Tinybot

中文文档 | Quick Start | Features | Commands

A lightweight personal AI assistant framework that integrates Large Language Models with multiple chat platforms, tool systems, and automation mechanisms.

Change log

flowchart TD
    UserTurn["Completed agent turn"] --> SaveSession["SessionHandler.save_turn"]
    SaveSession --> CaptureEvidence["capture_conversation_evidence"]
    CaptureEvidence --> EvidenceFiles["memory/conversations/*.jsonl"]
    CaptureEvidence --> Schedule["Memory extraction schedule"]

    Schedule --> DreamRun["Dream.run"]
    DreamRun --> HasEvidence{"Pending Conversation Evidence?"}
    HasEvidence -->|yes| EvidenceBatch["Read evidence after .evidence_cursor"]
    HasEvidence -->|no| LegacyBatch["Read legacy memory/history.jsonl after .dream_cursor"]

    EvidenceBatch --> Prompt["dream_phase1.md + current notes/views"]
    LegacyBatch --> Prompt
    Prompt --> LLMOps["LLM JSON Memory Operations"]
    LLMOps --> ParseOps["parse operations"]

    ParseOps --> OpType{"operation"}
    OpType -->|save| SaveNote["upsert MemoryNote"]
    OpType -->|supersede| Supersede["create replacement + mark old superseded"]
    OpType -->|reject| Reject["mark note rejected"]
    OpType -->|skip| Noop["no durable change"]

    SaveNote --> Notes["memory/notes.jsonl"]
    Supersede --> Notes
    Reject --> Notes

    Notes --> RefreshViews["refresh_memory_views"]
    RefreshViews --> ProjectView["memory/MEMORY.md"]
    RefreshViews --> UserView["USER.md"]
    RefreshViews --> SoulView["SOUL.md"]

    Notes --> RecallSelect["select_memory_recall"]
    RecallSelect --> RecallBlock["[MEMORY RECALL] system block"]
    RecallBlock --> AgentContext["ContextBuilder.build_messages"]

    Experience["ExperienceStore"] --> ExperienceBlock["[RELEVANT WORKFLOWS / RECOVERIES]"]
    Knowledge["KnowledgeStore / SessionKnowledgeStore"] --> KnowledgeBlock["Knowledge context"]
    ExperienceBlock --> AgentContext
    KnowledgeBlock --> AgentContext

flowchart LR
    Notes["Memory Notes\ncanonical JSONL"] --> Views["Memory Views\nMarkdown projection"]
    Notes --> Recall["Memory Recall\nprompt retrieval"]
    Evidence["Conversation Evidence\nraw source layer"] --> Dream["Dream\nbackground extraction"]
    Dream --> Notes

    ExplicitTools["Explicit memory tools\nforeground correction"] --> Notes
    Legacy["Legacy Markdown/history\nmigration + fallback"] --> Notes

    Experience["Experience\nhow to act"] --> AgentPrompt["Agent prompt"]
    Knowledge["Knowledge\nexternal document evidence"] --> AgentPrompt
    Recall --> AgentPrompt

    Views -. "inspection + compatibility" .-> AgentPrompt

Cowork now uses canonical architectures (adaptive_starter, team, generator_verifier, message_bus, shared_state, swarm), branch-aware session snapshots, Agent Step observation detail expansion, architecture-specific projections, and explicit branch result selection or merge controls.

Cowork now exposes versioned graph/trace snapshots, reusable JSON blueprints, budget-aware run controls, blocker panels, blueprint validation/preview APIs.

white mode

dark mode

✨ Core Highlights

Interactive Forms

Chatbot-agent

Agent cowork!

Cowork provides a shared multi-agent session model with architecture runtime policies, branch navigation, architecture-specific projections, observable Agent Steps, and explicit final-result selection.

🧠 Agentic DAG Task Scheduling

Automatically decomposes complex tasks into executable subtask DAGs, supporting:

• Intelligent Decomposition — LLM analyzes tasks and generates dependency-based subtask graphs
• Automatic Chain Execution — SubAgent completions automatically trigger dependent tasks
• Parallel Execution — Parallel-safe tasks run simultaneously for maximum efficiency
• Dynamic Adjustment — Add/remove subtasks during runtime

WebUI

🔄 Experience Self-Evolution System

A self-learning system that continuously improves from problem-solving experiences:

{
  "id": "exp_86788c0e",
  "timestamp": "2026-04-20T21:19:17",
  "tool_name": "exec",
  "error_type": "argument error",
  "error_message": "",
  "params": {},
  "outcome": "resolved",
  "resolution": "When using the opencli scroll command, pass exactly one argument to avoid argument-count errors. Check the command call format; valid examples are `scroll(distance)` or `scroll(selector)`, not multiple arguments. Validate argument counts before tool calls, using the opencli documentation or a test command to confirm API requirements.",
  "context_summary": "Browser automation: fixed argument errors and JavaScript syntax/type errors while using opencli by adjusting commands and adding defensive handling.",
  "confidence": 0.7,
  "session_key": "cli:direct",
  "merged_count": 0,
  "last_used_at": "2026-04-20T21:19:17",
  "category": "api",
  "tags": ["opencli", "scroll", "argument-error", "browser-automation"],
  "use_count": 0,
  "success_count": 0,
  "feedback_positive": 0,
  "feedback_negative": 0
}

• Semantic Experience Search — Vector-based search understands problem intent, not just keywords
• Auto Context Injection — Relevant past solutions automatically appear when you need them
• Proactive Error Diagnosis — Tool failures trigger automatic suggestions from resolved experiences
• Smart Confidence Model — Multi-dimensional scoring: usage frequency, success rate, freshness, feedback
• Automatic Categorization — Experiences tagged by category (path, permission, encoding, network, etc.)

🤖 SubAgent Asynchronous Execution

• Non-blocking Execution — Background tasks don't block main conversation
• Concurrency Control — Configurable max concurrency to prevent overload
• Heartbeat Monitoring — Auto-detects timeout tasks, prevents zombie processes
• Auto-notification — Automatically triggers main Agent to summarize resul