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.
Cherry-picked from PR #2201 by @Gutslabs.
session_search resolved hits to parent/root sessions but only excluded
the exact current_session_id. If the active session was a child
continuation (compression/delegation), its parent could still appear
as a 'past' conversation result.
Fix: resolve current_session_id to its lineage root before filtering,
so the entire active lineage (parent and children) is excluded.
Remove the [Files already read — do NOT re-read these] user message
that was injected into the conversation after context compression.
This message used role='user' for system-generated content, creating
a fake user turn that confused models about conversation state and
could contribute to task-redo behavior.
The file_tools.py read tracker (warn on 3rd consecutive read, block
on 4th+) already handles re-read prevention inline without injecting
synthetic messages.
Closes#2224.
Co-authored-by: Test <test@test.com>
Replace asyncio.run() with thread-local persistent event loops for
worker threads (e.g., delegate_task's ThreadPoolExecutor). asyncio.run()
creates and closes a fresh loop on every call, leaving cached
httpx/AsyncOpenAI clients bound to a dead loop — causing 'Event loop is
closed' errors during GC when parallel subagents clean up connections.
The fix mirrors the main thread's _get_tool_loop() pattern but uses
threading.local() so each worker thread gets its own long-lived loop,
avoiding both cross-thread contention and the create-destroy lifecycle.
Added 4 regression tests covering worker loop persistence, reuse,
per-thread isolation, and separation from the main thread's loop.
- Convert ~~text~~ to ~text~ (MarkdownV2 strikethrough)
- Protect ||text|| from pipe escaping (MarkdownV2 spoiler)
- Preserve > at line start as blockquote instead of escaping it
- Update _strip_mdv2() to strip ~strikethrough~ and ||spoiler|| markers
- Add tests covering new formatting paths and edge cases
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>
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>
* 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>
Cherry-picked from PR #2120 by @unclebumpy.
- from_env() now reads HONCHO_BASE_URL and enables Honcho when base_url
is set, even without an API key
- from_global_config() reads baseUrl from config root with
HONCHO_BASE_URL env var as fallback
- get_honcho_client() guard relaxed to allow base_url without api_key
for no-auth local instances
- Added HONCHO_BASE_URL to OPTIONAL_ENV_VARS registry
Result: Setting HONCHO_BASE_URL=http://localhost:8000 in ~/.hermes/.env
now correctly routes the Honcho client to a local instance.
When the user is on a custom provider (provider=custom, localhost, or
127.0.0.1 endpoint), /model <name> no longer tries to auto-detect a
provider switch. The model name changes on the current endpoint as-is.
To switch away from a custom endpoint, users must use explicit
provider:model syntax (e.g. /model openai-codex:gpt-5.2-codex).
A helpful tip is printed when changing models on a custom endpoint.
This prevents the confusing case where someone on LM Studio types
/model gpt-5.2-codex, the auto-detection tries to switch providers,
fails or partially succeeds, and requests still go to the old endpoint.
Also fixes the missing prompt_toolkit.auto_suggest mock stub in
test_cli_init.py (same issue already fixed in test_cli_new_session.py).
Follow-up to PR #2101 (InB4DevOps). Adds three missing context compressor
resets in reset_session_state():
- compression_count (displayed in status bar)
- last_total_tokens
- _context_probed (stale context-error flag)
Also fixes the test_cli_new_session.py prompt_toolkit mock (missing
auto_suggest stub) and adds a regression test for #2099 that verifies
all token counters and compressor state are zeroed on /new.
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>
Place a sentinel in _running_agents immediately after the "already
running" guard check passes — before any await. Without this, the
numerous await points between the guard (line 1324) and agent
registration (track_agent at line 4790) create a window where a
second message for the same session can bypass the guard and start
a duplicate agent, corrupting the transcript.
The await gap includes: hook emissions, vision enrichment (external
API call), audio transcription (external API call), session hygiene
compression, and the run_in_executor call itself. For messages with
media attachments the window can be several seconds wide.
The sentinel is wrapped in try/finally so it is always cleaned up —
even if the handler raises or takes an early-return path. When the
real AIAgent is created, track_agent() overwrites the sentinel with
the actual instance (preserving interrupt support).
