harness-rs 0.0.10

Rust agent framework based on harness-engineering principles (Lopopolo, Böckeler). Facade re-exporting the public surface of the harness-rs-* crates.
Documentation

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.

What you get

Layer What Crate
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

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/ for memory, recall, the learning loop, the scheduler, MCP, and end-to-end runs against a real model.

Status

Latest: v0.0.10 — the MCP client now speaks Streamable HTTP (remote servers) as well as stdio. Full history in CHANGELOG.md.

License

MIT OR Apache-2.0.