Previously, all project context files (AGENTS.md, .cursorrules, .hermes.md)
were loaded and concatenated into the system prompt. This bloated the prompt
with potentially redundant or conflicting instructions.
Now only ONE project context type is loaded, using priority order:
1. .hermes.md / HERMES.md (walk to git root)
2. AGENTS.md / agents.md (recursive directory walk)
3. CLAUDE.md / claude.md (cwd only, NEW)
4. .cursorrules / .cursor/rules/*.mdc (cwd only)
SOUL.md from HERMES_HOME remains independent and always loads.
Also adds CLAUDE.md as a recognized context file format, matching the
convention popularized by Claude Code.
Refactored the monolithic function into four focused helpers:
_load_hermes_md, _load_agents_md, _load_claude_md, _load_cursorrules.
Tests: replaced 1 coexistence test with 10 new tests covering priority
ordering, CLAUDE.md loading, case sensitivity, injection blocking.
Replace the fragile hardcoded context length system with a multi-source
resolution chain that correctly identifies context windows per provider.
Key changes:
- New agent/models_dev.py: Fetches and caches the models.dev registry
(3800+ models across 100+ providers with per-provider context windows).
In-memory cache (1hr TTL) + disk cache for cold starts.
- Rewritten get_model_context_length() resolution chain:
0. Config override (model.context_length)
1. Custom providers per-model context_length
2. Persistent disk cache
3. Endpoint /models (local servers)
4. Anthropic /v1/models API (max_input_tokens, API-key only)
5. OpenRouter live API (existing, unchanged)
6. Nous suffix-match via OpenRouter (dot/dash normalization)
7. models.dev registry lookup (provider-aware)
8. Thin hardcoded defaults (broad family patterns)
9. 128K fallback (was 2M)
- Provider-aware context: same model now correctly resolves to different
context windows per provider (e.g. claude-opus-4.6: 1M on Anthropic,
128K on GitHub Copilot). Provider name flows through ContextCompressor.
- DEFAULT_CONTEXT_LENGTHS shrunk from 80+ entries to ~16 broad patterns.
models.dev replaces the per-model hardcoding.
- CONTEXT_PROBE_TIERS changed from [2M, 1M, 512K, 200K, 128K, 64K, 32K]
to [128K, 64K, 32K, 16K, 8K]. Unknown models no longer start at 2M.
- hermes model: prompts for context_length when configuring custom
endpoints. Supports shorthand (32k, 128K). Saved to custom_providers
per-model config.
- custom_providers schema extended with optional models dict for
per-model context_length (backward compatible).
- Nous Portal: suffix-matches bare IDs (claude-opus-4-6) against
OpenRouter's prefixed IDs (anthropic/claude-opus-4.6) with dot/dash
normalization. Handles all 15 current Nous models.
- Anthropic direct: queries /v1/models for max_input_tokens. Only works
with regular API keys (sk-ant-api*), not OAuth tokens. Falls through
to models.dev for OAuth users.
Tests: 5574 passed (18 new tests for models_dev + updated probe tiers)
Docs: Updated configuration.md context length section, AGENTS.md
Co-authored-by: Test <test@test.com>
* fix: preserve Ollama model:tag colons in context length detection
The colon-split logic in get_model_context_length() and
_query_local_context_length() assumed any colon meant provider:model
format (e.g. "local:my-model"). But Ollama uses model:tag format
(e.g. "qwen3.5:27b"), so the split turned "qwen3.5:27b" into just
"27b" — which matches nothing, causing a fallback to the 2M token
probe tier.
Now only recognised provider prefixes (local, openrouter, anthropic,
etc.) are stripped. Ollama model:tag names pass through intact.
* fix: update claude-opus-4-6 and claude-sonnet-4-6 context length from 200K to 1M
Both models support 1,000,000 token context windows. The hardcoded defaults
were set before Anthropic expanded the context for the 4.6 generation.
Verified via models.dev and OpenRouter API data.
---------
Co-authored-by: kshitijk4poor <82637225+kshitijk4poor@users.noreply.github.com>
Co-authored-by: Test <test@test.com>
The colon-split logic in get_model_context_length() and
_query_local_context_length() assumed any colon meant provider:model
format (e.g. "local:my-model"). But Ollama uses model:tag format
(e.g. "qwen3.5:27b"), so the split turned "qwen3.5:27b" into just
"27b" — which matches nothing, causing a fallback to the 2M token
probe tier.
Now only recognised provider prefixes (local, openrouter, anthropic,
etc.) are stripped. Ollama model:tag names pass through intact.
