- Firecrawl scrape: 60s timeout via asyncio.wait_for + to_thread
(previously could hang indefinitely)
- Summarizer retries: 6 → 2 (one retry), reads timeout from
auxiliary.web_extract.timeout config (default 360s / 6min)
- Summarizer failure: falls back to truncated raw content (~5000 chars)
instead of useless error message, with guidance about config/model
- Config default: auxiliary.web_extract.timeout bumped 30 → 360s
for local model compatibility
Addresses Discord reports of agent hanging during web_extract.
- Add logger + debug log to read_nous_access_token() catch-all so token
refresh failures are observable instead of silently swallowed
- Tighten _is_nous_auxiliary_client() domain check to use proper URL
hostname parsing instead of substring match, preventing false-positives
on domains like not-nousresearch.com or nousresearch.com.evil.com
Three exfiltration vectors closed:
1. Browser URL exfil — agent could embed secrets in URL params and
navigate to attacker-controlled server. Now scans URLs for known
API key patterns before navigating (browser_navigate, web_extract).
2. Browser snapshot leak — page displaying env vars or API keys would
send secrets to auxiliary LLM via _extract_relevant_content before
run_agent.py's redaction layer sees the result. Now redacts snapshot
text before the auxiliary call.
3. Camofox annotation leak — accessibility tree text sent to vision
LLM could contain secrets visible on screen. Now redacts annotation
context before the vision call.
10 new tests covering URL blocking, snapshot redaction, and annotation
redaction for both browser and camofox backends.
* fix(gateway): honor default for invalid bool-like config values
* refactor: simplify web backend priority detection
Replace cascading boolean conditions with a priority-ordered loop.
Same behavior (verified against all 16 env var combinations),
half the lines, trivially extensible for new backends.
---------
Co-authored-by: aydnOktay <xaydinoktay@gmail.com>
Adds Exa (https://exa.ai) as a fourth web backend alongside Parallel,
Firecrawl, and Tavily. Follows the exact same integration pattern:
- Backend selection: config web.backend=exa or auto-detect from EXA_API_KEY
- Search: _exa_search() with highlights for result descriptions
- Extract: _exa_extract() with full text content extraction
- Lazy singleton client with x-exa-integration header
- Wired into web_search_tool and web_extract_tool dispatchers
- check_web_api_key() and requires_env updated
- CLI: hermes setup summary, hermes tools config, hermes config show
- config.py: EXA_API_KEY in OPTIONAL_ENV_VARS with metadata
- pyproject.toml: exa-py>=2.9.0,<3 in dependencies
Salvaged from PR #1850.
Co-authored-by: louiswalsh <louiswalsh@users.noreply.github.com>
Salvage of #3389 by @binhnt92 with reasoning fallback and retry logic added on top.
All 7 auxiliary LLM call sites now use extract_content_or_reasoning() which mirrors the main agent loop's behavior: extract content, strip think blocks, fall back to structured reasoning fields, retry on empty.
Closes#3389.
dict.get(key, default) returns None — not the default — when the key IS
present but explicitly set to null/~ in YAML. Calling .lower() on that
raises AttributeError.
Use (config.get(key) or fallback) so both missing keys and explicit nulls
coalesce to the intended default.
