Arnav

.github/workflows/ci.yml

@@ -2,7 +2,11 @@ name: CI
 
 on:
   push:
+    branches:
+      - main
   pull_request:
+    branches:
+      - main
 
 jobs:
   test:

README.md

@@ -33,8 +33,51 @@ This is not distributed inference for a single giant model. MeshMind is built ar
 - **Local-model runtime**: Uses Ollama-backed local models instead of remote APIs.
 - **Knowledge-aware nodes**: Load local knowledge files into specific nodes.
 - **Manual discovery fallback**: Useful for restrictive firewalls, unusual LANs, or offline field setups.
-- **Plugins**: Drop-in packages under `~/.meshmind/plugins/` (or `MESHMIND_PLUGIN_DIR`) merged into specialist nodes via `plugin: <id>` in `meshmind.yaml`. See [docs/plugins.md](docs/plugins.md) for layout and merge rules.
-- **Init presets / wizard**: `meshmind init <dir> --preset code` for a multi-specialist template; `meshmind init --wizard` for an interactive flow.
+
+---
+
+## What Makes MeshMind Different
+
+Other tools split one model across devices to make it run faster. **MeshMind does something different** — each device runs its own specialized AI agent, and they collaborate to solve complex queries that no single agent could answer alone.
+
+```
+Exo / mesh-llm:      "Split one 70B model across three laptops"
+MeshMind:            "A medical AI, a logistics AI, and a coordinator
+										 work as a team across three laptops"
+
+DISTRIBUTED INFERENCE   VS   DISTRIBUTED COLLABORATION
+```
+
+---
+
+## How It Works
+
+```
+User sends query
+		↓
+┌───────────────┐
+│  COORDINATOR  │  Decomposes query, classifies capabilities needed
+└───────────────┘
+		↓
+┌───────────────┐  ┌───────────────┐
+│ AGENT A       │  │ AGENT B       │  Each runs its own LLM + knowledge base
+│ Medical       │  │ Logistics     │  Processes in parallel via function calling
+└───────────────┘  └───────────────┘
+		↓                 ↓
+			┌───────────────┐
+			│  AGGREGATION  │  Combines responses into unified answer
+			└───────────────┘
+							 ↓
+User sees one coherent response
+Dashboard shows routing in real-time
+```
+
+1. Define your AI agents in a `meshmind.yaml` file
+2. Run `meshmind up` — agents discover each other automatically via mDNS
+3. Send a query — MeshMind decomposes it, routes sub-queries to the right agents, and aggregates their answers
+4. Everything runs locally. No internet. No cloud. No data leaves your network
+
+---
 
 ## Dashboard
 
@@ -51,7 +94,7 @@ Enable the dashboard on a coordinator with:
 
 ```yaml
 coordinator:
-  type: "coordinator"
+  type: 'coordinator'
   port: 8403
   ui: true
   ui_port: 8080
@@ -63,13 +106,12 @@ Then open:
 http://localhost:8080
 ```
 
-
 Paths on the same coordinator UI:
 
-- **`/dashboard`** — topology canvas, connected nodes, and live event log
-- **`/chat`** — full-page mesh query composer (same `POST /api/query` backend)
+- `/dashboard` - topology canvas, connected nodes, and live event log
+- `/chat` - full-page mesh query composer (same `POST /api/query` backend)
 
-If **`/chat`** returns `{"detail":"Not Found"}`, the running process is an older MeshMind build: stop the mesh (type `stop` in the `meshmind up` REPL, or Ctrl+C if you used `--no-interactive`), then from the repo root run `pip install -e .` and start again with `meshmind up` so the coordinator loads the current `meshmind.ui.app` routes.
+If `/chat` returns `{"detail":"Not Found"}`, the running process is an older MeshMind build: stop the mesh (Ctrl+C), then from the repo root run `pip install -e .` and start again with `meshmind up` so the coordinator loads the current `meshmind.ui.app` routes.
 
