When using Alibaba (DashScope) with an anthropic-compatible endpoint,
model names like qwen3.5-plus were being normalized to qwen3-5-plus.
Alibaba's API expects the dot. Added preserve_dots parameter to
normalize_model_name() and build_anthropic_kwargs().
Also fixed 401 auth: when provider is alibaba or base_url contains
dashscope/aliyuncs, use only the resolved API key (DASHSCOPE_API_KEY).
Never fall back to resolve_anthropic_token(), and skip Anthropic
credential refresh for DashScope endpoints.
Cherry-picked from PR #1748 by crazywriter1. Fixes#1739.
Six improvements to reduce information loss during context compression,
informed by analysis of Cline, OpenCode, Pi-mono, Codex, and ClawdBot:
1. Structured summary template — sections for Goal, Progress (Done/
In Progress/Blocked), Key Decisions, Relevant Files, Next Steps,
and Critical Context. Forces the summarizer to preserve each
category instead of writing a vague paragraph.
2. Iterative summary updates — on re-compression, the prompt says
'PRESERVE existing info, ADD new progress, UPDATE done/in-progress
status.' Previous summary is stored and fed back to the summarizer
so accumulated context survives across multiple compactions.
3. Token-budget tail protection — instead of fixed protect_last_n=4,
walks backward keeping ~20K tokens of recent context. Adapts to
message density: sessions with big tool results protect fewer
messages, short exchanges protect more. Falls back to protect_last_n
for small conversations.
4. Tool output pruning (pre-pass) — before the expensive LLM summary,
replaces old tool result contents with a placeholder. This is free
(no LLM call) and can save 30%+ of context by itself.
5. Scaled summary budget — instead of fixed 2500 tokens, allocates 20%
of compressed content tokens (clamped to 2000-8000). A 50-turn
conversation gets more summary space than a 10-turn one.
6. Richer summarizer input — tool calls now include arguments (up to
500 chars) and tool results keep up to 3000 chars (was 1500).
The summarizer sees 'terminal(git status) → M src/config.py'
instead of just '[Tool calls: terminal]'.
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.
In Docker/systemd/piped environments, the KawaiiSpinner animation
generates ~500 log lines per tool call. Now checks isatty() and
falls back to clean [tool]/[done] log lines in non-TTY contexts.
Interactive CLI behavior unchanged.
Based on work by 42-evey in PR #2203.
The official international DashScope endpoint uses dashscope-intl.aliyuncs.com
(per Alibaba docs), which the substring match on dashscope.aliyuncs.com misses
because of the hyphenated prefix.
If a tool_calls list contains a None entry (from malformed API response,
compression artifact, or corrupt session replay), convert_messages_to_anthropic
crashes with AttributeError: 'NoneType' object has no attribute 'get'.
Skip None and non-dict entries in the tool_calls iteration. Found via
chaos/fuzz testing with mixed valid/invalid tool_call entries.
Custom endpoint users (DashScope/Alibaba, Z.AI, Kimi, DeepSeek, etc.)
get wrong context lengths because their provider resolves as "openrouter"
or "custom", skipping the models.dev lookup entirely. For example,
qwen3.5-plus on DashScope falls to the generic "qwen" hardcoded default
(131K) instead of the correct 1M.
Add _infer_provider_from_url() that maps known API hostnames to their
models.dev provider IDs. When the explicit provider is generic
(openrouter/custom/empty), infer from the base URL before the models.dev
lookup. This resolves context lengths correctly for DashScope, Z.AI,
Kimi, MiniMax, DeepSeek, and Nous endpoints without requiring users to
manually set context_length in config.
Also refactors _is_known_provider_base_url() to use the same URL mapping,
removing the duplicated hostname list.
Cherry-picked from PR #2146 by @crazywriter1. Fixes#2104.
asyncio.run() creates and closes a fresh event loop each call. Cached
httpx/AsyncOpenAI clients bound to the dead loop crash on GC with
'Event loop is closed'. This hit vision_analyze on first use in CLI.
Two-layer fix:
- model_tools._run_async(): replace asyncio.run() with persistent
loop via _get_tool_loop() + run_until_complete()
- auxiliary_client._get_cached_client(): track which loop created
each async client, discard stale entries if loop is closed
6 regression tests covering loop lifecycle, reuse, and full vision
dispatch chain.
Co-authored-by: Test <test@test.com>
Cherry-picked from PR #2169 by @0xbyt4.
1. _strip_provider_prefix: skip Ollama model:tag names (qwen:0.5b)
2. Fuzzy match: remove reverse direction that made claude-sonnet-4
resolve to 1M instead of 200K
3. _has_content_after_think_block: reuse _strip_think_blocks() to
handle all tag variants (thinking, reasoning, REASONING_SCRATCHPAD)
4. models.dev lookup: elif→if so nous provider also queries models.dev
5. Disk cache fallback: use 5-min TTL instead of full hour so network
is retried soon
6. Delegate build: wrap child construction in try/finally so
_last_resolved_tool_names is always restored on exception
Two fixes for Telegram/gateway-specific bugs:
1. Anthropic adapter: strip orphaned tool_result blocks (mirror of
existing tool_use stripping). Context compression or session
truncation can remove an assistant message containing a tool_use
while leaving the subsequent tool_result intact. Anthropic rejects
these with a 400: 'unexpected tool_use_id found in tool_result
blocks'. The adapter now collects all tool_use IDs and filters out
any tool_result blocks referencing IDs not in that set.
2. Gateway: /reset and /new now bypass the running-agent guard (like
/status already does). Previously, sending /reset while an agent
was running caused the raw text to be queued and later fed back as
a user message with the same broken history — replaying the
corrupted session instead of resetting it. Now the running agent is
interrupted, pending messages are cleared, and the reset command
dispatches immediately.
Tests updated: existing tests now include proper tool_use→tool_result
pairs; two new tests cover orphaned tool_result stripping.
Co-authored-by: Test <test@test.com>
* feat: context pressure warnings for CLI and gateway
User-facing notifications as context approaches the compaction threshold.
Warnings fire at 60% and 85% of the way to compaction — relative to
the configured compression threshold, not the raw context window.
CLI: Formatted line with a progress bar showing distance to compaction.
Cyan at 60% (approaching), bold yellow at 85% (imminent).
◐ context ▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱▱▱▱▱▱ 60% to compaction 100k threshold (50%) · approaching compaction
⚠ context ▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱ 85% to compaction 100k threshold (50%) · compaction imminent
Gateway: Plain-text notification sent to the user's chat via the new
status_callback mechanism (asyncio.run_coroutine_threadsafe bridge,
same pattern as step_callback).
Does NOT inject into the message stream. The LLM never sees these
warnings. Flags reset after each compaction cycle.
Files changed:
- agent/display.py — format_context_pressure(), format_context_pressure_gateway()
- run_agent.py — status_callback param, _context_50/70_warned flags,
_emit_context_pressure(), flag reset in _compress_context()
- gateway/run.py — _status_callback_sync bridge, wired to AIAgent
- tests/test_context_pressure.py — 23 tests
* Merge remote-tracking branch 'origin/main' into hermes/hermes-7ea545bf
---------
Co-authored-by: Test <test@test.com>
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>
Cron jobs run unattended with no user present. Previously the agent had
send_message and clarify tools available, which makes no sense — the
final response is auto-delivered, and there's nobody to ask questions to.
Changes:
- Disable messaging and clarify toolsets for cron agent sessions
- Update cron platform hint to emphasize autonomous execution: no user
present, cannot ask questions, must execute fully and make decisions
- Update cronjob tool schema description to match (remove stale
send_message guidance)
* 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>
Custom endpoints (LM Studio, Ollama, vLLM, llama.cpp) silently fall
back to 2M tokens when /v1/models doesn't include context_length.
