The production stack for agents, RAG, guardrails, and observability. Largestack AI is a Python 3.11+ open-source framework for building typed agents, tool workflows, RAG assistants, guardrails, observability, and production-style AI workflow evaluation.
# Install Largestack
$ pip install largestack
$ largestack --help
$ python -c "import largestack; print(largestack.__version__)"
# Scaffold and validate
$ largestack init support-agent
$ cd support-agent
$ largestack doctor
✓ agents · tools · RAG · guardrails available
✓ traces, checks and deployment files readyStart from PyPI, review the public repository, open the framework documents, or contact RivaiLabs to scope a controlled AI workflow POC.
These links open the full Largestack documentation for install, quickstart, architecture, RAG, orchestration, security, observability and deployment.
Every run follows the same controlled path: input, planning, orchestration, agent/tool/RAG execution, guardrails, response, traces and deployable runtime.
The orchestrator is the traffic controller. It reads the incoming task, chooses sequential/parallel/router/supervisor/DAG-style execution, manages state, and keeps the run controlled from start to finish.
Instead of one chatbot doing everything, Largestack positions agents as typed runtime workers. Each agent carries role, provider/model, instructions, tool schemas, output rules, retry behavior, cost budget and trace context.
The workflow engine defines repeatable execution: start the task, route to agents, call tools or RAG, checkpoint state, apply checks, then return an auditable response. It supports Mermaid graph output and generated tests.
Largestack treats tools as safe external action boundaries. Tools define schemas, timeouts, retries, risk type, approval behavior, idempotency and audit metadata before agents touch APIs, search, databases or connectors.
The knowledge engine loads documents, chunks content, creates embeddings, stores vectors, retrieves relevant context, reranks evidence, cites sources and supports no-answer behavior when evidence is insufficient.
Memory gives the runtime continuity. Buffer memory helps the current run, long-term and vector-backed memory preserve reusable knowledge, and enterprise usage keeps user/session/tenant memory isolated.
This is the trust point. Guardrails inspect prompt injection, PII, topic rules, provider policies, tool permissions, approval needs and output risk before risky actions or final responses proceed.
Production teams need evidence. Largestack exposes trace IDs, run events, model calls, tool calls, RAG chunks, guardrail decisions, latency, costs, dashboard views and debugging context.
The production story does not stop at a notebook. Largestack covers project scaffolds, package build, wheel checks, generated tests, Docker runtime health, Compose and Helm deployment baseline.
This final capability frames the enterprise direction honestly: scale capacity, secure tenants, recover from failures, optimize resources and pass hardening gates such as load, backpressure and real Kubernetes proof.
The same runtime record connects the request, plan, tools, retrieval context, memory state, guardrail decisions, response, trace and deployment surface.
Agent demos are easy. Production-style AI systems need routing, tool boundaries, retrieval grounding, memory, policy checks, runtime visibility and a deployable software structure.
Agent code, RAG, tools, tracing, security and deployment often live in separate pieces. Largestack keeps the core runtime layers together.
The framework includes package structure, CLI scaffolding, docs, examples, deployment files, validation gates and trust center material.
Largestack is designed for copilots, assistants and automation flows that must be explainable, testable, observable and safer to operate.
The framework is organized around the way AI applications actually run: accept a request, plan the work, route execution, call tools, retrieve knowledge, enforce policy, emit traces and prepare the app for deployment.
Apps, APIs, CLIs or dashboards send a task, question or workflow request.
The agent understands intent, creates a plan and decides which runtime layers are required.
The orchestrator routes work across agents, tools, RAG, state and workflow steps.
Specialized agents research, analyze, reason, execute and return structured results.
Documents, vector stores and memory provide grounded context and continuity.
PII, policy, prompt safety, tool constraints, approvals and output checks reduce risk.
The runtime returns an answer, action result, structured output or workflow state.
Every run can emit traces, logs, metrics, cost signals, alerts and debugging context.
Install from PyPI, inspect the CLI, create a project, and run local validation. Requires Python 3.11+.
# Requires Python 3.11+
pip install largestack
largestack --help
python -c "import largestack; print(largestack.__version__)"from largestack import Agent
agent = Agent(
name="support_agent",
instructions="Answer with grounded, concise steps."
)
result = agent.run("Create a safe support-ticket workflow.")
print(result.content)# Create and validate a generated app
largestack init support-agent
cd support-agent
largestack run --input "Create a safe support-ticket workflow"
largestack doctor
✓ project generated
✓ runtime responded
✓ runtime checks completedThis HD map shows how Largestack sits between user interfaces, model providers, integrations, data stores and deployment infrastructure. The runtime core owns orchestration, agents, RAG, guardrails and observability.
Largestack AI Runtime Map
A step-by-step walkthrough — project layout, agents, orchestrator, RAG, validators, security and governance — with an interactive build flow and copy-paste commands.
Compact proof points from the public package, repository, runtime requirement, license, deployment path and security checks.
Use one runtime pattern across serious AI applications: typed agents for reasoning, tools and RAG for action and knowledge, guardrails for control, traces for review, and deployment files for pilots.
Classify, answer, escalate and trace support issues with policies and human handoff.
02Ground answers in internal documents with retrieval, citations and insufficient-evidence behavior.
03Model KYC, AML, approvals and compliance mapping with guarded stages.
04Inspect pull requests, call tools, enforce policy and generate structured review output.
05Use RAG and agents to draft answers from approved product and compliance sources.
06Generate test plans, coverage notes and validation steps from requirements.
07Route vendor requests through policy checks, risk notes and approval workflows.
08Analyze usage, surface waste and propose cost-control actions with traceable reasoning.
Largestack keeps the first-run experience simple while exposing the product surfaces teams expect when an AI application moves beyond a notebook: typed agents, tool boundaries, RAG grounding, guardrail checks, traces, tests, package validation and deployable infrastructure.
Agent/orchestrator APIs, tools, RAG, memory, guardrails, observability, CLI scaffolds, docs, examples and deployment files.
PyPI packaging, website, developer docs, example apps, trust center, clean comparison pages and POC onboarding.
External security review, Kubernetes install proof, load/backpressure validation, signed releases, support process and broader platform integrations.
Book a POC call to scope a secure AI assistant, RAG workflow, or automation pilot.