Harness Rs Cortexdb

crates.io license

harness-rs is a Rust framework for building AI agents that run in production: assistants, research and data agents, ops automation, coding agents, BI and process agents for a business. You bring a model and the tools for your domain; the framework supplies everything around the model that makes it behave like a dependable program instead of a chat window.

That "everything around the model" is the harness. An agent is a model plus a harness, and most of what decides whether an agent works — whether it stops, whether it is right, what it costs, whether anyone can tell afterwards what it did — lives in the harness, not in the model:

• Guides shape the request before the model sees it (feed-forward): rules, boundaries, the user's profile, recalled memory.
• Sensors check the work after each action (feedback): lint, format, tests, anything code can verify. Blocking signals go back to the model; mechanical fixes are applied directly as patches, without spending a model turn.
• Hooks observe and govern every lifecycle event — they can deny a tool call, inject text, or just record what happened.
• Acceptance checks decide when the work is actually done. The model stopping is its opinion; the check is the fact.
• Guards keep the run bounded: a token budget with staged compaction, stuck and monotony detection, per-call tool deadlines, a result-size ceiling that spills oversized payloads to disk, cancellation.

What sets it apart:

• A loop, not a call — ReAct with tool dispatch, sensor feedback and auto-fix, under a budget; guards for stuck detection, acceptance, spill, deadlines.
• Autonomy earned in stages — L1 report → L2 assisted (human gate) → L3 unattended (allowlist only). Graduate the agent as you build trust.
• Honest isolation — macOS Seatbelt, Linux Bubblewrap and containers enforce; a git worktree isolates changes, not capability, and reports itself that way.
• Memory that compounds — procedural, semantic and episodic, across sessions, with the backend chosen by a URL.
• Code does what code can — lint · format · git run as Sensors and Hooks, not model turns. Measured, not asserted.
• Compile-time type safety — tools, skills, guides, sensors and hooks are plain Rust types registered by proc-macro; structured output can be typed (run_typed::<T>).

How a run works

One call to AgentLoop::run takes a Task and returns an Outcome. In between:

  Task
   │
   ▼
 Context ── system prompt · tool schemas · history · metadata · policy
   │        (context budget sized from the model's context window)
   ▼
 Guides ── applied once, before the first model call (feed-forward)
   │
   ▼
 ┌─────────────────────────── each iteration (≤ max_iters) ──────────────────────┐
 │                                                                               │
 │  Compaction ── staged as the context fills: 60% … 95% of the input budget     │
 │      │                                                                        │
 │      ▼                                                                        │
 │  Model ── complete() or stream()  ─────────────▶  ModelOutput                 │
 │      │                                                │                       │
 │      │                                no tool calls ──┤                       │
 │      │                                                ▼                       │
 │      │                               Acceptance checks (+ sealed contract)    │
 │      │                                  fail ─▶ reason fed back, loop again   │
 │      │                                  pass ─▶ Outcome::Done ────────────────┼─▶
 │      ▼ tool calls                                                             │
 │  Tools ── leading read-only calls prefetched in parallel                      │
 │      │    each call: PreToolUse hook (may deny) → dispatch under deadline     │
 │      │    → result ceiling / spill to disk → PostToolUse hook                 │
 │      ▼                                                                        │
 │  Sensors ── signals back to the model · auto-fix patches applied to the world │
 │      │                                                                        │
 │      ▼                                                                        │
 │  Stuck / monotony detection ── spinning ─▶ forced answer · Outcome::Stuck     │
 │                                                                               │
 └── max_iters reached ─▶ forced final answer (tools off), Outcome::BudgetExhausted
     cancellation token ─▶ Outcome::Cancelled (partial text kept)

Every step above fires a lifecycle Event (SessionStart, PreModel, PostToolUse, PreCompact, AcceptanceChecked, BudgetWarning, …) through one hook bus. The loop's own behaviour and everything that watches it hang off that same seam:

 AgentLoop ── Event ──▶ HookBus   first Deny wins · Inject payloads concatenate
                          │
                          ├─▶ TelemetryHook       events + the run's bill (gen_ai.*)
                          │                       inside the loop's own spans
                          ├─▶ AuditHook           who asked, what was answered,
                          │                       which data was touched
                          ├─▶ SessionRecorder     every event as JSONL → replay
                          ├─▶ BroadcastHook       live feed for UIs and monitors
                          ├─▶ TranscriptRecorder  turns into any Memory, with a TTL
                          └─▶ your #[hook]        policy, approval gates, …

Crates

The workspace publishes as harness-rs-*. Upper layers build on lower ones; the middle row are siblings you pick à la carte. A crate boundary exists only where it lets you skip a dependency tree you might not want — heavy backends (SurrealDB, SQLite, gRPC, OTLP) sit behind features.

┌──────────────────────────────────────────────────────────────────────────────────┐
│ harness-cli · harness-serve      the `harness` binary · sessions, auth, audit    │
│                                  · http (axum) · grpc (tonic) · /metrics         │
├──────────────────────────────────────────────────────────────────────────────────┤
│ harness-loop                     ReAct loop · guards · acceptance · compaction   │
│                                  · hooks · traces + metrics · replay · sandbox   │
│                                  · subagents · scheduler · orchestrator          │
│                                  · loop engine (L1 → L2 → L3)                    │
├────────────────────┬──────────────────────────────────┬──────────────────────────┤
│ harness-models     │ harness-tools                    │ harness-memory           │
│ OpenAI-compatible  │ harness-tools-web                │ harness-cortexdb         │
│ Anthropic          │ harness-tools-docs               │                          │
│ Gemini native      │ harness-tools-sql                │ backend picked by URL    │
│ MockModel          │ harness-mcp-client               │                          │
├────────────────────┴──────────────────────────────────┴──────────────────────────┤
│ harness-core · harness-context  traits, lifecycle Event, default World runtime   │
│ harness-macros                  #[tool] #[skill] #[guide] #[sensor] #[hook]      │
└──────────────────────────────────────────────────────────────────────────────────┘