Co-authored-by: kshitijk4poor <82637225+kshitijk4poor@users.noreply.github.com>
* fix: detect context length for custom model endpoints via fuzzy matching + config override
Custom model endpoints (non-OpenRouter, non-known-provider) were silently
falling back to 2M tokens when the model name didn't exactly match what the
endpoint's /v1/models reported. This happened because:
1. Endpoint metadata lookup used exact match only — model name mismatches
(e.g. 'qwen3.5:9b' vs 'Qwen3.5-9B-Q4_K_M.gguf') caused a miss
2. Single-model servers (common for local inference) required exact name
match even though only one model was loaded
3. No user escape hatch to manually set context length
Changes:
- Add fuzzy matching for endpoint model metadata: single-model servers
use the only available model regardless of name; multi-model servers
try substring matching in both directions
- Add model.context_length config override (highest priority) so users
can explicitly set their model's context length in config.yaml
- Log an informative message when falling back to 2M probe, telling
users about the config override option
- Thread config_context_length through ContextCompressor and AIAgent init
Tests: 6 new tests covering fuzzy match, single-model fallback, config
override (including zero/None edge cases).
* fix: auto-detect local model name and context length for local servers
Cherry-picked from PR #2043 by sudoingX.
- Auto-detect model name from local server's /v1/models when only one
model is loaded (no manual model name config needed)
- Add n_ctx_train and n_ctx to context length detection keys for llama.cpp
- Query llama.cpp /props endpoint for actual allocated context (not just
training context from GGUF metadata)
- Strip .gguf suffix from display in banner and status bar
- _auto_detect_local_model() in runtime_provider.py for CLI init
Co-authored-by: sudo <sudoingx@users.noreply.github.com>
* fix: revert accidental summary_target_tokens change + add docs for context_length config
- Revert summary_target_tokens from 2500 back to 500 (accidental change
during patching)
- Add 'Context Length Detection' section to Custom & Self-Hosted docs
explaining model.context_length config override
---------
Co-authored-by: Test <test@test.com>
Co-authored-by: sudo <sudoingx@users.noreply.github.com>
* fix: banner skill count now respects disabled skills and platform filtering
The banner's get_available_skills() was doing a raw rglob scan of
~/.hermes/skills/ without checking:
- Whether skills are disabled (skills.disabled config)
- Whether skills match the current platform (platforms: frontmatter)
This caused the banner to show inflated skill counts (e.g. '100 skills'
when many are disabled) and list macOS-only skills on Linux.
Fix: delegate to _find_all_skills() from tools/skills_tool which already
handles both platform gating and disabled-skill filtering.
* fix: system prompt and slash commands now respect disabled skills
Two more places where disabled skills were still surfaced:
1. build_skills_system_prompt() in prompt_builder.py — disabled skills
appeared in the <available_skills> system prompt section, causing
the agent to suggest/load them despite being disabled.
2. scan_skill_commands() in skill_commands.py — disabled skills still
registered as /skill-name slash commands in CLI help and could be
invoked.
Both now load _get_disabled_skill_names() and filter accordingly.
* fix: skill_view blocks disabled skills
skill_view() checked platform compatibility but not disabled state,
so the agent could still load and read disabled skills directly.
Now returns a clear error when a disabled skill is requested, telling
the user to enable it via hermes skills or inspect the files manually.
---------
Co-authored-by: Test <test@test.com>
* perf: cache base_url.lower() via property, consolidate triple load_config(), hoist set constant
run_agent.py:
- Add base_url property that auto-caches _base_url_lower on every
assignment, eliminating 12+ redundant .lower() calls per API cycle
across __init__, _build_api_kwargs, _supports_reasoning_extra_body,
and the main conversation loop
- Consolidate three separate load_config() disk reads in __init__
(memory, skills, compression) into a single call, reusing the
result dict for all three config sections
model_tools.py:
- Hoist _READ_SEARCH_TOOLS set to module level (was rebuilt inside
handle_function_call on every tool invocation)
* Use endpoint metadata for custom model context and pricing
---------
Co-authored-by: kshitij <82637225+kshitijk4poor@users.noreply.github.com>
Add first-class GitHub Copilot and Copilot ACP provider support across
model selection, runtime provider resolution, CLI sessions, delegated
subagents, cron jobs, and the Telegram gateway.
This also normalizes Copilot model catalogs and API modes, introduces a
Copilot ACP OpenAI-compatible shim, and fixes service-mode auth by
resolving Homebrew-installed gh binaries under launchd.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
compress() checks both the head and tail neighbors when choosing the
summary message role. When only the tail collides, the role is flipped.
When BOTH roles would create consecutive same-role messages (e.g.
head=assistant, tail=user), the summary is merged into the first tail
message instead of inserting a standalone message that breaks role
alternation and causes API 400 errors.
The previous code handled head-side collision but left the tail-side
uncovered — long conversations would crash mid-reply with no useful
error, forcing the user to /reset and lose session history.
Based on PR #1186 by @alireza78a, with improved double-collision
handling (merge into tail instead of unconditional 'user' fallback).
