- Add agent/embeddings.py with Embedder protocol, FastEmbedEmbedder, OpenAIEmbedder
- Factory function get_embedder() reads provider from config.yaml embeddings section
- Lazy initialization — no startup impact, model loaded on first embed call
- cosine_similarity() and cosine_similarity_matrix() utility functions included
- Add fastembed as optional dependency in pyproject.toml
- 30 unit tests, all passing
Closes#675
The KawaiiSpinner animation would occasionally spam dozens of duplicate
lines instead of overwriting in-place with \r. This happened because
prompt_toolkit's StdoutProxy processes each flush() as a separate
run_in_terminal() call — when the write thread is slow (busy event loop
during long tool executions), each \r frame gets its own call, and the
terminal layout save/restore between calls breaks the \r overwrite
semantics.
Fix: rate-limit flush() calls to at most every 0.4s. Between flushes,
\r-frame writes accumulate in StdoutProxy's buffer. When flushed, they
concatenate into one string (e.g. \r frame1 \r frame2 \r frame3) and
are written in a single run_in_terminal() call where \r works correctly.
The spinner still animates (flush ~2.5x/sec) but each flush batches
~3 frames, guaranteeing the \r collapse always works. Most visible
with execute_code and terminal tools (3+ second executions).
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.
New config option:
security:
redact_secrets: false # default: true
When set to false, API keys, tokens, and passwords are shown in
full in read_file, search_files, and terminal output. Useful for
debugging auth issues where you need to verify the actual key value.
Bridged to both CLI and gateway via HERMES_REDACT_SECRETS env var.
The check is in redact_sensitive_text() itself, so all call sites
(terminal, file tools, log formatter) respect it.
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.
Complete Signal adapter using signal-cli daemon HTTP API.
Based on PR #268 by ibhagwan, rebuilt on current main with bug fixes.
Architecture:
- SSE streaming for inbound messages with exponential backoff (2s→60s)
- JSON-RPC 2.0 for outbound (send, typing, attachments, contacts)
- Health monitor detects stale SSE connections (120s threshold)
- Phone number redaction in all logs and global redact.py
Features:
- DM and group message support with separate access policies
- DM policies: pairing (default), allowlist, open
- Group policies: disabled (default), allowlist, open
- Attachment download with magic-byte type detection
- Typing indicators (8s refresh interval)
- 100MB attachment size limit, 8000 char message limit
- E.164 phone + UUID allowlist support
Integration:
- Platform.SIGNAL enum in gateway/config.py
- Signal in _is_user_authorized() allowlist maps (gateway/run.py)
- Adapter factory in _create_adapter() (gateway/run.py)
- user_id_alt/chat_id_alt fields in SessionSource for UUIDs
- send_message tool support via httpx JSON-RPC (not aiohttp)
- Interactive setup wizard in 'hermes gateway setup'
- Connectivity testing during setup (pings /api/v1/check)
- signal-cli detection and install guidance
Bug fixes from PR #268:
- Timestamp reads from envelope_data (not outer wrapper)
- Uses httpx consistently (not aiohttp in send_message tool)
- SIGNAL_DEBUG scoped to signal logger (not root)
- extract_images regex NOT modified (preserves group numbering)
- pairing.py NOT modified (no cross-platform side effects)
- No dual authorization (adapter defers to run.py for user auth)
- Wildcard uses set membership ('*' in set, not list equality)
- .zip default for PK magic bytes (not .docx)
No new Python dependencies — uses httpx (already core).
External requirement: signal-cli daemon (user-installed).
Tests: 30 new tests covering config, init, helpers, session source,
phone redaction, authorization, and send_message integration.
Co-authored-by: ibhagwan <ibhagwan@users.noreply.github.com>
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).
Improvements on top of PR #606 (auxiliary model configuration):
1. Gateway bridge: Added auxiliary.* and compression.summary_provider
config bridging to gateway/run.py so config.yaml settings work from
messaging platforms (not just CLI). Matches the pattern in cli.py.
2. Vision auto-fallback safety: In auto mode, vision now only tries
OpenRouter + Nous Portal (known multimodal-capable providers).
Custom endpoints, Codex, and API-key providers are skipped to avoid
confusing errors from providers that don't support vision input.
Explicit provider override (AUXILIARY_VISION_PROVIDER=main) still
allows using any provider.