Crate	What it is
harness-rs-core	The traits and types: Model, Tool, Guide, Sensor, Hook, Compactor, Skill, Context, World, Event
harness-rs-context	Default World runtime (Tokio process runner, system clock, in-memory KV)
harness-rs-loop	The agent loop and everything that runs with it, including its spans and metrics; feature otel adds OTLP export
harness-rs-models	ApiKind::build(base_url, model, key) for OpenAI-compatible, Anthropic and Gemini native; embeddings, image, speech; MockModel
harness-rs-tools	Files, shell (with background jobs), memory, skills, tasks, dates, browser, recall, sensors, MCP server
harness-rs-tools-web	GroundedWebSearch (the model's own search first, scraper fallback) and URL fetch
harness-rs-tools-docs	read_document: PDF/Word/Excel/PowerPoint parsed locally, LLM fallback
harness-rs-tools-sql	SELECT-only SQL with auto-LIMIT and result redaction
harness-rs-mcp-client	A remote MCP server's tools as harness tools
harness-rs-memory	Every memory/recall backend behind a URL: JSONL, sqlite://, mem:// / surrealkv:// / ws:// (SurrealDB), cortexdb+http:// / cortexdb:// — each behind a feature
harness-rs-cortexdb	Memory over a CortexDB brain
harness-rs-serve	Multi-session serving core; features http (axum), cors, grpc, prometheus (GET /metrics), otel
harness-rs-cli	The harness binary; feature otel exports runs when OTEL_EXPORTER_OTLP_ENDPOINT is set
harness-rs	Facade: use harness::prelude::*

Quick start

use harness_core::Task;
use harness_loop::AgentLoop;
use harness_models::ApiKind;
use harness_tools::fs::{ListDir, ReadFile};
use std::sync::Arc;

// One model API: protocol family + base_url + model + key. No hardcoded URLs.
let model = ApiKind::OpenAI.build("https://api.deepseek.com", "deepseek-chat", key);
let task = Task { description: "What is the workspace name?".into(), source: None, deadline: None };
let outcome = AgentLoop::boxed(model)          // `boxed` takes Arc<dyn Model>
    .with_tool(Arc::new(ReadFile))
    .with_tool(Arc::new(ListDir))
    .run(task, &mut harness_context::default_world("."))
    .await?;

The same builder takes the rest of the harness: with_guide, with_sensor, with_hook, with_acceptance, with_compact_policy, with_stuck_policy, with_tool_timeout, with_streaming, with_cancellation, with_recall. session() gives a multi-turn Session that keeps a stable prompt prefix so provider caches hit across turns.

Register tools, skills, guides, sensors and hooks with #[harness::tool] / #[skill] / #[guide] / #[sensor] / #[hook] — they auto-register via inventory. Scaffold a project with harness new; harness code is an approval-gated coding REPL built on the framework (--yolo to unattend).

Observability

Traces. Every run is an agent_run span with one child span per model call (chat {model}) and per tool call (execute_tool {tool}), opened by the loop itself, so parallel tool calls show up as overlapping bars and a failed call is marked as an error. Fields follow the OpenTelemetry GenAI conventions (gen_ai.usage.input_tokens, gen_ai.tool.name, …). Enable the otel feature and call harness_loop::otel::init_tracing_with_otlp (or add otel::otlp_layer to your own subscriber) to export to Jaeger, Tempo, SigNoz, Logfire or Langfuse. The CLI does the same when built with --features otel and OTEL_EXPORTER_OTLP_ENDPOINT is set.

agent_run ─────────────────────────────────────────────────  outcome=done
  chat deepseek-chat ────────                                in=1200 out=80
  execute_tool read_file       ─────                         ok
  execute_tool grep            ───────                       ok   (parallel)
  execute_tool shell_exec             ──── ✗                 error
  chat deepseek-chat                       ──────────────    in=2100 out=310

Events and totals. TelemetryHook adds per-call events and one run.end line with the bill: tokens, model/tool calls, failures, compaction savings, time to first token (target harness.telemetry; harness run --telemetry).

Metrics. The loop records through the metrics facade: model-call duration and token usage (gen_ai.client.operation.duration, gen_ai.client.token.usage), tool-call duration by tool and success, run duration by outcome, and tokens saved by compaction. See harness_loop::meters. With no recorder installed this is free. harness-rs-serve's prometheus feature installs one and serves GET /metrics.

Serving. ChatService attaches a TelemetryHook to every request and runs it in a chat_request span carrying the request id, which is the same id the audit trail and the replay recording use.

Records. SessionRecorder writes the run as JSONL, which harness trace pretty-prints, harness trace --html out.html turns into a self-contained timeline page (parallel calls overlapping, a click for each call's details), and harness replay reruns offline with no LLM calls. AuditHook keeps a compliance trail (optionally redacted and hash-chained). BroadcastHook streams the lifecycle live to any number of subscribers.

More

docs/guide.md — crates, composable layers, sandboxing, PII redaction, documents/OCR, telemetry, record/replay, grounded search, examples · examples/ — runnable agents, from deepseek-hello to ecommerce-ops-agent · docs/benchmarks.md — pass^k, guard ablation, cost · DESIGN.md · CHANGELOG.md · MIT