When a user is idle for longer than a configured TTL, nanobot **proactively** compresses the session context into a summary. This reduces token cost and first-token latency when the user returns — instead of re-processing a long stale context with an expired KV cache, the model receives a compact summary and fresh input.
The Enabled column in channels status and plugins list commands had a default green style that overrode the dim markup for disabled items. This caused no values to appear green instead of dimmed. Remove the default style to let cell-level markup control the display correctly.
On Windows, certain Unicode input (emoji, mixed-script text, surrogate
pairs) causes prompt_toolkit's FileHistory to crash with
UnicodeEncodeError when writing the history file.
Fix: wrap FileHistory with a _SafeFileHistory subclass that sanitizes
surrogate characters before writing, replacing invalid sequences instead
of crashing.
Fixes#2846
Allow config.json to reference environment variables via ${VAR_NAME}
syntax. Variables are resolved at runtime by resolve_config_env_vars(),
keeping the raw templates in the Pydantic model so save_config()
preserves them. This lets secrets live in a separate env file
(e.g. loaded by systemd EnvironmentFile=) instead of plain text
in config.json.
Fixes#2591
The "nanobot is thinking..." spinner was printing ANSI escape codes
literally in some terminals, causing garbled output like:
?[2K?[32m⠧?[0m ?[2mnanobot is thinking...?[0m
Root causes:
1. Console created without force_terminal=True, so Rich couldn't
reliably detect terminal capabilities
2. Spinner continued running during user input prompt, conflicting
with prompt_toolkit
Changes:
- Set force_terminal=True in _make_console() for proper ANSI handling
- Add stop_for_input() method to StreamRenderer
- Call stop_for_input() before reading user input in interactive mode
- Add tests for the new functionality
Replace single-stage MemoryConsolidator with a two-stage architecture:
- Consolidator: lightweight token-budget triggered summarization,
appends to HISTORY.md with cursor-based tracking
- Dream: cron-scheduled two-phase processor that analyzes HISTORY.md
and updates SOUL.md, USER.md, MEMORY.md via AgentRunner with
edit_file tools for surgical, fault-tolerant updates
New files: MemoryStore (pure file I/O), Dream class, DreamConfig,
/dream and /dream-log commands. 89 tests covering all components.
Implement the real GitHub device flow and Copilot token exchange for the GitHub Copilot provider.
Also route github-copilot models through a dedicated backend and strip the provider prefix before API requests.
Add focused regression coverage for provider wiring and model normalization.
Generated with GitHub Copilot, GPT-5.4.
- Rename WebSearchConfig references to the new WebToolsConfig root struct that wraps both search config and global proxy settings
- Add 'enable' flag to WebToolsConfig to allow fully disabling all web-related tools (WebSearch, WebFetch) at runtime
- Update AgentLoop and SubagentManager to receive the full web config object instead of separate web_search_config/web_proxy parameters
- Update CLI command initialization to pass the consolidated web config struct instead of split fields
- Change default web search provider from brave to duckduckgo for better out-of-the-box usability (no API key required)
Read serve host, port, and timeout from config by default, keep CLI flags higher priority, and bind the API to localhost by default for safer local usage.
Expose OpenAI-compatible chat completions and models endpoints through a single persistent API session, keeping the integration simple without adding multi-session isolation yet.
Add agent-level timezone configuration with a UTC default, propagate it into runtime context and heartbeat prompts, and document valid IANA timezone usage in the README.
Keep cron state workspace-scoped while only migrating legacy jobs into the default workspace. This preserves seamless upgrades for existing installs without polluting intentionally new workspaces.
Move channel-specific login logic from CLI into each channel class via a
new `login(force=False)` method on BaseChannel. The `channels login <name>`
command now dynamically loads the channel and calls its login() method.
- WeixinChannel.login(): calls existing _qr_login(), with force to clear saved token
- WhatsAppChannel.login(): sets up bridge and spawns npm process for QR login
- CLI no longer contains duplicate login logic per channel
- Update CHANNEL_PLUGIN_GUIDE to document the login() hook
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add a new WeChat (微信) channel that connects to personal WeChat using
the ilinkai.weixin.qq.com HTTP long-poll API. Protocol reverse-engineered
from @tencent-weixin/openclaw-weixin v1.0.2.