Files fixed:
- tools/tts_tool.py — _get_provider()
- tools/web_tools.py — _get_backend()
- tools/mcp_tool.py — MCPServerTask auth config
- trajectory_compressor.py — _detect_provider() and config loading
Co-authored-by: dieutx <dangtc94@gmail.com>
- add managed modal and gateway-backed tool integrations\n- improve CLI setup, auth, and configuration for subscriber flows\n- expand tests and docs for managed tool support
Three categories of cleanup, all zero-behavioral-change:
1. F-strings without placeholders (154 fixes across 29 files)
- Converted f'...' to '...' where no {expression} was present
- Heaviest files: run_agent.py (24), cli.py (20), honcho_integration/cli.py (34)
2. Simplify defensive patterns in run_agent.py
- Added explicit self._is_anthropic_oauth = False in __init__ (before
the api_mode branch that conditionally sets it)
- Replaced 7x getattr(self, '_is_anthropic_oauth', False) with direct
self._is_anthropic_oauth (attribute always initialized now)
- Added _is_openrouter_url() and _is_anthropic_url() helper methods
- Replaced 3 inline 'openrouter' in self._base_url_lower checks
3. Remove dead code in small files
- hermes_cli/claw.py: removed unused 'total' computation
- tools/fuzzy_match.py: removed unused strip_indent() function and
pattern_stripped variable
Full test suite: 6184 passed, 0 failures
E2E PTY: banner clean, tool calls work, zero garbled ANSI
* fix(security): add SSRF protection to vision_tools and web_tools
Both vision_analyze and web_extract/web_crawl accept arbitrary URLs
without checking if they target private/internal network addresses.
A prompt-injected or malicious skill could use this to access cloud
metadata endpoints (169.254.169.254), localhost services, or private
network hosts.
Adds a shared url_safety.is_safe_url() that resolves hostnames and
blocks private, loopback, link-local, and reserved IP ranges. Also
blocks known internal hostnames (metadata.google.internal).
Integrated at the URL validation layer in vision_tools and before
each website_policy check in web_tools (extract, crawl).
* test(vision): update localhost test to reflect SSRF protection
The existing test_valid_url_with_port asserted localhost URLs pass
validation. With SSRF protection, localhost is now correctly blocked.
Update the test to verify the block, and add a separate test for
valid URLs with ports using a public hostname.
* fix(security): harden SSRF protection — fail-closed, CGNAT, multicast, redirect guard
Follow-up hardening on top of dieutx's SSRF protection (PR #2630):
- Change fail-open to fail-closed: DNS errors and unexpected exceptions
now block the request instead of allowing it (OWASP best practice)
- Block CGNAT range (100.64.0.0/10): Python's ipaddress.is_private
does NOT cover this range (returns False for both is_private and
is_global). Used by Tailscale/WireGuard and carrier infrastructure.
- Add is_multicast and is_unspecified checks: multicast (224.0.0.0/4)
and unspecified (0.0.0.0) addresses were not caught by the original
four-check chain
- Add redirect guard for vision_tools: httpx event hook re-validates
each redirect target against SSRF checks, preventing the classic
redirect-based SSRF bypass (302 to internal IP)
- Move SSRF filtering before backend dispatch in web_extract: now
covers Parallel and Tavily backends, not just Firecrawl
- Extract _is_blocked_ip() helper for cleaner IP range checking
- Add 24 new tests (CGNAT, multicast, IPv4-mapped IPv6, fail-closed
behavior, parametrized blocked/allowed IP lists)
- Fix existing tests to mock DNS resolution for test hostnames
---------
Co-authored-by: dieutx <dangtc94@gmail.com>
Salvage of PR #1707 by @kshitijk4poor (cherry-picked with authorship preserved).
Adds Tavily as a third web backend alongside Firecrawl and Parallel, using the Tavily REST API via httpx.
- Backend selection via hermes tools → saved as web.backend in config.yaml
- All three tools supported: search, extract, crawl
- TAVILY_API_KEY in config registry, doctor, status, setup wizard
- 15 new Tavily tests + 9 backend selection tests + 5 config tests
- Backward compatible
Closes#1707
* feat(web): add Parallel as alternative web search/extract backend
Adds Parallel (parallel.ai) as a drop-in alternative to Firecrawl for
web_search and web_extract tools using the official parallel-web SDK.