Co-authored-by: alireza78a <alireza78.crypto@gmail.com>
- Add summary_base_url config option to compression block for custom
OpenAI-compatible endpoints (e.g. zai, DeepSeek, Ollama)
- Remove compression env var bridges from cli.py and gateway/run.py
(CONTEXT_COMPRESSION_* env vars no longer set from config)
- Switch run_agent.py to read compression config directly from
config.yaml instead of env vars
- Fix backwards-compat block in _resolve_task_provider_model to also
fire when auxiliary.compression.provider is 'auto' (DEFAULT_CONFIG
sets this, which was silently preventing the compression section's
summary_* keys from being read)
- Add test for summary_base_url config-to-client flow
- Update docs to show compression as config.yaml-only
Closes#1591
Based on PR #1702 by @uzaylisak
Adds .hermes.md / HERMES.md discovery for per-project agent configuration.
When the agent starts, it walks from cwd to the git root looking for
.hermes.md (preferred) or HERMES.md, strips any YAML frontmatter, and
injects the markdown body into the system prompt as project context.
- Nearest-first discovery (subdirectory configs shadow parent)
- Stops at git root boundary (no leaking into parent repos)
- YAML frontmatter stripped (structured config deferred to Phase 2)
- Same injection scanning and 20K truncation as other context files
- 22 comprehensive tests
Original implementation by ch3ronsa. Cherry-picked and adapted for current main.
Closes#681 (Phase 1)
After the first user→assistant exchange, Hermes now generates a short
descriptive session title via the auxiliary LLM (compression task config).
Title generation runs in a background thread so it never delays the
user-facing response.
Key behaviors:
- Fires only on the first 1-2 exchanges (checks user message count)
- Skips if a title already exists (user-set titles are never overwritten)
- Uses call_llm with compression task config (cheapest/fastest model)
- Truncates long messages to keep the title generation request small
- Cleans up LLM output: strips quotes, 'Title:' prefixes, enforces 80 char max
- Works in both CLI and gateway (Telegram/Discord/etc.)
Also updates /title (no args) to show the session ID alongside the title
in both CLI and gateway.
Implements #1426
When container_persistent=false, the inner mini-swe-agent cleanup only
runs 'docker stop' in the background, leaving containers in Exited state.
Now cleanup() also runs 'docker rm -f' to fully remove the container.
Also fixes pre-existing test failures in model_metadata (gpt-4.1 1M context),
setup tests (TTS provider step), and adds MockInnerDocker.cleanup().
Original fix by crazywriter1. Cherry-picked and adapted for current main.
Fixes#1679
Salvaged from PR #1708 by @kartikkabadi. Cherry-picked with authorship preserved.
Fixes pre-existing test failures from setup TTS prompt flow changes and environment-sensitive assumptions.
Co-authored-by: Kartik <user2@RentKars-MacBook-Air.local>
* feat: add optional smart model routing
Add a conservative cheap-vs-strong routing option that can send very short/simple turns to a cheaper model across providers while keeping the primary model for complex work. Wire it through CLI, gateway, and cron, and document the config.yaml workflow.
* fix(gateway): remove recursive ExecStop from systemd units, extend TimeoutStopSec to 60s
* fix(gateway): avoid recursive ExecStop in user systemd unit
* fix: extend ExecStop removal and TimeoutStopSec=60 to system unit
The cherry-picked PR #1448 fix only covered the user systemd unit.
The system unit had the same TimeoutStopSec=15 and could benefit
from the same 60s timeout for clean shutdown. Also adds a regression
test for the system unit.
---------
Co-authored-by: Ninja <ninja@local>
* feat(skills): add blender-mcp optional skill for 3D modeling
Control a running Blender instance from Hermes via socket connection
to the blender-mcp addon (port 9876). Supports creating 3D objects,
materials, animations, and running arbitrary bpy code.
Placed in optional-skills/ since it requires Blender 4.3+ desktop
with a third-party addon manually started each session.
* feat(acp): support slash commands in ACP adapter (#1532)
Adds /help, /model, /tools, /context, /reset, /compact, /version
to the ACP adapter (VS Code, Zed, JetBrains). Commands are handled
directly in the server without instantiating the TUI — each command
queries agent/session state and returns plain text.
Unrecognized /commands fall through to the LLM as normal messages.
/model uses detect_provider_for_model() for auto-detection when
switching models, matching the CLI and gateway behavior.
Fixes#1402
* fix(logging): improve error logging in session search tool (#1533)
* fix(gateway): restart on retryable startup failures (#1517)
* feat(email): add skip_attachments option via config.yaml
* feat(email): add skip_attachments option via config.yaml
Adds a config.yaml-driven option to skip email attachments in the
gateway email adapter. Useful for malware protection and bandwidth
savings.
Configure in config.yaml:
platforms:
email:
skip_attachments: true
Based on PR #1521 by @an420eth, changed from env var to config.yaml
(via PlatformConfig.extra) to match the project's config-first pattern.
* docs: document skip_attachments option for email adapter
* fix(telegram): retry on transient TLS failures during connect and send
Add exponential-backoff retry (3 attempts) around initialize() to
handle transient TLS resets during gateway startup. Also catches
TimedOut and OSError in addition to NetworkError.
