llmcode-cli 1.9.0

Open-source AI agent runtime for any LLM — production-grade coding agent with multi-layer memory, multi-agent orchestration, and defense-in-depth security

llmcode

Python-native coding agent runtime tuned for local LLMs
5-layer memory · synthesis-first multi-agent · per-model prompts for Qwen / Llama / DeepSeek

Quick Start · Why llmcode · Features · vs Other Tools · Configuration · Docs

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Why llmcode?

There are several great open-source AI coding agents now (opencode, Aider, Continue, etc). llmcode exists for a specific niche they don't fully serve:

You want a Claude Code-style coding agent that runs your own model on your own GPU, written in Python so it integrates with your existing Python LLM stack, with deep optimization for the smaller models you'll actually run locally.

If you check any of these boxes:

• You run vLLM, Ollama, or LM Studio with Qwen / Llama / DeepSeek locally
• You don't want another Node.js runtime in your stack (you already have Python)
• You've tried tools tuned for Claude/GPT and watched smaller models drown in the system prompt
• You need multi-agent coordination that doesn't over-spawn on local models
• You want persistent project memory that survives across sessions
• You care about CJK / multi-language terminal handling

then llmcode is for you.

If you mostly use cloud APIs and don't need any of the above, opencode is more mature and you should probably use it.

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Quick Start

pip install llmcode-cli

llmcode: command not found? pip installs scripts to ~/.local/bin (Linux/macOS) or %APPDATA%\Python\Scripts (Windows). Add it to your PATH:

export PATH="$HOME/.local/bin:$PATH"

With a local model (zero cost, fully offline):

mkdir -p ~/.llmcode
cat > ~/.llmcode/config.json << 'EOF'
{
  "model": "qwen3.5",
  "provider": {
    "base_url": "http://localhost:8000/v1"
  }
}
EOF

llmcode

With a cloud API:

cat > ~/.llmcode/config.json << 'EOF'
{
  "model": "claude-sonnet-4-6",
  "provider": {
    "base_url": "https://api.anthropic.com/v1",
    "api_key_env": "ANTHROPIC_API_KEY"
  }
}
EOF

llmcode

Docker (self-hosted):

docker pull ghcr.io/djfeu/llmcode:latest
docker run -it --rm \
  -v "$PWD:/workspace" \
  -v "$HOME/.llmcode:/home/llmcode/.llmcode" \
  --network host \
  ghcr.io/djfeu/llmcode

Modes

llmcode                       # Default fullscreen TUI
llmcode --provider ollama     # Auto-detect Ollama + interactive model selector
llmcode --mode plan           # Read-only mode, plan before execution
llmcode --yolo                # Auto-accept all permissions (dangerous)
llmcode -x "find large files" # Shell assistant: translate to command + execute
llmcode -q "explain this"     # Quick Q&A without TUI
llmcode --serve --port 8765   # Remote WebSocket server
llmcode --connect host:8765   # Connect to remote agent
llmcode --resume              # Resume from checkpoint

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How it compares

llmcode is deeply influenced by Claude Code's architecture and borrows proven patterns from opencode. Here's where it lands:

Feature	llmcode	opencode	Claude Code
Open source	✅ MIT	✅ MIT	❌
Language	Python	TypeScript	TypeScript
Local model first	✅	⚠️	❌
AGENTS.md / CLAUDE.md fallback	✅	✅	⚠️
LLM-driven /init	✅	✅	✅
Per-model system prompts	✅ (8)	✅ (7)	N/A
Qwen / Llama / DeepSeek tuned prompts	✅	❌	❌
Custom slash commands	✅	✅	✅
Tab agent cycling	✅	✅	❌
Skill router (auto match)	3-tier	manual	❌
Memory system	5-layer	basic	basic
Multi-agent coordinator	synthesis-first	task tool	❌
Context overlap detection	✅	❌	❌
Diminishing returns auto-stop	✅	❌	❌
Subagent resume (task_id)	✅	✅	❌
Plugin compatible with Claude Code ecosystem	✅	✅	✅
Cold start	370ms	unknown	600ms+
MCP servers	✅	✅	✅
YOLO mode	✅	✅	✅

Where llmcode is uniquely strong: 5-layer memory, synthesis-first multi-agent, diminishing returns detection, Qwen/Llama prompt tuning, Python-native integration.

Where opencode is stronger: Desktop & IDE variants, much wider community, more mature.

