Venice AI is a privacy-focused AI inference provider with support for
uncensored models and access to major proprietary models via their
anonymized proxy.
This integration adds:
- Complete model catalog with 25 models:
- 15 private models (Llama, Qwen, DeepSeek, Venice Uncensored, etc.)
- 10 anonymized models (Claude, GPT-5.2, Gemini, Grok, Kimi, MiniMax)
- Auto-discovery from Venice API with fallback to static catalog
- VENICE_API_KEY environment variable support
- Interactive onboarding via 'venice-api-key' auth choice
- Model selection prompt showing all available Venice models
- Provider auto-registration when API key is detected
- Comprehensive documentation covering:
- Privacy modes (private vs anonymized)
- All 25 models with context windows and features
- Streaming, function calling, and vision support
- Model selection recommendations
Privacy modes:
- Private: Fully private, no logging (open-source models)
- Anonymized: Proxied through Venice (proprietary models)
Default model: venice/llama-3.3-70b (good balance of capability + privacy)
Venice API: https://api.venice.ai/api/v1 (OpenAI-compatible)
* feat: add chunking mode for outbound messages
- Introduced `chunkMode` option in various account configurations to allow splitting messages by "length" or "newline".
- Updated message processing to handle chunking based on the selected mode.
- Added tests for new chunking functionality, ensuring correct behavior for both modes.
* feat: enhance chunking mode documentation and configuration
- Added `chunkMode` option to the BlueBubbles account configuration, allowing users to choose between "length" and "newline" for message chunking.
- Updated documentation to clarify the behavior of the `chunkMode` setting.
- Adjusted account merging logic to incorporate the new `chunkMode` configuration.
* refactor: simplify chunk mode handling for BlueBubbles
- Removed `chunkMode` configuration from various account schemas and types, centralizing chunk mode logic to BlueBubbles only.
- Updated `processMessage` to default to "newline" for BlueBubbles chunking.
- Adjusted tests to reflect changes in chunk mode handling for BlueBubbles, ensuring proper functionality.
* fix: update default chunk mode to 'length' for BlueBubbles
- Changed the default value of `chunkMode` from 'newline' to 'length' in the BlueBubbles configuration and related processing functions.
- Updated documentation to reflect the new default behavior for chunking messages.
- Adjusted tests to ensure the correct default value is returned for BlueBubbles chunk mode.
* fix(ui): enable save button only when config has changes
The save button in the Control UI config editor was not properly gating
on whether actual changes were made. This adds:
- `configRawOriginal` state to track the original raw config for comparison
- Change detection for both form mode (via computeDiff) and raw mode
- `hasChanges` check in canSave/canApply logic
- Set `configFormDirty` when raw mode edits occur
- Handle raw mode UI correctly (badge shows "Unsaved changes", no diff panel)
Fixes#1609
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat(gateway-tool): add config.patch action for safe partial config updates
Exposes the existing config.patch server method to agents, allowing safe
partial config updates that merge with existing config instead of replacing it.
- Add config.patch to GATEWAY_ACTIONS in gateway tool
- Add restart + sentinel logic to config.patch server method
- Extend ConfigPatchParamsSchema with sessionKey, note, restartDelayMs
- Add unit test for config.patch gateway tool action
Closes#1617
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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* feat: Add Ollama provider with automatic model discovery
- Add Ollama provider builder with automatic model detection
- Discover available models from local Ollama instance via /api/tags API
- Make resolveImplicitProviders async to support dynamic model discovery
- Add comprehensive Ollama documentation with setup and usage guide
- Add tests for Ollama provider integration
- Update provider index and model providers documentation
Closes#1531
* fix: Correct Ollama provider type definitions and error handling
- Fix input property type to match ModelDefinitionConfig
- Import ModelDefinitionConfig type properly
- Fix error template literal to use String() for type safety
- Simplify return type signature of discoverOllamaModels
* fix: Suppress unhandled promise warnings from ensureClawdbotModelsJson in tests
- Cast unused promise returns to 'unknown' to suppress TypeScript warnings
- Tests that don't await the promise are intentionally not awaiting it
- This fixes the failing test suite caused by unawaited async calls
* fix: Skip Ollama model discovery during tests
- Check for VITEST or NODE_ENV=test before making HTTP requests
- Prevents test timeouts and hangs from network calls
- Ollama discovery will still work in production/normal usage
* fix: Set VITEST environment variable in test setup
- Ensures Ollama discovery is skipped in all test runs
- Prevents network calls during tests that could cause timeouts
* test: Temporarily skip Ollama provider tests to diagnose CI failures
* fix: Make Ollama provider opt-in to avoid breaking existing tests
**Root Cause:**
The Ollama provider was being added to ALL configurations by default
(with a fallback API key of 'ollama-local'), which broke tests that
expected NO providers when no API keys were configured.