Add exponential-backoff retry (3 attempts) around send_message() for
NetworkError during message delivery, wrapping the existing Markdown
fallback logic.
Both imports are guarded with try/except ImportError for test
environments where telegram is mocked.
Based on PR #1527 by cmd8. Closes#1526.
* feat: permissive block_anchor thresholds and unicode normalization (#1539)
Salvaged from PR #1528 by an420eth. Closes#517.
Improves _strategy_block_anchor in fuzzy_match.py:
- Add unicode normalization (smart quotes, em/en-dashes, ellipsis,
non-breaking spaces → ASCII) so LLM-produced unicode artifacts
don't break anchor line matching
- Lower thresholds: 0.10 for unique matches (was 0.70), 0.30 for
multiple candidates — if first/last lines match exactly, the
block is almost certainly correct
- Use original (non-normalized) content for offset calculation to
preserve correct character positions
Tested: 3 new scenarios fixed (em-dash anchors, non-breaking space
anchors, very-low-similarity unique matches), zero regressions on
all 9 existing fuzzy match tests.
Co-authored-by: an420eth <an420eth@users.noreply.github.com>
* feat(cli): add file path autocomplete in the input prompt (#1545)
When typing a path-like token (./ ../ ~/ / or containing /),
the CLI now shows filesystem completions in the dropdown menu.
Directories show a trailing slash and 'dir' label; files show
their size. Completions are case-insensitive and capped at 30
entries.
Triggered by tokens like:
edit ./src/ma → shows ./src/main.py, ./src/manifest.json, ...
check ~/doc → shows ~/docs/, ~/documents/, ...
read /etc/hos → shows /etc/hosts, /etc/hostname, ...
open tools/reg → shows tools/registry.py
Slash command autocomplete (/help, /model, etc.) is unaffected —
it still triggers when the input starts with /.
Inspired by OpenCode PR #145 (file path completion menu).
Implementation:
- hermes_cli/commands.py: _extract_path_word() detects path-like
tokens, _path_completions() yields filesystem Completions with
size labels, get_completions() routes to paths vs slash commands
- tests/hermes_cli/test_path_completion.py: 26 tests covering
path extraction, prefix filtering, directory markers, home
expansion, case-insensitivity, integration with slash commands
* feat(privacy): redact PII from LLM context when privacy.redact_pii is enabled
Add privacy.redact_pii config option (boolean, default false). When
enabled, the gateway redacts personally identifiable information from
the system prompt before sending it to the LLM provider:
- Phone numbers (user IDs on WhatsApp/Signal) → hashed to user_<sha256>
- User IDs → hashed to user_<sha256>
- Chat IDs → numeric portion hashed, platform prefix preserved
- Home channel IDs → hashed
- Names/usernames → NOT affected (user-chosen, publicly visible)
Hashes are deterministic (same user → same hash) so the model can
still distinguish users in group chats. Routing and delivery use
the original values internally — redaction only affects LLM context.
Inspired by OpenClaw PR #47959.
* fix(privacy): skip PII redaction on Discord/Slack (mentions need real IDs)
Discord uses <@user_id> for mentions and Slack uses <@U12345> — the LLM
needs the real ID to tag users. Redaction now only applies to WhatsApp,
Signal, and Telegram where IDs are pure routing metadata.
Add 4 platform-specific tests covering Discord, WhatsApp, Signal, Slack.
* feat: smart approvals + /stop command (inspired by OpenAI Codex)
* feat: smart approvals — LLM-based risk assessment for dangerous commands
Adds a 'smart' approval mode that uses the auxiliary LLM to assess
whether a flagged command is genuinely dangerous or a false positive,
auto-approving low-risk commands without prompting the user.
Inspired by OpenAI Codex's Smart Approvals guardian subagent
(openai/codex#13860).
Config (config.yaml):
approvals:
mode: manual # manual (default), smart, off
Modes:
- manual — current behavior, always prompt the user
- smart — aux LLM evaluates risk: APPROVE (auto-allow), DENY (block),
or ESCALATE (fall through to manual prompt)
- off — skip all approval prompts (equivalent to --yolo)
When smart mode auto-approves, the pattern gets session-level approval
so subsequent uses of the same pattern don't trigger another LLM call.
When it denies, the command is blocked without user prompt. When
uncertain, it escalates to the normal manual approval flow.
The LLM prompt is carefully scoped: it sees only the command text and
the flagged reason, assesses actual risk vs false positive, and returns
a single-word verdict.
* feat: make smart approval model configurable via config.yaml
Adds auxiliary.approval section to config.yaml with the same
provider/model/base_url/api_key pattern as other aux tasks (vision,
web_extract, compression, etc.).
Config:
auxiliary:
approval:
provider: auto
model: '' # fast/cheap model recommended
base_url: ''
api_key: ''
Bridged to env vars in both CLI and gateway paths so the aux client
picks them up automatically.
* feat: add /stop command to kill all background processes
Adds a /stop slash command that kills all running background processes
at once. Currently users have to process(list) then process(kill) for
each one individually.