Adds _query_local_context_length() which queries server-specific APIs:
- LM Studio: /api/v1/models (max_context_length + loaded instances)
- Ollama: /api/show (model_info + num_ctx parameters)
- llama.cpp: /props (n_ctx from default_generation_settings)
- vLLM: /v1/models/{model} (max_model_len)
Prefers loaded instance context over max (e.g., 122K loaded vs 1M max).
Results are cached via save_context_length() to avoid repeated queries.
Also fixes detect_local_server_type() misidentifying LM Studio as
Ollama (LM Studio returns 200 for /api/tags with an error body).
When LM Studio has a model loaded with a custom context size (e.g.,
122K), prefer that over the model's max_context_length (e.g., 1M).
This makes the TUI status bar show the actual runtime context window.
Instead of defaulting to 2M for unknown local models, query the server
API for the real context length. Supports Ollama (/api/show), vLLM
(max_model_len), and LM Studio (/v1/models). Results are cached to
avoid repeated queries.
Closes#1911
- insights.py: Pre-compute SELECT queries as class constants instead of
f-string interpolation at runtime. _SESSION_COLS is now evaluated once
at class definition time.
- hermes_state.py: Add identifier quoting and whitelist validation for
ALTER TABLE column names in schema migrations.
- Add 4 tests verifying no injection vectors in SQL query construction.
* 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>
After #1675 removed ANTHROPIC_BASE_URL env var support, the Anthropic
provider base URL was hardcoded to https://api.anthropic.com. Now reads
model.base_url from config.yaml as an override, falling back to the
default when not set. Also applies to the auxiliary client.
Cherry-picked from PR #1949 by @rivercrab26.
Co-authored-by: rivercrab26 <rivercrab26@users.noreply.github.com>
_align_boundary_backward only checked messages[idx-1] to decide if
the compress-end boundary splits a tool_call/result group. When an
assistant issues 3+ parallel tool calls, their results span multiple
consecutive messages. If the boundary fell in the middle of that group,
the parent assistant was summarized away and orphaned tool results were
silently deleted by _sanitize_tool_pairs.
Now walks backward through all consecutive tool results to find the
parent assistant, then pulls the boundary before the entire group.
6 regression tests added in tests/test_compression_boundary.py.
Co-authored-by: Guts <Gutslabs@users.noreply.github.com>
SOUL.md now loads in slot #1 of the system prompt, replacing the
hardcoded DEFAULT_AGENT_IDENTITY. This lets users fully customize
the agent's identity and personality by editing ~/.hermes/SOUL.md
without it conflicting with the built-in identity text.
When SOUL.md is loaded as identity, it's excluded from the context
files section to avoid appearing twice. When SOUL.md is missing,
empty, unreadable, or skip_context_files is set, the hardcoded
DEFAULT_AGENT_IDENTITY is used as a fallback.
The default SOUL.md (seeded on first run) already contains the full
Hermes personality, so existing installs are unaffected.
Co-authored-by: Test <test@test.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>
MiniMax: Add M2.7 and M2.7-highspeed as new defaults across provider
model lists, auxiliary client, metadata, setup wizard, RL training tool,
fallback tests, and docs. Retain M2.5/M2.1 as alternatives.
OpenRouter: Add grok-4.20-beta, nemotron-3-super-120b-a12b:free,
trinity-large-preview:free, glm-5-turbo, and hunter-alpha to the
model catalog.
MiniMax changes based on PR #1882 by @octo-patch (applied manually
due to stale conflicts in refactored pricing module).
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>
Replaces all remaining print() calls in compress() with logger.info()
and logger.warning() for consistency with the rest of the module.
Inspired by PR #1822.
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
Four small fixes:
1. model_tools.py: Tool import failures logged at WARNING instead of
DEBUG. If a tool module fails to import (syntax error, missing dep),
the user now sees a warning instead of the tool silently vanishing.
2. hermes_cli/config.py: Remove duplicate 'import sys' (lines 19, 21).
3. agent/model_metadata.py: Remove 6 duplicate entries in
DEFAULT_CONTEXT_LENGTHS dict. Python keeps the last value, so no
functional change, but removes maintenance confusion.
4. hermes_state.py: Add missing self._lock to the LIKE query in
resolve_session_id(). The exact-match path used get_session()
(which locks internally), but the prefix fallback queried _conn
without the lock.
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
The fuzzy match for model context lengths iterated dict insertion
order. Shorter model names (e.g. 'gpt-5') could match before more
specific ones (e.g. 'gpt-5.4-pro'), returning the wrong context
length.
Sort by key length descending so more specific model names always
match first.
The summary message role was determined only by the last head message,
ignoring the first tail message. This could create consecutive user
messages (rejected by Anthropic) when the tail started with 'user'.
Now checks both neighbors. Priority: avoid colliding with the head
(already committed). If the chosen role also collides with the tail,
flip it — but only if flipping wouldn't re-collide with the head.
When tool_choice was 'none', the code did 'pass' — no tool_choice
was sent but tools were still included in the request. Anthropic
defaults to 'auto' when tools are present, so the model could still
call tools despite the caller requesting 'none'.
Fix: omit tools entirely from the request when tool_choice is 'none',
which is the only way to prevent tool use with the Anthropic API.
The module-level auxiliary_is_nous was set to True by _try_nous() and
never reset. In long-running gateway processes, once Nous was resolved
as auxiliary provider, the flag stayed True forever — even if
subsequent resolutions chose a different provider (e.g. OpenRouter).
This caused Nous product tags to be sent to non-Nous providers.
Reset the flag at the start of _resolve_auto() so only the winning
provider's flag persists.
When two consecutive assistant messages had mixed content types (one
string, one list), the merge logic just replaced the earlier message
entirely with the later one (fixed[-1] = m), silently dropping the
earlier message's content.
Apply the same normalization pattern used in the tool_use merge path
(lines 952-956): convert both to list format before concatenating.
This preserves all content from both messages.
* fix: thread safety for concurrent subagent delegation
Four thread-safety fixes that prevent crashes and data races when
running multiple subagents concurrently via delegate_task:
1. Remove redirect_stdout/stderr from delegate_tool — mutating global
sys.stdout races with the spinner thread when multiple children start
concurrently, causing segfaults. Children already run with
quiet_mode=True so the redirect was redundant.
2. Split _run_single_child into _build_child_agent (main thread) +
_run_single_child (worker thread). AIAgent construction creates
httpx/SSL clients which are not thread-safe to initialize
concurrently.
3. Add threading.Lock to SessionDB — subagents share the parent's
SessionDB and call create_session/append_message from worker threads
with no synchronization.
4. Add _active_children_lock to AIAgent — interrupt() iterates
_active_children while worker threads append/remove children.
5. Add _client_cache_lock to auxiliary_client — multiple subagent
threads may resolve clients concurrently via call_llm().
Based on PR #1471 by peteromallet.
* feat: Honcho base_url override via config.yaml + quick command alias type
Two features salvaged from PR #1576:
1. Honcho base_url override: allows pointing Hermes at a remote
self-hosted Honcho deployment via config.yaml:
honcho:
base_url: "http://192.168.x.x:8000"
When set, this overrides the Honcho SDK's environment mapping
(production/local), enabling LAN/VPN Honcho deployments without
requiring the server to live on localhost. Uses config.yaml instead
of env var (HONCHO_URL) per project convention.
2. Quick command alias type: adds a new 'alias' quick command type
that rewrites to another slash command before normal dispatch:
quick_commands:
sc:
type: alias
target: /context
Supports both CLI and gateway. Arguments are forwarded to the
target command.
Based on PR #1576 by redhelix.
