TigerHarness 0.1.3

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

# Core (claude -p backend only; Claude Code CLI must be on PATH)
pip install tigerharness

# With the official claude-agent-sdk backend
pip install tigerharness[anthropic]

# With Slack bridge support
pip install tigerharness[slack]

# With memory support
pip install tigerharness[memory]

# With memory + RAG (local embeddings, free)
pip install tigerharness[memory-rag]

# Everything
pip install tigerharness[all]

Or with uv:

uv add tigerharness
uv add tigerharness --extra all

Quick start

Scaffold a new project

# Create config files in the current directory
tigerharness init --name researcher

# Or with tiger-memory support
tigerharness init --name researcher --memory

# This creates:
#   personas/researcher.md     -- edit your agent's instructions
#   .env                       -- fill in Slack tokens
#   tiger-memory.config.yaml   -- (only with --memory)

Task runner

# 1. Point tigerharness at your personas directory
export TIGERHARNESS_PERSONAS_DIR=./personas

# 2. Assign a task (5 iterations)
python -m tigerharness.task_runner assign \
    --to researcher \
    --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 .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

Tiger memory

# 1. Copy the example config
cp examples/tiger-memory.config.yaml tiger-memory.config.yaml
# Edit: set store.root and sources.project_path

# 2. Initialize the memory store
tiger-memory --config tiger-memory.config.yaml init

# 3. Bootstrap (one-time backfill from existing transcripts)
tiger-memory --config tiger-memory.config.yaml bootstrap --dry-run
tiger-memory --config tiger-memory.config.yaml bootstrap

# 4. Rebuild (incremental, run after each session)
tiger-memory --config tiger-memory.config.yaml rebuild

# 5. Search memory
tiger-memory --config tiger-memory.config.yaml search "solar energy"

# 6. Pin a must-memorize fact
tiger-memory --config tiger-memory.config.yaml 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 sample configs:

• tiger-memory.config.yaml -- annotated memory config
• personas/researcher.md -- sample persona prompt
• env.example -- Slack bridge env template

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