Inspired by OpenAI Codex's separation of interrupt (Ctrl+C stops current
turn) from /stop (cleans up background processes). See openai/codex#14602.
Ctrl+C continues to only interrupt the active agent turn — background
dev servers, watchers, etc. are preserved. /stop is the explicit way
to clean them all up.
* feat: first-class plugin architecture + hide status bar cost by default (#1544)
The persistent status bar now shows context %, token counts, and
duration but NOT $ cost by default. Cost display is opt-in via:
display:
show_cost: true
in config.yaml, or: hermes config set display.show_cost true
The /usage command still shows full cost breakdown since the user
explicitly asked for it — this only affects the always-visible bar.
Status bar without cost:
⚕ claude-sonnet-4 │ 12K/200K │ 6% │ 15m
Status bar with show_cost: true:
⚕ claude-sonnet-4 │ 12K/200K │ 6% │ $0.06 │ 15m
* feat: improve memory prioritization + aggressive skill updates (inspired by OpenAI Codex)
* feat: improve memory prioritization — user preferences over procedural knowledge
Inspired by OpenAI Codex's memory prompt improvements (openai/codex#14493)
which focus memory writes on user preferences and recurring patterns
rather than procedural task details.
Key insight: 'Optimize for reducing future user steering — the most
valuable memory prevents the user from having to repeat themselves.'
Changes:
- MEMORY_GUIDANCE (prompt_builder.py): added prioritization hierarchy
and the core principle about reducing user steering
- MEMORY_SCHEMA (memory_tool.py): reordered WHEN TO SAVE list to put
corrections first, added explicit PRIORITY guidance
- Memory nudge (run_agent.py): now asks specifically about preferences,
corrections, and workflow patterns instead of generic 'anything'
- Memory flush (run_agent.py): now instructs to prioritize user
preferences and corrections over task-specific details
* feat: more aggressive skill creation and update prompting
Press harder on skill updates — the agent should proactively patch
skills when it encounters issues during use, not wait to be asked.
Changes:
- SKILLS_GUIDANCE: 'consider saving' → 'save'; added explicit instruction
to patch skills immediately when found outdated/wrong
- Skills header: added instruction to update loaded skills before finishing
if they had missing steps or wrong commands
- Skill nudge: more assertive ('save the approach' not 'consider saving'),
now also prompts for updating existing skills used in the task
- Skill nudge interval: lowered default from 15 to 10 iterations
- skill_manage schema: added 'patch it immediately' to update triggers
* feat: first-class plugin architecture (#1555)
Plugin system for extending Hermes with custom tools, hooks, and
integrations — no source code changes required.
Core system (hermes_cli/plugins.py):
- Plugin discovery from ~/.hermes/plugins/, .hermes/plugins/, and
pip entry_points (hermes_agent.plugins group)
- PluginContext with register_tool() and register_hook()
- 6 lifecycle hooks: pre/post tool_call, pre/post llm_call,
on_session_start/end
- Namespace package handling for relative imports in plugins
- Graceful error isolation — broken plugins never crash the agent
Integration (model_tools.py):
- Plugin discovery runs after built-in + MCP tools
- Plugin tools bypass toolset filter via get_plugin_tool_names()
- Pre/post tool call hooks fire in handle_function_call()
CLI:
- /plugins command shows loaded plugins, tool counts, status
- Added to COMMANDS dict for autocomplete
Docs:
- Getting started guide (build-a-hermes-plugin.md) — full tutorial
building a calculator plugin step by step
- Reference page (features/plugins.md) — quick overview + tables
- Covers: file structure, schemas, handlers, hooks, data files,
bundled skills, env var gating, pip distribution, common mistakes
Tests: 16 tests covering discovery, loading, hooks, tool visibility.
* fix: hermes update causes dual gateways on macOS (launchd)
Three bugs worked together to create the dual-gateway problem:
1. cmd_update only checked systemd for gateway restart, completely
ignoring launchd on macOS. After killing the PID it would print
'Restart it with: hermes gateway run' even when launchd was about
to auto-respawn the process.
2. launchd's KeepAlive.SuccessfulExit=false respawns the gateway
after SIGTERM (non-zero exit), so the user's manual restart
created a second instance.
3. The launchd plist lacked --replace (systemd had it), so the
respawned gateway didn't kill stale instances on startup.
Fixes:
- Add --replace to launchd ProgramArguments (matches systemd)
- Add launchd detection to cmd_update's auto-restart logic
- Print 'auto-restart via launchd' instead of manual restart hint
* fix: add launchd plist auto-refresh + explicit restart in cmd_update
Two integration issues with the initial fix:
1. Existing macOS users with old plist (no --replace) would never
get the fix until manual uninstall/reinstall. Added
refresh_launchd_plist_if_needed() — mirrors the existing
refresh_systemd_unit_if_needed(). Called from launchd_start(),
launchd_restart(), and cmd_update.
