Implement reasoning extraction and enhance assistant message handling
- Added a new method `_extract_reasoning` to extract reasoning content from assistant messages, accommodating multiple formats from various providers. - Updated message handling to ensure all assistant messages include reasoning content for API compatibility, preserving multi-turn reasoning context. - Enhanced logging to capture reasoning details for debugging and analysis. - Modified the TODO.md to reflect changes in planning and task management, emphasizing the need for structured task decomposition and progress tracking.
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TODO.md
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TODO.md
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@ -177,56 +177,48 @@ These items need to be addressed ASAP:
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---
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## 2. Context Management (complements Subagents)
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## 2. Planning & Task Management 📋
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**Problem:** Context grows unbounded during long conversations. Trajectory compression exists for training data post-hoc, but live conversations lack intelligent context management.
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**Problem:** Agent handles tasks reactively without explicit planning. Complex multi-step tasks lack structure, progress tracking, and the ability to decompose work into manageable chunks.
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**Ideas:**
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- [ ] **Incremental summarization** - Compress old tool outputs on-the-fly during conversations
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- Trigger when context exceeds threshold (e.g., 80% of max tokens)
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- Preserve recent turns fully, summarize older tool responses
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- Could reuse logic from `trajectory_compressor.py`
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- [ ] **Task decomposition tool** - Break complex requests into subtasks:
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```
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User: "Set up a new Python project with FastAPI, tests, and Docker"
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- [ ] **Semantic memory retrieval** - Vector store for long conversation recall
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- Embed important facts/findings as conversation progresses
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- Retrieve relevant memories when needed instead of keeping everything in context
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- Consider lightweight solutions: ChromaDB, FAISS, or even a simple embedding cache
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Agent creates plan:
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├── 1. Create project structure and requirements.txt
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├── 2. Implement FastAPI app skeleton
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├── 3. Add pytest configuration and initial tests
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├── 4. Create Dockerfile and docker-compose.yml
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└── 5. Verify everything works together
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```
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- Each subtask becomes a trackable unit
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- Agent can report progress: "Completed 3/5 tasks"
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- [ ] **Working vs. episodic memory** distinction
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- Working memory: Current task state, recent tool results (always in context)
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- Episodic memory: Past findings, tried approaches (retrieved on demand)
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- Clear eviction policies for each
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- [ ] **Progress checkpoints** - Periodic self-assessment:
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- After N tool calls or time elapsed, pause to evaluate
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- "What have I accomplished? What remains? Am I on track?"
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- Detect if stuck in loops or making no progress
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- Could trigger replanning if approach isn't working
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- [ ] **Explicit plan storage** - Persist plan in conversation:
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- Store as structured data (not just in context)
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- Update status as tasks complete
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- User can ask "What's the plan?" or "What's left?"
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- Survives context compression (plans are protected)
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**Files to modify:** `run_agent.py` (add memory manager), possibly new `tools/memory_tool.py`
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- [ ] **Failure recovery with replanning** - When things go wrong:
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- Record what failed and why
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- Revise plan to work around the issue
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- "Step 3 failed because X, adjusting approach to Y"
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- Prevents repeating failed strategies
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**Files to modify:** `run_agent.py` (add planning hooks), new `tools/planning_tool.py`
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---
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## 3. Self-Reflection & Course Correction 🔄
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**Problem:** Current retry logic handles malformed outputs but not semantic failures. Agent doesn't reason about *why* something failed.
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**Ideas:**
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- [ ] **Meta-reasoning after failures** - When a tool returns an error or unexpected result:
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```
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Tool failed → Reflect: "Why did this fail? What assumptions were wrong?"
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→ Adjust approach → Retry with new strategy
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```
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- Could be a lightweight LLM call or structured self-prompt
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- [ ] **Planning/replanning module** - For complex multi-step tasks:
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- Generate plan before execution
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- After each step, evaluate: "Am I on track? Should I revise the plan?"
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- Store plan in working memory, update as needed
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- [ ] **Approach memory** - Remember what didn't work:
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- "I tried X for this type of problem and it failed because Y"
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- Prevents repeating failed strategies in the same conversation
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**Files to modify:** `run_agent.py` (add reflection hooks in tool loop), new `tools/reflection_tool.py`
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---
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## 4. Tool Composition & Learning 🔧
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## 3. Tool Composition & Learning 🔧
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**Problem:** Tools are atomic. Complex tasks require repeated manual orchestration of the same tool sequences.
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@ -257,7 +249,7 @@ These items need to be addressed ASAP:
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---
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## 5. Dynamic Skills Expansion 📚
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## 4. Dynamic Skills Expansion 📚
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**Problem:** Skills system is elegant but static. Skills must be manually created and added.
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@ -286,7 +278,7 @@ These items need to be addressed ASAP:
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---
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## 6. Task Continuation Hints 🎯
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## 5. Task Continuation Hints 🎯
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**Problem:** Could be more helpful by suggesting logical next steps.
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@ -336,7 +328,7 @@ These items need to be addressed ASAP:
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---
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## 8. Resource Awareness & Efficiency 💰
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## 6. Resource Awareness & Efficiency 💰
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**Problem:** No awareness of costs, time, or resource usage. Could be smarter about efficiency.
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@ -373,7 +365,7 @@ These items need to be addressed ASAP:
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---
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## 10. Project-Local Context 💾
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## 7. Project-Local Context 💾
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**Problem:** Valuable context lost between sessions.
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@ -393,7 +385,7 @@ These items need to be addressed ASAP:
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---
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## 11. Graceful Degradation & Robustness 🛡️
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## 8. Graceful Degradation & Robustness 🛡️
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**Problem:** When things go wrong, recovery is limited. Should fail gracefully.
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@ -414,7 +406,7 @@ These items need to be addressed ASAP:
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---
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## 12. Tools & Skills Wishlist 🧰
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## 9. Tools & Skills Wishlist 🧰
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*Things that would need new tool implementations (can't do well with current tools):*
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@ -481,7 +473,7 @@ These items need to be addressed ASAP:
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---
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## 13. Messaging Platform Integrations 💬
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## 10. Messaging Platform Integrations 💬
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**Problem:** Agent currently only works via `cli.py` which requires direct terminal access. Users may want to interact via messaging apps from their phone or other devices.
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@ -525,7 +517,7 @@ These items need to be addressed ASAP:
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---
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## 14. Scheduled Tasks / Cron Jobs ⏰
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## 11. Scheduled Tasks / Cron Jobs ⏰
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**Problem:** Agent only runs on-demand. Some tasks benefit from scheduled execution (daily summaries, monitoring, reminders).
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@ -570,7 +562,7 @@ These items need to be addressed ASAP:
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---
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## 15. Text-to-Speech (TTS) 🔊
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## 12. Text-to-Speech (TTS) 🔊
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**Problem:** Agent can only respond with text. Some users prefer audio responses (accessibility, hands-free use, podcasts).
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---
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## 16. Speech-to-Text / Audio Transcription 🎤
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## 13. Speech-to-Text / Audio Transcription 🎤
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**Problem:** Users may want to send voice memos instead of typing. Agent is blind to audio content.
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