title	Ollama Agent Harness
description	Local-first Ollama agent harness with tools, documents, Telegram, email, tracing, learning, and a browser UI
author	Bradliebs
ms.date	2026-06-05
ms.topic	overview
keywords	ollama agent local-first multimodal telegram email
estimated_reading_time	7

What is this?

Ollama Agent Harness is a local-first agent runtime that wraps Ollama models with a browser UI, tool dispatch, permissions, session management, and learning infrastructure. Everything runs on your machine. No cloud accounts, no API keys beyond Ollama itself.

You chat with a model, it can call tools (read/write files, run bash, search the web, analyze images, transcribe audio, generate documents, send emails), and the harness manages permissions, context, and history.

v0.6.4 brings semantic memory via the bundled ccmem concept-cell service (auto-started on port 8765), the Apex family of autonomous experiences (/goal natural-language expander, PDF → wiki blueprint, Kanban bridge, competitor-research renderer, personal memory wiki, daily morning-priority prompt), persistent workspace path so start.bat only asks once, and a smarter empty-synthesis fallback that leads with any files the model wrote and falls back to short labelled bullets instead of raw page dumps. See the CHANGELOG for the full history.

What's new since v0.6.5 (in development)

Landed on main but not yet cut into a numbered release:

• Autonomous Lead Agent — hand the harness one task and the lead agent plans it into a graph of sub-agent workstreams, dispatches them in parallel via the orchestrator, verifies the merged result against the toolchain, and re-plans until it passes or a budget is exhausted — no human interaction. Use HARNESS_LEAD=1 headless in the CLI, the /auto <task> chat command, or POST /api/lead/run (streaming). See docs/LEAD-AGENT.md.
• Governed Agent Loop v1 — a shadow-first governance pass beside the product path: confidence-mode labels, per-answer self-critique, a working-memory snapshot, and a human-gated review queue that writes to durable memory only on explicit approval. Idle replays re-ask drained answers and re-enter the same review queue. See docs/GOVERNED-LOOP.md.
• New HTTP surface for the loop and supporting subsystems: /api/working-memory, /api/review-queue/*, /api/replay-*, /api/governed-metrics, plus /api/webhooks/* (including dead-letter redelivery) and /api/mycelium/* (router inspection, learning curve, feedback).
• Workspace vs install clarification — set HARNESS_PROJECT_DIR to keep user data out of the install dir, and promote user-wide credentials (e.g. SMTP) to OS env vars so they stop going stale per workspace. See Workspace vs install.

What's new in v0.6.5

Shipped since the v0.6.4 release:

• Build Mode — a simplified, non-developer flow for describing a goal and letting the harness plan and build it.
• /wiki and /memory-wiki slash commands, plus research commands, built on reusable renderers.
• Evidence-first verification — a side-effect ledger and research skill that check work before presenting it.
• Autonomy reliability fixes — the Build-it loop now launches correctly in isolated workspaces and streams live tool activity to the dashboard.
• OLLAMA_HOST support, tool-output compression, and plain-English intent chips.

🌅 Apex-class autonomous experiences

A small family of end-to-end experiences composed from existing harness primitives: the /goal natural-language expander, a PDF → wiki blueprint, a triage/doing/done Kanban bridge that auto-promotes cards into the autonomy plan, a competitor-research report renderer, a personal memory wiki, and a daily morning-priority prompt. See docs/APEX-FEATURES.md for the full tour with copy-pasteable commands.