Features:
- QR code login flow (nanobot weixin login)
- HTTP long-poll message receiving (getupdates)
- Text message sending with proper WeixinMessage format
- Media download with AES-128-ECB decryption (image/voice/file/video)
- Voice-to-text from WeChat + Groq Whisper fallback
- Quoted message (ref_msg) support
- Session expiry detection and auto-pause
- Server-suggested poll timeout adaptation
- Context token caching for replies
- Auto-discovery via channel registry
No WebSocket, no Node.js bridge, no local WeChat client needed — pure
HTTP with a bot token obtained via QR code scan.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add strip_think() to helpers.py as single source of truth
- Filter deltas in agent loop before dispatching to consumers
- Implement send_delta in TelegramChannel with progressive edit_message_text
- Remove duplicate think filtering from CLI stream.py and telegram.py
- Remove legacy fake streaming (send_message_draft) from Telegram
- Default Telegram streaming to true
- Update CHANNEL_PLUGIN_GUIDE.md with streaming documentation
Made-with: Cursor
Move ThinkingSpinner and StreamRenderer into a dedicated module to keep
commands.py focused on orchestration. Uses Rich Live with manual refresh
(auto_refresh=False) and ellipsis overflow for stable streaming output.
Made-with: Cursor
Preserve the provider and agent-loop streaming primitives plus the CLI experiment scaffolding so this work can be resumed later without blocking urgent bug fixes on main.
Made-with: Cursor
Merge process_direct() and process_direct_outbound() into a single
interface returning OutboundMessage | None. This eliminates the
dual-path detection logic in CLI single-message mode that relied on
inspect.iscoroutinefunction to distinguish between the two APIs.
Extract status rendering into a pure function build_status_content()
in utils/helpers.py, decoupling it from AgentLoop internals.
Made-with: Cursor
Only use process_direct_outbound when the agent loop actually exposes it as an async method, and otherwise fall back to the legacy process_direct path. This keeps the new CLI render-metadata flow without breaking existing test doubles or older direct-call implementations.
Made-with: Cursor
Keep status output responsive while estimating current context from session history, dropping low-value queue/subagent counters, and marking command-style replies for plain-text rendering in CLI. Also route direct CLI calls through outbound metadata so help/status formatting stays explicit instead of relying on content heuristics.
Made-with: Cursor
Exclude openai_codex alongside github_copilot from generated config,
filter OAuth-only providers out of the onboarding wizard, and clarify in
README that OAuth login stores session state outside config. Also unify
the GitHub Copilot login command spelling and add regression tests.
Made-with: Cursor
- Mask sensitive fields (api_key/token/secret/password) in all display
surfaces, showing only the last 4 characters
- Replace all emoji with pure ASCII labels for consistent cross-platform
terminal rendering
- Extract _print_summary_panel helper, eliminating 5x duplicate table
construction in _show_summary
- Replace 3 one-line wrapper functions with declarative _SETTINGS_SECTIONS
dispatch tables and _MENU_DISPATCH in run_onboard
- Extract _handle_model_field / _handle_context_window_field into a
_FIELD_HANDLERS registry, shrinking _configure_pydantic_model
- Return FieldTypeInfo NamedTuple from _get_field_type_info for clarity
- Replace global mutable _PROVIDER_INFO / _CHANNEL_INFO with @lru_cache
- Use vars() instead of dir() in _get_channel_info for reliable config
class discovery
- Defer litellm import in model_info.py so non-wizard CLI paths stay fast
- Clarify README Quick Start wording (Add -> Configure)
Cherry-pick from d6acf1a with manual merge resolution.
Keep onboarding edits in draft state until users choose Done or Save and
Exit, so backing out or discarding the wizard no longer persists partial
changes.
Co-Authored-By: Jason Zhao <144443939+JasonZhaoWW@users.noreply.github.com>
--workspace and --config now work as initial defaults in interactive mode:
- The wizard starts with these values pre-filled
- Users can view and modify them in the wizard
- Final saved config reflects user's choices
This makes the CLI args more useful for interactive sessions while
still allowing full customization through the wizard.
- Add `_SELECT_FIELD_HINTS` for select fields with predefined choices
(e.g., reasoning_effort: low/medium/high with hint text)
- Add `_select_with_back()` using prompt_toolkit for custom key bindings
- Support Escape and Left arrow keys to go back in menus
- Apply to field config, provider selection, and channel selection menus
- Add model_info.py module with litellm-based model lookup
- Provide autocomplete suggestions for model names
- Auto-fill context_window_tokens when model changes (only at default)
- Add "Get recommended value" option for manual context lookup
- Dynamically load provider keywords from registry (no hardcoding)
Resolves#2018
