Quickstart

Get Kazma running and answering messages in under 10 minutes. This guide is the install source of truth — keep it in lockstep with pyproject.toml, setup.ps1 / setup.sh, and Settings → Packages.

After kazma serve, open / — that is chat, not the dashboard. First-run asks for one provider key and one model. Inspectors live under More.

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1. Prerequisites

Requirement	Detail
Python	>=3.11, <3.15 (declared in pyproject.toml). 3.12 or 3.13 recommended.
Git	For cloning / operator upgrades via kazma update (git install path).
An LLM provider key	At least one of: OpenAI, Anthropic, Google (ADC), DeepSeek, xAI, OpenRouter, NVIDIA NIM, Mistral, Together, Cohere, Fireworks, Perplexity, AI21 — or a local server (Ollama / LM Studio). Azure OpenAI and AWS Bedrock are also supported natively.
Node.js (optional)	Only if you want to build/serve the Docusaurus docs site (docs/). Not required to run Kazma itself.

Note on extras: A bare pip install -e . already includes the agent, Web UI, TUI (textual is a core dep), gateways, swarm, and the document-generation libraries. The rag extra (chromadb, sentence-transformers, sqlite-vec) is what you add for vector memory / dense recall — see Memory & RAG. There is no [cli] extra; kazma-cli ships in the wheel.

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2. Install

Choose one path. All of them produce the same console scripts: kazma, kazma-tui, kazma-web.

Repo README has a short Windows-friendly Quick Start (ports, ERR_CONNECTION_RESET). Prefer this guide for depth.

Path A — Bootstrap script (recommended)

git clone https://github.com/Mubder/kazma.git
cd kazma

# Windows (PowerShell) — never chain with && / ||; use ; and $LASTEXITCODE
.\setup.ps1

# Linux / macOS / WSL
chmod +x setup.sh
./setup.sh

The script: requires Python 3.11+, installs uv if missing, runs uv sync --extra rag --extra dev --extra tui, copies .env.example → .env when needed, and import-checks LangGraph / aiosqlite / textual. It does not install [all] (that pulls torch, Playwright, WeasyPrint, Temporal, E2B).

Path B — Editable install (manual)

git clone https://github.com/Mubder/kazma.git kazma
cd kazma
python -m venv .venv

Activate the venv for your platform:

# Linux / macOS / WSL (bash, zsh)
source .venv/bin/activate

# Windows (PowerShell)
.venv\Scripts\Activate.ps1

:: Windows (CMD)
.venv\Scripts\activate.bat

# Same extras as the bootstrap scripts:
uv sync --extra rag --extra dev --extra tui
# or:
pip install -e ".[rag,dev,tui]"
# Everything:
# uv sync --all-extras
# pip install -e ".[all]"

Optional extras (only install the ones you need; Settings → Packages can add them later):

Extra	Packages	Enables
[rag]	chromadb, sentence-transformers, sqlite-vec	Vector memory / dense recall
[dev]	pytest, pytest-asyncio, pytest-cov, pytest-mock, ruff, mypy, locust, pre-commit	Tests + lint + git hooks
[test]	pytest stack + pytest-timeout + fakeredis	CI-lighter than dev
[tui]	textual, python-bidi	Terminal dashboard (kazma-tui; textual is also core)
[observability]	prometheus-client	/metrics scrape endpoint
[web]	playwright	Browser-automation skill (then playwright install chromium)
[push]	pywebpush	Web Push for turn-complete (self-disables if missing)
[document]	reportlab, python-docx, openpyxl, pypdf, …	Same libs as core; extra kept for explicit installs
[ocr]	pytesseract, pdf2image, pillow	OCR (needs system Tesseract)
[convert]	weasyprint	HTML→PDF (needs OS fonts / GTK on some hosts)
[document-platform]	document + ocr + convert + pymupdf + pypdfium2	Full Document Intelligence engines
[docling]	docling	Local hard-PDF salvage after PyMuPDF
[index]	tree-sitter, tree-sitter-python, tree-sitter-javascript	Codebase index (regex fallback always works)
[sandbox]	e2b-code-interpreter	Firecracker python_exec (E2B_API_KEY)
[durable]	temporalio	Temporal-wrapped swarm (KAZMA_TEMPORAL_HOST)
[database]	psycopg, pymysql, pymongo	Extra drivers for the database-client skill
[postgres]	psycopg[binary,pool], langgraph-checkpoint-postgres	Multi-replica shared state
[all]	meta — every extra above	Convenience; heavy

