largestack 1.1.1

Largestack AI — a Beta Python framework for typed agents, tools, RAG, guardrails, observability, and orchestration

Largestack AI

Largestack AI (largestack) is a Python 3.11+ framework for AI agents, RAG, guardrails, observability, workflow orchestration, and tool-based automation.

pip install largestack

Largestack is built for developers and teams moving from AI demos to production-style AI workflows. The largestack Python framework helps teams structure agents, retrieval, guardrails, traces, and orchestration without starting from a blank file.

• Website: https://largestack.ai
• Docs: https://docs.largestack.ai
• PyPI: https://pypi.org/project/largestack/
• GitHub: https://github.com/Rivailabs/largestack

Largestack requires Python 3.11+.

Largestack AI is an open-source Python framework for building AI agents with guardrails, cost tracking, and traces built in — so you can put agents in front of real users without wiring the safety layer yourself.

It is designed for developers building support agents, RAG assistants, code reviewers, and workflow automations who want structure (typed agents, tools, retrieval, guardrails, observability) instead of a blank file.

Status: Beta (v1.1.1), maintained by a single developer. Largestack installs and runs, ships a large test suite, and is a good fit for prototypes, internal experiments, and learning. It has not been independently audited, load-tested at scale, or certified for any regulated or enterprise use. The checks listed below are internal smoke/soak runs on the maintainer's own machines, not third-party validation — evaluate it for your own use case before relying on it.

See docs/known-limitations.md for an honest, up-to-date list of what is and isn't proven.

Install

pip install largestack

Verify:

largestack --help
python -c "import largestack; print(largestack.__version__)"

---

Why Largestack?

Most agent frameworks solve only one layer: agents, chains, RAG, or observability. Largestack brings the main production surfaces together:

Layer	What Largestack provides
Agents	Agent, typed agents, role-based agents, multi-agent teams
Tools	Safe tool calling, schemas, retries, timeout controls, approval policies
Workflows	Sequential, parallel, router, supervisor, graph/DAG-style orchestration
RAG	Loaders, chunking, retrievers, rerankers, vector stores, citations, no-answer behavior
Guardrails	PII checks, injection controls, topic/sensitive data policies, tool/provider policies
Memory	Buffer, long-term, vector-backed, shared and isolated memory patterns
Observability	Traces, cost tracking, event logs, dashboard APIs, OTEL helpers
Enterprise	RBAC, audit trail, tenant scoping, SSO/session modules, payment/billing scaffolds
Deployment	Docker, Compose, Helm charts, CI validation, release evidence
Testing	Unit, integration, security, RAG eval, live provider validation, generated project checks

---

Development quickstart

1. Open a source checkout

git clone https://github.com/Rivailabs/largestack.git
cd largestack

2. Create environment

python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip setuptools wheel

3. Install editable development dependencies

For normal source development:

python -m pip install -e ".[dev]"

For CPU-only PyTorch dependency resolution on Linux/macOS:

PIP_EXTRA_INDEX_URL=https://download.pytorch.org/whl/cpu \
python -m pip install -e ".[dev]"

4. Run a first validation

python -m pytest tests/unit/test_memory.py -q --tb=short

5. Run the full suite

python -m pytest tests -q --tb=short -ra

---

Minimal agent example

import asyncio
from largestack import Agent

async def main():
    agent = Agent(
        name="assistant",
        llm="deepseek/deepseek-chat",
        instructions="Be concise and practical."
    )
    result = await agent.run("Explain Largestack in one sentence.")
    print(result.content)

asyncio.run(main())

For deterministic tests, use the built-in test/offline model patterns instead of a live cloud provider.

---

Live provider setup

DeepSeek:

export LARGESTACK_DEEPSEEK_API_KEY="your_key_here"
python examples/01_hello/main.py

OpenAI:

export LARGESTACK_OPENAI_API_KEY="your_key_here"
export LARGESTACK_DEFAULT_MODEL="openai/gpt-4o-mini"
python examples/01_hello/main.py

Never commit .env or paste API keys into source files.

---

LLM/API provider support

Largestack is provider-switchable. The core agent, workflow, RAG, guardrail, and observability layers run through a model string such as openai/gpt-4o-mini, anthropic/claude-sonnet-4-6, deepseek/deepseek-chat, litellm/groq/llama-3.1-70b-versatile, or local/llama3.2.

Largestack supports OpenAI/GPT, Anthropic/Claude, DeepSeek, LiteLLM, Ollama/local models, and many OpenAI-compatible providers through the capability matrix below. Support depth varies by provider — adapters marked "Partial" have not all been through live end-to-end validation, so verify the specific provider/model you depend on.