How it works

flowchart LR
    A["🖥️ Install\nNode.js + Ollama"] -->|pull a model| B["🤖 Start Harness\nstart.bat / ./start.sh"]
    B -->|opens browser| C["🌐 Chat UI\nhttp://127.0.0.1:4300"]
    C -->|type a message| D["💬 AI Responds\nusing local model"]
    D -->|needs a file?| E["🔧 Tools\nread, write, search, run"]
    E -->|result| D
    D -->|learns| F["🧠 Memory\nskills, patterns, history"]
    F -.->|next session| C

    style A fill:#1a1a2e,stroke:#50c878,color:#fff
    style B fill:#1a1a2e,stroke:#8ab4f8,color:#fff
    style C fill:#1a1a2e,stroke:#bb86fc,color:#fff
    style D fill:#1a1a2e,stroke:#ffb050,color:#fff
    style E fill:#1a1a2e,stroke:#ff5050,color:#fff
    style F fill:#1a1a2e,stroke:#50c878,color:#fff

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Your first 5 minutes

graph TD
    S1["① Install Node.js<br/><i>nodejs.org → LTS → Next through everything</i>"] --> S2
    S2["② Install Ollama<br/><i>ollama.com → Download → ollama pull llama3.2</i>"] --> S3
    S3["③ Start the Harness<br/><i>Double-click start.bat or run ./start.sh</i>"] --> S4
    S4["④ Pick a model<br/><i>Choose llama3.2 from the dropdown</i>"] --> S5
    S5["⑤ Start chatting!<br/><i>Try: 'List the files in this project'</i>"]

    S1 ~~~ N1["💡 Already have Node?<br/>Skip to step 2"]
    S3 ~~~ N2["💡 No terminal?<br/>Just double-click start.bat"]

    style S1 fill:#16213e,stroke:#50c878,color:#e0e0e0,stroke-width:2px
    style S2 fill:#16213e,stroke:#50c878,color:#e0e0e0,stroke-width:2px
    style S3 fill:#16213e,stroke:#8ab4f8,color:#e0e0e0,stroke-width:2px
    style S4 fill:#16213e,stroke:#bb86fc,color:#e0e0e0,stroke-width:2px
    style S5 fill:#16213e,stroke:#ffb050,color:#e0e0e0,stroke-width:2px
    style N1 fill:#0d1117,stroke:#555,color:#888,stroke-dasharray:5 5
    style N2 fill:#0d1117,stroke:#555,color:#888,stroke-dasharray:5 5

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Quick start

Prerequisites

• Node.js 18+
• Ollama running locally with at least one model pulled (e.g. ollama pull llama3.2)

Option A — Windows installer (easiest)

Download Harness-Setup.exe from the latest release, run it, and double-click the desktop shortcut. The installer checks for Node.js and Ollama automatically.

Option B — Double-click (from source)

1. Clone this repo
2. Double-click start.bat (Windows) or run ./start.sh (Mac/Linux)
3. Open http://127.0.0.1:4300 in your browser

These launchers run the assistant profile (HARNESS_PROFILE=assistant) — the same harness with its proactive "Jarvis" features (ambient daily brief, voice, chat channels) on by default.

Option C — Terminal (from source)

npm install
npm run ui

Open http://127.0.0.1:4300 in your browser. That is the full UI — start chatting in the main panel. This dev path runs a plain harness; set HARNESS_PROFILE=assistant first to turn the proactive assistant features on.

CLI mode

npm run start -- -p "Summarize the project" --model llama3.2

Run npm run start -- --help for all CLI flags including --mode, --max-turns, --validate-output, and helper model routing.

Validation

npm run typecheck
npm test -- --runInBand

With the UI server running, smoke-test the browser:

npm run smoke:ui -- http://127.0.0.1:4300/

To validate the current checkout without accidentally reusing a stale local UI server, run the fresh smoke. It starts its own server on the default smoke port and fails if that port is already occupied:

npm run smoke:ui:fresh

To inspect local automation jobs without modifying runtime state, build first and run the automation job audit:

npm run build
npm run audit:automation-jobs

To archive known test-created duplicate automation jobs after reviewing the audit output, run the cleanup command. It writes an archive JSON under .harness/automations/archive/ before updating the active jobs file:

npm run cleanup:automation-jobs -- --apply

Smoke-test the Mycelium API route-inspection surface with a temporary seeded graph:

npm run build
npm run smoke:mycelium

Run a paired AutoResearch experiment when you want to compare a baseline model, prompt, skill, or routing configuration against a candidate with frozen benchmark evidence:

npm run experiment:run -- --manifest cookbook/auto-research.manifest.example.json --dry-run

See AutoResearch experiments for the manifest shape, execution command, history views, and promotion-evidence rules.