---------
Co-authored-by: peteromallet <peteromallet@users.noreply.github.com>
Co-authored-by: redhelix <redhelix@users.noreply.github.com>
Add Alibaba Cloud (DashScope) as a first-class inference provider
using the Anthropic-compatible endpoint. This gives access to Qwen
models (qwen3.5-plus, qwen3-max, qwen3-coder-plus, etc.) through
the same api_mode as native Anthropic.
Also add ANTHROPIC_BASE_URL env var support so users can point the
Anthropic provider at any compatible endpoint.
Changes:
- auth.py: Add alibaba ProviderConfig + ANTHROPIC_BASE_URL on anthropic
- models.py: Add alibaba to catalog, labels, aliases (dashscope/aliyun/qwen), provider order
- runtime_provider.py: Add alibaba resolution (anthropic_messages api_mode) + ANTHROPIC_BASE_URL
- model_metadata.py: Add Qwen model context lengths (128K)
- config.py: Add DASHSCOPE_API_KEY, DASHSCOPE_BASE_URL, ANTHROPIC_BASE_URL env vars
Usage:
hermes --provider alibaba --model qwen3.5-plus
# or via aliases:
hermes --provider qwen --model qwen3-max
* fix: prevent infinite 400 failure loop on context overflow (#1630)
When a gateway session exceeds the model's context window, Anthropic may
return a generic 400 invalid_request_error with just 'Error' as the
message. This bypassed the phrase-based context-length detection,
causing the agent to treat it as a non-retryable client error. Worse,
the failed user message was still persisted to the transcript, making
the session even larger on each attempt — creating an infinite loop.
Three-layer fix:
1. run_agent.py — Fallback heuristic: when a 400 error has a very short
generic message AND the session is large (>40% of context or >80
messages), treat it as a probable context overflow and trigger
compression instead of aborting.
2. run_agent.py + gateway/run.py — Don't persist failed messages:
when the agent returns failed=True before generating any response,
skip writing the user's message to the transcript/DB. This prevents
the session from growing on each failure.
3. gateway/run.py — Smarter error messages: detect context-overflow
failures and suggest /compact or /reset specifically, instead of a
generic 'try again' that will fail identically.
* fix(skills): detect prompt injection patterns and block cache file reads
Adds two security layers to prevent prompt injection via skills hub
cache files (#1558):
1. read_file: blocks direct reads of ~/.hermes/skills/.hub/ directory
(index-cache, catalog files). The 3.5MB clawhub_catalog_v1.json
was the original injection vector — untrusted skill descriptions
in the catalog contained adversarial text that the model executed.
2. skill_view: warns when skills are loaded from outside the trusted
~/.hermes/skills/ directory, and detects common injection patterns
in skill content ("ignore previous instructions", "<system>", etc.).
Cherry-picked from PR #1562 by ygd58.
* fix(tools): chunk long messages in send_message_tool before dispatch (#1552)
Long messages sent via send_message tool or cron delivery silently
failed when exceeding platform limits. Gateway adapters handle this
via truncate_message(), but the standalone senders in send_message_tool
bypassed that entirely.
- Apply truncate_message() chunking in _send_to_platform() before
dispatching to individual platform senders
- Remove naive message[i:i+2000] character split in _send_discord()
in favor of centralized smart splitting
- Attach media files to last chunk only for Telegram
- Add regression tests for chunking and media placement
Cherry-picked from PR #1557 by llbn.
* fix(approval): show full command in dangerous command approval (#1553)
Previously the command was truncated to 80 chars in CLI (with a
[v]iew full option), 500 chars in Discord embeds, and missing entirely
in Telegram/Slack approval messages. Now the full command is always
displayed everywhere:
- CLI: removed 80-char truncation and [v]iew full menu option
- Gateway (TG/Slack): approval_required message includes full command
in a code block
- Discord: embed shows full command up to 4096-char limit
- Windows: skip SIGALRM-based test timeout (Unix-only)
- Updated tests: replaced view-flow tests with direct approval tests
Cherry-picked from PR #1566 by crazywriter1.
* fix(cli): flush stdout during agent loop to prevent macOS display freeze (#1624)
The interrupt polling loop in chat() waited on the queue without
invalidating the prompt_toolkit renderer. On macOS, the StdoutProxy
buffer only flushed on input events, causing the CLI to appear frozen
during tool execution until the user typed a key.
Fix: call _invalidate() on each queue timeout (every ~100ms, throttled
to 150ms) to force the renderer to flush buffered agent output.
* fix(claw): warn when API keys are skipped during OpenClaw migration (#1580)
When --migrate-secrets is not passed (the default), API keys like
OPENROUTER_API_KEY are silently skipped with no warning. Users don't
realize their keys weren't migrated until the agent fails to connect.
Add a post-migration warning with actionable instructions: either
re-run with --migrate-secrets or add the key manually via
hermes config set.
Cherry-picked from PR #1593 by ygd58.
* fix(security): block sandbox backend creds from subprocess env (#1264)
Add Modal and Daytona sandbox credentials to the subprocess env
blocklist so they're not leaked to agent terminal sessions via
printenv/env.
Cherry-picked from PR #1571 by ygd58.
* fix(gateway): cap interrupt recursion depth to prevent resource exhaustion (#816)
When a user sends multiple messages while the agent keeps failing,
_run_agent() calls itself recursively with no depth limit. This can
exhaust stack/memory if the agent is in a failure loop.
Add _MAX_INTERRUPT_DEPTH = 3. When exceeded, the pending message is
logged and the current result is returned instead of recursing deeper.
The log handler duplication bug described in #816 was already fixed
separately (AIAgent.__init__ deduplicates handlers).
* fix(gateway): /model shows active fallback model instead of config default (#1615)
When the agent falls back to a different model (e.g. due to rate
limiting), /model still showed the config default. Now tracks the
effective model/provider after each agent run and displays it.
Cleared when the primary model succeeds again or the user explicitly
switches via /model.
Cherry-picked from PR #1616 by MaxKerkula. Added hasattr guard for
test compatibility.
* feat(gateway): inject reply-to message context for out-of-session replies (#1594)
When a user replies to a Telegram message, check if the quoted text
exists in the current session transcript. If missing (from cron jobs,
background tasks, or old sessions), prepend [Replying to: "..."] to
the message so the agent has context about what's being referenced.
- Add reply_to_text field to MessageEvent (base.py)
- Populate from Telegram's reply_to_message (text or caption)
- Inject context in _handle_message when not found in history
Based on PR #1596 by anpicasso (cherry-picked reply-to feature only,
excluded unrelated /server command and background delegation changes).
* fix: recognize Claude Code OAuth credentials in startup gate (#1455)
The _has_any_provider_configured() startup check didn't look for
Claude Code OAuth credentials (~/.claude/.credentials.json). Users
with only Claude Code auth got the setup wizard instead of starting.
Cherry-picked from PR #1455 by kshitijk4poor.
* perf: use ripgrep for file search (200x faster than find)
search_files(target='files') now uses rg --files -g instead of find.
Ripgrep respects .gitignore, excludes hidden dirs by default, and has
parallel directory traversal — ~200x faster on wide trees (0.14s vs 34s
benchmarked on 164-repo tree).
Falls back to find when rg is unavailable, preserving hidden-dir
exclusion and BSD find compatibility.
Salvaged from PR #1464 by @light-merlin-dark (Merlin) — adapted to
preserve hidden-dir exclusion added since the original PR.
* refactor(tts): replace NeuTTS optional skill with built-in provider + setup flow
Remove the optional skill (redundant now that NeuTTS is a built-in TTS
provider). Replace neutts_cli dependency with a standalone synthesis
helper (tools/neutts_synth.py) that calls the neutts Python API directly
in a subprocess.
