitions + skills scan + git state every
launch; now snapshotted to ~/.hermes/cache/banner_snapshot.json keyed on
(config.yaml, .env, checkout rev, toolsets) and replayed on warm launches
with a background refresh. Agent tool list is still computed fresh.
- _resolve_active_context_length probed the Nous portal /models (~200ms
network) per launch; the tool-search gate now prefers the on-disk
context cache when present.
- schema reconciliation re-executed SCHEMA_SQL in a scratch SQLite DB
(~85ms) per SessionDB(); the reference parse is now disk-memoized by
DDL hash (live-DB diffing still runs every startup).
- bundled-skills sync (~120-170ms rglob/hash) moved off the startup path
to a daemon thread; plugin discovery starts in the background and every
synchronous consumer joins via discover_plugins().
- hermes_cli.auth imported httpx eagerly (~30ms); now a lazy proxy that
test monkeypatching still reaches (setattr forwards to the real module).
- fast chat launch: unambiguous 'hermes'/'hermes chat' invocations skip
building all ~40 subcommand parsers (bails to full dispatch on anything
else, incl. container mode).
- -w path: git worktree add runs with checkout.workers=8 (0.6s→0.2s) and
overlaps HermesCLI construction; --skills preload runs in the background
and is folded in at agent init (finalize_preloaded_skills, same
fail-loud contract for fully-unknown skill lists); stale-worktree prune
moved off the banner path.
Warm results (PTY time-to-banner, 5-run): hermes 1.80s → 0.38-0.40s;
hermes -w -s hermes-agent-dev --yolo 2.82s → 0.57-0.69s.
model_tools._TOOL_ERROR_MAX_LEN (2000, '...' marker) and the new
registry._MAX_TOOL_ERROR_CHARS (2048, '… [truncated]' marker) were two
caps for the same budget; text on the dispatch exception path passed
both. Alias the sanitizer's cap to the registry constant so the
tool-error budget lives in one home. model_tools already imports from
tools.registry at module level, so no import-cycle risk.
A kanban worker that fires a cron job in-process no longer leaks its task
identity into the cron agent.
The worker is a normal `hermes chat -q` CLI agent whose default toolset
includes `cronjob`, running with HERMES_KANBAN_TASK legitimately set in its
own environment. `cronjob(action="run")` calls run_one_job() -> run_job()
in that same process, so the cron AIAgent was misidentified as that worker:
kanban toolset force-added, kanban-worker protocol injected into its system
prompt, and kanban_complete defaulting task_id to $HERMES_KANBAN_TASK --
letting an unrelated cron job close the worker's task and overwrite real
results.
Fixed with a ContextVar (`non_dispatcher_owned_context`), not by clearing
os.environ. The env is process-global and shared with three concurrent
readers that all need the real values:
* the worker's own claim heartbeat -- run_agent._touch_activity ->
heartbeat_current_worker_from_env reads TASK/CLAIM_LOCK/RUN_ID, and the
cron-run heartbeat thread drives it every 10s. Clearing them silently
no-ops the heartbeat, so after DEFAULT_CLAIM_TTL_SECONDS (15 min) the
dispatcher reclaims a task whose worker is still alive and re-dispatches
it -- the same duplicate-work failure from the other direction.
* the gateway's kanban watchers, which do their own HERMES_KANBAN_BOARD
save/restore around a slow decompose_task() LLM call.
* concurrent cron jobs, which take a *shared* read lock
(_terminal_cwd_lock.acquire_read) and so interleave: job A clears, job B
snapshots empty, A restores, B clears and its restore no-ops -- the
worker's identity is destroyed permanently.
`is_dispatcher_owned_worker_context()` is now the single predicate every
HERMES_KANBAN_* identity gate consults before trusting those vars. It also
closes a pre-existing gap in agent/skill_utils.py, which read the vars
without consulting the delegate_task ContextVar at all; the `kanban` verdict
additionally bypasses _ENV_DETECT_CACHE, since a context-dependent answer
must not be memoized process-wide.
HERMES_KANBAN_BOARD/DB/WORKSPACES_ROOT are left untouched, so the #20074
board pin and the dispatcher's path overrides keep working.
Tests: 18 new, including thread-isolation, concurrent-cron-jobs, and an AST
invariant over _default_spawn that fails if the dispatcher gains a var that
is neither identity-gated nor explicitly classified behaviour-only. All six
mutations are caught, including one that reintroduces the os.environ clear.
tests/cron/ + kanban suites 440 passed; model_tools/skill_utils/boards 63
passed; ruff clean.
Reported and diagnosed by Geoff Friesen (#78961), who identified the symptom
and the exact gating mechanism.
Co-authored-by: Geoff Friesen <gfriesen1@users.noreply.github.com>
Port from nearai/ironclaw#5149 (the describe-first live-hardening fix in
their progressive tool disclosure work): when a model invokes a deferred
tool through the tool_call bridge without the schema-required arguments,
return the tool's parameter schema instead of dispatching blind.
Pre-fix, a blind call produced an opaque downstream failure
("[TOOL_ERROR] Tool execution failed: KeyError: 'document_id'") that
teaches the model nothing about what the tool expects — IronClaw observed
cheap models looping ~30 identical invalid calls until the iteration
budget died. Post-fix, the model repairs the call in one round-trip.
- tools/tool_search.py: new validate_deferred_call_args() — key-absence
check of schema 'required' fields only; no type checking (coerce_tool_args
already repairs types downstream); fails open on any validator error so
it can never block a legitimate dispatch.
- model_tools.py: probe after the scope gate in the bridge dispatch.
- agent/tool_executor.py: probe in both unwrap sites (concurrent +
sequential) before the underlying tool replaces the bridge; sequential
path flattens the payload to match its {"error": str} wrapping.