- Backend selection via WEB_SEARCH_BACKEND env var (auto/parallel/firecrawl)
- Auto mode prefers Firecrawl when both keys present; Parallel when sole backend
- web_crawl remains Firecrawl-only with clear error when unavailable
- Lazy SDK imports, interrupt support, singleton clients
- 16 new unit tests for backend selection and client config
Co-authored-by: s-jag <s-jag@users.noreply.github.com>
* fix: add PARALLEL_API_KEY to config registry and fix web_crawl policy tests
Follow-up for Parallel backend integration:
- Add PARALLEL_API_KEY to OPTIONAL_ENV_VARS (hermes doctor, env blocklist)
- Add to set_config_value api_keys list (hermes config set)
- Add to doctor keys display
- Fix 2 web_crawl policy tests that didn't set FIRECRAWL_API_KEY
(needed now that web_crawl has a Firecrawl availability guard)
* refactor: explicit backend selection via hermes tools, not auto-detect
Replace the auto-detect backend selection with explicit user choice:
- hermes tools saves WEB_SEARCH_BACKEND to .env when user picks a provider
- _get_backend() reads the explicit choice first
- Fallback only for manual/legacy config (uses whichever key is present)
- _is_provider_active() shows [active] for the selected web backend
- Updated tests, docs, and .env.example to remove 'auto' mode language
* refactor: use config.yaml for web backend, not env var
Match the TTS/browser pattern — web.backend is stored in config.yaml
(set by hermes tools), not as a WEB_SEARCH_BACKEND env var.
- _load_web_config() reads web: section from config.yaml
- _get_backend() reads web.backend from config, falls back to key detection
- _configure_provider() saves to config dict (saved to config.yaml)
- _is_provider_active() reads from config dict
- Removed WEB_SEARCH_BACKEND from .env.example, set_config_value, docs
- Updated all tests to mock _load_web_config instead of env vars
---------
Co-authored-by: s-jag <s-jag@users.noreply.github.com>
- Default enabled: false (zero overhead when not configured)
- Fast path: cached disabled state skips all work immediately
- TTL cache (30s) for parsed policy — avoids re-reading config.yaml
on every URL check
- Missing shared files warn + skip instead of crashing all web tools
- Lazy yaml import — missing PyYAML doesn't break browser toolset
- Guarded browser_tool import — fail-open lambda fallback
- check_website_access never raises for default path (fail-open with
warning log); only raises with explicit config_path (test mode)
- Simplified enforcement code in web_tools/browser_tool — no more
try/except wrappers since errors are handled internally
- 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 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).
- 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.
The web_extract_tool was stripping the 'url' key during its output
trimming step, but documentation in 3 places claimed it was present.
This caused KeyError when accessing result['url'] in execute_code
scripts, especially when extracting from multiple URLs.
Changes:
- web_tools.py: Add 'url' back to trimmed_results output
- code_execution_tool.py: Add 'title' to _TOOL_STUBS docstring and
_TOOL_DOC_LINES so docs match actual {url, title, content, error}
response format
On top of PR #460: self-hosted Firecrawl instances don't require an API
key (USE_DB_AUTHENTICATION=false), so don't force users to set a dummy
FIRECRAWL_API_KEY when FIRECRAWL_API_URL is set. Also adds a proper
self-hosting section to the configuration docs explaining what you get,
what you lose, and how to set it up (Docker stack, tradeoffs vs cloud).
Added 2 more tests (URL-only without key, neither-set raises).
Adds optional FIRECRAWL_API_URL environment variable to support
self-hosted Firecrawl deployments alongside the cloud service.
- Add FIRECRAWL_API_URL to optional env vars in hermes_cli/config.py
- Update _get_firecrawl_client() in tools/web_tools.py to accept custom API URL
- Add tests for client initialization with/without URL
- Document new env var in installation and config guides
- 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.
- Introduced new skills for extracting text from PDFs, scanned documents, and images using OCR and document parsing tools.
- Added detailed documentation for usage and installation of `pymupdf` and `marker-pdf` for local extraction.
- Implemented scripts for text extraction with both lightweight and high-quality options, including support for various document formats.
