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A Python-based terminal AI programming assistant — collaborate with AI directly in your project directory: read/write files, execute commands, browse the web, and maintain memory across sessions.

Why Invincat

Invincat is designed for real engineering work in local repositories, not demo-only chat.

• Terminal-native workflow: stay in your project directory and use AI without switching IDEs or browser tabs.
• Execution with guardrails: shell/file/network actions are approval-gated by default, with optional auto-approve for trusted flows.
• Plan-first delivery mode: /plan lets teams review and approve checklists before execution, reducing risky one-shot edits.
• Long-context durability: micro compression + offload keep long sessions usable without losing operational history.
• Practical memory model: user/project memory stores persist conventions across sessions and are inspectable via /memory.
• Extensible architecture: MCP tools, skills, and subagents allow adapting the assistant to team-specific workflows.

Agent Architecture

Invincat uses a multi-agent runtime with clear role boundaries.

Execution Flow

1. User Input enters the session router.
2. If /plan mode is active, input is routed to the Planner Agent; otherwise to the Main Agent.
3. Main Agent executes tools (file/shell/web/MCP) under approval and middleware guardrails.
4. After a non-trivial turn completes, Memory Agent runs asynchronously to extract durable user/project memory updates.
5. When needed, Main Agent delegates bounded subtasks to local or async subagents.

Agent Roles and Responsibilities

Runtime Guardrails

• Planner mode uses both visible-tool filtering and runtime allow-list enforcement.
• Memory store files are protected by middleware and updated only through the memory pipeline.
• Memory extraction runs in post-turn async middleware (aafter_agent) so it does not block user-visible responses.

Documentation

• Chinese guide: doc/README_CN.md
• Memory design (Chinese): doc/MEMORY_DESIGN.md
• Memory design (English): doc/MEMORY_DESIGN_EN.md

Installation

Requirements: Python 3.11+

# Install from PyPI
pip install invincat-cli

Or install from source:

git clone https://github.com/dog-qiuqiu/invincat.git
cd invincat
pip install -e .

Quick Start

# Start in your project directory
cd ~/my-project
invincat-cli

After the first launch, run /model to configure the model and API Key, then you can start the conversation directly.

Model Configuration

Configure via Interface

Run /model command to open the model management interface:

1. Press Ctrl+N to register a new model
2. Fill in the provider, model name, and API Key
3. Select from the list and press Enter to activate

Supported Providers

For OpenAI-compatible interfaces (DeepSeek, Zhipu, local Ollama, etc.), simply set the base_url to connect.

Environment Variables

Basic Usage

Type your question or task directly in the input box and press Enter to send. AI will automatically select the appropriate tools to complete the task:

Search for the latest usage of LangGraph interrupt

Command Mode (/ prefix)

/clear
/threads
/model
... ...

Press Tab to autocomplete available commands. See Slash Commands for the complete list.

Plan Mode

Use planner mode when you want to discuss and approve a plan before execution:

/plan

Then describe your task in chat. The planner agent will:

• analyze requirements with read-only tools
• write a todo list (write_todos)
• ask for explicit approval (approve_plan)

After approval, planner mode exits and keeps the approved checklist visible. The approved checklist is then handed to the main agent for execution. If you reject the plan, the planner stays in planning mode so you can refine requirements and regenerate the checklist.

Exit planner mode anytime:

/exit-plan

/exit-plan also cancels an in-flight planner turn and drops queued planner handoff actions, so no stale plan execution will continue after exit.

File References

Use @ in your message to reference files, and AI will read and understand their content:

@src/main.py Are there any potential performance issues in this file?

Tool Approval

When AI performs operations like file writing, shell commands, or network requests, it will pause by default for confirmation:

Auto-approve Mode: Press Shift+Tab to toggle. When enabled, all tool calls are automatically approved, suitable for trusted task scenarios. The status bar will display an AUTO indicator.

⚠️ It's recommended to enable auto-approve only after you're familiar with the task content.

Input Line Breaks

Press Ctrl+J in the input box to insert a line break, suitable for entering longer code or paragraphs.

Context Management

Micro Compression

A lightweight compression that runs automatically before each model call, no LLM involved, taking <1ms.