Native skills (browser, calendar, document-generator, document-platform, database) always load. Calling a tool whose backend isn’t installed returns an install-hint instead of crashing. Install the extra to activate that backend.

Document Intelligence first use: open /documents after start (core text parsers work without extras). For convert/redact engines: pip install -e ".[document-platform]". Optional system packages: Tesseract (OCR), ClamAV (malware scan), LibreOffice (some conversions). Guide: Document Intelligence.

Path C — Production Docker Compose

cp .env.example .env      # then edit .env (see step 3)
docker compose up -d --build

The image installs .[rag,postgres,document-platform], listens on container port 8000, and compose maps host 9090 → 8000 (HOST_PORT to override). Health check hits /health/ready every 30 s (300 s start period). See Deployment.

Do not start with raw python -m uvicorn on Windows — that forces a Proactor loop and silently drops Postgres checkpoints. Use kazma serve / the guard.

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3. Configure

Kazma reads configuration from three layers (in increasing precedence for runtime reads):

1. kazma.yaml — declarative defaults (the source of truth on first boot).
2. ConfigStore (SQLite) — kazma-data/settings.db; overrides kazma.yaml after first boot.
3. Environment variables — win in specific helpers (e.g. KAZMA_SECRET, KAZMA_VECTOR_PATH).

Full precedence rules are documented in Configuration → Override Precedence.

Minimal .env

Copy .env.example to .env and set at least one provider key:

# Linux / macOS / WSL
cp .env.example .env

# Windows (PowerShell)
Copy-Item .env.example .env

# Required for the OpenAI provider:
OPENAI_API_KEY=sk-...

# Or, for DeepSeek (key stored via provider config, not a dedicated env var —
# see Configuration). Local servers (Ollama/LM Studio) need no key.

# Optional: Telegram gateway
# TELEGRAM_BOT_TOKEN=123456:ABC...

# Optional: protect HITL approval endpoints (recommended for any non-localhost deploy)
# KAZMA_SECRET=generate-a-long-random-string

Important: Only OPENAI_API_KEY and KAZMA_API_KEY are read as generic env-var fallbacks by the LLM provider. Other providers (DeepSeek, Anthropic, xAI, …) are keyed through the ConfigStore provider list / kazma.yaml — there are no dedicated DEEPSEEK_API_KEY / ANTHROPIC_API_KEY env vars read by the code. See Configuration → API keys.

Check kazma.yaml

Open kazma.yaml and confirm:

agent:
  name: kazma
  language: ar        # 'ar' enables RTL + Arabic UI; 'en' for English
  rtl: true

models:
  default: gpt-4o-mini
  router: kazma       # Kazma's own router — not an import of LiteLLM
  fallback: gpt-4o-mini

llm:
  base_url: https://api.openai.com/v1
  model: gpt-4o-mini
  max_tokens: 4096
  temperature: 0.7
  timeout: 60.0
  input_cost_per_1m: 0.15
  output_cost_per_1m: 0.6

ui:
  host: 127.0.0.1
  port: 9090

The complete key-by-key reference is in Configuration.