3. Comprehensive tests (46 new):
- _get_auxiliary_provider env var resolution (8 tests)
- _resolve_forced_provider with all provider types (8 tests)
- Per-task provider routing integration (4 tests)
- Vision auto-fallback safety (7 tests)
- Config bridging logic (11 tests)
- Gateway/CLI bridge parity (2 tests)
- Vision model override via env var (2 tests)
- DEFAULT_CONFIG shape validation (4 tests)
4. Docs: Added auxiliary_client.py to AGENTS.md project structure.
Updated module docstring with separate text/vision resolution chains.
Tests: 2429 passed (was 2383).
- Added support for auxiliary model overrides in the configuration, allowing users to specify providers and models for vision and web extraction tasks.
- Updated the CLI configuration example to include new auxiliary model settings.
- Enhanced the environment variable mapping in the CLI to accommodate auxiliary model configurations.
- Improved the resolution logic for auxiliary clients to support task-specific provider overrides.
- Updated relevant documentation and comments for clarity on the new features and their usage.
Skills can now declare runtime prerequisites (env vars, CLI binaries) via
YAML frontmatter. Skills with unmet prerequisites are excluded from the
system prompt so the agent never claims capabilities it can't deliver, and
skill_view() warns the agent about what's missing.
Three layers of defense:
- build_skills_system_prompt() filters out unavailable skills
- _find_all_skills() flags unmet prerequisites in metadata
- skill_view() returns prerequisites_warning with actionable details
Tagged 12 bundled skills that have hard runtime dependencies:
gif-search (TENOR_API_KEY), notion (NOTION_API_KEY), himalaya, imessage,
apple-notes, apple-reminders, openhue, duckduckgo-search, codebase-inspection,
blogwatcher, songsee, mcporter.
Closes#658Fixes#630
browser_vision now saves screenshots persistently to ~/.hermes/browser_screenshots/
and returns the screenshot_path in its JSON response. The model can include
MEDIA:<path> in its response to share screenshots as native photos.
Changes:
- browser_tool.py: Save screenshots persistently, return screenshot_path,
auto-cleanup files older than 24 hours, mkdir moved inside try/except
- telegram.py: Add send_image_file() — sends local images via bot.send_photo()
- discord.py: Add send_image_file() — sends local images via discord.File
- slack.py: Add send_image_file() — sends local images via files_upload_v2()
(WhatsApp already had send_image_file — no changes needed)
- prompt_builder.py: Updated Telegram hint to list image extensions,
added Discord and Slack MEDIA: platform hints
- browser.md: Document screenshot sharing and 24h cleanup
- send_file_integration_map.md: Updated to reflect send_image_file is now
implemented on Telegram/Discord/Slack
- test_send_image_file.py: 19 tests covering MEDIA: .png extraction,
send_image_file on all platforms, and screenshot cleanup
Partially addresses #466 (Phase 0: platform adapter gaps for send_image_file).
Kimi Code (platform.kimi.ai) issues API keys prefixed sk-kimi- that require:
1. A different base URL: api.kimi.com/coding/v1 (not api.moonshot.ai/v1)
2. A User-Agent header identifying a recognized coding agent
Without this fix, sk-kimi- keys fail with 401 (wrong endpoint) or 403
('only available for Coding Agents') errors.
Changes:
- Auto-detect sk-kimi- key prefix and route to api.kimi.com/coding/v1
- Send User-Agent: KimiCLI/1.0 header for Kimi Code endpoints
- Legacy Moonshot keys (api.moonshot.ai) continue to work unchanged
- KIMI_BASE_URL env var override still takes priority over auto-detection
- Updated .env.example with correct docs and all endpoint options
- Fixed doctor.py health check for Kimi Code keys
Reference: https://github.com/MoonshotAI/kimi-cli (platforms.py)
Updated the _generate_summary method to attempt summary generation using the auxiliary model first, with a fallback to the main model. If both attempts fail, the method now returns None instead of a placeholder, allowing the caller to handle missing summaries appropriately. This change enhances the robustness of context compression and improves logging for failure scenarios.
Reduces token usage and latency for most tasks by defaulting to
medium reasoning effort instead of xhigh. Users can still override
via config or CLI flag. Updates code, tests, example config, and docs.
Enhance message compression by adding a method to clean up orphaned tool-call and tool-result pairs. This ensures that the API receives well-formed messages, preventing errors related to mismatched IDs. The new functionality includes removing orphaned results and adding stub results for missing calls, improving overall message integrity during compression.
Add a 'platforms' field to SKILL.md frontmatter that restricts skills
to specific operating systems. Skills with platforms: [macos] only
appear in the system prompt, skills_list(), and slash commands on macOS.
Skills without the field load everywhere (backward compatible).
