When IntentRouter matches a direct-mode agent (no tools), but the task
content suggests tool needs (shell, search, file ops, etc.), the routing
now falls through to the default agent which has full tool access.
This fixes the issue where "帮我执行个命令" would be routed to
direct_agent and fail because direct mode doesn't support tool calling.
Also restored "你好" in direct_agent keywords since it's correctly
handled now — greetings don't need tools, direct mode is fine.
Hardcoded model names like 'openai/gpt-4o-mini' or 'anthropic/claude-sonnet'
cause 'No provider available' errors when the specific provider isn't configured.
Using 'default' lets the system pick the available provider automatically.
1. InMemoryMessageBus.request(): fix param name (timeout→timeout_seconds) to match ABC
2. InMemoryMessageBus: track consumer tasks, cancel on unsubscribe
3. InMemoryMessageBus: _try_resolve_pending() in queue consumer path
4. evolve_soul(): use "default" category when patterns is empty
5. quick_classify(): use delimiter-based prompt to mitigate injection risk
6. Use asyncio.get_running_loop() instead of deprecated get_event_loop()
1. Critical: Add missing TaskResult import in plan_exec_engine.py
2. Critical: Fix ReWOOEngine param name (max_steps → max_plan_steps)
3. Major: Remove duplicate token counting in reflexion.py
4. Major: LLM audit failure now passes (trusts rule check) instead of failing
5. Major: Fix dict iteration with del using list() copy in lifecycle.py
6. Major: Fix Chinese content tokenization using regex split instead of space split
7. Minor: _is_positive_mention now checks all occurrences, not just the first
Phase B:
- U1: CostAwareRouter with 3-layer routing (rule/LLM/capability matching)
- U6: OrganizationContext with agent profiles and capability-based discovery
- U7: AlignmentGuard with constraint injection and cascade detection
Phase C:
- U8: Soul dynamic evolution with version tracking and reflection-triggered updates
- U9: Auction mechanism as optional advanced routing mode
- U10: Server integration + end-to-end integration tests
250 new tests passing across all units.
- Shell whitelist: use exact binary match instead of startswith
- Shell audit log: use deque(maxlen=10000) to cap memory
- Terminal history: use deque(maxlen) for O(1) eviction
- Path optimizer: cap _pending_paths at 50 entries per task_type
- Pitfall detector: only add tips to matching steps, not all
- Experience store: handle non-numeric _parse_time_window input
- Extract shared is_safe_url() to utils/security.py (DRY)
- Workflow condition evaluator: handle float() ValueError
- Enhanced chat CLI with adaptive mode and session management
- Added pipeline reflection and schema extensions
- Upgraded BaiduSearch and WebSearch tools with advanced capabilities
- Expanded server routes for skills and chat
- Added session store enhancements
- New chat module and pipeline reflection support
- Orchestrator accepts optional message_bus parameter; workers publish
task.progress messages via MessageBus after each subtask execution
- AgentPool accepts optional message_bus; auto-registers agents on
create and auto-unregisters on remove
- app.py initializes MessageBus from config and injects into AgentPool
- ServerConfig adds bus configuration field
- 5 new tests, all passing
- AgentMessage: message model with sender/recipient/topic/payload/correlation_id
- MessageBus Protocol: publish/subscribe/unsubscribe/request/broadcast/health_check
- InMemoryMessageBus: asyncio.Queue-based implementation for testing
- RedisMessageBus: Redis Streams (XADD/XREADGROUP) implementation with
consumer groups, message acknowledgment, and dead letter queue
- create_message_bus() factory with graceful Redis→InMemory fallback
- Request-response pattern via correlation_id + asyncio.Future
- 13 new tests, all passing
- AskHumanTool: Human-in-the-Loop tool for Chat mode, pushes questions
via WebSocket callback and waits for user reply via asyncio.Future
- Token streaming: execute_stream() now uses chat_stream() instead of
chat(), yielding token-type ReActEvents for each StreamChunk
- _build_response_from_stream() static method constructs LLMResponse
from accumulated stream data
- Export AskHumanTool from tools/__init__.py
- 12 new tests (7 AskHumanTool + 5 token streaming), all passing
Add HeadroomRetrieveTool that allows LLM to retrieve original
uncompressed data from CCR cache via Function Calling. Auto-registered
when HeadroomCompressor is active and available.
Add compression config to ServerConfig (following telemetry pattern),
create compressor in create_app, pass through AgentPool to
ConfigDrivenAgent, and inject into ReActEngine.execute() calls.
Add HeadroomCompressor implementing CompressionStrategy Protocol with
content-type routing (JSON→SmartCrusher, code→CodeCompressor), CCR
reversible compression cache, and graceful degradation when headroom-ai
is not installed.
Add runtime-checkable CompressionStrategy Protocol with compress(),
compress_tool_result(), and is_available() methods. Add compress_tool_result
and is_available to existing ContextCompressor. Add create_compressor()
factory function with headroom/summary provider routing and ImportError
fallback.
Add telemetry module with tracing (agent/tool/llm/pipeline_step spans),
metrics (5 histograms/counters), and setup with optional OTLP exporters.
Uses no-op pattern when opentelemetry not installed. GenAI Semantic
Conventions for LLM spans. Integrated into ReactEngine, LLMGateway,
ToolBase, and FastAPI app.
Add StepRetryPolicy with jitter-based exponential backoff, SagaOrchestrator
with LIFO compensation pattern, integrate retry_policy and compensate
fields into PipelineStage/PipelineStep schema, add GEO pipeline
compensation definitions for all 7 steps.
Add PipelineStateMemory/Redis/PG backends, PipelineStateManager with
Redis Sorted Set hot state + PostgreSQL JSONB cold persistence.
Integrated into PipelineEngine with state persistence calls at each
step transition.
Add WebCrawlTool (Crawl4AI wrapper with graceful degradation),
SchemaExtractTool (extruct-based Schema.org extraction), and
SchemaGenerateTool (JSON-LD generation with optional pydantic-schemaorg
validation). All tools work without optional dependencies.
Add MCPServerConfig dataclass with stdio/streamable_http/sse transport
validation, MCPManager for declarative YAML-driven MCP server lifecycle
(start_all/stop_all), tool discovery and registration. Integrated
into FastAPI lifespan startup/shutdown.
chiguyong