hermes-agent/TODO.md

Hermes Agent - Future Improvements

Ideas for enhancing the agent's capabilities, generated from self-analysis of the codebase.

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1. Memory & Context Management 🧠

Problem: Context grows unbounded during long conversations. Trajectory compression exists for training data post-hoc, but live conversations lack intelligent context management.

Ideas:

• Incremental summarization - Compress old tool outputs on-the-fly during conversations

  • Trigger when context exceeds threshold (e.g., 80% of max tokens)
  • Preserve recent turns fully, summarize older tool responses
  • Could reuse logic from trajectory_compressor.py

• Semantic memory retrieval - Vector store for long conversation recall

  • Embed important facts/findings as conversation progresses
  • Retrieve relevant memories when needed instead of keeping everything in context
  • Consider lightweight solutions: ChromaDB, FAISS, or even a simple embedding cache

• Working vs. episodic memory distinction

  • Working memory: Current task state, recent tool results (always in context)
  • Episodic memory: Past findings, tried approaches (retrieved on demand)
  • Clear eviction policies for each

Files to modify: run_agent.py (add memory manager), possibly new tools/memory_tool.py

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2. Self-Reflection & Course Correction 🔄

Problem: Current retry logic handles malformed outputs but not semantic failures. Agent doesn't reason about why something failed.

Ideas:

• Meta-reasoning after failures - When a tool returns an error or unexpected result:

  Tool failed → Reflect: "Why did this fail? What assumptions were wrong?"
  → Adjust approach → Retry with new strategy
  

  • Could be a lightweight LLM call or structured self-prompt

• Planning/replanning module - For complex multi-step tasks:

  • Generate plan before execution
  • After each step, evaluate: "Am I on track? Should I revise the plan?"
  • Store plan in working memory, update as needed

• Approach memory - Remember what didn't work:

  • "I tried X for this type of problem and it failed because Y"
  • Prevents repeating failed strategies in the same conversation

Files to modify: run_agent.py (add reflection hooks in tool loop), new tools/reflection_tool.py

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3. Tool Composition & Learning 🔧

Problem: Tools are atomic. Complex tasks require repeated manual orchestration of the same tool sequences.

Ideas:

• Macro tools / Tool chains - Define reusable tool sequences:

  research_topic:
    description: "Deep research on a topic"
    steps:
      - web_search: {query: "$topic"}
      - web_extract: {urls: "$search_results.urls[:3]"}
      - summarize: {content: "$extracted"}
  

  • Could be defined in skills or a new macros/ directory
  • Agent can invoke macro as single tool call

• Tool failure patterns - Learn from failures:

  • Track: tool, input pattern, error type, what worked instead
  • Before calling a tool, check: "Has this pattern failed before?"
  • Persistent across sessions (stored in skills or separate DB)

• Parallel tool execution - When tools are independent, run concurrently:

  • Detect independence (no data dependencies between calls)
  • Use asyncio.gather() for parallel execution
  • Already have async support in some tools, just need orchestration

Files to modify: model_tools.py, toolsets.py, new tool_macros.py

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4. Dynamic Skills Expansion 📚

Problem: Skills system is elegant but static. Skills must be manually created and added.

Ideas:

• Skill acquisition from successful tasks - After completing a complex task:

  • "This approach worked well. Save as a skill?"
  • Extract: goal, steps taken, tools used, key decisions
  • Generate SKILL.md automatically
  • Store in user's skills directory

• Skill templates - Common patterns that can be parameterized:

  # Debug {language} Error
  1. Reproduce the error
  2. Search for error message: `web_search("{error_message} {language}")`
  3. Check common causes: {common_causes}
  4. Apply fix and verify
  

• Skill chaining - Combine skills for complex workflows:

  • Skills can reference other skills as dependencies
  • "To do X, first apply skill Y, then skill Z"
  • Directed graph of skill dependencies

Files to modify: tools/skills_tool.py, skills/ directory structure, new skill_generator.py

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5. Task Continuation Hints 🎯

Problem: Could be more helpful by suggesting logical next steps.

Ideas:

• Suggest next steps - At end of a task, suggest logical continuations:
  • "Code is written. Want me to also write tests / docs / deploy?"
  • Based on common workflows for task type
  • Non-intrusive, just offer options

Files to modify: run_agent.py, response generation logic

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6. Uncertainty & Honesty Calibration 🎚️

Problem: Sometimes confidently wrong. Should be better calibrated about what I know vs. don't know.

Ideas:

• Source attribution - Track where information came from:

  • "According to the docs I just fetched..." vs "From my training data (may be outdated)..."
  • Let user assess reliability themselves

• Cross-reference high-stakes claims - Self-check for made-up details:

  • When stakes are high, verify with tools before presenting as fact
  • "Let me verify that before you act on it..."

Files to modify: run_agent.py, response generation logic

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7. Resource Awareness & Efficiency 💰

Problem: No awareness of costs, time, or resource usage. Could be smarter about efficiency.

