Sikao-Engine/KimiX
The next-gen lightweight coding agent cli
安装Install
dsh plugin --profile web add github:Sikao-Engine/KimiX把 Sikao-Engine/KimiX 加入你的 DSH 配置（web profile）即可启用。
READMEREADME
Kimi-CLI-X
Install from Source
python install.py
pip Install
pip install kimix
python -m kimix.cli
# or
kimix
python -m kimix
Note: This repo supports not only KIMI LLM but also various API keys! Like OpenAI, Anthropic, etc. Default config templates are in
docs/; usekimix --config=xx.jsonafter setup.
Why Kimi-CLI-X?
Kimi-CLI-X is a deep optimization of the original Kimi-CLI, focusing on prompt efficiency, tool reliability, and extensibility, plus new tools for real-world development.
Optimizations
- Lean system prompts — Compressed initial prompts and tool descriptions down to ~2000 tokens while covering nearly all built-in tools.
- Hardened permissions & validation — Properly handles Shell, Glob, and other tool validations to reduce retry loops from failures.
- Better subprocess output — Redirects large outputs to temp files and filters redundant logs for easier backend retrieval.
- Simpler concurrency — Streamlined design for subprocesses, sub-agents, and background tasks.
- Programmable prompts — Allows custom system prompt injection at the upper layer for flexible scenarios.
- Explicit conversation management — Clearer multi-task orchestration and state tracking.
- Write-and-validate — Auto format checks and warnings on strict config files to prevent model-hallucinated errors.
- Multi-API support — Import custom configs compatible with OpenAI, Anthropic, and more.
- Verified backends — Tested against kimi, xai, anthropic, openai_legacy, openai_responses, google_genai/gemini, vertexai/vertex, plus 25+ Hermes-ported providers (deepseek, openrouter, xiaomi, zai, minimax, bedrock, ...). See
kimi-cli/tests/core/test_create_llm.pyand the Supported Providers list.
New Capabilities
| Capability | Description |
|---|---|
| Interactive shell tools | Start and continue Bash/Powershell/Run sessions via task_id, with optional wait_for_pattern. |
| Docx / PDF conversion | Built-in document conversion without external deps. |
| Python script execution | Agent can run Python scripts directly. |
| Error logging | Records tool-call errors for model backtracking and improvement. |
| Script system | Combines prompts with Python logic to orchestrate complex tasks. |
| Enhanced web fetch (fetch_url) | Headless-browser-based Markdown output (not plain text), supports output_path and auto-truncation for超长 content; zero external service dependency. |
Best-of-N sampling (AgentSwarm parallel_sample) |
Run the SAME task N times in isolated workspaces (git worktree / temp copy), pick the winner via self_eval or majority selection, then apply and verify the winning diff — never a silent accept. |
Scriptable Workflows (Core Advantage)
Unlike traditional CLI interaction where you type commands one by one, Kimi-CLI-X lets you write Python scripts to orchestrate entire workflows. You can combine prompts, loops, conditionals, and tool calls into fully automated, reproducible task pipelines:
from kimix import *
from pathlib import Path
clear_default_context()
for i in Path('docs').glob('*.md'):
prompt(f'''According to the new git commits, update document `{i}`''')
Benefits:
- Batch automation: Use native Python syntax (
forloops, file globbing) to fire tasks at multiple files at once. - Complex orchestration: Freely compose mode switches, tool calls, and logic into multi-stage, multi-branch workflows.
- Reproducible & maintainable: Workflows live as version-controlled scripts, not ephemeral chat history.
Context Memory Architecture
Kimi-CLI-X embeds an automatic context memory system inside the KimiSoul core loop, keeping long conversations coherent without manual intervention. Three layers work together:
1. Conversation History Index (HistoryIndex)
Every user/assistant message is automatically indexed by BM25 inverted index (N-gram, n=2) on append, persisted to <session>/history_index/<id>.json, and survives process restarts. Cap at 500 rounds; oldest evicted automatically.
2. Automatic Context Compaction (SimpleCompaction)
Triggered when context token ratio hits compaction_trigger_ratio or free space falls below reserved_context_size:
- Retention policy: Recent N rounds kept verbatim (depth adapted by
adaptive_preserve_depth— deepened on errors, thinking, multi-file edits, etc.); first message always kept (primacy effect). - LLM summarization: Old messages compressed into structured summaries via a lightweight LLM call; thinking blocks discarded.