---

Features

Local-LLM optimization

This is llmcode's core focus. Local models behave very differently from Claude / GPT:

• They drown in big system prompts. llmcode's 3-tier skill router only injects skills that match the current intent — keyword match → TF-IDF similarity → optional LLM classifier. No more "all 28 skills loaded every turn".
• They follow instructions too literally. llmcode has separate per-model system prompts for Qwen, Llama, DeepSeek, Kimi, Codex, Gemini, GPT, and Claude — auto-selected from model name.
• They tend to repeat themselves. llmcode's diminishing returns detection auto-stops when continuation produces < 500 new tokens for 3+ iterations in a row.
• They over-spawn agents. llmcode's coordinator forces a synthesis step before delegation, asking "should I delegate at all?" before splitting work.

Memory system (5 layers)

Layer	Purpose	Lifetime
L0 Governance	Project rules from CLAUDE.md / AGENTS.md / .llmcode/governance.md	Permanent, always loaded
L1 Working	Current task scratch space	Ephemeral
L2 Project	Long-term project knowledge with 4-type taxonomy (user/feedback/project/reference)	Persistent, DreamTask consolidates
L3 Task	Multi-session task state machine (PLAN→DO→VERIFY→CLOSE→DONE)	Cross-session
L4 Summary	Past session summaries	Persistent

Plus typed memory with MEMORY.md index, 25KB hard limit, and content validation that rejects derivable content (git logs, code dumps, file path lists).

See docs/memory.md for the full guide.

Coordinator with synthesis-first

user task → synthesize → should_delegate? → decompose → spawn/resume → wait → aggregate

The coordinator's first action is not decomposition — it's a synthesis check that asks the LLM "do I actually need to delegate this, and if so, what do I already know vs. what needs investigation?" This catches 30-50% of cases where naive coordinators would have spawned 3-5 unnecessary workers for trivial tasks.

Plus subagent resume — pass resume_member_ids to continue existing workers instead of spawning fresh, so multi-stage workflows keep their accumulated context.

See docs/coordinator.md for the full tutorial.

Tools

Category	Tools
File I/O	read_file, write_file, edit_file, multi_edit (with resolve_path workspace boundary check)
Search	glob_search, grep_search, tool_search
Execution	bash (21-point security), agent (sub-agents)
Git	git_status, git_diff, git_log, git_commit, git_push, git_stash, git_branch
Notebook	notebook_read, notebook_edit
Computer Use	screenshot, mouse_click, keyboard_type, key_press, scroll, mouse_drag
Task Lifecycle	task_plan, task_verify, task_close
Scheduling	cron_create, cron_list, cron_delete
IDE	ide_open, ide_diagnostics, ide_selection
Swarm	swarm_create, swarm_list, swarm_message, swarm_delete, coordinate
Skills	skill_load (LLM-driven loading on top of auto-router)

Smart per-model tool selection: GPT models get apply_patch (unified diff format), other models get edit_file. Auto-detected from model name.

Path resolution: resolve_path() auto-corrects wrong absolute paths from LLM (e.g. llm-code vs llm_code confusion) with workspace boundary check to prevent path traversal.

Security

• 21-point bash security — injection detection, network access control, credential paths, recursive operation warnings, etc.
• MCP instruction sanitization — strips prompt injection patterns
• Bash output secret scanning — auto-redacts AWS/GitHub/JWT keys before they enter LLM context
• Environment variable filtering — sensitive vars replaced with [FILTERED]
• File protection — .env, SSH keys, *.pem blocked on write
• Workspace boundary checks — file tools refuse paths outside the project tree

Terminal UI

• Native text selection — uses mouse=False + plain Text rendering so terminal native selection works (handles CJK correctly)
• Cmd+V auto-detect — text via bracketed paste, image via clipboard fallback
• Shift+Tab cycles agents — BUILD → PLAN → SUGGEST → BUILD
• PageUp/Down + Shift+↑/↓ — scrollback navigation
• /yolo — toggle auto-accept
• /init — generate AGENTS.md from repo analysis
• /copy — copy last response to clipboard
• /search — cross-session FTS5 search
• Background task indicator — status bar shows running/pending tasks
• Vim mode — full motions, operators, text objects

Hooks (24 events)

{
  "hooks": [
    {"event": "post_tool_use", "tool_pattern": "write_file|edit_file", "command": "ruff format {path}"},
    {"event": "session.*", "command": "echo $HOOK_EVENT >> ~/agent.log", "on_error": "ignore"}
  ]
}

Categories: tool, command, prompt, agent, session, http.