Ideas:

• Tool result caching - Don't repeat identical operations:

  • Cache web searches, extractions within a session
  • Invalidation based on time-sensitivity of query
  • Hash-based lookup: same input → cached output

• Lazy evaluation - Don't fetch everything upfront:

  • Get summaries first, full content only if needed
  • "I found 5 relevant pages. Want me to deep-dive on any?"

Files to modify: model_tools.py, new resource_tracker.py

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8. Collaborative Problem Solving 🤝

Problem: Interaction is command/response. Complex problems benefit from dialogue.

Ideas:

• Assumption surfacing - Make implicit assumptions explicit:

  • "I'm assuming you want Python 3.11+. Correct?"
  • "This solution assumes you have sudo access..."
  • Let user correct before going down wrong path

• Checkpoint & confirm - For high-stakes operations:

  • "About to delete 47 files. Here's the list - proceed?"
  • "This will modify your database. Want a backup first?"
  • Configurable threshold for when to ask

Files to modify: run_agent.py, system prompt configuration

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9. Project-Local Context 💾

Problem: Valuable context lost between sessions.

Ideas:

• Project awareness - Remember project-specific context:

  • Store .hermes/context.md in project directory
  • "This is a Django project using PostgreSQL"
  • Coding style preferences, deployment setup, etc.
  • Load automatically when working in that directory

• Handoff notes - Leave notes for future sessions:

  • Write to .hermes/notes.md in project
  • "TODO for next session: finish implementing X"
  • "Known issues: Y doesn't work on Windows"

Files to modify: New project_context.py, auto-load in run_agent.py

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10. Graceful Degradation & Robustness 🛡️

Problem: When things go wrong, recovery is limited. Should fail gracefully.

Ideas:

• Fallback chains - When primary approach fails, have backups:

  • web_extract fails → try browser_navigate → try web_search for cached version
  • Define fallback order per tool type

• Partial progress preservation - Don't lose work on failure:

  • Long task fails midway → save what we've got
  • "I completed 3/5 steps before the error. Here's what I have..."

• Self-healing - Detect and recover from bad states:

  • Browser stuck → close and retry
  • Terminal hung → timeout and reset

Files to modify: model_tools.py, tool implementations, new fallback_manager.py

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11. Tools & Skills Wishlist 🧰

Things that would need new tool implementations (can't do well with current tools):

High-Impact

• Audio/Video Transcription 🎬

  • Transcribe audio files, podcasts, YouTube videos
  • Extract key moments from video
  • Currently blind to multimedia content
  • Could potentially use whisper via terminal, but native tool would be cleaner

• Diagram Rendering 📊

  • Render Mermaid/PlantUML to actual images
  • Can generate the code, but rendering requires external service or tool
  • "Show me how these components connect" → actual visual diagram

Medium-Impact

• Document Generation 📄

  • Create styled PDFs, Word docs, presentations
  • Can do basic PDF via terminal tools, but limited

• Diff/Patch Tool 📝

  • Surgical code modifications with preview
  • "Change line 45-50 to X" without rewriting whole file
  • Show diffs before applying
  • Can use diff/patch but a native tool would be safer

Skills to Create

• Domain-specific skill packs:

  • DevOps/Infrastructure (Terraform, K8s, AWS)
  • Data Science workflows (EDA, model training)
  • Security/pentesting procedures

• Framework-specific skills:

  • React/Vue/Angular patterns
  • Django/Rails/Express conventions
  • Database optimization playbooks

• Troubleshooting flowcharts:

  • "Docker container won't start" → decision tree
  • "Production is slow" → systematic diagnosis

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Priority Order (Suggested)

1. Memory & Context Management - Biggest impact on complex tasks
2. Self-Reflection - Improves reliability and reduces wasted tool calls
3. Project-Local Context - Practical win, keeps useful info across sessions
4. Tool Composition - Quality of life, builds on other improvements
5. Dynamic Skills - Force multiplier for repeated tasks

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Removed Items (Unrealistic)

The following were removed because they're architecturally impossible:

• Proactive suggestions / Prefetching - Agent only runs on user request, can't interject
• Session save/restore across conversations - Agent doesn't control session persistence
• User preference learning across sessions - Same issue
• Clipboard integration - No access to user's local system clipboard
• Voice/TTS playback - Can generate audio but can't play it to user
• Set reminders - No persistent background execution

The following were removed because they're already possible:

• HTTP/API Client → Use curl or Python requests in terminal
• Structured Data Manipulation → Use pandas in terminal
• Git-Native Operations → Use git CLI in terminal
• Symbolic Math → Use SymPy in terminal
• Code Quality Tools → Run linters (eslint, black, mypy) in terminal
• Testing Framework → Run pytest, jest, etc. in terminal
• Translation → LLM handles this fine, or use translation APIs

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Last updated: $(date +%Y-%m-%d) 🤖