harness

Agent = Model + Harness. This is the Harness — the scaffolding that turns an LLM into an autonomous agent. Any domain: research, ops, assistants, data work, coding.

A Rust framework for production-oriented AI agents, built on the harness engineering discipline (Böckeler/Thoughtworks, Lopopolo/OpenAI, 2026). Compile-time type-safe, deterministic-first, observable. Rationale in DESIGN.md.

What you get

Quick start

use harness_loop::AgentLoop;
use harness_models::ApiKind;
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> {
    // One model API: protocol family + base_url + model + key. No hardcoded URLs.
    let model = ApiKind::OpenAI.build("https://api.deepseek.com", "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 with harness new. See examples/.

Loop engineering

When one agent run isn't enough — you want it to run on a cadence, verify itself, stay on budget, and escalate when unsure — harness-loop-engine gives you trusted recurring loops:

use harness_loop_engine::{LoopEngine, patterns};

// A daily, report-only (L1) triage loop. Maker proposes, checker verifies,
// the gate escalates, the budget caps spend, memory carries state forward.
let report = LoopEngine::new(patterns::daily_triage(), model)
    .with_maker_tool(read_only_tool)
    .run_once().await;

Loops earn autonomy in stages — L1 report → L2 assisted (human gates every change) → L3 unattended (allowlisted actions only). After a verified L3 approval, an ActionExecutor performs the project-specific side effect (commit, PR, comment, patch, ticket update). See DESIGN.md §11.5.

For Agents

Building or operating agents with harness, or landing in this repo as a coding agent? Start here:

• Map: harness-core defines every trait (Model, Tool, Memory, Sensor, Guide, Hook) with zero heavy deps — read it first. Everything else builds on it; the dependency graph is in DESIGN.md §4.
• Add a tool/skill: annotate a function with #[harness::tool] or #[skill] — no registry edits, inventory collects it at link time. Skills follow the agentskills.io spec.
• Pick a model: always ApiKind::{OpenAI,Anthropic,Gemini}.build(base_url, model, key). There are no hardcoded provider URLs — pass base_url yourself.
• Don't burn tokens on what code can do: lint/format/git/file-moves run deterministically via Sensors and Hooks, not the model. Context is scarce — the Compactor manages it; don't stuff everything into one prompt.
• Isolate, don't interrupt: permissions are burned in at sandbox spawn (WorktreeSandbox / ContainerSandbox), not prompted per call.
• Run loops responsibly: unattended loops make unattended mistakes. Keep LoopSpec.intent honest, start at L1, set a TokenBudget, graduate levels only as you build trust. Verification stays on you.

Status

Latest: v0.0.15 — new harness-loop-engine (loop engineering) + simplified, hardcoded-URL-free model API (ApiKind). History in CHANGELOG.md.

License

MIT OR Apache-2.0.