- tests: TestDeferredCallSchemaProbe — blind call returns schema (not
KeyError), valid/optional calls dispatch, unvalidatable tools fail open,
out-of-scope rejection unchanged.
Teknium review changes on the tiered policy:
1. threshold_pct default 10 -> 5 (listing budget = min(5% of context,
listing_max_tokens)); unknown-context fallback 20K -> 10K.
2. Tier 2 no longer leaves the model blind: when even names-only doesn't
fit, the bridge description now carries a one-line-per-server summary
('cloudflare (3320 tools)') plus an instruction to search FIRST rather
than substitute a generic tool or claim the capability is missing —
the measured tier-2 failure mode (core-tool substitution) at zero
meaningful token cost (~50 tokens/server).
3. Listing degradation is now PER SERVER, largest first: one oversized
server (Cloudflare) collapses to its summary line while small
co-attached servers (Linear) keep their full per-tool listings
('mixed' form). Previously global: attaching Cloudflare next to
Linear silently cost Linear its listing. Greedy fit is deterministic
(size then label) so the rendered block stays byte-stable per catalog
— prompt-prefix cache safe.
E2E on real captures (defaults, 200K ctx): linear alone -> tier 1 full;
unreal alone -> tier 2 groups (5% budget) / tier 1 names at 1M;
cloudflare alone -> tier 2 groups; linear+cloudflare -> tier 1 MIXED
(linear fully listed, cloudflare summarized). 48/48 tests.
Cloudflare's flat API MCP ships 61 property keys that violate Anthropic's
^[a-zA-Z0-9_.-]{1,64}$ pattern (query-filter params like 'issue_class~neq'
and 'meta.<field>[<operator>]'). One bad key anywhere in the tools array
400s the ENTIRE request — measured live: Anthropic, Bedrock, Google Vertex,
and Azure all rejected an eager 3,320-tool Cloudflare request at validation,
before token limits even applied.
- schema_sanitizer: rename non-conforming property keys deterministically
(bad chars -> '_', 64-char truncation, collision dedup with numeric
suffixes), nested schemas included; required[] remapped alongside
- unrename_tool_args(): reverse map applied in coerce_tool_args at dispatch,
so the MCP server receives the original wire names; recurses into object
values and array items
- deterministic on both sides: the rename map is recomputed from the
registry's original schema at dispatch time, no state carried
E2E on the real capture: 61 -> 0 violations across 3,320 tools; round-trip
verified on get_accounts_intel_attacksurfacereport_issues (5 '~neq' keys).
Tier 0: no MCP/plugin tools -> everything eager (pass-through).
Tier 1: deferred tools whose catalog listing fits min(threshold_pct%
of context, listing_max_tokens) -> bridge + skills-style
listing, degrading to names-only over budget.
Tier 2: listing over budget even names-only (Cloudflare's flat API
surface: 3,320 tools, names alone ~32K tokens) -> bare bridge,
discovery through tool_search only.
The old activation threshold (defer only when schemas > threshold_pct
of context) let mid-size catalogs ride eager and pay full schema cost;
with servers like Cloudflare (~597K tokens of schema, would not even
fit a 200K window) the binary gate is the wrong shape. Activation is
now driven purely by deferrable-tool presence; threshold_pct is
repurposed as the listing budget's context-relative leg.
- AssemblyResult gains tier + listing_form for observability
- listing_max_tokens default 4000 -> 20000 (cap 60000) so an 830-tool
catalog keeps a names-only listing while Cloudflare-scale drops to
bare bridge
- E2E verified against real captures: Linear 24 tools -> tier 1 full,
Epic UE 5.8 830 tools -> tier 1 names-only, Cloudflare 3,320 tools
-> tier 2 (both 200K and 1M context)
_resolve_active_context_length() called get_model_context_length() with the
model id alone, so provider-enforced windows (e.g. Codex OAuth's 272K for
gpt-5.x vs the direct API's 1.05M) never reached the tool-search activation
gate — it sized against generic metadata for the same slug.
Resolve the runtime provider for the configured model and pass provider,
base_url, and api_key through. If credential resolution fails (offline, no
keys), degrade to a provider+base_url-only lookup so the static
provider-aware fallbacks still apply; explicit model.context_length keeps
short-circuiting as before (#46620). Gap flagged during review of #16735.
_cli's show_banner() calls _resolve_active_context_length() at every
startup. For non-OpenRouter providers (e.g. minimax-cn, kimi-coding,
custom endpoints) the resolver falls through to step 6 (OpenRouter
live /models fetch), which blocks ~2-3s per CLI launch and adds up
to 7+ minutes when openrouter.ai is unreachable through a proxy that
403s CONNECT (#46620).
Two complementary changes:
1. model_tools.py: read model.context_length from config.yaml and pass
it as config_context_length to get_model_context_length. The
step-0 config override short-circuits the entire resolution chain
including the OpenRouter fetch. No network call is made when the
user has set the value explicitly.
2. agent/model_metadata.py: replace flat timeout=10 with (5, 10)
tuple at all five sites (fetch_model_metadata + four endpoint
probes). urllib3 can otherwise block for 10s per retry stage
through proxies that 403 CONNECT. The tuple bounds connect at 5s
while still allowing slow reads.
Complements the in-flight PR #46685 (which adds HERMES_DISABLE_MODEL_METADATA
env var + same timeout tuple change for fetch_model_metadata). This PR
extends the timeout fix to the other four endpoint probes and adds the
config-override path that addresses the slow-but-reachable scenario
where env-var disable is too heavy-handed.
Refs #46620, PR #46685.
(cherry picked from commit e7faa34199f553d2b1d30c4009856a983cc87707)
Teknium