2. cmd_update relied on KeepAlive respawn after SIGTERM rather than
explicit launchctl stop/start. This caused races: launchd would
respawn the old process before the PID file was cleaned up.
Now does explicit stop+start (matching how systemd gets an
explicit systemctl restart), with plist refresh first so the
new --replace flag is picked up.
---------
Co-authored-by: Ninja <ninja@local>
Co-authored-by: alireza78a <alireza78a@users.noreply.github.com>
Co-authored-by: Oktay Aydin <113846926+aydnOktay@users.noreply.github.com>
Co-authored-by: JP Lew <polydegen@protonmail.com>
Co-authored-by: an420eth <an420eth@users.noreply.github.com>
- Add 'emoji' field to ToolEntry and 'get_emoji()' to ToolRegistry
- Add emoji= to all 50+ registry.register() calls across tool files
- Add get_tool_emoji() helper in agent/display.py with 3-tier resolution:
skin override → registry default → hardcoded fallback
- Replace hardcoded emoji maps in run_agent.py, delegate_tool.py, and
gateway/run.py with centralized get_tool_emoji() calls
- Add 'tool_emojis' field to SkinConfig so skins can override per-tool
emojis (e.g. ares skin could use swords instead of wrenches)
- Add 11 tests (5 registry emoji, 6 display/skin integration)
- Update AGENTS.md skin docs table
Based on the approach from PR #1061 by ForgingAlex (emoji centralization
in registry). This salvage fixes several issues from the original:
- Does NOT split the cronjob tool (which would crash on missing schemas)
- Does NOT change image_generate toolset/requires_env/is_async
- Does NOT delete existing tests
- Completes the centralization (gateway/run.py was missed)
- Hooks into the skin system for full customizability
Add base_url/api_key overrides for auxiliary tasks and delegation so users can
route those flows straight to a custom OpenAI-compatible endpoint without
having to rely on provider=main or named custom providers.
Also clear gateway session env vars in test isolation so the full suite stays
deterministic when run from a messaging-backed agent session.
Allow cron runs to keep using send_message for additional destinations, but
skip same-target sends when the scheduler will already auto-deliver the final
response there. Add prompt/tool guidance, docs, and regression coverage for
origin/home-channel resolution and thread-aware comparisons.
Remove diary-style memory framing from the system prompt and memory tool
schema, explicitly steer task/session logs to session_search, and clarify
that session_search is for cross-session recall after checking the current
conversation first. Add regression tests for the updated guidance text.
Seed ~/.hermes/SOUL.md when missing, load SOUL only from HERMES_HOME, and inject raw SOUL content without wrapper text. If the file exists but is empty, nothing is added to the system prompt.
Adapt PR #916 onto current main by replacing the old context summary marker
with a clearer handoff wrapper, updating the summarization prompt for
resume-oriented summaries, and preserving the current call_llm-based
compression path.
* fix: Home Assistant event filtering now closed by default
Previously, when no watch_domains or watch_entities were configured,
ALL state_changed events passed through to the agent, causing users
to be flooded with notifications for every HA entity change.
Now events are dropped by default unless the user explicitly configures:
- watch_domains: list of domains to monitor (e.g. climate, light)
- watch_entities: list of specific entity IDs to monitor
- watch_all: true (new option — opt-in to receive all events)
A warning is logged at connect time if no filters are configured,
guiding users to set up their HA platform config.
All 49 gateway HA tests + 52 HA tool tests pass.
* docs: update Home Assistant integration documentation
- homeassistant.md: Fix event filtering docs to reflect closed-by-default
behavior. Add watch_all option. Replace Python dict config example with
YAML. Fix defaults table (was incorrectly showing 'all'). Add required
configuration warning admonition.
- environment-variables.md: Add HASS_TOKEN and HASS_URL to Messaging section.
- messaging/index.md: Add Home Assistant to description, architecture
diagram, platform toolsets table, and Next Steps links.
* fix(terminal): strip provider env vars from background and PTY subprocesses
Extends the env var blocklist from #1157 to also cover the two remaining
leaky paths in process_registry.py:
- spawn_local() PTY path (line 156)
- spawn_local() background Popen path (line 197)
Both were still using raw os.environ, leaking provider vars to background
processes and interactive PTY sessions. Now uses the same dynamic
_HERMES_PROVIDER_ENV_BLOCKLIST from local.py.
Explicit env_vars passed to spawn_local() still override the blocklist,
matching the existing behavior for callers that intentionally need these.
Gap identified by PR #1004 (@PeterFile).
* feat(delegate): add observability metadata to subagent results
Enrich delegate_task results with metadata from the child AIAgent:
- model: which model the child used
- exit_reason: completed | interrupted | max_iterations
- tokens.input / tokens.output: token counts
- tool_trace: per-tool-call trace with byte sizes and ok/error status
Tool trace uses tool_call_id matching to correctly pair parallel tool
calls with their results, with a fallback for messages without IDs.
