TigerHarness 0.2.0

A generic Claude Code agent harness: iterative task execution, Slack integration, and persistent memory management.

TigerHarness

A generic Claude Code agent harness: iterative task execution, Slack integration, and persistent memory management.

Sub-packages

Package	Description
tigerharness.agent_sdk	Backend-agnostic agent SDK. Same caller code, swappable runtimes: claude -p subprocess, Anthropic's claude-agent-sdk, OpenAI's openai-agents (planned).
tigerharness.task_runner	Fire-and-forget iterative task execution. Drives a persona through N Claude turns with periodic /compact.
tigerharness.slack_bridge	Slack Socket Mode bridge. Forwards DMs to a claude -p backend and posts replies back to the thread.
tigerharness.tiger_memory	Persistent agent memory: archive, journal, briefing with lazy rebuild, kind-decay must-memorize, and drill-down.

Installation

Pick the strategy that matches how you want to use tigerharness:

Option A — Scope it to one folder (recommended for teams)

Best when you want a self-contained teams/ directory that owns its own tigerharness install — nothing global, nothing borrowed:

mkdir -p ~/projects/teams && cd ~/projects/teams
uv init --bare                       # creates a minimal pyproject.toml
uv add 'tigerharness[all]'           # adds dep, creates .venv + uv.lock
uv run tigerharness init             # interactive team scaffolder

tigerharness only exists inside this folder's .venv; from then on you invoke it with uv run tigerharness ....

Option B — Install as a global CLI

Best when you want tigerharness available everywhere, like git or gh:

uv tool install 'tigerharness[all]'  # one-time; puts `tigerharness` on PATH
tigerharness init                    # works from any directory

Equivalent with pipx: pipx install 'tigerharness[all]'.

Option C — Traditional pip into an active venv

python -m venv .venv && source .venv/bin/activate
pip install 'tigerharness[all]'
tigerharness init

Choosing extras

tigerharness has zero hard dependencies by design. Optional features are gated behind extras so you don't pay disk/install cost for things you don't use:

Extra	Pulls in	Enables / required for
(none)	—	init scaffolder, task-runner with the default claude -p subprocess backend
[anthropic]	claude-agent-sdk	The official Claude Agent SDK backend (anthropic_sdk)
[slack]	slack-bolt, aiohttp, python-dotenv	slack-bridge (Slack Socket Mode DM bridge)
[memory]	pyyaml	tiger-memory (per-persona persistent memory; substring search only)
[memory-rag]	pyyaml, fastembed, sqlite-vec	tiger-memory with semantic search via local embeddings (free, ~50 MB model download on first use)
[memory-rag-openai]	pyyaml, openai, sqlite-vec	tiger-memory with semantic search via OpenAI embeddings (API key needed, no model download)
[all]	union of everything above	Everything works out of the box

Pick the union that matches what you'll use, e.g. 'tigerharness[slack,memory,anthropic]' for a Slack-fronted agent with persistent memory and the official SDK backend.

Heads up: the install commands quote the extras ('tigerharness[all]') because zsh treats [ as a glob character. In bash you can drop the quotes, but quoting always works.

Quick start

Scaffold a team and its first persona

tigerharness init is interactive — it walks you through picking (or creating) a team and adding a persona inside it. Every persona always belongs to a team, and a team is a self-contained directory:

tigers/
├── configs/
│   ├── personas.yaml              # team registry (auto-updated)
│   └── .env                       # Slack tokens (gitignored)
├── skills/
│   └── README.md                  # drop team-shared skills here
├── personas/
│   ├── chief/
│   │   └── prompt.md              # the persona's system prompt
│   └── scout/
│       └── prompt.md
└── memories/
    ├── chief/
    │   └── tiger-memory.config.yaml   # per-persona memory config
    └── scout/
        └── tiger-memory.config.yaml