- Updated web extraction functionality to handle PDF URLs directly, enhancing usability for academic papers and documents.
- 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.
- 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.
- Implemented functionality to load ephemeral prefill messages from a JSON file, enhancing few-shot priming capabilities for the agent.
- Introduced a mechanism to load an ephemeral system prompt from environment variables or configuration files, ensuring dynamic prompt adjustments at API-call time.
- Updated the CLI and agent initialization to utilize the new prefill messages and system prompt, improving the overall interaction experience.
- Enhanced configuration options with new environment variables for prefill messages and system prompts, allowing for greater customization without persistence.
- Introduced a shared interrupt signaling mechanism to allow tools to check for user interrupts during long-running operations.
- Updated the AIAgent to handle interrupts more effectively, ensuring in-progress tool calls are canceled and multiple interrupt messages are combined into one prompt.
- Enhanced the CLI configuration to include container resource limits (CPU, memory, disk) and persistence options for Docker, Singularity, and Modal environments.
- Improved documentation to clarify interrupt behaviors and container resource settings, providing users with better guidance on configuration and usage.
- Introduced a new DebugSession class in tools/debug_helpers.py to centralize debug logging functionality, replacing duplicated code across various tool modules.
- Updated image_generation_tool.py, mixture_of_agents_tool.py, vision_tools.py, web_tools.py, and others to utilize the new DebugSession for logging tool calls and saving debug logs.
- Enhanced maintainability and consistency in debug logging practices across the codebase.
- Introduced logging functionality in cli.py, run_agent.py, scheduler.py, and various tool modules to replace print statements with structured logging.
- Enhanced error handling and informational messages to improve debugging and monitoring capabilities.
- Ensured consistent logging practices across the codebase, facilitating better traceability and maintenance.
- Introduced `cli-config.yaml.example` to provide a template for configuring the CLI behavior, including model settings, terminal tool configurations, agent behavior, and toolsets.
- Created `cli.py` for an interactive terminal interface, allowing users to start the Hermes Agent with various options and toolsets.
- Added `hermes` launcher script for convenient CLI access.
- Updated `model_tools.py` to support quiet mode for suppressing output during tool initialization and execution.
- Enhanced logging in various tools to respect quiet mode, improving user experience by reducing unnecessary output.
- Added `prompt_toolkit` to `requirements.txt` for improved CLI interaction capabilities.
- Created `TODO.md` for future improvements and enhancements to the Hermes Agent framework.
- Updated logging configuration in `run_agent.py` to suppress debug messages from additional third-party libraries, reducing noise in logs.
- Enhanced shell scripts for terminal tasks to utilize Singularity for containerized execution, including pre-build SIF image logic and improved logging.
- Refactored tool initialization in `mixture_of_agents_tool.py`, `vision_tools.py`, and `web_tools.py` to implement lazy loading of API clients, optimizing resource usage and error handling.
- Updated ephemeral system prompts in shell scripts to provide clearer guidance on task execution and resource usage.
- Introduced normalization functions for tool statistics and error counts to ensure consistent schema across all trajectory entries, facilitating compatibility with HuggingFace datasets.
- Updated batch processing to utilize normalized tool stats and error counts, improving data integrity.
- Refactored vision tools and mixture of agents tool to integrate with OpenRouter API, replacing Nous Research API references and updating model configurations.
- Enabled reasoning capabilities in API calls for enhanced response quality across various tools.
- Improved error handling and API key validation for OpenRouter integration.
- Added support for tracking partial results and tool error counts in batch processing.
- Implemented filtering of corrupted entries during batch file combination based on valid tool names.
- Updated terminal tool to improve command execution and error handling, including retry logic for transient failures.
- Refactored model tools to use a simple terminal tool with no session persistence.
- Improved logging and error messages for invalid API responses and tool calls.
- Introduced chunked processing for large content in web tools to manage size limitations effectively.
