Astromesh

CI / CD

Packages

Info

Agent Execution Pipeline

Define agents in YAML. Astromesh handles the rest — from input safety checks to model routing to tool execution.

01 Query

Query

User sends a query to the agent via REST API or WebSocket

View docs →

02 Input Guardrails

Input Guardrails

PII detection, max length check, topic filtering on input

View docs →

03 Prompt Engine

Prompt Engine

Jinja2 renders the system prompt with memory context and variables

View docs →

04 Model Router

Model Router

Routes to the best provider using cost, latency, or quality strategy

View docs →

05 Tool Calls

Tool Calls

Executes tools (internal, MCP, webhook, RAG) as directed by the LLM

View docs →

06 Output Guardrails

Output Guardrails

Content filter, cost limit, PII redaction on output

View docs →

07 Response

Response

Final response returned to the caller and persisted to memory

View docs →

Read full pipeline documentation

NEW adk-v0.1.5

Astromesh ADK

Agent Development Kit

Build AI agents in pure Python. Decorators, auto-generated schemas, multi-agent teams — powered by the same runtime engine under the hood.

my_agent.py

1from astromesh_adk import agent, tool

2 

3@tool(description="Search the web")

4async def search(query: str) -> str:

5 return await fetch_results(query)

6 

7@agent(

8 name="assistant",

9 model="openai/gpt-4o",

10 tools=[search],

11)

12async def assistant(ctx):

13 """You are a research assistant."""

14 return None

Decorators Python-first API

Auto Schema From type hints

6 Providers One string config

Multi-Agent 4 team patterns

CLI 5 commands

Local + Remote One codebase

$ pip install astromesh-adk

$ astromesh-adk run my_agent.py:assistant "What is quantum computing?"

Get Started with ADK Read the Docs

Features

Everything you need to build, deploy, and scale AI agents.

core

6 LLM Providers

Connect to any model, local or cloud

Details

Ollama, OpenAI-compatible, vLLM, llama.cpp, HuggingFace TGI, ONNX Runtime. Automatic failover with circuit breaker (3 failures → 60s cooldown).

core

6 Orchestration Patterns

From simple to multi-agent

Details

ReAct (think-act-observe), Plan & Execute, Parallel Fan-Out, Pipeline, Supervisor (delegate to workers), Swarm (agents hand off conversations).

3 Memory Types Persistent context across conversations

Conversational (Redis/PG/SQLite), Semantic (pgvector/ChromaDB/Qdrant/FAISS), Episodic (PostgreSQL). Strategies: sliding window, summary, token budget.

RAG Pipeline Document ingestion to retrieval

4 chunking strategies (fixed, recursive, sentence, semantic), 3 embedding providers, 4 vector stores, 2 rerankers (cross-encoder, Cohere).

Mesh Discovery (Maia) Nodes find each other automatically

Gossip-based discovery, failure detection (alive→suspect→dead), leader election, least-connections routing. No manual peer configuration.

Rust Extensions 5-50x speedup on CPU-bound paths

Optional native Rust extensions via PyO3 for chunking, PII detection, tokenization, rate limiting. Pure Python fallback when not compiled.

Developer Experience

Your Toolkit

Three tools, one workflow. Define agents in YAML, run them instantly, debug with full traces — all wired together.

CLI Copilot VS Code

astromeshctl

Your command center

16 commands to scaffold, run, debug, monitor, and deploy agents — without ever leaving the terminal.

01

$ new agent Scaffold a YAML agent

02

$ run Execute instantly

03

$ traces Full execution trees

04

$ metrics Token & cost tracking

05

$ doctor Health diagnostics

06

$ ask AI copilot, inline

CLI Reference

Built-in AI Copilot

Answers that know your project

An AI assistant embedded in both CLI and VS Code that understands your agents, configs, and runtime — ask anything.

01

$ ask "Why is this slow?" Performance insights

02

$ ask "Add a RAG pipeline" Config generation

03

$ ask "Explain this trace" Debugging help

04

$ ask "Compare providers" Decision support

05

$ Copilot Chat panel Interactive in VS Code

06

$ Context-aware Reads your YAML files

Copilot Guide

VS Code Extension

Your editor becomes mission control

7 integrated features turn VS Code into a full agent development environment — from IntelliSense to live traces.

01

$ YAML IntelliSense Auto-complete & validation

02

$ ▶ Play Button Run agents from editor

03

$ Traces Panel Expandable span trees

04

$ Metrics Dashboard Real-time charts

05

$ Workflow Visualizer DAG visualization

06

$ Copilot Chat AI assistant panel

Extension Docs

Coming Soon

Connect to remote Astromesh clusters from VS Code — deploy, monitor, and orchestrate agents across production environments, all from your editor.