**Solution:**
- Removed the default fallback API key for Ollama
- Ollama provider now requires explicit configuration via:
- OLLAMA_API_KEY environment variable, OR
- Ollama profile in auth store
- Updated documentation to reflect the explicit configuration requirement
- Added a test to verify Ollama is not added by default
This fixes all 4 failing test suites:
- checks (node, test, pnpm test)
- checks (bun, test, bunx vitest run)
- checks-windows (node, test, pnpm test)
- checks-macos (test, pnpm test)
Closes#1531
* fix: detect Anthropic 'Request size exceeds model context window' as context overflow
Anthropic now returns 'Request size exceeds model context window' instead of
the previously detected 'prompt is too long' format. This new error message
was not recognized by isContextOverflowError(), causing auto-compaction to
NOT trigger. Users would see the raw error twice without any recovery attempt.
Changes:
- Add 'exceeds model context window' and 'request size exceeds' to
isContextOverflowError() detection patterns
- Add tests that fail without the fix, verifying both the raw error
string and the JSON-wrapped format from Anthropic's API
- Add test for formatAssistantErrorText to ensure the friendly
'Context overflow' message is shown instead of the raw error
Note: The upstream pi-ai package (@mariozechner/pi-ai) also needs a fix
in its OVERFLOW_PATTERNS regex: /exceeds the context window/i should be
changed to /exceeds.*context window/i to match both 'the' and 'model'
variants for triggering auto-compaction retry.
* fix(tests): remove unused imports and helper from test files
Remove WorkspaceBootstrapFile references and _makeFile helper that were
incorrectly copied from another test file. These caused type errors and
were unrelated to the context overflow detection tests.
* fix: trigger auto-compaction on context overflow promptError
When the LLM rejects a request with a context overflow error that surfaces
as a promptError (thrown exception rather than streamed error), the existing
auto-compaction in pi-coding-agent never triggers. This happens because the
error bypasses the agent's message_end → agent_end → _checkCompaction path.
This fix adds a fallback compaction attempt directly in the run loop:
- Detects context overflow in promptError (excluding compaction_failure)
- Calls compactEmbeddedPiSessionDirect (bypassing lane queues since already in-lane)
- Retries the prompt after successful compaction
- Limits to one compaction attempt per run to prevent infinite loops
Fixes: context overflow errors shown to user without auto-compaction attempt
* style: format compact.ts and run.ts with oxfmt
* fix: tighten context overflow match (#1627) (thanks @rodrigouroz)
---------
Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Peter Steinberger <steipete@gmail.com>
* feat(discord): add exec approval forwarding to DMs
Add support for forwarding exec approval requests to Discord DMs,
allowing users to approve/deny command execution via interactive buttons.
Features:
- New DiscordExecApprovalHandler that connects to gateway and listens
for exec.approval.requested/resolved events
- Sends DMs with embeds showing command details and 3 buttons:
Allow once, Always allow, Deny
- Configurable via channels.discord.execApprovals with:
- enabled: boolean
- approvers: Discord user IDs to notify
- agentFilter: only forward for specific agents
- sessionFilter: only forward for matching session patterns
- Updates message embed when approval is resolved or expires
Also fixes exec completion routing: when async exec completes after
approval, the heartbeat now uses a specialized prompt to ensure the
model relays the result to the user instead of responding HEARTBEAT_OK.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: generic exec approvals forwarding (#1621) (thanks @czekaj)
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Peter Steinberger <steipete@gmail.com>
* fix: reduce log noise for node disconnect/late invoke errors
- Handle both 'node not connected' and 'node disconnected' errors at info level
- Return success with late:true for unknown invoke IDs instead of error
- Add 30-second throttle to skills change listener to prevent rapid-fire probes
- Add tests for isNodeUnavailableError and late invoke handling
* fix: clean up skills refresh timer and listener on shutdown
Store the return value from registerSkillsChangeListener() and call it
on gateway shutdown. Also clear any pending refresh timer. This follows
the same pattern used for agentUnsub and heartbeatUnsub.
* refactor: simplify KISS/YAGNI - inline checks, remove unit tests for internal utilities
* fix: reduce gateway log noise (#1607) (thanks @petter-b)
* test: align agent id casing expectations (#1607)
---------
Co-authored-by: Peter Steinberger <steipete@gmail.com>
When native slash commands are executed in Telegram topics/forums, the
originating topic context was not being preserved. This caused sub-agent
announcements to be delivered to the wrong topic.
Root cause: Native slash command context did not set OriginatingChannel
and OriginatingTo, causing session delivery context to fallback to the
user's personal ID instead of the group ID + topic.
Fix: Added OriginatingChannel and OriginatingTo to native slash command
context, ensuring topic information is preserved for sub-agent announcements.
Related session fields:
- lastThreadId: preserved via MessageThreadId
- lastTo: now correctly set to group ID via OriginatingTo
- deliveryContext: includes threadId for proper routing