Marketplace

Compatible with Claude Code's plugin ecosystem.

/skill                       # Browse skills
/plugin install obra/superpowers
/mcp                         # Browse MCP servers

Sources: Official (anthropics/claude-plugins-official), Community, npm, GitHub.

---

Configuration

{
  "model": "qwen3.5",
  "provider": {
    "base_url": "http://localhost:8000/v1",
    "timeout": 120
  },
  "permissions": {
    "mode": "prompt"
  },
  "model_routing": {
    "sub_agent": "qwen3.5-32b",
    "compaction": "qwen3.5-7b",
    "fallback": "qwen3.5-7b"
  },
  "skill_router": {
    "enabled": true,
    "tier_a": true,
    "tier_b": true,
    "tier_c": false
  },
  "diminishing_returns": {
    "enabled": true,
    "min_continuations": 3,
    "min_delta_tokens": 500
  },
  "swarm": {
    "enabled": true,
    "synthesis_enabled": true,
    "max_members": 5
  },
  "thinking": { "mode": "adaptive", "budget_tokens": 10000 },
  "dream": { "enabled": true, "min_turns": 3 },
  "hooks": []
}

Config locations (low → high precedence)

1. ~/.llmcode/config.json — User global
2. .llmcode/config.json — Project
3. .llmcode/config.local.json — Local (gitignored)
4. CLI flags / env vars

Optional features

pip install llmcode-cli[voice]          # Voice input via STT
pip install llmcode-cli[computer-use]   # GUI automation
pip install llmcode-cli[ide]            # IDE integration
pip install llmcode-cli[telemetry]      # OpenTelemetry tracing
pip install llmcode-cli[treesitter]     # Tree-sitter multi-language repo map

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Docs

• Memory system — 5-layer architecture, typed taxonomy, DreamTask
• Coordinator — synthesis-first orchestration, resume mechanism
• Architecture — high-level system overview
• Plugins — building plugins
• Tools — tool reference
• Configuration — all config options

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Architecture

llm_code/               29,000+ lines Python
├── api/                Provider abstraction (OpenAI-compat + Anthropic)
├── cli/                CLI entry point, TUI launcher, oneshot modes (-x/-q)
│   └── templates/      LLM-driven command templates (init.md, etc)
├── runtime/            ReAct engine, 5-layer memory, skill router,
│                       compression, hooks, permissions, checkpoint,
│                       dream, VCR, speculative execution, telemetry,
│                       file protection, sandbox, secret scanner,
│                       conversation DB, tree-sitter repo map
│   └── prompts/        Per-model system prompts (anthropic, gpt,
│                       gemini, qwen, llama, deepseek, kimi, codex)
├── tools/              30+ tools with deferred loading + security
├── task/               PLAN/DO/VERIFY/CLOSE state machine
├── hida/               Dynamic context loading (10-type classifier)
├── mcp/                MCP client (4 transports) + OAuth + health checks
├── marketplace/        Plugin system + security scanning
├── lsp/                Language Server Protocol client
├── remote/             WebSocket server/client + SSH proxy
├── vim/                Vim engine
├── voice/              STT (Whisper, Google, Anthropic backends)
├── computer_use/       GUI automation
├── cron/               Task scheduler
├── ide/                IDE bridge (WebSocket JSON-RPC)
├── swarm/              Multi-agent coordinator (synthesis-first)
└── utils/              Notebook, diff, hyperlinks, search
tests/                  3,696 tests across 270+ files

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Contributing

git clone https://github.com/DJFeu/llmcode
cd llmcode
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest                  # 3,696 tests
ruff check llm_code/    # lint

Looking for contributors interested in:

• More provider integrations (Anthropic native, OpenAI, Google, xAI, DeepSeek)
• More built-in skills (especially for Python-specific workflows)
• IDE integrations (VS Code, JetBrains, Neovim)
• i18n / l10n
• Per-model prompt tuning for additional model families
• Documentation, tutorials, examples
• Real-world usage feedback (especially on local Qwen/Llama/DeepSeek)

Requirements

• Python 3.11+
• An LLM server (vLLM, Ollama, LM Studio, or any OpenAI-compatible cloud API)

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License

MIT