Also handles the edge case where a message arrives while the sentinel
is set but no real agent exists yet: the message is queued via the
adapter's pending-message mechanism instead of attempting to call
interrupt() on the sentinel object.
MiniMax's default base URL was /v1 which caused runtime_provider to
default to chat_completions mode (OpenAI-style Authorization: Bearer
header). MiniMax rejects this with a 401 because they require the
Anthropic-style x-api-key header.
Changes:
- auth.py: Change default inference_base_url for minimax and minimax-cn
from /v1 to /anthropic
- runtime_provider.py: Auto-correct stale /v1 URLs from existing .env
files to /anthropic, and always default minimax/minimax-cn providers
to anthropic_messages mode
- Update tests to reflect new defaults, add tests for stale URL
auto-correction and explicit api_mode override
Based on PR #2100 by @devorun. Fixes#2094.
Co-authored-by: Test <test@test.com>
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.
* fix(codex): treat reasoning-only responses as incomplete, not stop
When a Codex Responses API response contains only reasoning items
(encrypted thinking state) with no message text or tool calls, the
_normalize_codex_response method was setting finish_reason='stop'.
This sent the response into the empty-content retry loop, which
burned 3 retries and then failed — exactly the pattern Nester
reported in Discord.
Two fixes:
1. _normalize_codex_response: reasoning-only responses (reasoning_items_raw
non-empty but no final_text) now get finish_reason='incomplete', routing
them to the Codex continuation path instead of the retry loop.
2. Incomplete handling: also checks for codex_reasoning_items when deciding
whether to preserve an interim message, so encrypted reasoning state is
not silently dropped when there is no visible reasoning text.
Adds 4 regression tests covering:
- Unit: reasoning-only → incomplete, reasoning+content → stop
- E2E: reasoning-only → continuation → final answer succeeds
- E2E: encrypted reasoning items preserved in interim messages
* fix(codex): ensure reasoning items have required following item in API input
Follow-up to the reasoning-only response fix. Three additional issues
found by tracing the full replay path:
1. _chat_messages_to_responses_input: when a reasoning-only interim
message was converted to Responses API input, the reasoning items
were emitted as the last items with no following item. The Responses
API requires a following item after each reasoning item (otherwise:
'missing_following_item' error, as seen in OpenHands #11406). Now
emits an empty assistant message as the required following item when
content is empty but reasoning items were added.
2. Duplicate detection: two consecutive reasoning-only incomplete
messages with identical empty content/reasoning but different
encrypted codex_reasoning_items were incorrectly treated as
duplicates, silently dropping the second response's reasoning state.
Now includes codex_reasoning_items in the duplicate comparison.
3. Added tests for both the API input conversion path and the duplicate
detection edge case.
Research context: verified against OpenCode (uses Vercel AI SDK, no
retry loop so avoids the issue), Clawdbot (drops orphaned reasoning
blocks entirely), and OpenHands (hit the missing_following_item error).
Our approach preserves reasoning continuity while satisfying the API
constraint.
---------
Co-authored-by: Test <test@test.com>
* fix: persist ACP sessions to disk so they survive process restarts
The ACP adapter stored sessions entirely in-memory. When the editor
restarted the ACP subprocess (idle timeout, crash, system sleep/wake,
editor restart), all sessions were lost. The editor's load_session /
resume_session calls would fail to find the session, forcing a new
empty session and losing all conversation history.