How it works: Groups conversation messages by "tool call groups", keeps a dynamic recent window intact, and compresses older large tool outputs in two levels:

• cleared-light: richer placeholder near the cutoff (keeps head/tail signals)
• cleared-heavy: stronger placeholder for older groups (keeps concise summary)

Compressible Tool Outputs:

Not Compressed: agent/subagent results, ask_user responses, MCP tool outputs, compact_conversation results.

Tune micro compression with environment variables:

INVINCAT_MICRO_COMPACT_KEEP_RECENT_GROUPS=3
INVINCAT_MICRO_COMPACT_DYNAMIC_GROUP_FACTOR=12
INVINCAT_MICRO_COMPACT_MAX_KEEP_RECENT_GROUPS=8
INVINCAT_MICRO_COMPACT_LIGHT_NEAR_CUTOFF_GROUPS=2
INVINCAT_MICRO_COMPACT_MIN_COMPRESS_CHARS=240

💡 Micro compression only affects the context sent to the model, does not modify persisted state, and complete history is still saved in checkpoints.

Auto Compression

When context window usage exceeds 80%, the system automatically compresses older messages into summaries to free up space, requiring no manual operation. The status bar token count turns orange above 70% and red above 90% as warnings.

Manual Compression

/offload

Or equivalently /compact. After execution, it shows how many messages were compressed and how many tokens were freed.

Memory System

AI can remember your preferences, project conventions, and important information across sessions.

Memory Architecture Highlights

• JSON single source of truth: runtime memory uses memory_user.json and memory_project.json only, which keeps reads/writes auditable and deterministic.
• Dual-scope isolation: separates cross-project personal preferences (user) from repository conventions (project) to avoid memory pollution.
• Read/write pipeline decoupling:
  • RefreshableMemoryMiddleware is responsible only for loading/rendering/injecting memory.
  • MemoryAgentMiddleware is responsible only for post-turn extraction and structured writes.
• Async post-turn extraction: memory updates run after main responses, so memory persistence does not block interactive latency.
• Incremental extraction with recovery: consumes only delta messages after last successful cursor, with full-history fallback when history is rewritten.
• Evidence-aware project memory: project scope favors durable conventions backed by tool evidence and avoids transient session noise.
• Deterministic invalid-fact cleanup: stale or contradicted active memories can be removed by rule-based validation, reducing long-lived wrong memory.
• Strong write safety: schema validation, dedup/conflict guards, path whitelist, and atomic write (tmp + os.replace) prevent corruption.
• Transparent and operable: /memory provides full-screen live inspection and management for both scopes.

Memory Governance Innovation

This project treats memory as a governed subsystem, not just a longer chat history.

• Decision/write split: the memory agent decides operations, while runtime validators and guards decide what is actually persisted.
• Typed lifecycle model: memory evolves through explicit operations (create/update/rescore/retier/archive/delete/noop) instead of opaque free-form rewrites.
• Built-in drift control: invalid-fact cleanup, scoring/tiering, and archive/delete semantics prevent memory from becoming stale or bloated.
• Evidence-gated project memory: project conventions require durable signals and tool evidence, reducing accidental writes from transient noise.
• Operator-friendly governance: memory stores are inspectable JSON, and /memory provides live visibility for review and correction.

Memory Runtime Architecture

flowchart LR
    A[Conversation Turn] --> B[Main Agent Response]
    B --> C[MemoryAgentMiddleware aafter_agent]
    C --> D{Non-trivial + completed + throttle passed?}
    D -- No --> E[Skip extraction]
    D -- Yes --> F[Incremental slice by cursor + anchor]
    F --> G[Collect tool evidence]
    G --> H[Structured ops JSON]
    H --> I[Validate + guardrails]
    I --> J[Atomic write memory_user.json / memory_project.json]
    J --> K[Next turn RefreshableMemoryMiddleware injects active memory]

Memory Files

AGENTS.md is deprecated for runtime memory injection. The runtime memory pipeline now uses memory_*.json as the single source of truth.