Deploy Your Way

From a single command to a production Kubernetes cluster.

Standalone Astromesh Node Docker Maia Mesh Maia + GPU Helm ArgoCD

Copy

git clone https://github.com/monaccode/astromesh.git
cd astromesh
uv sync --extra all
astromeshctl init --dev
astromeshd --config ./config

Copy

# Download latest .deb from GitHub Releases
curl -LO https://github.com/monaccode/astromesh/releases/latest/download/astromesh_<VERSION>_amd64.deb

# Install and start
sudo apt install ./astromesh_<VERSION>_amd64.deb
sudo astromeshctl init
sudo systemctl enable --now astromeshd

Copy

# Single node with Ollama
docker compose -f recipes/single-node.yml up -d
curl http://localhost:8000/v1/health

Copy

# 3-node mesh: gateway + worker + inference
docker compose -f recipes/mesh-3node.yml up -d
curl http://localhost:8000/v1/mesh/state

Copy

# Mesh with NVIDIA GPU inference
docker compose -f recipes/mesh-gpu.yml up -d

Copy

cd deploy/helm/astromesh
helm dependency update
helm install astromesh . -f values-dev.yaml \
  --namespace astromesh --create-namespace
kubectl get pods -n astromesh

Copy

# Multi-environment GitOps deployment
kubectl apply -f deploy/gitops/argocd/applicationset.yaml
# Creates: astromesh-dev, astromesh-staging, astromesh-prod

NEW orbit-v0.1.0

Astromesh Orbit

Deploy to Any Cloud

One command to provision a production-ready Astromesh stack on GCP. Cloud-native managed services, declarative config, and an escape hatch to raw Terraform.

orbit.yaml

1apiVersion: astromesh/v1

2kind: OrbitDeployment

3metadata:

4 name: my-astromesh

5 environment: production

6spec:

7 provider:

8 name: gcp

9 project: my-project-123

10 region: us-central1

One Command orbit apply

Cloud-Native Managed services

Multi-Cloud GCP first, then AWS & Azure

Escape Hatch Eject to raw Terraform

Marketplace GCP Marketplace ready

Secure Defaults VPC, IAM, Auth Proxy

What gets provisioned:

Cloud Run Runtime / Cloud API / Studio

Cloud SQL PostgreSQL 16

Memorystore Redis

Secret Manager JWT / Provider Keys

$ pip install astromesh-orbit[gcp]

$ astromeshctl orbit apply --preset starter

Deploy with Orbit Read the Docs

NEW node-v0.18.0

Astromesh Node

Deploy as a Native System Service

Install Astromesh as a first-class OS service on Linux, macOS, and Windows. Platform packages, automatic restarts, CLI management, and 7 runtime profiles — no containers required.

Choose your platform:

Debian / Ubuntu sudo apt install ./astromesh_latest_amd64.deb

RHEL / Fedora sudo dnf install ./astromesh_latest_x86_64.rpm

macOS sudo ./install.sh

Windows .\install.ps1

System Service systemd, launchd, WinSvc

CLI Management astromeshctl

7 Profiles full, gateway, worker...

Cross-Platform Linux, macOS, Windows

Auto-Restart Restart on failure

Health Checks doctor + watchdog

$ sudo astromeshctl init --profile full

$ astromeshctl status

● Running v0.18.0 1 agent loaded 1 provider healthy

Get Started with Node Read the Docs

Define an Agent in 20 Lines

Create a YAML file, start the daemon, call the API.

config/agents/hello.agent.yaml

apiVersion: astromesh/v1
kind: Agent
metadata:
  name: hello-agent

spec:
  identity:
    description: A minimal test agent

  model:
    primary:
      provider: ollama
      model: llama3.1:8b
      endpoint: http://ollama:11434
      parameters:
        temperature: 0.7

  prompts:
    system: |
      You are a helpful assistant.
      Keep responses brief.

  orchestration:
    pattern: react
    max_iterations: 3

Terminal

curl -X POST http://localhost:8000/v1/agents/hello-agent/run \
  -H "Content-Type: application/json" \
  -d '{"query": "What is 2+2?", "session_id": "demo"}'

Response

{
  "agent": "hello-agent",
  "response": "2 + 2 = 4",
  "session_id": "demo",
  "tokens_used": 42,
  "provider": "ollama",
  "pattern": "react",
  "iterations": 1
}