Changes:
- SessionManager now persists each session as a JSON file under
~/.hermes/acp_sessions/<session_id>.json
- get_session() transparently restores from disk when not in memory
- update_cwd(), fork_session(), list_sessions() all check disk
- server.py calls save_session() after prompt completion, /reset,
/compact, and model switches
- cleanup() and remove_session() delete disk files too
- Sessions have a 7-day TTL; expired sessions are pruned on startup
- Atomic writes via tempfile + os.replace to prevent corruption
- 11 new tests covering persistence, disk restoration, and TTL expiry
* refactor: use SessionDB instead of JSON files for ACP session persistence
Replace the standalone JSON file persistence layer with SessionDB
(~/.hermes/state.db) integration. ACP sessions now:
- Share the same DB as CLI and gateway sessions
- Are searchable via session_search (FTS5)
- Get token tracking, cost tracking, and session titles for free
- Follow existing session pruning policies
Key changes:
- _get_db() lazily creates a SessionDB, resolving HERMES_HOME
dynamically (not at import time) for test compatibility
- _persist() creates session record + replaces messages in DB
- _restore() loads from DB with source='acp' filter
- cwd stored in model_config JSON field (no schema migration)
- Model values coerced to str to handle mock agents in tests
- Removed: json files, sessions_dir, ttl_days, _expire logic
- Tests updated: DB-backed persistence, FTS search, tool_call
round-tripping, source filtering
---------
Co-authored-by: Test <test@test.com>
Authored by Lovre Pešut (rovle). Migrates from deprecated find_one(labels=...)
to get(sandbox_name) with deterministic naming (hermes-{task_id}), plus legacy
fallback via list(labels=...) for pre-migration sandboxes.
When a cron job references a skill that is no longer installed,
_build_job_prompt() now logs a warning and injects a user-visible notice
into the prompt instead of raising RuntimeError. The job continues with
any remaining valid skills and the user prompt.
Adds 4 regression tests for missing skill handling.
find_one is being deprecated. Primary lookup now uses get() with a
deterministic sandbox name (hermes-{task_id}). A legacy fallback via
list(labels=...) ensures sandboxes created before this migration are
still resumable.
Authored by dusterbloom. Closes#1911.
Pre-computes SQL query strings at class definition time in insights.py,
adds identifier quoting for ALTER TABLE DDL in hermes_state.py, and adds
4 regression tests verifying query construction safety.
The merge at e7844e9c re-introduced a line in _build_child_agent() that
references _saved_tool_names — a variable only defined in _run_single_child().
This caused NameError on every delegate_task call, completely breaking
subagent delegation.
Moves the child._delegate_saved_tool_names assignment to _run_single_child()
where _saved_tool_names is actually defined, keeping the save/restore in the
same scope as the try/finally block.
Adds two regression tests from PR #2038 (YanSte).
Also fixes the same issue reported in PR #2048 (Gutslabs).
Co-authored-by: Yannick Stephan <yannick.stephan@gmail.com>
Co-authored-by: Guts <gutslabs@users.noreply.github.com>
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>
The gateway approval system previously intercepted bare 'yes'/'no' text
from the user's next message to approve/deny dangerous commands. This was
fragile and dangerous — if the agent asked a clarify question and the user
said 'yes' to answer it, the gateway would execute the pending dangerous
command instead. (Fixes#1888)
Changes:
- Remove bare text matching ('yes', 'y', 'approve', 'ok', etc.) from
_handle_message approval check
- Add /approve and /deny as gateway-only slash commands in the command
registry
- /approve supports scoping: /approve (one-time), /approve session,
/approve always (permanent)
- Add 5-minute timeout for stale approvals
- Gateway appends structured instructions to the agent response when a
dangerous command is pending, telling the user exactly how to respond
- 9 tests covering approve, deny, timeout, scoping, and verification
that bare 'yes' no longer triggers execution
Credit to @solo386 and @FlyByNight69420 for identifying and reporting
this security issue in PR #1971 and issue #1888.
Co-authored-by: Test <test@test.com>
Three bugs prevented providers like MiniMax from using their
Anthropic-compatible endpoints (e.g. api.minimax.io/anthropic):
1. _VALID_API_MODES was missing 'anthropic_messages', so explicit
api_mode config was silently rejected and defaulted to
chat_completions.
2. API-key provider resolution hardcoded api_mode to 'chat_completions'
without checking model config or detecting Anthropic-compatible URLs.
3. run_agent.py auto-detection only recognized api.anthropic.com, not
third-party endpoints using the /anthropic URL convention.