# Fully interactive — prompts for persona name, team, slack/memory opts
tigerharness init

# Non-interactive — creates team 'tigers' with persona 'chief'
tigerharness init --persona chief --team tigers --yes

# Add a second persona to the same team
tigerharness init --persona scout --team tigers --yes

# Skip Slack or memory generation
tigerharness init --persona chief --team tigers --no-memory --no-slack --yes

Task runner

# 1. Point tigerharness at your team's persona registry
export TIGERHARNESS_PERSONAS_CONFIG=./tigers/configs/personas.yaml

# 2. Assign a task (5 iterations)
python -m tigerharness.task_runner assign \
    --to chief \
    --prompt "Research the latest developments in solar energy" \
    --iters 5

# 3. Check status
python -m tigerharness.task_runner list

# 4. View logs
python -m tigerharness.task_runner logs <task-id>

Slack bridge

# 1. Fill in your Slack tokens in tigers/configs/.env (from api.slack.com)
#    SLACK_APP_TOKEN=xapp-...
#    SLACK_BOT_TOKEN=xoxb-...
#    ALLOWED_SLACK_USER_IDS=U0123ABC

# 2. Run the bridge
python -m tigerharness.slack_bridge

Multi-team mode — one bridge process can serve N teams (one Slack app per team, separate bot identities, separate threads.json). Opt in by creating slack-bridge.yaml in your teams dir; subsequent tigerharness init runs auto-register each team. See docs/slack-bridge.md#multi-team-mode for the full setup.

Tiger memory

Each persona has its own memory config under tigers/memories/<persona>/tiger-memory.config.yaml. Edit it to point at your Claude Code project path, then:

--config is a top-level option for the tiger-memory sub-command, so it must appear before the verb:

# Save typing — the same path is reused everywhere
CFG=tigers/memories/chief/tiger-memory.config.yaml

# 1. Initialize the memory store (per persona)
tigerharness tiger-memory --config $CFG init

# 2. Bootstrap (one-time backfill from existing transcripts)
tigerharness tiger-memory --config $CFG bootstrap --dry-run
tigerharness tiger-memory --config $CFG bootstrap

# 3. Rebuild (incremental, run after each session)
tigerharness tiger-memory --config $CFG rebuild

# 4. Search memory
tigerharness tiger-memory --config $CFG search "solar energy"

# 5. Pin a must-memorize fact
tigerharness tiger-memory --config $CFG pin "Prefers solar over wind"

Configuration

All paths are resolved from environment variables -- no hardcoded paths.

Variable	Default	Description
TIGERHARNESS_STATE_DIR	~/.local/state/tigerharness-tasks/	Task runner state directory
TIGERHARNESS_PERSONAS_DIR	(none)	Directory containing <name>.md prompt files
TIGERHARNESS_SLACK_ENV	.env	Path to slack-bridge .env file
TIGERHARNESS_AGENT_CWD	.	Working directory for the Claude agent
TIGERHARNESS_AGENT_PROMPT	(none)	Path to the agent's system prompt
TIGERHARNESS_SLACK_BRIDGE_DIR	(none)	Path to slack-bridge service dir (for notify CLI)
TIGERHARNESS_ATTACHMENT_DIR	/tmp/slack-attachments	Where to stage downloaded files
TIGER_MEMORY_CONFIG	(none)	Path to tiger-memory YAML config
TIGER_MEMORY_CLI	(none)	Path to tiger-memory CLI binary

Examples

See examples/ for a fully-populated sample team folder (examples/tigers/) and standalone reference configs:

• examples/tigers/ -- sample team scaffolded by tigerharness init
• tiger-memory.config.yaml -- annotated memory config (standalone)
• env.example -- Slack bridge env template (standalone)

Requirements

• Python 3.11+
• For the default claude_p backend: the Claude Code CLI (claude) on PATH.
• For the anthropic_sdk backend: install with [anthropic] extra; pulls in claude-agent-sdk.

License

MIT