Add TTS provider selection to hermes setup:
- 'hermes setup' now prompts for TTS provider after model selection
- 'hermes setup tts' available as standalone section
- Selecting NeuTTS checks for deps and offers to install:
espeak-ng (system) + neutts[all] (pip)
- ElevenLabs/OpenAI selections prompt for API keys
- Tool status display shows NeuTTS install state
Changes:
- Remove optional-skills/mlops/models/neutts/ (skill + CLI scaffold)
- Add tools/neutts_synth.py (standalone synthesis subprocess helper)
- Move jo.wav/jo.txt to tools/neutts_samples/ (bundled default voice)
- Refactor _generate_neutts() — uses neutts API via subprocess, no
neutts_cli dependency, config-driven ref_audio/ref_text/model/device
- Add TTS setup to hermes_cli/setup.py (SETUP_SECTIONS, tool status)
- Update config.py defaults (ref_audio, ref_text, model, device)
* fix(docker): add explicit env allowlist for container credentials (#1436)
Docker terminal sessions are secret-dark by default. This adds
terminal.docker_forward_env as an explicit allowlist for env vars
that may be forwarded into Docker containers.
Values resolve from the current shell first, then fall back to
~/.hermes/.env. Only variables the user explicitly lists are
forwarded — nothing is auto-exposed.
Cherry-picked from PR #1449 by @teknium1, conflict-resolved onto
current main.
Fixes#1436
Supersedes #1439
* fix: email send_typing metadata param + ☤ Hermes staff symbol
- email.py: add missing metadata parameter to send_typing() to match
BasePlatformAdapter signature (PR #1431 by @ItsChoudhry)
- README.md: ⚕ → ☤ — the caduceus is Hermes's staff, not the
medical Staff of Asclepius (PR #1420 by @rianczerwinski)
* fix(whatsapp): support LID format in self-chat mode (#1556)
WhatsApp now uses LID (Linked Identity Device) format alongside classic
@s.whatsapp.net. Self-chat detection checked only the classic format,
breaking self-chat mode for users on newer WhatsApp versions.
- Check both sock.user.id and sock.user.lid for self-chat detection
- Accept 'append' message type in addition to 'notify' (self-chat
messages arrive as 'append')
- Track sent message IDs to prevent echo-back loops with media
- Add WHATSAPP_DEBUG env var for troubleshooting
Based on PR #1556 by jcorrego (manually applied due to cherry-pick
conflicts).
* fix: detect Claude Code version dynamically for OAuth user-agent
The _CLAUDE_CODE_VERSION was hardcoded to '2.1.2' but Anthropic
rejects OAuth requests when the spoofed user-agent version is too
far behind the current Claude Code release. The error is a generic
400 with just 'Error' as the message, making it very hard to diagnose.
Fix: detect the installed version via 'claude --version' at import
time, falling back to a bumped static constant (2.1.74) when Claude
Code isn't installed. This means users who keep Claude Code updated
never hit stale-version rejections.
Reported by Jack — changing the version string to match the installed
claude binary fixed persistent OAuth 400 errors immediately.
---------
Co-authored-by: buray <ygd58@users.noreply.github.com>
Co-authored-by: lbn <llbn@users.noreply.github.com>
Co-authored-by: crazywriter1 <53251494+crazywriter1@users.noreply.github.com>
Co-authored-by: Max K <MaxKerkula@users.noreply.github.com>
Co-authored-by: Angello Picasso <angello.picasso@devsu.com>
Co-authored-by: kshitij <kshitijk4poor@users.noreply.github.com>
Co-authored-by: jcorrego <jcorrego@users.noreply.github.com>
Add Kilo Gateway (kilo.ai) as an API-key provider with OpenAI-compatible
endpoint at https://api.kilo.ai/api/gateway. Supports 500+ models from
Anthropic, OpenAI, Google, xAI, Mistral, MiniMax via a single API key.
- Register kilocode in PROVIDER_REGISTRY with aliases (kilo, kilo-code,
kilo-gateway) and KILOCODE_API_KEY / KILOCODE_BASE_URL env vars
- Add to model catalog, CLI provider menu, setup wizard, doctor checks
- Add google/gemini-3-flash-preview as default aux model
- 12 new tests covering registration, aliases, credential resolution,
runtime config
- Documentation updates (env vars, config, fallback providers)
- Fix setup test index shift from provider insertion
Inspired by PR #1473 by @amanning3390.
Co-authored-by: amanning3390 <amanning3390@users.noreply.github.com>
Add support for OpenCode Zen (pay-as-you-go, 35+ curated models) and
OpenCode Go ($10/month subscription, open models) as first-class providers.
Both are OpenAI-compatible endpoints resolved via the generic api_key
provider flow — no custom adapter needed.
Files changed:
- hermes_cli/auth.py — ProviderConfig entries + aliases
- hermes_cli/config.py — OPENCODE_ZEN/GO API key env vars
- hermes_cli/models.py — model catalogs, labels, aliases, provider order
- hermes_cli/main.py — provider labels, menu entries, model flow dispatch
- hermes_cli/setup.py — setup wizard branches (idx 10, 11)
- agent/model_metadata.py — context lengths for all OpenCode models
- agent/auxiliary_client.py — default aux models
- .env.example — documentation
Co-authored-by: DevAgarwal2 <DevAgarwal2@users.noreply.github.com>
* feat: add Vercel AI Gateway as a first-class provider
Adds AI Gateway (ai-gateway.vercel.sh) as a new inference provider
with AI_GATEWAY_API_KEY authentication, live model discovery, and
reasoning support via extra_body.reasoning.
Based on PR #1492 by jerilynzheng.
* feat: add AI Gateway to setup wizard, doctor, and fallback providers
* test: add AI Gateway to api_key_providers test suite
* feat: add AI Gateway to hermes model CLI and model metadata
Wire AI Gateway into the interactive model selection menu and add
context lengths for AI Gateway model IDs in model_metadata.py.
* feat: use claude-haiku-4.5 as AI Gateway auxiliary model
* revert: use gemini-3-flash as AI Gateway auxiliary model
* fix: move AI Gateway below established providers in selection order
---------
Co-authored-by: jerilynzheng <jerilynzheng@users.noreply.github.com>
Co-authored-by: jerilynzheng <zheng.jerilyn@gmail.com>
The URL is now the primary element — displayed in a bordered box
before the browser auto-open attempt. Works for users who SSH into
remote servers where webbrowser.open() silently fails.
Put the authorization URL front and center instead of treating it as
a fallback. Most Hermes users run on remote servers via SSH where
webbrowser.open() silently fails.
Adds our own OAuth login and token refresh flow, independent of Claude
Code CLI. Mirrors the PKCE flow used by pi-ai (clawdbot) and OpenCode:
- run_hermes_oauth_login(): full PKCE authorization code flow
- Opens browser to claude.ai/oauth/authorize
- User pastes code#state back
- Exchanges for access + refresh tokens
- Stores in ~/.hermes/.anthropic_oauth.json (our own file)
- Also writes to ~/.claude/.credentials.json for backward compat
- refresh_hermes_oauth_token(): automatic token refresh
- POST to console.anthropic.com/v1/oauth/token with refresh_token
- Updates both credential files on success
- Credential resolution priority updated:
1. ANTHROPIC_TOKEN env var
2. CLAUDE_CODE_OAUTH_TOKEN env var
3. Hermes OAuth credentials (~/.hermes/.anthropic_oauth.json) ← NEW
4. Claude Code credentials (~/.claude/.credentials.json)
5. ANTHROPIC_API_KEY env var
Uses same CLIENT_ID, endpoints, scopes, and PKCE parameters as
Claude Code / OpenCode / pi-ai. Token refresh happens automatically
before each API call via _try_refresh_anthropic_client_credentials.