- Cascade handling: When already-compacted content is compressed again (depth ≥3), switches to
COMPACT_CASCADEprompt to prevent information degradation. - Post-compaction, all rounds marked
is_compactedin HistoryIndex for future retrieval.
3. Auto History Retrieval + On-Demand Recall
- Auto retrieval (
_maybe_auto_retrieve_history): Each round, if user input ≥10 chars, BM25-searches HistoryIndex for matching compacted rounds; injects matches aboveauto_retrieve_history_thresholdas[Auto-retrieved from past conversation]. retrievetool: the agent can actively search all archived history (including compacted rounds) by natural-language query, returning verbatim excerpts with relevance scores (or fetch a turn byid).
┌──────────────┐ append ┌──────────────┐ overflow ┌──────────────────┐
│ Context │ ───────────► │ HistoryIndex │ ────────────► │ SimpleCompaction │
│ (live window)│ │ (BM25 index) │ │ (LLM summary) │
└──────────────┘ └──────────────┘ └──────────────────┘
▲ │ │
│ auto-retrieve │ │
└────────────────────────────┘ │
│ Retrieve (agent主动recall) │
└────────────────────────────────────────────────────────────┘
Agent Harness: Self-Regulation & Reminders
The KimiSoul core loop actively keeps long runs on track — no manual babysitting. It works in CLI, server, and sub-agent sessions alike.
- Verification Gate — a turn can't end while todos are unfinished, or while files were edited without running any check. Failing checks are fed back to the agent to fix.
- Anti-loop detection — catches repeated edits to the same file across different tools, and the same error recurring without a root-cause fix; nudges the agent to change strategy.
- Todo reminders — unfinished todos are periodically re-surfaced at the end of the context, so goals never drift out of attention.
- Compact reminders — when context fills up (~70%), the agent is prompted to compact on its own terms before forced auto-compaction kicks in.
- Budget reminders (opt-in) — wrap-up warnings as the per-turn step/time budget runs out, so the agent finishes gracefully instead of being cut off.
- Context meter — when context usage shifts materially, the agent is reminded to recall past history with the
Retrievetool. - Decision-aware compaction — compaction summaries preserve a
Decisions & Conclusionsand aVerification Statussection, so early decisions and verified work survive. - Context pruning — stale tool outputs, thinking blocks, and near-duplicate content are automatically elided to reclaim context space.
todo_write
The todo_write tool tracks multi-step plans:
- Incremental updates with append/overwrite modes, fuzzy title matching, and per-todo notes.
- Nested sub-todos via
todo_write(send the full tree) ortodo_update(parent=...);todo_update(complete=True)finishes a subtree in one call.
Best-of-N Sampling
AgentSwarm's parallel_sample mode runs the same task N times in isolated workspaces (git worktree / temp copy), picks the winner by model self-evaluation or majority vote, then applies and verifies the winning diff. Failures are explicit errors — never silently accepted.
Documentation Index
Tutorials
| Document | Description |
|---|---|
docs/tutorials/1_quick_start_en.md |
Quick start guide: Git submodules, uv env setup, CLI args, and interactive commands. |
docs/tutorials/2_long_task_en.md |
Long task strategy in KimiX. |
docs/tutorials/3_builtin_tools_en.md |
Complete built-in tool guide: file I/O, search, code execution, process management, doc conversion, plan mode, sub-agents, plus prompt strategies and best practices. |
docs/tutorials/4_skills_en.md |
Custom skill authoring: design principles, directory structure, SKILL.md spec, resource organization, testing, packaging, and installation. |
docs/tutorials/5_server_en.md |
HTTP server tutorial: FastAPI + SSE, OpenCode-compatible REST API, session management, event streaming, SSE CLI debugger, dummy mode, and client implementation. |
docs/tutorials/6_multi_provider_en.md |
Multi-provider configuration: route sub-agents and planner to different LLM providers with role-tagged sub_providers. |
Config Reference
| File | Description |
|---|---|
docs/config.json |
Sample model config with model, url, api_key, capabilities, etc. |
.kimix/config.json |
Workspace behavior config: protected_write_paths, protected_read_paths, forbidden_commands, etc. |
.kimix/skill.json |
Workspace skill directory config: skill_dir field (string or array) for extra skill directories, resolved relative to workspace. |
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