Introduction

Flux Agent provides a calm, minimal architecture to design, connect, observe and optimize AI agents and their flows.

Built for production from day one: clear roles and goals, memory strategies, tool integration, and first‑class observability. Use it for customer support, data enrichment, and business automation.

Quick start

Download the starter and run the server.
Define an agent (role, goals, tools).
Connect webhooks / HTTP endpoints.
Send a test task and observe the session.

# 1) Clone
                        curl -L https://example.com/flux-agent-starter.tar.gz | tar xz
                        cd flux-agent-starter

                        # 2) Run (dev)
                        python -m venv .venv && source .venv/bin/activate
                        pip install -r requirements.txt
                        python app.py

                        # 3) Open local UI
                        open http://localhost:5173

Core concepts

Agent — A role with goals, memory, and a toolset.
Flow — Steps: Interpret → Plan → Tools → Memory → Output.
Memory — Short‑term session notes and long‑term knowledge.
Observability — Traces, logs, metrics, session replay.

Agent architecture

Agents are configured with a minimal JSON schema. Example:

{
                        "id": "support-agent",
                        "role": "Answer customer questions with a helpful, concise tone.",
                        "goals": ["Resolve issues", "Collect context", "Escalate when needed"],
                        "tools": ["kb.search", "http.post", "form.fill"],
                        "memory": {
                            "short_term": {"limit": 12},
                            "long_term": {"provider": "vector", "namespace": "support"}
                        }
                        }

Flows

A flow is a deterministic skeleton; models fill in the semantics. Example pseudo:

async function runFlow(input){
                        const intent = await interpret(input)
                        const plan = await planSteps(intent)
                        const results = []
                        for(const step of plan){
                            const r = await invokeTool(step)
                            results.push(r)
                        }
                        await remember({intent, plan, results})
                        return await synthesizeOutput(results)
                        }

Memory

Two layers: short‑term (session window) and long‑term (vector or key‑value). Use tags to route retrieval.

Tools

Tools are adapters (HTTP, DB, queues). Each tool advertises schema and cost to help the planner.

Name	Type	Notes
kb.search	vector	Retrieve docs by semantic query
http.post	network	Call external API endpoints
form.fill	automation	Fill forms, validate, submit

Observability

Record trace spans, logs and counters. Attach a session ID to follow the full path.

REST API

Minimal endpoints to enqueue tasks and read sessions.

POST /v1/tasks
                        Content-Type: application/json
                        {
                        "agent": "support-agent",
                        "input": "Where is my order?",
                        "meta": {"user_id": "u_123"}
                        }

                        GET /v1/sessions/:id

Webhooks

Receive events for task lifecycle. Example payload:

{
                        "type": "task.completed",
                        "id": "evt_9q2m",
                        "task": {"id": "t_55x", "agent": "support-agent"},
                        "result": {"status": "ok", "latency_ms": 420}
                        }

Examples

Customer Support Agent (KB + forms)
Data Enrichment (web + CRM)
Business Automation (n8n/Make + queues)

FAQ

Is this model‑agnostic? Yes. Bring your preferred LLM(s) and switch by policy.

Can I run without a DB? For demos yes; for production, use a proper store.

Changelog

v0.1 — Initial public docs, core concepts, API, and examples.