Auto Memory Update

Memory updates are triggered after non-trivial completed turns, with:

• incremental extraction: consume only messages added since the previous memory extraction in the same thread
• cursor invalidation fallback: if history is rewritten (for example, compaction/checkpoint replay), fallback to one full-history pass
• turn-interval throttling
• keyword-based early triggers (preferences/rules/conventions)
• time/file cooldown guards

Tune behavior via environment variables:

INVINCAT_MEMORY_CONTEXT_MESSAGES=0
INVINCAT_MEMORY_MIN_TURN_INTERVAL=1
INVINCAT_MEMORY_MIN_SECONDS_BETWEEN_RUNS=0
INVINCAT_MEMORY_FILE_COOLDOWN_SECONDS=0

INVINCAT_MEMORY_CONTEXT_MESSAGES=0 means no cap on the incremental delta since the last memory extraction. Set a positive integer to cap the delta to recent N messages.

By default the memory agent runs after every non-trivial turn (MIN_TURN_INTERVAL=1, no wall-clock or file cooldown) so memory stays in sync with the latest signal. Raise the values to re-enable throttling if the extraction cost becomes a concern.

For production tuning (cost-sensitive setups), a practical starting point is:

INVINCAT_MEMORY_MIN_TURN_INTERVAL=2
INVINCAT_MEMORY_MIN_SECONDS_BETWEEN_RUNS=8
INVINCAT_MEMORY_FILE_COOLDOWN_SECONDS=5

Troubleshooting Project Memory Not Updating

If project memory updates appear rare, check in this order:

1. Is the turn non-trivial and completed? Very short confirmations (ok, thanks, 继续) are skipped.
2. Did evidence come from supported tools? Project evidence extraction prioritizes read_file, edit_file, write_file, execute, bash, shell.
3. Is evidence durable and convention-like? Temporary logs or one-off statuses are intentionally ignored.
4. Is throttling active? MIN_TURN_INTERVAL, wall-clock cooldown, or file cooldown can suppress runs.
5. Was history rewritten? Cursor mismatch triggers fallback behavior; check whether compaction/replay happened.
6. Did writes fail guardrails? Invalid/conflicting operations are dropped by schema and safety validation.

Quick verification path:

1. Run one concrete, non-trivial turn that states a stable project rule.
2. Ensure at least one supporting read/execute tool result exists in that turn.
3. Open /memory and check the project tab for new or updated active items.

Memory Design Docs

• Memory Design (Chinese)
• Memory Design (English)

Memory Manager UI

/memory

Open the full-screen memory manager for live inspection of memory stores:

• separate pages for user and project scope (1 / 2, or Tab to switch)
• highlights key fields (status, id, section, content) for each item
• supports r (refresh), a (show/hide archived), Esc (close)

Skill System

Skills are predefined workflow templates for reusing complex task steps.

Using Skills

/skill:web-research Search for LangGraph best practices
/skill:code-review Check code quality in src/ directory

Skill Locations

Creating Custom Skills

/skill-creator

Starts an interactive wizard that guides you through creating and saving new skills.

Session Management

View and Switch Sessions

/threads

Opens the session browser, displaying all historical conversations (time, message count, branch, etc.).

Start New Conversation

/clear

Clears the current conversation and starts a new session (old sessions are still saved and can be retrieved via /threads).

Slash Commands

Type / in the input box and press Tab to view and autocomplete all commands.

Session

Model & Interface

Context & Memory

Tools & Extensions

Others

FAQ

Q: No response on first launch? You need to configure the model first. Run /model → Press Ctrl+N to register a model → Fill in the API Key.

Q: How to interrupt a running task? Press Esc to interrupt the current AI response; if AI is waiting for tool approval, Esc acts as a rejection.

Q: Context too long causing slow response? Run /offload to manually compress history, or wait for automatic compression (triggers when usage exceeds 80%).

Q: How to make AI remember my coding preferences? Just tell AI directly, for example "Remember: my project uses 4-space indentation, no semicolons", and AI will automatically save it to memory files at the appropriate time.

Q: How to share skills across different projects? Place skill files in the ~/.invincat/agent/skills/ directory for global availability; place in .invincat/skills/ for current project only.