* 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>
* 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
Salvaged from PR #1104 by kshitijk4poor. Closes#683.
Adds a persistent status bar to the CLI showing model name, context
window usage with visual bar, estimated cost, and session duration.
Responsive layout degrades gracefully for narrow terminals.
Changes:
- agent/usage_pricing.py: shared pricing table, cost estimation with
Decimal arithmetic, duration/token formatting helpers
- agent/insights.py: refactored to reuse usage_pricing (eliminates
duplicate pricing table and formatting logic)
- cli.py: status bar with FormattedTextControl fragments, color-coded
context thresholds (green/yellow/orange/red), enhanced /usage with
cost breakdown, 1Hz idle refresh for status bar updates
- tests/test_cli_status_bar.py: status bar snapshot, width collapsing,
usage report with/without pricing, zero-priced model handling
- tests/test_insights.py: verify zero-priced providers show as unknown
Salvage fixes:
- Resolved conflict with voice status bar (both coexist in layout)
- Import _format_context_length from hermes_cli.banner (moved since PR)
Co-authored-by: kshitijk4poor <kshitijk4poor@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.
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>
anthropic/claude-opus-4.6 (OpenRouter format) was being sent as
claude-opus-4.6 to the Anthropic API, which expects claude-opus-4-6
(hyphens, not dots).
normalize_model_name() now converts dots to hyphens after stripping
the provider prefix, matching Anthropic's naming convention.
Fixes 404: 'model: claude-opus-4.6 was not found'
Fixes Anthropic OAuth/subscription authentication end-to-end:
Auth failures (401 errors):
- Add missing 'claude-code-20250219' beta header for OAuth tokens. Both
clawdbot and OpenCode include this alongside 'oauth-2025-04-20' — without
it, Anthropic's API rejects OAuth tokens with 401 authentication errors.
- Fix _fetch_anthropic_models() to use canonical beta headers from
_COMMON_BETAS + _OAUTH_ONLY_BETAS instead of hardcoding.
Token refresh:
- Add _refresh_oauth_token() — when Claude Code credentials from
~/.claude/.credentials.json are expired but have a refresh token,
automatically POST to console.anthropic.com/v1/oauth/token to get
a new access token. Uses the same client_id as Claude Code / OpenCode.
- Add _write_claude_code_credentials() — writes refreshed tokens back
to ~/.claude/.credentials.json, preserving other fields.
- resolve_anthropic_token() now auto-refreshes expired tokens before
returning None.
Config contamination:
- Anthropic's _model_flow_anthropic() no longer saves base_url to config.
Since resolve_runtime_provider() always hardcodes Anthropic's URL, the
stale base_url was contaminating other providers when users switched
without re-running 'hermes model' (e.g., Codex hitting api.anthropic.com).
- _update_config_for_provider() now pops base_url when passed empty string.
- Same fix in setup.py.
Flow/UX (hermes model command):
- CLAUDE_CODE_OAUTH_TOKEN env var now checked in credential detection
- Reauthentication option when existing credentials found
- run_oauth_setup_token() runs 'claude setup-token' as interactive
subprocess, then auto-detects saved credentials
- Clean has_creds/needs_auth flow in both main.py and setup.py
Tests (14 new):
- Beta header assertions for claude-code-20250219
- Token refresh: successful refresh with credential writeback, failed
refresh returns None, no refresh token returns None
- Credential writeback: new file creation, preserving existing fields
- Auto-refresh integration in resolve_anthropic_token()
- CLAUDE_CODE_OAUTH_TOKEN fallback, credential file auto-discovery
- run_oauth_setup_token() (5 scenarios)
Haiku models don't support extended thinking at all. Without this
guard, claude-haiku-4-5-20251001 would receive type=enabled +
budget_tokens and return a 400 error.
Incorporates the fix from PR #1127 (by frizynn) on top of #1128's
adaptive thinking refactor.
Verified live with Claude Code OAuth:
claude-opus-4-6 → adaptive thinking ✓
claude-haiku-4-5 → no thinking params ✓
claude-sonnet-4 → enabled thinking ✓
For Claude 4.6 models (Opus and Sonnet), the Anthropic API rejects
budget_tokens when thinking.type is 'adaptive'. This was causing a
400 error: 'thinking.adaptive.budget_tokens: Extra inputs are not
permitted'.
Changes:
- Send thinking: {type: 'adaptive'} without budget_tokens for 4.6
- Move effort control to output_config: {effort: ...} per Anthropic docs
- Map Hermes effort levels to Anthropic effort levels (xhigh->max, etc.)
- Narrow adaptive detection to 4.6 models only (4.5 still uses manual)
- Add tests for adaptive thinking on 4.6 and manual thinking on pre-4.6
Fixes#1126
Remaining issues from deep scan:
Adapter (agent/anthropic_adapter.py):
- Add _sanitize_tool_id() — Anthropic requires IDs matching [a-zA-Z0-9_-],
now strips invalid chars and ensures non-empty (both tool_use and tool_result)
- Empty tool result content → '(no output)' placeholder (Anthropic rejects empty)
- Set temperature=1 when thinking type='enabled' on older models (required)
- normalize_model_name now case-insensitive for 'Anthropic/' prefix
- Fix stale docstrings referencing only ~/.claude/.credentials.json
Agent loop (run_agent.py):
- Guard memory flush path (line ~2684) — was calling self.client.chat.completions
which is None in anthropic_messages mode. Now routes through Anthropic client.
- Guard summary generation path (line ~3171) — same crash when reaching
iteration limit. Now builds proper Anthropic kwargs and normalizes response.
- Guard retry summary path (line ~3200) — same fix for the summary retry loop.
All three self.client.chat.completions.create() calls outside the main
loop now have anthropic_messages branches to prevent NoneType crashes.
Fixes from comprehensive code review and cross-referencing with
clawdbot/OpenCode implementations:
CRITICAL:
- Add one-shot guard (anthropic_auth_retry_attempted) to prevent
infinite 401 retry loops when credentials keep changing
- Fix _is_oauth_token(): managed keys from ~/.claude.json are NOT
regular API keys (don't start with sk-ant-api). Inverted the logic:
only sk-ant-api* is treated as API key auth, everything else uses
Bearer auth + oauth beta headers
HIGH:
- Wrap json.loads(args) in try/except in message conversion — malformed
tool_call arguments no longer crash the entire conversation
- Raise AuthError in runtime_provider when no Anthropic token found
(was silently passing empty string, causing confusing API errors)
- Remove broken _try_anthropic() from auxiliary vision chain — the
centralized router creates an OpenAI client for api_key providers
which doesn't work with Anthropic's Messages API
MEDIUM:
- Handle empty assistant message content — Anthropic rejects empty
content blocks, now inserts '(empty)' placeholder
- Fix setup.py existing_key logic — set to 'KEEP' sentinel instead
of None to prevent falling through to the auth choice prompt
- Add debug logging to _fetch_anthropic_models on failure
Tests: 43 adapter tests (2 new for token detection), 3197 total passed
- Add _fetch_anthropic_models() to hermes_cli/models.py — hits the
Anthropic /v1/models endpoint to get the live model catalog. Handles
both API key and OAuth token auth headers.
- Wire it into provider_model_ids() so both 'hermes model' and
'hermes setup model' show the live list instead of a stale static one.
- Update static _PROVIDER_MODELS fallback with full current catalog:
opus-4-6, sonnet-4-6, opus-4-5, sonnet-4-5, opus-4, sonnet-4, haiku-4-5
- Update model_metadata.py with context lengths for all current models.