Fixes:
- Add 'anthropic_messages' to _VALID_API_MODES
- API-key providers now check model config api_mode and auto-detect
URLs ending in /anthropic
- run_agent.py and fallback logic detect /anthropic URL convention
- 5 new tests covering all scenarios
Users can now either:
- Set MINIMAX_BASE_URL=https://api.minimax.io/anthropic (auto-detected)
- Set api_mode: anthropic_messages in model config (explicit)
- Use custom_providers with api_mode: anthropic_messages
Co-authored-by: Test <test@test.com>
When provider: custom is set in config.yaml with base_url and api_key,
those values are now used instead of falling back to OPENAI_BASE_URL and
OPENAI_API_KEY env vars. Also reads the 'api' field as an alternative to
'api_key' for config compatibility.
Cherry-picked from PR #1762 by crazywriter1.
Co-authored-by: crazywriter1 <53251494+crazywriter1@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>
Add unauthorized_dm_behavior config (pair|ignore) with global default
and per-platform override. WhatsApp can silently drop unknown DMs
instead of sending pairing codes.
Adapted config bridging to work with gw_data dict (pre-construction)
rather than config object. Dropped implementation plan document.
Co-authored-by: Frederico Ribeiro <fr@tecompanytea.com>
The previous copilot_model_api_mode() checked the catalog's
supported_endpoints first and picked /chat/completions when a model
supported both endpoints. This is wrong — GPT-5+ models should use
the Responses API even when the catalog lists both.
Replicate opencode's shouldUseCopilotResponsesApi() logic:
- GPT-5+ models (gpt-5.4, gpt-5.3-codex, etc.) → Responses API
- gpt-5-mini → Chat Completions (explicit exception)
- Everything else (gpt-4o, claude, gemini, etc.) → Chat Completions
- Model ID pattern is the primary signal, catalog is secondary
The catalog fallback now only matters for non-GPT-5 models that might
exclusively support /v1/messages (e.g. Claude via Copilot).
Models are auto-detected from the live catalog at
api.githubcopilot.com/models — no hardcoded list required for
supported models, only a static fallback for when the API is
unreachable.
Builds on PR #1879's Copilot integration with critical auth improvements
modeled after opencode's implementation:
- Add hermes_cli/copilot_auth.py with:
- OAuth device code flow (copilot_device_code_login) using the same
client_id (Ov23li8tweQw6odWQebz) as opencode and Copilot CLI
- Token type validation: reject classic PATs (ghp_*) with a clear
error message explaining supported token types
- Proper env var priority: COPILOT_GITHUB_TOKEN > GH_TOKEN > GITHUB_TOKEN
(matching Copilot CLI documentation)
- copilot_request_headers() with Openai-Intent, x-initiator, and
Copilot-Vision-Request headers (matching opencode)
- Update auth.py:
- PROVIDER_REGISTRY copilot entry uses correct env var order
- _resolve_api_key_provider_secret delegates to copilot_auth for
the copilot provider with proper token validation
- Update models.py:
- copilot_default_headers() now includes Openai-Intent and x-initiator
- Update main.py:
- _model_flow_copilot offers OAuth device code login when no token
is found, with manual token entry as fallback
- Shows supported vs unsupported token types
- 22 new tests covering token validation, env var priority, header
generation, and integration with existing auth infrastructure
* Improve tool batching independence checks
* fix: address review feedback on path-aware batching
- Log malformed/non-dict tool arguments at debug level before
falling back to sequential, instead of silently swallowing
the error into an empty dict
- Guard empty paths in _paths_overlap (unreachable in practice
due to upstream filtering, but makes the invariant explicit)
- Add tests: malformed JSON args, non-dict args, _paths_overlap
unit tests including empty path edge cases
- web_crawl is not a registered tool (only web_search/web_extract
are); no addition needed to _PARALLEL_SAFE_TOOLS
---------
Co-authored-by: kshitij <82637225+kshitijk4poor@users.noreply.github.com>
- Strip '_tools' suffix from internal toolset identifiers in the banner
(e.g. 'web_tools' -> 'web', 'homeassistant_tools' -> 'homeassistant')
- Stop appending '_tools' to unavailable toolset names
- Replace 6 hardcoded hex colors (#B8860B, #FFBF00, #FFF8DC) in toolset
rows, overflow line, and MCP server rows with the skin variables
(dim, accent, text) already resolved at the top of the function
Inspired by PR #1871 by @kshitijk4poor.
Adds 4 tests.
* 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>