Cherry-picked from PR #872 by @omerkaz, with fixes:
- Fixed parallel tool call trace pairing (was always updating last entry)
- Removed redundant 'iterations' field (identical to existing 'api_calls')
- Added test for parallel tool call trace correctness
Co-authored-by: omerkaz <omerkaz@users.noreply.github.com>
* feat(stt): add free local whisper transcription via faster-whisper
Replace OpenAI-only STT with a dual-provider system mirroring the TTS
architecture (Edge TTS free / ElevenLabs paid):
STT: faster-whisper local (free, default) / OpenAI Whisper API (paid)
Changes:
- tools/transcription_tools.py: Full rewrite with provider dispatch,
config loading, local faster-whisper backend, and OpenAI API backend.
Auto-downloads model (~150MB for 'base') on first voice message.
Singleton model instance reused across calls.
- pyproject.toml: Add faster-whisper>=1.0.0 as core dependency
- hermes_cli/config.py: Expand stt config to match TTS pattern with
provider selection and per-provider model settings
- agent/context_compressor.py: Fix .strip() crash when LLM returns
non-string content (dict from llama.cpp, None). Fixes#1100 partially.
- tests/: 23 new tests for STT providers + 2 for compressor fix
- docs/: Updated Voice & TTS page with STT provider table, model sizes,
config examples, and fallback behavior
Fallback behavior:
- Local not installed → OpenAI API (if key set)
- OpenAI key not set → local whisper (if installed)
- Neither → graceful error message to user
Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com>
---------
Co-authored-by: omerkaz <omerkaz@users.noreply.github.com>
Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com>
* fix: prevent model/provider mismatch when switching providers during active gateway
When _update_config_for_provider() writes the new provider and base_url
to config.yaml, the gateway (which re-reads config per-message) can pick
up the change before model selection completes. This causes the old model
name (e.g. 'anthropic/claude-opus-4.6') to be sent to the new provider's
API (e.g. MiniMax), which fails.
Changes:
- _update_config_for_provider() now accepts an optional default_model
parameter. When provided and the current model.default is empty or
uses OpenRouter format (contains '/'), it sets a safe default model
for the new provider.
- All setup.py callers for direct-API providers (zai, kimi, minimax,
minimax-cn, anthropic) now pass a provider-appropriate default model.
- _setup_provider_model_selection() now validates the 'Keep current'
choice: if the current model uses OpenRouter format and wouldn't work
with the new provider, it warns and switches to the provider's first
default model instead of silently keeping the incompatible name.
Reported by a user on Home Assistant whose gateway started sending
'anthropic/claude-opus-4.6' to MiniMax's API after running hermes setup.
* fix: auxiliary client uses main model for custom/local endpoints instead of gpt-4o-mini
When a user runs a local server (e.g. Qwen3.5-9B via OPENAI_BASE_URL),
the auxiliary client (context compression, vision, session search) would
send requests for 'gpt-4o-mini' or 'google/gemini-3-flash-preview' to
the local server, which only serves one model — causing 404 errors
mid-task.
Changes:
- _try_custom_endpoint() now reads the user's configured main model via
_read_main_model() (checks OPENAI_MODEL → HERMES_MODEL → LLM_MODEL →
config.yaml model.default) instead of hardcoding 'gpt-4o-mini'.
- resolve_provider_client() auto mode now detects when an OpenRouter-
formatted model override (containing '/') would be sent to a non-
OpenRouter provider (like a local server) and drops it in favor of
the provider's default model.
- Test isolation fixes: properly clear env vars in 'nothing available'
tests to prevent host environment leakage.
The old message referenced 'hermes setup' which doesn't handle
skill-specific env vars. Updated to direct users to load the skill
in the local CLI (which triggers the secure prompt) or add the key
to ~/.hermes/.env manually.
When a skill declares required_environment_variables in its YAML
frontmatter, missing env vars trigger a secure TUI prompt (identical
to the sudo password widget) when the skill is loaded. Secrets flow
directly to ~/.hermes/.env, never entering LLM context.
Key changes:
- New required_environment_variables frontmatter field for skills
- Secure TUI widget (masked input, 120s timeout)
- Gateway safety: messaging platforms show local setup guidance
- Legacy prerequisites.env_vars normalized into new format
- Remote backend handling: conservative setup_needed=True
- Env var name validation, file permissions hardened to 0o600
- Redact patterns extended for secret-related JSON fields
- 12 existing skills updated with prerequisites declarations
- ~48 new tests covering skip, timeout, gateway, remote backends
- Dynamic panel widget sizing (fixes hardcoded width from original PR)
Cherry-picked from PR #723 by kshitijk4poor, rebased onto current main
with conflict resolution.
Fixes#688
Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
- gateway/run.py: Take main's _resolve_gateway_model() helper
- hermes_cli/setup.py: Re-apply nous-api removal after merge brought
it back. Fix provider_idx offset (Custom is now index 3, not 4).