 ## Benchmarks
 
@@ -89,6 +131,14 @@ Current benchmarking priorities:
 - disconnect and rejoin behavior
 - failure handling under node loss
 
+Run current benchmark suite:
+
+```bash
+meshmind benchmark --suite minimum --runs 5 -o benchmark-report.json
+```
+
+---
+
 ## Quick Start
 
 Devices running MeshMind can discover each other automatically over local networks. The default generated project also exposes a local dashboard on the configured `ui_port`.
@@ -110,7 +160,7 @@ Prerequisites:
 Clone the repo and bootstrap the environment:
 
 ```bash
-git clone https://github.com/Nexarion-Distributed-AI/MeshMind.git
+git clone https://github.com/MeshMind-Labs/MeshMind.git
 cd MeshMind
 
 # Windows PowerShell
@@ -130,7 +180,7 @@ source .venv/bin/activate
 # .\.venv\Scripts\Activate.ps1
 ```
 
-Run the environment diagnostics:
+Run environment diagnostics:
 
 ```bash
 meshmind doctor
@@ -145,30 +195,12 @@ meshmind config validate -c meshmind.yaml
 meshmind up -c meshmind.yaml
 ```
 
-**Presets:** `meshmind init <dir> --preset default` (same as omitting `--preset`) uses the generic assistant + coordinator template. `meshmind init <dir> --preset code` generates a reviewer + security + coordinator mesh and, by default, copies bundled demo plugins `demo_review` and `demo_security` into your plugin directory. Use `--skip-bundled-plugins` to only write YAML. In a normal terminal, bare `meshmind init` (no name, default options) starts the same interactive wizard as `meshmind init --wizard`; the wizard shows a Rich preview and asks for confirmation before creating files.
-
-**Wizard:** `meshmind init --wizard` interactively chooses a preset and project name (optional NAME as the first argument).
-
-**Plugins CLI:** `meshmind plugin list`, `meshmind plugin list --available` / `meshmind plugin catalog`, `meshmind plugin install <path-or-bundled-id>`, `meshmind plugin sync -c meshmind.yaml`, `meshmind plugin validate <path-or-id>`, `meshmind plugin remove <id>`. Details: [docs/plugins.md](docs/plugins.md).
-
-**Two-terminal workflow (Claude Code–style):** the terminal where you run `meshmind up` starts an **interactive REPL** with a **`›`** prompt. Mesh **INFO** logs are written to **`.meshmind/logs/mesh-<mesh-name>.log`** next to your YAML (not mixed into that REPL). Open a **second** terminal and tail them:
-
-```bash
-meshmind logs -f -c meshmind.yaml
-```
-
-**REPL behavior:** use **`/help`** (or `help`) for commands. **`/status`**, **`/urls`**, **`/query …`**, and **`/stop`** (or `stop` / `exit`) work with or without the leading slash. **Enter** submits; **Ctrl+J** inserts a newline for multi-line questions. Answers are rendered with **Rich** (metadata panel + markdown). **Tab** completes commands and configured node names. Input history is stored in **`.meshmind/repl_history`** next to your `meshmind.yaml`.
-
-Query from either terminal: in the REPL type a line (it is sent as a query) or **`/query …`**; or from another shell:
+Query it from another terminal:
 
 ```bash
 meshmind query "Summarize the latest AI trends in 5 bullets" -c meshmind.yaml -t 300
 ```
 
-**CLI output format:** `meshmind query` defaults to **`--format auto`** — **markdown** when stdout is a TTY (styled panel + markdown body), **plain text** when not (e.g. pipes/CI). Use **`--format json`** for a stable JSON object (`result`, `nodes_used`, `duration`, `unavailable_nodes`, `trace`).
-
-For a **single** terminal with the old log stream on stdout (e.g. CI), use `meshmind up -c meshmind.yaml --log-to-stdout`. To skip the REPL and wait until Ctrl+C only: `--no-interactive`.
-
 ### Python SDK
 
 MeshMind also provides a Python SDK for programmatic mesh creation:
@@ -205,37 +237,37 @@ await mesh.stop()
 