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4. Run

Web UI (most common)

kazma serve              # default host 127.0.0.1, port 9090
kazma serve 9091         # if 9090 is taken (common on Windows + WSL/Docker)

On a watched host (Scheduled Task / systemd / launchd), pick up a git pull with the guard — do not kill python/uvicorn by hand:

& '.venv\Scripts\python.exe' scripts\service\kazma_guard.py --reload

Open the URL printed in the terminal (e.g. http://127.0.0.1:9090 or :9091). Use http, not https. Navigate to Chat and send a message.

Smoke-test:

curl http://127.0.0.1:9091/health

Binding security: Default host is 127.0.0.1. Non-loopback (KAZMA_HOST=0.0.0.0) requires a strong KAZMA_SECRET or the process exits. On loopback, a secret is auto-generated for the process if unset (not persisted).
Windows ERR_CONNECTION_RESET on :9090: often a stale WSL/Docker portproxy on 9090, not Kazma — see Troubleshooting §15.0 and run kazma serve 9091.

TUI

kazma-tui

A Textual dashboard with tabs for Dashboard, Chat, Files, Traces, Swarm, Settings. The TUI is primarily a read-only observability view of the core singletons (it initializes ModelRegistry and SwarmEngine on first launch if they don’t exist).

kazma ask (no web server)

The same LangGraph supervisor, without uvicorn. Tokens stream to stdout; tool lines go to stderr.

kazma ask "What files define the supervisor graph?"
kazma ask --plan "Add a rate limiter to the API"
kazma ask --json "fix the tests"          # NDJSON events (token/tool/done)
echo "summarize README.md" | kazma ask --json --no-stream -

On a TTY, danger tools prompt y/N (or a = allow for this session). Piped stdin / kazma ask - fail closed (no HITL on a consumed stdin). --yolo is the explicit headless escape hatch. Workspace is cwd (--workspace PATH to override).

ACP: kazma acp is Agent Client Protocol JSON-RPC on stdio. Point Zed / JetBrains at that command. The agent streams session/update chunks and tool calls, and asks the editor to approve danger tools via session/request_permission.

Verify with the CLI

kazma status

Probes the running server (/api/gateway/status, /api/swarm/status) and prints Python/Kazma versions, config path, and key package versions.

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5. Send your first message

Once kazma serve is running, the fastest loop is the Web UI chat. Behind the scenes:

1. Your text is POSTed to /api/chat/stream (SSE).
2. The supervisor node calls the active LLM with the registered tools.
3. If the LLM invokes a danger tool (e.g. file_write, shell_exec), execution pauses and an approval_required SSE event is emitted.
4. Approve via the Web UI button → POST /api/approve/\{thread_id\} → the graph resumes with Command(resume=\{"approved": true\}).

You can watch tool calls, token usage, and cost stream back as SSE events. See API & Extension Points → SSE event contract.

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6. Enable a second channel (Telegram, optional)

TELEGRAM_BOT_TOKEN=123456:ABC-DEF...

connectors:
  telegram:
    enabled: true

Restart the server. The Telegram adapter polls via long-polling (or a webhook at /api/webhooks/telegram if configured). Try /help, /status, /model in the bot.

Platform isolation: Your Telegram chat_id, user_id, and message_id never enter the LangGraph state — they live in the SessionStore and are re-attached on reply via _build_target_id(). See Gateways & Platforms.

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7. Next steps

If you want to…	Read
Understand the engine	Architecture
Tune every setting	Configuration
Add a custom tool / skill	Skills, MCP & Tools
Run a multi-worker swarm	Swarm Orchestration
Lock down a production deploy	Security & Safety + Deployment
Use Kazma in Arabic	Arabic & Cultural Features

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Documentation Audit Notes

• The previous README’s “4-layer memory pipeline” is partially wired — see Memory & RAG → Honest status. Quickstart deliberately avoids implying automatic memory retrieval, since RAG in the chat path requires the LLM to voluntarily call memory_search.
• tiktoken is not a declared dependency; token counting falls back to a chars/4 heuristic unless you pip install tiktoken yourself.