Implementation:
- skill_matches_platform() in tools/skills_tool.py — core filter
- Wired into all 3 discovery paths: prompt_builder.py, skills_tool.py,
skill_commands.py
- 28 new tests across 3 test files
New bundled Apple/macOS skills (all platforms: [macos]):
- imessage — Send/receive iMessages via imsg CLI
- apple-reminders — Manage Reminders via remindctl CLI
- apple-notes — Manage Notes via memo CLI
- findmy — Track devices/AirTags via AppleScript + screen capture
Docs updated: CONTRIBUTING.md, AGENTS.md, creating-skills.md,
skills.md (user guide)
These direct providers don't return cost in API responses and their
per-token pricing isn't readily available externally. Treat as local
models with zero cost so they appear in /insights without fake estimates.
When the user only has a z.ai/Kimi/MiniMax API key (no OpenRouter key),
auxiliary tasks (context compression, web summarization, session search)
now fall back to the configured direct provider instead of returning None.
Resolution chain: OpenRouter -> Nous -> Custom endpoint -> Codex OAuth
-> direct API-key providers -> None.
Uses cheap/fast models for auxiliary tasks:
- zai: glm-4.5-flash
- kimi-coding: kimi-k2-turbo-preview
- minimax/minimax-cn: MiniMax-M2.5-highspeed
Vision auxiliary intentionally NOT modified — vision needs multimodal
models (Gemini) that these providers don't serve.
Adds DEFAULT_CONTEXT_LENGTHS entries for kimi-k2.5 (262144), kimi-k2-thinking
(262144), kimi-k2-turbo-preview (262144), kimi-k2-0905-preview (131072),
MiniMax-M2.5/M2.5-highspeed/M2.1 (204800), and glm-4.5/4.5-flash (131072).
Avoids unnecessary 2M-token probe on first use with direct providers.
Authored by manuelschipper. Adds GLM-4.7 and GLM-5 context lengths (202752)
to model_metadata.py. The key priority fix (prefer OPENAI_API_KEY for
non-OpenRouter endpoints) was already applied in PR #295; merged the Z.ai
mention into the comment.
Issues found and fixed during deep code path review:
1. CRITICAL: Prefix matching returned wrong prices for dated model names
- 'gpt-4o-mini-2024-07-18' matched gpt-4o ($2.50) instead of gpt-4o-mini ($0.15)
- Same for o3-mini→o3 (9x), gpt-4.1-mini→gpt-4.1 (5x), gpt-4.1-nano→gpt-4.1 (20x)
- Fix: use longest-match-wins strategy instead of first-match
- Removed dangerous key.startswith(bare) reverse matching
2. CRITICAL: Top Tools section was empty for CLI sessions
- run_agent.py doesn't set tool_name on tool response messages (pre-existing)
- Insights now also extracts tool names from tool_calls JSON on assistant
messages, which IS populated for all sessions
- Uses max() merge strategy to avoid double-counting between sources
3. SELECT * replaced with explicit column list
- Skips system_prompt and model_config blobs (can be thousands of chars)
- Reduces memory and I/O for large session counts
4. Sets in overview dict converted to sorted lists
- models_with_pricing / models_without_pricing were Python sets
- Sets aren't JSON-serializable — would crash json.dumps()
5. Negative duration guard
- end > start check prevents negative durations from clock drift
6. Model breakdown sort fallback
- When all tokens are 0, now sorts by session count instead of arbitrary order
7. Removed unused timedelta import
Added 6 new tests: dated model pricing (4), tool_calls JSON extraction,
JSON serialization safety. Total: 69 tests.
Custom OAI endpoints, self-hosted models, and local inference should NOT
show fabricated cost estimates. Changed default pricing from $3/$12 per
million tokens to $0/$0 for unrecognized models.
- Added _has_known_pricing() to distinguish commercial vs custom models
- Models with known pricing show $ amounts; unknown models show 'N/A'
- Overview shows asterisk + note when some models lack pricing data
- Gateway format adds '(excludes custom/self-hosted models)' note
- Added 7 new tests for custom model cost handling
Inspired by Claude Code's /insights, adapted for Hermes Agent's multi-platform
architecture. Analyzes session history from state.db to produce comprehensive
usage insights.