Tests and smoke scripts that need .harness/** state should create the fixture inside the test or script and restore the prior state before exiting. Do not depend on ignored local files already existing in a developer checkout.

For local timeout checks against a real Ollama model, run the optional long-prompt smoke after building:

npm run build
npm run smoke:long-prompt

Override the model, host, prompt size, timeout, or context window with HARNESS_LONG_PROMPT_MODEL, OLLAMA_HOST, HARNESS_LONG_PROMPT_LINES, HARNESS_LONG_PROMPT_TIMEOUT_MS, and HARNESS_LONG_PROMPT_NUM_CTX.

Operating services

Agentic Service Mode handles ongoing service requests before model selection. Requests such as reminders, bullet journals, and daily site checks create local operating services under .harness/services/ instead of asking the model to build an app or write task files.

See Operating Services for the storage contract, deterministic commands, scheduler behavior, Discovery detail flow, and model-agnostic routing rules.

Autonomy mode

Run the harness against itself, draining IMPLEMENTATION_PLAN.md task by task, with no human in the loop:

$env:HARNESS_MODEL = "kimi-k2.5:cloud"   # see model matrix below
$env:FORGE_MAX_ITERATIONS = "10"
npm run autonomy            # full run
npm run autonomy:dry        # preview one iteration without spending tokens
npm run autonomy:stop       # graceful stop signal
npm run autonomy:reset      # clear .forge-state.json + .forge-stop

Tasks in IMPLEMENTATION_PLAN.md may declare anchors (read-only file context the model gets inline) and a target (the file to edit):

- [ ] verify-permissions-deny-first — Add a focused jest test under `src/permissions/`...
  - anchor: src/permissions/engine.ts
  - anchor: src/permissions/engine.test.ts
  - target: src/permissions/engine.test.ts

Live progress: .forge-state.json (one-shot), .forge-history.jsonl (append-only per iteration), .forge-run.log (mirrored console output), .forge-debug.jsonl (raw model exchanges, only with HARNESS_DEBUG_LOG set or npm run autonomy:debug). The web UI surfaces the autonomy HUD in the topbar with a click-through log tail modal.

Model capability matrix

What we measured running real autonomy iterations on this codebase (May 2026). "Writes correct code" means the model picked the task, called file_write/file_edit, and produced output that passed npm run typecheck.

Model	Backend	Tool calls	Writes correct code	Notes
kimi-k2.5:cloud	ollama (Pro)	✅ native	✅ yes with anchors	First model to land an autonomy commit end-to-end. Recommended.
gpt-oss:120b-cloud	ollama	✅ native	⚠️ writes code, often wrong code	Explores well but may scaffold generic structure instead of doing the task.
gpt-oss:20b-cloud	ollama	✅ native	❓ untested at length	Same family as 120b, ~6× faster.
qwen3-coder:480b-cloud	ollama	❌ chats	❌ no	Refuses to use tools on this codebase.
deepseek-v3.1:671b-cloud	ollama	❓ untested	❓ untested
qwen2.5-coder:14b	ollama (local, ~9GB)	✅ but wrong tools	❌ no	Loops on reflect/promote_pattern, ignores tool whitelist.
qwen2.5-coder:7b	ollama (local, ~4GB)	⚠️ JSON-as-text	❌ no	Mitigated by inline tool-call parser; still picks wrong tools.
gemma4:e4b, gemma4:26b	ollama (local)	❌ chats	❌ no	Conversational only.
llama3.1-8b	cerebras	✅ but wrong tools	❌ no	Hallucinates recall/remember; only Cerebras free model accessible.
gpt-oss-120b	cerebras	❌ 404 on free	❌ no	Listed in /v1/models but free tier returns 404.
qwen-3-235b-a22b-instruct-2507	cerebras	⚠️ rate-limited	❓ untested	Free RPM is shared at the tier level; usually 429s.
gpt-4.1, gpt-4.1-mini, o3-mini	github	❓ untested	❓ untested	High expected value; needs GITHUB_TOKEN with Models scope. 50-150 RPD free.
kimi-k2-instruct	groq	❓ untested	❓ untested	Same Kimi lineage; free, 14,400 RPD.