- Fix thinking parameter for 4.5+ models: use type='adaptive' instead
of type='enabled' (Anthropic deprecated 'enabled' for newer models,
warns at runtime). Detects model version from the model name string.
Verified live:
hermes model → Anthropic → auto-detected creds → shows 7 live models
hermes chat --provider anthropic --model claude-opus-4-6 → works
The critical bug: read_claude_code_credentials() only looked at
~/.claude/.credentials.json, but Claude Code's native binary (v2.x,
Bun-compiled) stores credentials in ~/.claude.json at the top level
as 'primaryApiKey'. The .credentials.json file is only written by
older npm-based installs.
Now checks both locations in priority order:
1. ~/.claude.json → primaryApiKey (native binary, v2.x)
2. ~/.claude/.credentials.json → claudeAiOauth.accessToken (legacy)
Verified live: hermes model → Anthropic → auto-detected credentials →
claude-sonnet-4-20250514 → 'Hello there, how are you?' (5 words)
* fix: stop rejecting unlisted models + auto-detect from /models endpoint
validate_requested_model() now accepts models not in the provider's API
listing with a warning instead of blocking. Removes hardcoded catalog
fallback for validation — if API is unreachable, accepts with a warning.
Model selection flows (setup + /model command) now probe the provider's
/models endpoint to get the real available models. Falls back to
hardcoded defaults with a clear warning when auto-detection fails:
'Could not auto-detect models — use Custom model if yours isn't listed.'
Z.AI setup no longer excludes GLM-5 on coding plans.
* fix: use hermes-agent.nousresearch.com as HTTP-Referer for OpenRouter
OpenRouter scrapes the favicon/logo from the HTTP-Referer URL for app
rankings. We were sending the GitHub repo URL, which gives us a generic
GitHub logo. Changed to the proper website URL so our actual branding
shows up in rankings.
Changed in run_agent.py (main agent client) and auxiliary_client.py
(vision/summarization clients).
Feedback fixes:
1. Revert _convert_vision_content — vision is handled by the vision_analyze
tool, not by converting image blocks inline in conversation messages.
Removed the function and its tests.
2. Add Anthropic to 'hermes model' (cmd_model in main.py):
- Added to provider_labels dict
- Added to providers selection list
- Added _model_flow_anthropic() with Claude Code credential auto-detection,
API key prompting, and model selection from catalog.
3. Wire up Anthropic as a vision-capable auxiliary provider:
- Added _try_anthropic() to auxiliary_client.py using claude-sonnet-4
as the vision model (Claude natively supports multimodal)
- Added to the get_vision_auxiliary_client() auto-detection chain
(after OpenRouter/Nous, before Codex/custom)
Cache tracking note: the Anthropic cache metrics branch in run_agent.py
(cache_read_input_tokens / cache_creation_input_tokens) is in the correct
place — it's response-level parsing, same location as the existing
OpenRouter cache tracking. auxiliary_client.py has no cache tracking.
After studying clawdbot (OpenClaw) and OpenCode implementations:
## Beta headers
- Add interleaved-thinking-2025-05-14 and fine-grained-tool-streaming-2025-05-14
as common betas (sent with ALL auth types, not just OAuth)
- OAuth tokens additionally get oauth-2025-04-20
- API keys now also get the common betas (previously got none)
## Vision/image support
- Add _convert_vision_content() to convert OpenAI multimodal format
(image_url blocks) to Anthropic format (image blocks with base64/url source)
- Handles both data: URIs (base64) and regular URLs
## Role alternation enforcement
- Anthropic strictly rejects consecutive same-role messages (400 error)
- Add post-processing step that merges consecutive user/assistant messages
- Handles string, list, and mixed content types during merge
## Tool choice support
- Add tool_choice parameter to build_anthropic_kwargs()
- Maps OpenAI values: auto→auto, required→any, none→omit, name→tool
## Cache metrics tracking
- Anthropic uses cache_read_input_tokens / cache_creation_input_tokens
(different from OpenRouter's prompt_tokens_details.cached_tokens)
- Add api_mode-aware branch in run_agent.py cache stats logging
## Credential refresh on 401
- On 401 error during anthropic_messages mode, re-read credentials
via resolve_anthropic_token() (picks up refreshed Claude Code tokens)
- Rebuild client if new token differs from current one
- Follows same pattern as Codex/Nous 401 refresh handlers
## Tests
- 44 adapter tests (8 new: vision conversion, role alternation, tool choice)
- Updated beta header tests to verify new structure
- Full suite: 3198 passed, 0 regressions
Root cause: two issues combined to create visual spam on Telegram/Discord:
1. build_tool_preview() preserved newlines from tool arguments. A preview
like 'import os\nprint("...")' rendered as 2+ visual lines per
progress entry on messaging platforms. This affected execute_code most
(code always has newlines), but could also hit terminal, memory,
send_message, session_search, and process tools.
2. No deduplication of identical progress messages. When models iterate
with execute_code using the same boilerplate code (common pattern),
each call produced an identical progress line. 9 calls x 2 visual
lines = 18 lines of identical spam in one message bubble.
Fixes:
- Added _oneline() helper to collapse all whitespace (newlines, tabs) to
single spaces. Applied to ALL code paths in build_tool_preview() —
both the generic path and every early-return path that touches user
content (memory, session_search, send_message, process).
- Added dedup in gateway progress_callback: consecutive identical messages
are collapsed with a repeat counter, e.g. 'execute_code: ... (x9)'
instead of 9 identical lines. The send_progress_messages async loop
handles dedup tuples by updating the last progress_line in-place.
* fix: ClawHub skill install — use /download ZIP endpoint
The ClawHub API v1 version endpoint only returns file metadata
(path, size, sha256, contentType) without inline content or download
URLs. Our code was looking for inline content in the metadata, which
never existed, causing all ClawHub installs to fail with:
'no inline/raw file content was available'
Fix: Use the /api/v1/download endpoint (same as the official clawhub
CLI) to download skills as ZIP bundles and extract files in-memory.
Changes:
- Add _download_zip() method that downloads and extracts ZIP bundles
- Retry on 429 rate limiting with Retry-After header support
- Path sanitization and binary file filtering for security
- Keep _extract_files() as a fallback for inline/raw content
- Also fix nested file lookup (version_data.version.files)
* chore: lower default compression threshold from 85% to 50%
Triggers context compression earlier — at 50% of the model's context
window instead of 85%. Updated in all four places where the default
is defined: context_compressor.py, cli.py, run_agent.py, config.py,
and gateway/run.py.
* fix: /reasoning command output ordering, display, and inline think extraction
Three issues with the /reasoning command:
1. Output interleaving: The command echo used print() while feedback
used _cprint(), causing them to render out-of-order under
prompt_toolkit's patch_stdout. Changed echo to use _cprint() so
all output renders through the same path in correct order.
2. Reasoning display not working: /reasoning show toggled a flag
but reasoning never appeared for models that embed thinking in
inline <think> blocks rather than structured API fields. Added
fallback extraction in _build_assistant_message to capture
<think> block content as reasoning when no structured reasoning
fields (reasoning, reasoning_content, reasoning_details) are
present. This feeds into both the reasoning callback (during
tool loops) and the post-response reasoning box display.
3. Feedback clarity: Added checkmarks to confirm actions, persisted
show/hide to config (was session-only before), and aligned the
status display for readability.
Tests: 7 new tests for inline think block extraction (41 total).