- tests/hermes_cli/test_setup.py: Fix custom setup test index (3→4)
Add centralized call_llm() and async_call_llm() functions that own the
full LLM request lifecycle:
1. Resolve provider + model from task config or explicit args
2. Get or create a cached client for that provider
3. Format request args (max_tokens handling, provider extra_body)
4. Make the API call with max_tokens/max_completion_tokens retry
5. Return the response
Config: expanded auxiliary section with provider:model slots for all
tasks (compression, vision, web_extract, session_search, skills_hub,
mcp, flush_memories). Config version bumped to 7.
Migrated all auxiliary consumers:
- context_compressor.py: uses call_llm(task='compression')
- vision_tools.py: uses async_call_llm(task='vision')
- web_tools.py: uses async_call_llm(task='web_extract')
- session_search_tool.py: uses async_call_llm(task='session_search')
- browser_tool.py: uses call_llm(task='vision'/'web_extract')
- mcp_tool.py: uses call_llm(task='mcp')
- skills_guard.py: uses call_llm(provider='openrouter')
- run_agent.py flush_memories: uses call_llm(task='flush_memories')
Tests updated for context_compressor and MCP tool. Some test mocks
still need updating (15 remaining failures from mock pattern changes,
2 pre-existing).
Vision auto-mode previously only tried OpenRouter, Nous, and Codex
for multimodal — deliberately skipping custom endpoints with the
assumption they 'may not handle vision input.' This caused silent
failures for users running local multimodal models (Qwen-VL, LLaVA,
Pixtral, etc.) without any cloud API keys.
Now custom endpoints are tried as a last resort in auto mode. If the
model doesn't support vision, the API call fails gracefully — but
users with local vision models no longer need to manually set
auxiliary.vision.provider: main in config.yaml.
Reported by @Spadav and @kotyKD.
Skills can now declare fallback_for_toolsets, fallback_for_tools,
requires_toolsets, and requires_tools in their SKILL.md frontmatter.
The system prompt builder filters skills automatically based on which
tools are available in the current session.
- Add _read_skill_conditions() to parse conditional frontmatter fields
- Add _skill_should_show() to evaluate conditions against available tools
- Update build_skills_system_prompt() to accept and apply tool availability
- Pass valid_tool_names and available toolsets from run_agent.py
- Backward compatible: skills without conditions always show; calling
build_skills_system_prompt() with no args preserves existing behavior
Closes#539
The summary message was always injected as 'user' role, which causes
consecutive user messages when the last preserved head message is also
'user'. Some APIs reject this (400 error), and it produces malformed
training data.
Fix: check the role of the last head message and pick the opposite role
for the summary — 'user' after assistant/tool, 'assistant' after user.
Based on PR #328 by johnh4098. Closes#328.
The 'openai' provider was redundant — using OPENAI_BASE_URL +
OPENAI_API_KEY with provider: 'main' already covers direct OpenAI API.
Provider options are now: auto, openrouter, nous, codex, main.
- Removed _try_openai(), _OPENAI_AUX_MODEL, _OPENAI_BASE_URL
- Replaced openai tests with codex provider tests
- Updated all docs to remove 'openai' option and clarify 'main'
- 'main' description now explicitly mentions it works with OpenAI API,
local models, and any OpenAI-compatible endpoint
Tests: 2467 passed.
The Codex Responses API (chatgpt.com/backend-api/codex) supports
vision via gpt-5.3-codex. This was verified with real API calls
using image analysis.
Changes to _CodexCompletionsAdapter:
- Added _convert_content_for_responses() to translate chat.completions
multimodal format to Responses API format:
- {type: 'text'} → {type: 'input_text'}
- {type: 'image_url', image_url: {url: '...'}} → {type: 'input_image', image_url: '...'}
- Fixed: removed 'stream' from resp_kwargs (responses.stream() handles it)
- Fixed: removed max_output_tokens and temperature (Codex endpoint rejects them)
Provider changes:
- Added 'codex' as explicit auxiliary provider option
- Vision auto-fallback now includes Codex (OpenRouter → Nous → Codex)
since gpt-5.3-codex supports multimodal input
- Updated docs with Codex OAuth examples
Tested with real Codex OAuth token + ~/.hermes/image2.png — confirmed
working end-to-end through the full adapter pipeline.
Tests: 2459 passed.
Users can now set provider: "openai" for auxiliary tasks (vision, web
extract, compression) to use OpenAI's API directly with their
OPENAI_API_KEY. This hits api.openai.com/v1 with gpt-4o-mini as the
default model — supports vision since GPT-4o handles image input.
Provider options are now: auto, openrouter, nous, openai, main.
Changes:
- agent/auxiliary_client.py: added _try_openai(), "openai" case in
_resolve_forced_provider(), updated auxiliary_max_tokens_param()
to use max_completion_tokens for OpenAI
- Updated docs: cli-config.yaml.example, AGENTS.md, and user-facing
configuration.md with Common Setups section showing OpenAI,
OpenRouter, and local model examples
- 3 new tests for OpenAI provider resolution
Tests: 2459 passed (was 2429).
