releases | changelog | pkg.go.dev | leather.sh | pate.sh
Local agent infrastructure in one stdlib-only Go binary.
Leather runs declarative agents on your workstation, server, or Raspberry Pi: scheduled jobs, one-shot runs, webhook-driven workflows, tool calling, and auditable outputs.
No Python stack. No hosted control plane. No broker, telemetry, or dependency pile.
cat > summarizer.agent.md <<'EOF'
---
name: summarizer
---
You are a concise planning assistant. Output bullet points only.
EOF
leather validate --agent-dir .
leather run summarizer.agent.mdSixteen demos in examples/, each one make target away.
Example 02: scheduled agent
cp examples/env.example examples/.env
$EDITOR examples/.env
make example-02
Example 06: multi-agent curing + devtools UI
make example-06
Same code path also runs a profiled 100-webhook burst — 500 LLM calls, 965K tokens, five fan-out/fan-in stages — end to end in 190s at 6.5% avg host CPU and no measurable IO pressure. Leather isn't the bottleneck; the model is. Full profile in examples/11-high-volume-ci.
| Agents | *.agent.md — Markdown front and a system prompt with 0-N turns and variable support. |
| Tools | Skills, toolsets, MCP server support, and shell-mcp (make build-shell-mcp) for turning shell commands into agent tools. |
| Curings | Bind agents to queues for flexible multi-stage routing. |
| Queues | Filesystem FIFO — configurable depth, backpressure, concurrency, dead-letter routing, and single-use parameterized queues. |
| Workers | Goroutines with hooks into queues. |
| Tannery | Pipelines connecting agents, curings, workers, and artifacts, with fan-in / fan-out routing. |
| DevTools | Stdlib-JS UI with session timeline, event inspector, curing flow diagram, and live SSE. |
| Notifications | Telegram and Signal backends. |
A complete agent:
---
name: summarizer
---
You are a concise planning assistant. Output bullet points only.A schedule for it (*.lifecycle.yaml, optional):
agent: summarizer
schedule: "0 9 * * *"
model: llama3
prompt: Summarize the three most important things to do today.Run it:
leather validate # check everything parses
leather run ~/.leather/agents/summarizer.agent.md # run once
leather serve --pretty --stats # run on scheduleChaining two agents behind a webhook — one triages, one reviews, an artifact comes out the other end — is the same primitives composed: see the GUIDE.md: two-agent pipeline recipe and examples/06-multi-agent-curing.
From source:
git clone https://github.com/TGPSKI/leather
cd leather
make build && make build-shell-mcp
make installWith go install:
go install github.com/TGPSKI/leather/cmd/leather@latest
go install github.com/TGPSKI/leather/cmd/shell-mcp@latestVerify the install — no LLM endpoint required:
leather --version # prints version
make example-01 # runs a mock-LLM example end-to-endEvery command above needs a model and an LLM endpoint from somewhere. All
three forms set the same values — flag wins over env var, env var wins over
config.yaml:
# flag
leather run agent.md --model llama3 --llm-endpoint http://localhost:11434# env var
export LEATHER_MODEL=llama3
export LEATHER_LLM_ENDPOINT=http://localhost:11434# config.yaml
model: llama3
llm_endpoint: http://localhost:11434leather speaks the OpenAI Chat Completions API, so cloud endpoints work the
same way. The bearer token resolves inline value → pass → env var, in that
order:
export LEATHER_LLM_API_KEY="sk-..."
leather serve --llm-endpoint https://api.openai.com --model gpt-4o-mini# config.yaml + unix pass
llm_endpoint: https://api.openai.com
model: gpt-4o-mini
llm_api_key:
pass: openai/api-key # `pass show openai/api-key`
env: OPENAI_API_KEY # fallback when pass is empty or unavailable- Implementation guide → docs/GUIDE.md — every file format plus recipes for each pattern above
- Architecture & data flow → docs/ARCHITECTURE.md
- Glossary → docs/GLOSSARY.md
- Per-package docs → docs/modules/
- Agent contributor guide → AGENTS.md
- Domain-specific subagent guides → .subagents/