# harness
> **Agent = Model + Harness.** This crate is the *Harness* — the modular
> scaffolding that turns an LLM into an autonomous agent (any domain — research,
> ops, assistants, data work, coding — not coding-only).
A Rust framework for building production agents, based on the *harness
engineering* discipline (Böckeler / Thoughtworks and Lopopolo / OpenAI, 2026).
Full architectural rationale in **[DESIGN.md](DESIGN.md)**.
## What you get
| **Models** | OpenAI-compatible · Anthropic · Gemini · scriptable mock | `harness-models` |
| **Tools** | fs · shell (risk-gated) · web search/fetch | `harness-tools-fs/shell/web` |
| **Loop** | ReAct + tool dispatch + sensor feedback + auto-fix + final-synthesis | `harness-loop` |
| **Skills · Guides · Hooks · Sensors** | proc-macro registered; agentskills.io-compliant | `harness-macros`, `harness-skills` |
| **Memory** | `Memory` trait + JSONL store + cheap-model distillation | `harness-core`, `harness-context` |
| **Recall** | cross-session search (FTS5 / CJK trigram), owner-scoped | `harness-recall-sqlite` |
| **Learning loop** | self-evolving skills + memory at session end | `harness-loop`, `harness-tools-skills` |
| **Scheduler** | cron-style agent jobs + delivery (stdout / email) | `harness-scheduler` |
| **MCP** | server (expose tools) + client (consume any MCP server, via `rmcp`) | `harness-mcp`, `harness-mcp-client` |
| **Compactor · Sandbox · Blueprint · Observability · CLI** | progressive compaction · git-worktree isolation · state machine · JSONL traces · `harness` CLI | — |
## Quick start
```rust
use harness_loop::AgentLoop;
use harness_models::{OpenAiCompat, providers::DEEPSEEK};
use harness_tools_fs::{ListDir, ReadFile};
use harness_context::default_world;
use harness_core::Task;
use std::sync::Arc;
#[tokio::main]
async fn main() -> Result<(), harness::HarnessError> {
let model = OpenAiCompat::with_key(DEEPSEEK, "deepseek-chat",
std::env::var("DEEPSEEK_API_KEY").unwrap());
let mut world = default_world(".");
let outcome = AgentLoop::new(model)
.with_tool(Arc::new(ReadFile))
.with_tool(Arc::new(ListDir))
.run(Task { description: "What is the workspace name?".into(),
source: None, deadline: None }, &mut world)
.await?;
println!("{outcome:?}");
Ok(())
}
```
Register tools/skills/guides/sensors/hooks with `#[harness::tool]` /
`#[skill]` / `#[guide]` / `#[sensor]` / `#[hook]` — they auto-register via
`inventory`. Scaffold a project with `harness new`.
See **[examples/](examples/)** for memory, recall, the learning loop, the
scheduler, MCP, and end-to-end runs against a real model.
## Status
Latest: **v0.0.11** — SSRF-safe HTTP connect for the MCP client
(`connect_http_with_client`). Full history in **[CHANGELOG.md](CHANGELOG.md)**.
## License
MIT OR Apache-2.0.