Backends are configured via HARNESS_BACKEND (or --backend flag) plus the appropriate *_API_KEY env var. harness doctor lists every configured backend and reports whether its key is set.

Recommended models for tool use

Not every model can call tools. These are the ones that work with the harness.

Local Ollama — best picks

Model	Size	Best role	Pull command
qwen3:8b	~5 GB	Main local agent	ollama pull qwen3:8b
qwen3:14b	~9 GB	Better planner	ollama pull qwen3:14b
qwen2.5-coder:7b	~4 GB	Coding agent	ollama pull qwen2.5-coder:7b
qwen2.5-coder:14b	~9 GB	Stronger coder	ollama pull qwen2.5-coder:14b
llama3.1:8b	~5 GB	Stable fallback	ollama pull llama3.1:8b
phi4-mini	~2 GB	Fast router	ollama pull phi4-mini
devstral	~15 GB	Coding agent	ollama pull devstral
mistral-small3.2	~15 GB	Function calling	ollama pull mistral-small3.2
granite4.1:8b	~5 GB	Structured tasks	ollama pull granite4.1:8b
gemma4:26b	~16 GB	Heavy reasoning	ollama pull gemma4:26b

Quick start set (fits most GPUs):

ollama pull qwen3:8b
ollama pull qwen2.5-coder:7b
ollama pull phi4-mini
ollama pull llama3.1:8b

Mistral API (free tier)

Model ID	Best role
mistral-small-latest	General agent — best starting point
devstral-small-latest	Coding agent — test first
codestral-latest	Code generation specialist
ministral-8b-latest	Cheap router
magistral-small-latest	Reasoning/review
mistral-medium-latest	Better planner (rate-limited)
mistral-large-latest	Highest quality (use sparingly)

Configure with MISTRAL_API_KEY and --backend mistral.

Recommended stack

Planner:   qwen3:8b (local) or mistral-small-latest (API)
Coder:     qwen2.5-coder:7b (local) or devstral-small-latest (API)
Router:    phi4-mini (local) or ministral-8b-latest (API)
Reviewer:  llama3.1:8b (local) or magistral-small-latest (API)

UI tabs

The browser UI keeps five primary tabs in the left sidebar, with everything else under a More (⋯) menu:

Tab	What it does
💬 Chats	Your past conversations; new/export sessions
📁 Files	Browse files in this project
⚡ Abilities	Reusable instructions (skills) the AI can follow
🧠 Memory	What the AI remembers across sessions
⚙ Automations	Saved sequences of AI actions (workflows under .harness/workflows/)

The More menu groups the remaining tabs:

Group	Tabs
Build & use	🤖 Agents · 👥 Squads · ⚡ Triggers · ✅ Tasks · 🪪 Identity
Search & knowledge	🔎 Search Files (vector index) · 🏛 Memory Bank (semantic memory) · 🔮 Discoveries · 🧬 Code Intel
History & diagnostics	📜 Runs · 📋 Events · 🛡 Activity Log · 📈 Learning · 💓 Health
Admin & backup	🛠 Tools (registry, permissions, kill switch, capability grants) · 📦 Backups · 📎 Generated Files
Advanced	🚀 Autonomy · 🍄 AI Router (adaptive routing network) · 🤝 Commitments

The right side has a Settings panel for Ollama host, generation parameters, model routing, media tools, output validation, and safety mode. Settings are saved to .harness/settings.json.