* feat: add /reasoning command to gateway (Telegram/Discord/etc)
The /reasoning command only existed in the CLI — messaging platforms
had no way to view or change reasoning settings. This adds:
1. /reasoning command handler in the gateway:
- No args: shows current effort level and display state
- /reasoning <level>: sets reasoning effort (none/low/medium/high/xhigh)
- /reasoning show|hide: toggles reasoning display in responses
- All changes saved to config.yaml immediately
2. Reasoning display in gateway responses:
- When show_reasoning is enabled, prepends a 'Reasoning' block
with the model's last_reasoning content before the response
- Collapses long reasoning (>15 lines) to keep messages readable
- Uses last_reasoning from run_conversation result dict
3. Plumbing:
- Added _show_reasoning attribute loaded from config at startup
- Propagated last_reasoning through _run_agent return dict
- Added /reasoning to help text and known_commands set
- Uses getattr for _show_reasoning to handle test stubs
* fix: improve Kimi model selection — auto-detect endpoint, add missing models
Kimi Coding Plan setup:
- New dedicated _model_flow_kimi() replaces the generic API-key flow
for kimi-coding. Removes the confusing 'Base URL' prompt entirely —
the endpoint is auto-detected from the API key prefix:
sk-kimi-* → api.kimi.com/coding/v1 (Kimi Coding Plan)
other → api.moonshot.ai/v1 (legacy Moonshot)
- Shows appropriate models for each endpoint:
Coding Plan: kimi-for-coding, kimi-k2.5, kimi-k2-thinking, kimi-k2-thinking-turbo
Moonshot: full model catalog
- Clears any stale KIMI_BASE_URL override so runtime auto-detection
via _resolve_kimi_base_url() works correctly.
Model catalog updates:
- Added kimi-for-coding (primary Coding Plan model) and kimi-k2-thinking-turbo
to models.py, main.py _PROVIDER_MODELS, and model_metadata.py context windows.
- Updated User-Agent from KimiCLI/1.0 to KimiCLI/1.3 (Kimi's coding
endpoint whitelists known coding agents via User-Agent sniffing).
- 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)
Model selection now comes exclusively from config.yaml (set via
'hermes model' or 'hermes setup'). The LLM_MODEL env var is no longer
read or written anywhere in production code.
Why: env vars are per-process/per-user and would conflict in
multi-agent or multi-tenant setups. Config.yaml is file-based and
can be scoped per-user or eventually per-session.
Changes:
- cli.py: Read model from CLI_CONFIG only, not LLM_MODEL/OPENAI_MODEL
- hermes_cli/auth.py: _save_model_choice() no longer writes LLM_MODEL
to .env
- hermes_cli/setup.py: Remove 12 save_env_value('LLM_MODEL', ...)
calls from all provider setup flows
- gateway/run.py: Remove LLM_MODEL fallback (HERMES_MODEL still works
for gateway process runtime)
- cron/scheduler.py: Same
- agent/auxiliary_client.py: Remove LLM_MODEL from custom endpoint
model detection
Phase 2 of the provider router migration — route the main agent's
client construction and fallback activation through
resolve_provider_client() instead of duplicated ad-hoc logic.
run_agent.py:
- __init__: When no explicit api_key/base_url, use
resolve_provider_client(provider, raw_codex=True) for client
construction. Explicit creds (from CLI/gateway runtime provider)
still construct directly.
- _try_activate_fallback: Replace _resolve_fallback_credentials and
its duplicated _FALLBACK_API_KEY_PROVIDERS / _FALLBACK_OAUTH_PROVIDERS
dicts with a single resolve_provider_client() call. The router
handles all provider types (API-key, OAuth, Codex) centrally.
- Remove _resolve_fallback_credentials method and both fallback dicts.
agent/auxiliary_client.py:
- Add raw_codex parameter to resolve_provider_client(). When True,
returns the raw OpenAI client for Codex providers instead of wrapping
in CodexAuxiliaryClient. The main agent needs this for direct
responses.stream() access.
3251 passed, 2 pre-existing unrelated failures.
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).
Route all remaining ad-hoc auxiliary LLM call sites through
resolve_provider_client() so auth, headers, and API format (Chat
Completions vs Responses API) are handled consistently in one place.
Files changed:
- tools/openrouter_client.py: Replace manual AsyncOpenAI construction
with resolve_provider_client('openrouter', async_mode=True). The
shared client module now delegates entirely to the router.
- tools/skills_guard.py: Replace inline OpenAI client construction
(hardcoded OpenRouter base_url, manual api_key lookup, manual
headers) with resolve_provider_client('openrouter'). Remove unused
OPENROUTER_BASE_URL import.
- trajectory_compressor.py: Add _detect_provider() to map config
base_url to a provider name, then route through
resolve_provider_client. Falls back to raw construction for
unrecognized custom endpoints.
- mini_swe_runner.py: Route default case (no explicit api_key/base_url)
through resolve_provider_client('openrouter') with auto-detection
fallback. Preserves direct construction when explicit creds are
passed via CLI args.
- agent/auxiliary_client.py: Fix stale module docstring — vision auto
mode now correctly documents that Codex and custom endpoints are
tried (not skipped).
Three interconnected fixes for auxiliary client infrastructure:
1. CENTRALIZED PROVIDER ROUTER (auxiliary_client.py)
Add resolve_provider_client(provider, model, async_mode) — a single
entry point for creating properly configured clients. Given a provider
name and optional model, it handles auth lookup (env vars, OAuth
tokens, auth.json), base URL resolution, provider-specific headers,
and API format differences (Chat Completions vs Responses API for
Codex). All auxiliary consumers should route through this instead of
ad-hoc env var lookups.
Refactored get_text_auxiliary_client, get_async_text_auxiliary_client,
and get_vision_auxiliary_client to use the router internally.
2. FIX CODEX VISION BYPASS (vision_tools.py)
vision_tools.py was constructing a raw AsyncOpenAI client from the
sync vision client's api_key/base_url, completely bypassing the Codex
Responses API adapter. When the vision provider resolved to Codex,
the raw client would hit chatgpt.com/backend-api/codex with
chat.completions.create() which only supports the Responses API.
Fix: Added get_async_vision_auxiliary_client() which properly wraps
Codex into AsyncCodexAuxiliaryClient. vision_tools.py now uses this
instead of manual client construction.
3. FIX COMPRESSION FALLBACK + VISION ERROR HANDLING
- context_compressor.py: Removed _get_fallback_client() which blindly
looked for OPENAI_API_KEY + OPENAI_BASE_URL (fails for Codex OAuth,
API-key providers, users without OPENAI_BASE_URL set). Replaced
with fallback loop through resolve_provider_client() for each
known provider, with same-provider dedup.
- vision_tools.py: Added error detection for vision capability
failures. Returns clear message to the model when the configured
model doesn't support vision, instead of a generic error.
Addresses #886
Allow users to interact with Hermes by sending and receiving emails.
Uses IMAP polling for incoming messages and SMTP for replies with
proper threading (In-Reply-To, References headers).
Integrates with all 14 gateway extension points: config, adapter
factory, authorization, send_message tool, cron delivery, toolsets,
prompt hints, channel directory, setup wizard, status display, and
env example.
65 tests covering config, parsing, dispatch, threading, IMAP fetch,
SMTP send, attachments, and all integration points.
- 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.
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
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
Issue #263: Telegram/Discord/WhatsApp/Slack now show tool call details
based on display.tool_progress in config.yaml.
Changes:
- gateway/run.py: 'verbose' mode shows full args (keys + JSON, 200 char
max). 'all' mode preview increased from 40 to 80 chars. Added missing
tool emojis (execute_code, delegate_task, clarify, skill_manage,
search_files).
- agent/display.py: Added execute_code, delegate_task, clarify,
skill_manage to primary_args. Added 'code' and 'goal' to fallback keys.
- run_agent.py: Pass function_args dict to tool_progress_callback so
gateway can format based on its own verbosity config.