Features:
- Overview stats: sessions, messages, tokens, estimated cost, active time
- Model breakdown: per-model sessions, tokens, and cost estimation
- Platform breakdown: CLI vs Telegram vs Discord etc. (unique to Hermes)
- Tool usage ranking: most-used tools with percentages
- Activity patterns: day-of-week chart, peak hours, streaks
- Notable sessions: longest, most messages, most tokens, most tool calls
- Cost estimation: real pricing data for 25+ models (OpenAI, Anthropic,
DeepSeek, Google, Meta) with fuzzy model name matching
- Configurable time window: --days flag (default 30)
- Source filtering: --source flag to filter by platform
Three entry points:
- /insights slash command in CLI (supports --days and --source flags)
- /insights slash command in gateway (compact markdown format)
- hermes insights CLI subcommand (standalone)
Includes 56 tests covering pricing helpers, format helpers, empty DB,
populated DB with multi-platform data, filtering, formatting, and edge cases.
Replaces the unsafe 128K fallback for unknown models with a descending
probe strategy (2M → 1M → 512K → 200K → 128K → 64K → 32K). When a
context-length error occurs, the agent steps down tiers and retries.
The discovered limit is cached per model+provider combo in
~/.hermes/context_length_cache.yaml so subsequent sessions skip probing.
Also parses API error messages to extract the actual context limit
(e.g. 'maximum context length is 32768 tokens') for instant resolution.
The CLI banner now displays the context window size next to the model
name (e.g. 'claude-opus-4 · 200K context · Nous Research').
Changes:
- agent/model_metadata.py: CONTEXT_PROBE_TIERS, persistent cache
(save/load/get), parse_context_limit_from_error(), get_next_probe_tier()
- agent/context_compressor.py: accepts base_url, passes to metadata
- run_agent.py: step-down logic in context error handler, caches on success
- cli.py + hermes_cli/banner.py: context length in welcome banner
- tests: 22 new tests for probing, parsing, and caching
Addresses #132. PR #319's approach (8K default) rejected — too conservative.
When an LLM returns null/empty tool call arguments, json.loads()
produces None. build_tool_preview then crashes with
"argument of type 'NoneType' is not iterable" on the `in` check.
Return None early when args is falsy.
Authored by satelerd. Adds native WhatsApp media sending for images, videos,
and documents via MEDIA: tags. Also includes conflict resolution with edit_message
feature, Telegram hint fix (only advertise supported media types), and import cleanup.
When base_url points to a non-OpenRouter endpoint (e.g. Z.ai),
OPENROUTER_API_KEY incorrectly takes priority over OPENAI_API_KEY,
sending the wrong credentials. This causes 401 errors on the main
inference path and forces users to comment out OPENROUTER_API_KEY,
which then breaks auxiliary clients (compression, vision).
Fix: check whether base_url contains "openrouter" and swap the key
priority accordingly. Also adds GLM-4.7 and GLM-5 context lengths
to DEFAULT_CONTEXT_LENGTHS.
Authored by Farukest. Fixes#389.
Replaces hardcoded forward-slash string checks ('/.git/', '/.hub/') with
Path.parts membership test in _find_all_skills() and scan_skill_commands().
On Windows, str(Path) uses backslashes so the old filter never matched,
causing quarantined skills to appear as installed.
When the auxiliary client (used for context compression summaries) fails
— e.g. due to a stale OpenRouter API key after switching to a local LLM
— fall back to the user's active endpoint (OPENAI_BASE_URL) instead of
returning a useless static summary string.
This handles the common scenario where a user switches providers via
'hermes model' but the old provider's API key remains in .env. The
auxiliary client picks up the stale key, fails (402/auth error), and
previously compression would produce garbage. Now it gracefully retries
with the working endpoint.
On successful fallback, the working client is cached for future
compressions in the same session so the fallback cost is paid only once.
Ref: #348
The hidden directory filter used hardcoded forward-slash strings like
'/.git/' and '/.hub/' to exclude internal directories. On Windows,
Path returns backslash-separated strings, so the filter never matched.
This caused quarantined skills in .hub/quarantine/ to appear as
installed skills and available slash commands on Windows.
Replaced string-based checks with Path.parts membership test which
works on both Windows and Unix.
Add a /send-media endpoint to the WhatsApp bridge and corresponding
adapter methods so the agent can send files as native WhatsApp
attachments instead of plain-text URLs/paths.
- bridge.js: new POST /send-media endpoint using Baileys' native
image/video/document/audio message types with MIME detection
- base.py: add send_video(), send_document(), send_image_file()
with text fallbacks; route MEDIA: tags by file extension instead
of always treating them as voice messages
- whatsapp.py: implement all media methods via a shared
_send_media_to_bridge() helper; override send_image() to download
URLs to local cache and send as native photos
- prompt_builder.py: update WhatsApp and Telegram platform hints so
the agent knows it can use MEDIA:/path tags to send native media