Key concepts

Tools

Built-in tools include file_read, file_write, file_edit, file_move, file_delete, make_directory, bash, list_files, grep, web_fetch, web_search, web_read, image_analyze, audio_transcribe, document_export, email_send, email_draft, create_skill, install_skill, desktop_screenshot, browser_bookmarks, browser_navigate, browser_click, browser_fill, browser_read, browser_screenshot, browser_close, calendar_read, calendar_write, slack_notify, telegram_notify, and more. Each tool has a risk level (low/medium/high) and can be individually disabled from the Tools tab.

Browser tools (browser_navigate, browser_click, browser_fill) are disabled by default and require a capability grant — they interact with live websites via Playwright.

The bash tool spawns a single executable without a shell. Shell control operators (;, |, &&, redirects, command substitution) outside quoted arguments are rejected — quote them and they pass through verbatim (e.g. python -c "import x; print(x)"). Use make_directory instead of mkdir, file_read/list_files instead of cat/dir, and pipe through a script file instead of cmd-style pipes.

Allowed External Paths

File tools are confined to the project directory by default. To let the agent read and write under other folders (e.g. D:\Downloads\AI) without permission prompts, add them under Settings → 📂 Allowed External Paths. The allowlist matches recursively, applies to both reads and writes, and bypasses the permission-prompt timeout. Configure per folder; precedence is exact match → ancestor match → blocked.

agent-outputs redirect

When the agent writes to a bare filename (e.g. notes.md, no directory), the harness redirects the write into the configured agent-outputs directory (default ./agent-outputs/, override with HARNESS_AGENT_OUTPUT_DIR). The file_write response leads with the resolved absolute path so the model uses it for follow-up commands. The bash tool mirrors this: a bare script-extension arg (python notes.py, node run.js) is auto-resolved against the agent-outputs directory when it does not exist in the cwd, so the agent's first run-after-write Just Works.

Environment knobs

• HARNESS_PROJECT_DIR — where the harness stores user data (.harness/, sessions, memory, API keys). Defaults to the cwd; auto-redirects to ~/apex-workspace if the cwd is the install dir. See Workspace vs install.
• HARNESS_AGENT_OUTPUT_DIR — override the agent-outputs directory (absolute or project-relative).
• HARNESS_PERMISSION_PROMPT_TIMEOUT_MS — change the default permission-prompt timeout (default 5 minutes). Lower it for autonomous runs; pair with Allowed External Paths to avoid prompts entirely.
• HARNESS_VERIFY_PATH_CLAIMS — set to 1 to append an ⚠️ Unverified file references: footer to assistant text whenever it cites a file path that does not exist on disk. Off by default.
• HARNESS_FILE_WRITE_REDIRECTS — JSON of pattern → directory rules to route specific writes (e.g. lottery-*.py → a sibling repo). Overrides the agent-outputs redirect when matched.

Document generation

The document_export tool creates CSV, Excel (.xlsx), Word (.docx), and PDF files directly from chat. Numbers, percentages, and currency values are auto-formatted in Excel. Tables are supported in Word and PDF. All documents are redirected to the configured Agent Files directory.

Email

The email_send tool sends real emails via SMTP with optional file attachments. Configure SMTP credentials in Settings → API Keys (HARNESS_SMTP_HOST, HARNESS_SMTP_USER, HARNESS_SMTP_PASS). For Gmail, use an App Password. Sent emails are archived under .harness/email/sent/.

Telegram bot

Talk to Oracle from your phone via Telegram. Create a bot with @BotFather, paste the token in Settings → Telegram Bot, and start chatting. Supports:

• Text messages — Oracle responds via the chat API
• Photos — analyzed with the vision model
• Files — PDF, CSV, Excel, images auto-detected and processed
• Voice notes — transcribed and responded to
• /task — add tasks to the autonomy plan
• /schedule every 6h Check prices — create recurring automation jobs
• /status — check readiness scores
• Inline progress — see tool calls happening in real time
• Notifications — automation job results pushed to your chat

Discord bot

Talk to the harness from any Discord server. Create a bot at the Discord Developer Portal, enable Message Content Intent, and set HARNESS_DISCORD_BOT_TOKEN. Optionally restrict to specific channels with HARNESS_DISCORD_ALLOWED_CHANNEL_IDS.