Config usage:
display:
tool_progress: verbose # off | new | all | verbose
The OpenAI API returns content: null on assistant messages with tool
calls. msg.get('content', '') returns None when the key exists with
value None, causing TypeError on len(), string concatenation, and
.strip() in downstream code paths.
Fixed 4 locations that process conversation messages:
- agent/auxiliary_client.py:84 — None passed to API calls
- cli.py:1288 — crash on content[:200] and len(content)
- run_agent.py:3444 — crash on None.strip()
- honcho_integration/session.py:445 — 'None' rendered in transcript
13 other instances were verified safe (already protected, only process
user/tool messages, or use the safe pattern).
Pattern: msg.get('content', '') → msg.get('content') or ''
Fixes#276
The OpenAI API returns content: null on assistant messages that only
contain tool calls. msg.get('content', '') returns None (not '') when
the key exists with value None, causing TypeError on len() and string
concatenation in _generate_summary and compress.
Fix: msg.get('content') or '' — handles both missing keys and None.
Tests from PR #216 (@Farukest). Fix also in PR #215 (@cutepawss).
Both PRs had stale branches and couldn't be merged directly.
Closes#211
Updated the authentication mechanism to store Codex OAuth tokens in the Hermes auth store located at ~/.hermes/auth.json instead of the previous ~/.codex/auth.json. This change includes refactoring related functions for reading and saving tokens, ensuring better management of authentication states and preventing conflicts between different applications. Adjusted tests to reflect the new storage structure and improved error handling for missing or malformed tokens.
print_above() used \033[K (erase-to-end-of-line) to clear the spinner
line before printing text above it. This causes garbled escape codes when
prompt_toolkit's patch_stdout is active in CLI mode.
Switched to the same spaces-based clearing approach used by stop() —
overwrite with blanks, then carriage return back to start of line.
Updated test assertion to match the new clearing method.
When subagents run via delegate_task, the user now sees real-time
progress instead of silence:
CLI: tree-view activity lines print above the delegation spinner
🔀 Delegating: research quantum computing
├─ 💭 "I'll search for papers first..."
├─ 🔍 web_search "quantum computing"
├─ 📖 read_file "paper.pdf"
└─ ⠹ working... (18.2s)
Gateway (Telegram/Discord): batched progress summaries sent every
5 tool calls to avoid message spam. Remaining tools flushed on
subagent completion.
Changes:
- agent/display.py: add KawaiiSpinner.print_above() to print
status lines above an active spinner without disrupting animation.
Uses captured stdout (self._out) so it works inside the child's
redirect_stdout(devnull).
- tools/delegate_tool.py: add _build_child_progress_callback()
that creates a per-child callback relaying tool calls and
thinking events to the parent's spinner (CLI) or progress
queue (gateway). Each child gets its own callback instance,
so parallel subagents don't share state. Includes _flush()
for gateway batch completion.
- run_agent.py: fire tool_progress_callback with '_thinking'
event when the model produces text content. Guarded by
_delegate_depth > 0 so only subagents fire this (prevents
gateway spam from main agent). REASONING_SCRATCHPAD/think/
reasoning XML tags are stripped before display.
Tests: 21 new tests covering print_above, callback builder,
thinking relay, SCRATCHPAD filtering, batching, flush, thread
isolation, delegate_depth guard, and prefix handling.
- Implement logic to distinguish between "full" memory errors and actual failures in the `_detect_tool_failure` function.
- Add JSON parsing to identify specific error messages related to memory limits, improving error handling for memory-related tools.
- Replace `hermes login` with `hermes model` for selecting providers and managing authentication.
- Update documentation and CLI commands to reflect the new provider selection process.
- Introduce a new redaction system for logging sensitive information.
- Enhance Codex model discovery by integrating API fetching and local cache.
- Adjust max turns configuration logic for better clarity and precedence.
- Improve error handling and user feedback during authentication processes.
- Enhanced Codex model discovery by fetching available models from the API, with fallback to local cache and defaults.
- Updated the context compressor's summary target tokens to 2500 for improved performance.
- Added external credential detection for Codex CLI to streamline authentication.
- Refactored various components to ensure consistent handling of authentication and model selection across the application.
- Added _max_tokens_param method in AIAgent to return appropriate max tokens parameter based on the provider (OpenAI vs. others).
- Updated API calls in AIAgent to utilize the new max tokens handling.
- Introduced auxiliary_max_tokens_param function in auxiliary_client for consistent max tokens management across auxiliary clients.
- Refactored multiple tools to use auxiliary_max_tokens_param for improved compatibility with different models and providers.
KawaiiSpinner used a two-phase clear+redraw approach: first write
\r + spaces to blank the line, then \r + new frame. When running
inside prompt_toolkit's patch_stdout proxy, each phase could trigger
a separate repaint, causing visible flickering every 120ms.
Replace with a single \r\033[K (carriage return + ANSI erase-to-EOL)
write so the line is cleared and redrawn atomically.
The security scanner (skills_guard.py) was only wired into the hub install path.
All other write paths to persistent state — skills created by the agent, memory
entries, cron prompts, and context files — bypassed it entirely. This closes
those gaps:
- file_operations: deny-list blocks writes to ~/.ssh, ~/.aws, ~/.hermes/.env, etc.
- code_execution_tool: filter secret env vars from sandbox child process
- skill_manager_tool: wire scan_skill() into create/edit/patch/write_file with rollback
- skills_guard: add "agent-created" trust level (same policy as community)
- memory_tool: scan content for injection/exfil before system prompt injection
- prompt_builder: scan AGENTS.md, .cursorrules, SOUL.md for prompt injection
- cronjob_tools: scan cron prompts for critical threats before scheduling
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added functionality to include product attribution tags for Nous Portal in auxiliary API calls.
- Introduced a mechanism to determine if the auxiliary client is backed by Nous Portal, affecting the extra body of requests.
- Updated various tools to utilize the new extra body configuration for enhanced tracking in API calls.
- Modified the `_wrap` function to append a failure suffix without applying red coloring, simplifying the failure message format.
- Introduced temporary debug logging in the `execute_code` function to track enabled and sandbox tools, aiding in troubleshooting.
- Captured stdout at spinner creation to prevent redirection issues from child agents.
- Replaced direct print statements with a new `_write` method for consistent output handling during spinner animation and final message display.
- Enhanced code maintainability and clarity by centralizing output logic.
- Eliminated the `_raw_write` function to simplify output handling in the `KawaiiSpinner` class.
- Updated spinner animation and final message display to use standard print statements, ensuring compatibility with prompt_toolkit.
- Improved code clarity and maintainability by reducing complexity in the output rendering process.
- Added a new function `_raw_write` to write directly to stdout, bypassing prompt_toolkit's interference with ANSI escapes and carriage returns.
- Updated the `KawaiiSpinner` class to utilize `_raw_write` for rendering spinner animations and final messages, ensuring proper display in terminal environments.
- Improved the clarity of output handling during spinner operations, enhancing user experience during tool execution.
- Added skills configuration options in cli-config.yaml.example, including a nudge interval for skill creation reminders.
- Implemented skills guidance in AIAgent to prompt users to save reusable workflows after complex tasks.
- Enhanced skills indexing in the prompt builder to include descriptions from SKILL.md files for better context.
- Updated the agent's behavior to periodically remind users about potential skills during tool-calling iterations.
- Updated the MEMORY_GUIDANCE text to improve clarity by rephrasing the usage instructions for the memory tool, emphasizing its diary-like functionality.
- Introduced MEMORY_GUIDANCE and SESSION_SEARCH_GUIDANCE to improve agent's contextual awareness and proactive assistance.
- Updated AIAgent to conditionally include tool-aware guidance in prompts based on available tools.
- Enhanced descriptions in memory and session search schemas for clearer user instructions on when to utilize these features.
