* feat(telegram): add webhook mode as alternative to polling
When TELEGRAM_WEBHOOK_URL is set, the adapter starts an HTTP webhook
server (via python-telegram-bot's start_webhook()) instead of long
polling. This enables cloud platforms like Fly.io and Railway to
auto-wake suspended machines on inbound HTTP traffic.
Polling remains the default — no behavior change unless the env var
is set.
Env vars:
TELEGRAM_WEBHOOK_URL Public HTTPS URL for Telegram to push to
TELEGRAM_WEBHOOK_PORT Local listen port (default 8443)
TELEGRAM_WEBHOOK_SECRET Secret token for update verification
Cherry-picked and adapted from PR #2022 by SHL0MS. Preserved all
current main enhancements (network error recovery, polling conflict
detection, DM topics setup).
Co-authored-by: SHL0MS <SHL0MS@users.noreply.github.com>
* fix: send_document call in background task delivery + vision download timeout
Two fixes salvaged from PR #2269 by amethystani:
1. gateway/run.py: adapter.send_file() → adapter.send_document()
send_file() doesn't exist on BasePlatformAdapter. Background task
media files were silently never delivered (AttributeError swallowed
by except Exception: pass).
2. tools/vision_tools.py: configurable image download timeout via
HERMES_VISION_DOWNLOAD_TIMEOUT env var (default 30s), plus guard
against raise None when max_retries=0.
The third fix in #2269 (opencode-go auth config) was already resolved
on main.
Co-authored-by: amethystani <amethystani@users.noreply.github.com>
---------
Co-authored-by: SHL0MS <SHL0MS@users.noreply.github.com>
Co-authored-by: amethystani <amethystani@users.noreply.github.com>
Three safety gaps in vision_analyze_tool:
1. Local files accepted without checking if they're actually images —
a renamed text file would get base64-encoded and sent to the model.
Now validates magic bytes (PNG, JPEG, GIF, BMP, WebP, SVG).
2. No website policy enforcement on image URLs — blocked domains could
be fetched via the vision tool. Now checks before download.
3. No redirect check — if an allowed URL redirected to a blocked domain,
the download would proceed. Now re-checks the final URL.
Fixed one test that needed _validate_image_url mocked to bypass DNS
resolution on the fake blocked.test domain (is_safe_url does DNS
checks that were added after the original PR).
Co-authored-by: GutSlabs <GutSlabs@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.
* docs: unify hooks documentation — add plugin hooks to hooks page, add session:end event
The hooks page only documented gateway event hooks (HOOK.yaml system).
The plugins page listed plugin hooks (pre_tool_call, etc.) that weren't
referenced from the hooks page, which was confusing.
Changes:
- hooks.md: Add overview table showing both hook systems
- hooks.md: Add Plugin Hooks section with available hooks, callback
signatures, and example
- hooks.md: Add missing session:end gateway event (emitted but undocumented)
- hooks.md: Mark pre_llm_call, post_llm_call, on_session_start,
on_session_end as planned (defined in VALID_HOOKS but not yet invoked)
- hooks.md: Update info box to cross-reference plugin hooks
- hooks.md: Fix heading hierarchy (gateway content as subsections)
- plugins.md: Add cross-reference to hooks page for full details
- plugins.md: Mark planned hooks as (planned)
* fix: browser_vision ignores auxiliary.vision.timeout config
browser_vision called call_llm() without passing a timeout parameter,
so it always used the 30-second default in auxiliary_client.py. This
made vision analysis with local models (llama.cpp, ollama) impossible
since they typically need more than 30s for screenshot analysis.
Now browser_vision reads auxiliary.vision.timeout from config.yaml
(same config key that vision_analyze already uses) and passes it
through to call_llm().
Also bumped the default vision timeout from 30s to 120s in both
browser_vision and vision_analyze — 30s is too aggressive for local
models and the previous default silently failed for anyone running
vision locally.
Fixes user report from GamerGB1988.
httpx.AsyncClient awaits event hooks. The sync _ssrf_redirect_guard
returned None, causing 'object NoneType can't be used in await
expression' on any vision_analyze call that followed redirects.
Caught during live PTY testing of the merged SSRF protection.
* 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>
Path('~/.hermes/image.png').is_file() returns False because Path
doesn't expand tilde. This caused the tool to fall through to URL
validation, which also failed, producing a confusing error:
'Invalid image source. Provide an HTTP/HTTPS URL or a valid local
file path.'
Fix: use os.path.expanduser() before constructing the Path object.
Added two tests for tilde expansion (success and nonexistent file).
Reads auxiliary.vision.timeout from config.yaml (default: 30s) and
passes it to async_call_llm. Useful for slow local vision models
that need more than 30 seconds.
Setting is in config.yaml (not .env) since it's not a secret:
auxiliary:
vision:
timeout: 120
Based on PR #2306.
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
When vision_analyze_tool fails, the except block was returning a
generic 'could not be analyzed' message that gave the agent no
actionable information about the failure cause.
Replace the generic message with the actual exception string so the
agent can distinguish between backend errors, missing dependencies,
network failures, and unsupported image paths.
Also add an 'error' field to the failure response for structured
error handling by callers.
Fixes#1034
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).
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
Previously the early return for unconfigured vision model was silent.
Now logs an error so the failure is visible in logs for debugging.
Inspired by PR #839 by aydnOktay.
Co-authored-by: aydnOktay <aydnOktay@users.noreply.github.com>
Authored by aydnOktay. Improves URL validation with urlparse, adds exc_info
to error logs for full stack traces, and tightens type hints.
Resolved merge conflict in _handle_vision_analyze: kept PR's string formatting
with our AUXILIARY_VISION_MODEL env var logic.
- 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.
- 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.
- 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.
- Updated the vision tool to accept both HTTP/HTTPS URLs and local file paths for image analysis.
- Implemented caching of user-uploaded images in local directories to ensure reliable access for the vision tool, addressing issues with ephemeral URLs.
- Enhanced platform adapters (Discord, Telegram, WhatsApp) to download and cache images, allowing for immediate analysis and enriched message context.
- Added a new method to auto-analyze images attached by users, enriching the conversation with detailed descriptions.
- Improved documentation for image handling processes and updated related functions for clarity and efficiency.
- 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 new browser automation tools in `browser_tool.py` for navigating, interacting with, and extracting content from web pages using the agent-browser CLI and Browserbase cloud execution.
- Updated `.env.example` to include new configuration options for Browserbase API keys and session settings.
- Enhanced `model_tools.py` and `toolsets.py` to integrate browser tools into the existing tool framework, ensuring consistent access across toolsets.
- Updated `README.md` with setup instructions for browser tools and their usage examples.
- Added new test script `test_modal_terminal.py` to validate Modal terminal backend functionality.
- Improved `run_agent.py` to support browser tool integration and logging enhancements for better tracking of API responses.
- Updated batch processing to include robust resume functionality by scanning completed prompts based on content rather than indices, improving recovery from failures.
- Implemented retry logic for image downloads with exponential backoff to handle transient failures effectively.
- Refined image generation tool to utilize the FLUX 2 Pro model, updating descriptions and parameters for clarity and consistency.
- Added new configuration scripts for GLM 4.7 and Imagen tasks, enhancing usability and logging capabilities.
- Removed outdated scripts and test files to streamline the codebase.
- 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.