Slack notifications

Send one-way Slack notifications through slack_notify. Create a Slack incoming webhook, set HARNESS_SLACK_WEBHOOK_URL in Settings -> API Keys or the environment, and keep the tool disabled until you grant the communications capability for a specific run.

Mission Control

The welcome screen shows a Mission Control dashboard with:

• Readiness scores for Chat, Coding, Research, Automation, and Full Autonomy
• Autonomy Run Builder with task creation form and one-click start
• Document Studio for generating briefs, reports, and specs
• Job templates for daily digest, hotel monitor, weekly report, and email reminder

Chat commands

• /task Create a report — add a task to the autonomy plan
• /schedule every 24h Send daily summary — create a recurring automation job
• Type / to see all available slash commands

Permissions

Three permission modes control tool execution:

• default — prompts for confirmation on medium/high-risk tool calls
• acceptEdits — auto-approves file edits, prompts for everything else
• dontAsk — auto-approves everything (use with caution)

The kill switch (Tools tab or Ctrl+Shift+K) blocks all tool calls until released.

Capability grants

High-risk surfaces (shell execution, background jobs, self-modifying code) require explicit time-limited grants with required controls before they can be used. Create and revoke grants from the Tools tab. All grant lifecycle events (created, revoked, expired) are recorded in .harness/capabilities/audit.jsonl.

Capabilities are classified by posture:

• available — usable without a grant
• gated — requires an explicit grant with required controls
• design-only — connector not yet implemented
• blocked — denied by default, no grant path

Shell command allowlist

When a background automation job needs to run a shell command, it must have active arbitrary-shell and background-autonomous-jobs grants AND match a command allowlist preset. Four presets are built in:

• git-read-status — git status, git diff --stat, git log --oneline
• file-discovery — dir, rg --files, Get-ChildItem (rejects .. path traversal)
• tool-version — node --version, npm --version, git --version
• project-validation — npm run typecheck, npm run build, npm run smoke:ui

Commands that do not match a preset are denied even with active grants.

Skills

Skills are structured prompts that teach the model domain-specific tasks. They live in .harness/skills/ (runtime) and .github/skills/ (repo). The Skill Curator optionally archives stale skills and proposes merges.

MCP runtime

The Tools dashboard includes a curated MCP catalog and a local MCP runtime panel. Runtime server definitions are persisted under .harness/mcp/servers.json; starting a server launches an external process and therefore requires an active arbitrary-shell capability grant. The first runtime layer supports configure, list, start, stop, status, and visible configured-tool metadata. Protocol-level tool invocation can build on this process manager without bypassing grants or audit logs.

Sessions and context

Chat sessions persist under .harness/sessions/. Context continuity detects model context length and manages conversation history. Sessions can be forked and resumed.

Output validation

Optional deterministic checks on the model's final answer. Built-in profiles: oracle-prime, factual-answer, coding-answer, tool-result-summary. Custom profiles can be authored from Settings or .harness/output-validation-profiles.json. See VALIDATION-PROFILES.md for the full reference.

Media tools

• Image analysis — auto-detects an installed Ollama vision model; or set one in Settings / HARNESS_VISION_MODEL
• Audio transcription — auto-detects an OpenAI Whisper install on PATH (pip install -U openai-whisper); or set a command in Settings / HARNESS_AUDIO_TRANSCRIBE_COMMAND

Agent identity

Give your agent a name, avatar emoji, and personality. Presets include professional, friendly, concise, mentor, creative, and pirate. Save multiple profiles and switch between them. The agent name appears in chat bubbles, the topbar, and session history.

Full Autonomy mode

Click ⚡ Full Autonomy in Settings to set dontAsk mode and enable all tools in one click. All 9 gated capabilities auto-grant for 8 hours. Kill switch (Ctrl+Shift+K) remains the emergency stop.

Mycelium context router

An adaptive graph system that learns which combinations of tools, skills, and memories work best for different queries. The network reinforces successful routes and decays unused ones. View the graph in the 🍄 AI Router tab.

