Micro‑Orchestration

A DIY playbook for spinning up a multi‑agent workflow using nothing but free, open‑source tools — ideal for cash‑strapped startups, weekend hackers and privacy‑sensitive teams.

Why “micro” orchestration?

• Zero cloud fees — run everything on a single laptop or a $5‑month VPS.
• Data stays local — no PII leaves your box.
• Full hackability — every layer is MIT or Apache‑licensed, so you can dive into the code.

The idea is to replace pay‑per‑token SaaS stacks with local inference + open‑source tooling, while still enjoying agent‑level coordination.

The FOSS Component Menu

Layer	Pick‑1 Tool	Licence	Why It Fits
LLM inference	llama.cpp + Mistral‑7B	MIT / Apache‑2.0	7 B model runs on a mid‑range GPU or even CPU; full local control
Model gateway	LiteLLM Proxy	MIT	One OpenAI‑style endpoint for 100 + models, plus cost & guard‑rails
Agent graph	LangGraph	MIT	Graph‑native orchestration; open‑source core, no vendor lock‑in
(Alt.)	CrewAI	MIT	Lightweight, LangChain‑free multi‑agent framework
Memory / Vector DB	Chroma	Apache‑2.0	Drop‑in, serverless, zero cost
(Scale option)	Milvus	Apache‑2.0	Handles billions of vectors when you outgrow Chroma
Async & retries	Temporal	Apache‑2.0	Durable workflow engine; restarts agents exactly where they crashed

All seven pieces are free to install and run offline.

30‑Minute Quick‑Start

Prereqs: macOS/Linux, 16 GB RAM, Python 3.10, Homebrew/apt + Docker.

Step 0.  Pull a local model

brew install ollama                # or curl script on Linux

ollama run mistral:7b              # downloads ≈4 GB and starts REST server

The Ollama runtime wraps GGUF files so they stream efficiently on CPU/GPU  .

Step 1.  Stand up a unified LLM endpoint

pip install litellm

litellm –model ollama/mistral –api_base http://localhost:11434

Now any library that expects an OpenAI‑style /v1/chat/completions URL will talk to your local model — cost tracking and guard‑rails included  .

Step 2.  Install the orchestrator

pip install langgraph                # or: pip install crewai

LangGraph lets you describe agents and edges as a directed graph; every node can pause, stream or retry without extra code  .

Step 3.  Add a vector memory

pip install chromadb

python -m chromadb –path ./db       # starts local serverless store

Chroma’s Apache licence keeps the stack 100 % FOSS  .

Step 4.  Code a three‑agent micro‑flow

from langgraph.graph import StateGraph

from litellm import completion

import chromadb, os

os.environ[“OPENAI_API_BASE”] = “http://localhost:4000”  # LiteLLM proxy

memory = chromadb.Client().get_or_create_collection(“project”)

def planner(state): …

def researcher(state): …

def drafter(state): …

def reflector(state): …

g = StateGraph()

g.add_node(“plan”, planner)

g.add_node(“research”, researcher, after=”plan”)

g.add_node(“draft”, drafter, after=”research”)

g.add_node(“reflect”, reflector, after=”draft”).loop_until(criteria)

g.compile().invoke({“objective”: “Write launch email”})

Outcome: a self‑critiquing email generator that never touches the public cloud.

Step 5.  Make it durable & asynchronous

Wrap the invoke() call in a Temporal Workflow to survive reboots and network blips:

@workflow.defn

class EmailCampaignWF:

    @workflow.run

    async def run(self, objective):

        return await g.invoke({“objective”: objective})

Temporal checkpoints every step, restarts failed tasks, and scales workers horizontally  .

Total elapsed wall‑clock ≈ 30 minutes once dependencies are cached.

Performance & Cost Snapshot

Resource	Typical Footprint
RAM at idle (Mistral 7B Q4)	~9 GB CPU, 7 GB GPU
Tokens/sec (M1 Pro, llama.cpp)	30‑35 t/s
Monthly cost	$0 cloud spend; only your electricity bill

After the first gigabytes, every additional token is essentially free — a stark contrast to $2‑$8 per million output tokens on GPT‑4‑class APIs.

Common Pitfalls & Fixes

Symptom	Root Cause	Fix
“CUDA out of memory”	Q4 model too big	Use mistral.Q2_K.gguf or CPU inference
Agents loop forever	No reflection stop condition	Add max_iterations or a reward threshold
Requests time‑out	LiteLLM proxy defaults to 30 s	LITELLM_TIMEOUT=120 env var

Where to Go Next

1. Policy Guard‑rails — integrate an open policy LLM (e.g., caispp/policies) as a Governor node.
2. Multi‑model routing — point LiteLLM at both local Mistral and an occasional cloud GPT‑4o for “hard” tasks.
3. Observability — scrape LangGraph traces into Grafana or use Temporal’s built‑in Web UI.
4. Fleet Scaling — swap Chroma for Milvus when you hit millions of embeddings  .

Bottom Line

You no longer need a VC budget to explore agentic AI. In half an hour, a single developer can:

• run a modern 7 B model locally;
• expose it via LiteLLM’s unified API with budgets & guard‑rails;
• orchestrate multiple agents and memory with LangGraph (or CrewAI);
• achieve crash‑proof durability using Temporal;
• store vectors in a free Apache‑licensed database.

All seven layers are 100 % open source and privacy‑preserving.  Micro‑orchestration puts an enterprise‑grade AI control‑tower on your laptop — and leaves your AWS bill at $0.