 ## Example Configuration
 
-`meshmind.yaml` defines the mesh, default model settings, and the participating nodes:
+`meshmind.yaml` defines the mesh, default model settings, and participating nodes:
 
 ```yaml
 mesh:
-  name: "my-team"
-  discovery: "mdns"
+  name: 'my-team'
+  discovery: 'mdns'
 
 defaults:
-  model: "gemma3:1b"
+  model: 'gemma3:1b'
 
 nodes:
   analyst:
-    type: "specialist"
+    type: 'specialist'
     port: 8401
     system_prompt: |
       You are a data analyst. Analyze data and provide insights.
     capabilities:
-      - "data_analysis"
-      - "statistics"
+      - 'data_analysis'
+      - 'statistics'
 
   writer:
-    type: "specialist"
+    type: 'specialist'
     port: 8402
     system_prompt: |
       You are a technical writer. Write clear documentation.
     capabilities:
-      - "writing"
-      - "documentation"
+      - 'writing'
+      - 'documentation'
 
   coordinator:
-    type: "coordinator"
+    type: 'coordinator'
     port: 8403
     ui: true
     ui_port: 8080
@@ -245,17 +277,19 @@ nodes:
 
 ```bash
 meshmind init <name>
-meshmind up [-c meshmind.yaml] [--no-interactive] [--log-to-stdout] [--log-file PATH]
-meshmind logs [-c meshmind.yaml] [-f] [-n 200]
+meshmind up
 meshmind down
 meshmind stats
 meshmind nodes
 meshmind query "text"
+meshmind benchmark
 meshmind doctor
 meshmind config validate
 meshmind config show
 ```
 
+---
+
 ## Architecture
 
 ```text
@@ -271,6 +305,8 @@ meshmind/
 |- ui/            FastAPI dashboard
 ```
 
+---
+
 ## Current Status
 
 MeshMind is currently an early open source release focused on:
@@ -289,6 +325,8 @@ Current focus areas:
 - offline/manual-peer workflows
 - mobile-node interoperability
 
+---
+
 ## Current Limitations
 
 - Large models may exceed available RAM and create unstable first-run behavior.
@@ -297,21 +335,25 @@ Current focus areas:
 - Multi-device routing is implemented, but guaranteed speedup has not yet been comprehensively benchmarked.
 - Mobile-node support is still experimental and not yet a stable documented flow.
 
+---
+
 ## Guides
 
 - [2-device LAN guide](docs/lan-two-device.md)
+- [Testing and benchmarking guide](docs/tests.md)
 - [Known limitations](docs/known-limitations.md)
-- [Vision](VISION.md)
+
+---
 
 ## Testing
 
-Run the automated test suite from the repository root:
+Run the automated test suite from repository root:
 
 ```bash
 python -m pytest tests -q
 ```
 
-The current test suite covers:
+The current suite covers:
 
 - protocol serialization
 - routing behavior
@@ -320,6 +362,8 @@ The current test suite covers:
 - CLI helper logic
 - SDK lifecycle behavior
 
+---
+
 ## Use Cases
 
 - private local developer assistants
@@ -328,6 +372,8 @@ The current test suite covers:
 - offline or degraded-network operations
 - edge and field-device intelligence coordination
 
+---
+
 ## Contributing
 
 See:
@@ -342,6 +388,8 @@ python -m pip install -e .
 python -m pytest tests -q
 ```
 
+---
+
 ## License
 
 MeshMind is licensed under the [Apache License 2.0](LICENSE).

docker/configs/coordinator.node.yaml

@@ -6,6 +6,10 @@ node:
     - coordination
     - query_routing
     - response_aggregation
+  # knowledge_domains:
+  #   - coordination
+  # knowledge:
+  #   - ./data/knowledge.json
 
 ai:
   model: gemma3:1b

docker/configs/specialist-research.node.yaml

@@ -5,6 +5,11 @@ node:
   capabilities:
     - research
     - analysis
+  knowledge_domains:
+    - research
+    - analysis
+  # knowledge:
+  #   - ./data/knowledge.json
 
 ai:
   model: gemma3:1b

docker/configs/specialist-writing.node.yaml

@@ -5,6 +5,11 @@ node:
   capabilities:
     - writing
     - summarization
+  knowledge_domains:
+    - writing
+    - summarization
+  # knowledge:
+  #   - ./data/knowledge.json
 
 ai:
   model: gemma3:1b