Automation

Schedule recurring jobs with optional shell commands. Create, edit, toggle, and delete jobs from the Runs tab. The automation scheduler runs due jobs when the system is idle, respecting kill switch and capability grants. Run history with output viewing is available in the Runs tab.

Project structure

src/            TypeScript source (core, tools, web server, permissions, automation, learning)
ui/             Browser UI (index.html, app.js, chatHistory.js)
docs/           Additional guides (MODEL-PRESETS, VALIDATION-PROFILES, GETTING-STARTED)
cookbook/        Code examples and integration guides
scripts/        Build, smoke, and release scripts
.harness/       Runtime state (settings, sessions, skills, memory, automations) — gitignored

Storage

Workspace vs install

The harness separates the install directory (where the code lives, e.g. H:\ollama-agent-harness-master) from the project directory (where your data lives, e.g. D:\Brad\Downloads\AI). Set HARNESS_PROJECT_DIR to point at the project directory; if you launch from inside the install dir without setting it, the server auto-redirects to ~/apex-workspace so user data never lands next to source code.

One consequence: .harness/api-keys.json is per workspace. Credentials you want to share across every workspace (SMTP, third-party API keys) should be set as OS environment variables instead — the harness reads env vars whenever a key is absent from api-keys.json, so promoting a credential to an env var and removing it from per-workspace files prevents drift.

Runtime state

All runtime state goes under .harness/ in your project directory:

Path	Contents
.harness/settings.json	Server and UI configuration
.harness/sessions/	Chat session transcripts
.harness/skills/	Runtime skill definitions
.harness/memory/	Agent memory entries
.harness/capabilities/audit.jsonl	Grant and automation audit log
.harness/automations/	Scheduled job definitions and output
.harness/curator/	Skill curator log and merge proposals
.harness/workflows/	Declarative workflow definitions
.harness/mycelium/	Adaptive routing graph
.harness/mcp/	Local MCP runtime server definitions
.harness/desktop/	Desktop screenshots
.harness/email/drafts/	Email draft files
.harness/email/sent/	Sent email archive
.harness/documents/	Generated documents (Markdown, HTML, PDF, DOCX)
.harness/evidence/	Run evidence cards (automation, autonomy)
.harness/telegram-chat-ids.json	Telegram notification recipients

Releasing

The release pipeline is single-source-of-truth for version metadata and gated on a green CI run for the same commit.

npm run release:bump -- 0.4.0   # update package.json + lockfile + installer + provenance
git add -A
git commit -m "chore(release): bump to v0.4.0"
git push origin master           # triggers CI
npm run release:ready            # one-shot pre-flight: clean tree, upstream sync, CHANGELOG, versions, typecheck, tag
git tag -a v0.4.0 -m "v0.4.0"
git push origin v0.4.0           # triggers Release workflow once CI is green

• npm run release:bump is the only supported way to bump the version. It syncs package.json, package-lock.json, installer/harness-installer.nsi, and release-provenance.json together so they cannot drift.
• npm run release:ready is read-only and runs every gate that CI and the Release workflow will hit. Treat its output as the go/no-go signal before pushing a tag.
• Add a matching ## Ollama Agent Harness vX.Y.Z section to CHANGELOG.md before tagging. CI runs npm run verify:changelog and refuses to publish a tag whose notes would be empty.
• The Release workflow refuses to publish if the CI workflow on the same commit did not conclude successfully. Push master first, wait for CI to go green, then push the tag.

See docs/RELEASE-PIPELINE.md for the full runbook including failure recovery.

More information

• START-HERE.md — complete beginner guide (install Node.js, install Ollama, first chat)
• CHANGELOG.md — release notes for every version
• System breakdown — single-page reference covering every subsystem, env flag, and operational runbook
• Model presets guide — beginner-friendly model recommendations
• Validation profiles — output validation reference
• Mycelium router — adaptive context routing reference
• 20/10 Roadmap — product roadmap
• GitHub Releases — download the latest release