Provider/API path	Model string example	Env/config	Status
OpenAI / GPT	openai/gpt-4o-mini	LARGESTACK_OPENAI_API_KEY	Verified primary adapter path
Anthropic / Claude	anthropic/claude-sonnet-4-6	LARGESTACK_ANTHROPIC_API_KEY	Adapter-only — native adapter implemented, not live-verified (run check_connection with your key)
DeepSeek	deepseek/deepseek-chat	LARGESTACK_DEEPSEEK_API_KEY	Live E2E validated
LiteLLM gateway	litellm/<provider>/<model>	Provider-specific LiteLLM env vars	Partial; downstream capability varies
Local OpenAI-compatible	local/<model>	LARGESTACK_OPENAI_COMPATIBLE_BASE_URL	Partial; gateway/model capability varies
Ollama native	ollama/<model>	LARGESTACK_OLLAMA_BASE_URL optional	Verified (local); native chat — tools via ollama_openai/
Azure OpenAI	azure/<deployment>	LARGESTACK_AZURE_OPENAI_KEY, LARGESTACK_AZURE_OPENAI_ENDPOINT	Partial; deployment-specific
Groq, Mistral, OpenRouter, xAI, Cerebras, SambaNova, NVIDIA	<provider>/<model>	LARGESTACK_<PROVIDER>_API_KEY	Partial/OpenAI-compatible; verify live
Google/Gemini	google/<model>	LARGESTACK_GOOGLE_API_KEY	Verified (chat + tools + structured live)
Cohere, Bedrock	<provider>/<model>	Provider env/credentials	Partial/adapter-only; feature support differs

Inspect the runtime matrix:

python - <<'PY'
from largestack import provider_support_matrix
for row in provider_support_matrix():
    print(row["provider"], row["status"], "tools=", row["tool_calling"], "structured=", row["structured_output"])
PY

Run the provider-switchable flow demo offline:

python examples/provider_flow_demo/main.py

Run the same flow against GPT:

export LARGESTACK_OPENAI_API_KEY="your_key_here"
export LARGESTACK_DEFAULT_MODEL="openai/gpt-4o-mini"
export LARGESTACK_FLOW_DEMO_LIVE=1
python examples/provider_flow_demo/main.py

Run the same flow against Claude:

export LARGESTACK_ANTHROPIC_API_KEY="your_key_here"
export LARGESTACK_DEFAULT_MODEL="anthropic/claude-sonnet-4-6"
export LARGESTACK_FLOW_DEMO_LIVE=1
python examples/provider_flow_demo/main.py

Run the same flow against a local OpenAI-compatible endpoint:

export LARGESTACK_OPENAI_COMPATIBLE_BASE_URL="http://localhost:11434/v1"
export LARGESTACK_OPENAI_COMPATIBLE_API_KEY="ollama"
export LARGESTACK_DEFAULT_MODEL="local/llama3.2"
export LARGESTACK_FLOW_DEMO_LIVE=1
python examples/provider_flow_demo/main.py

---

Flow demo

The quickest workflow demo is examples/provider_flow_demo/main.py. It runs offline by default and can be switched to any configured provider by changing only LARGESTACK_DEFAULT_MODEL.

flowchart LR
    U[User task] --> I[Intake agent]
    I --> P[Planner agent]
    P --> R[Responder agent]
    R --> O[Final answer]

What the demo proves:

• one task flows through three agents,
• DAG dependencies control execution order,
• each agent can use the same model string or provider family,
• offline TestModel validation requires no API key,
• live mode works with GPT, DeepSeek, Gemini, LiteLLM, or local-compatible providers when credentials are configured (the Claude/Anthropic adapter is implemented but not yet live-verified — see the provider table).

---

Built-in example areas

Example	Purpose
examples/00_offline_test_model.py	Offline deterministic model check
examples/01_hello	Basic provider-backed agent
examples/02_tools	Tool calling
examples/03_team	Multi-agent/team behavior
examples/04_guards	Guardrails/security behavior
examples/05_rag_knowledge	RAG with knowledge files
examples/06_streaming	Streaming responses
examples/07_structured	Structured outputs
examples/08_mcp_server	MCP server pattern
examples/10_full_app	Integrated app pattern
examples/provider_flow_demo	Provider-switchable workflow demo
examples/rag_basic	Basic RAG assistant
examples/fintech_kyc	BFSI/KYC style workflow
examples/riva_ai	Riva/Largestack demo pipelines

---

Internal checks

These are checks the maintainer runs locally before publishing. They are not independent audits, certifications, or production guarantees — read them as "the author exercised this path on their own machine."

Check	What it means
Unit + security test suite	Full suite runs in CI on Python 3.11–3.13; coverage gated ≥75% on the core wedge
Live DeepSeek e2e	Typed output, cost tracking, and tool calling run against the real DeepSeek API in CI (when the API-key secret is set; auto-skips otherwise)
Provider support matrix	Present, with explicit verified/partial adapter statuses
Offline provider flow demo	Runs deterministically with TestModel, no API key
Package build + twine check	Passes locally
Docker /health smoke	Container builds and the health endpoint responds
Local soak run	A repeated test loop ran for several hours without crashing — a stability smoke check, not a load or concurrency test

For exactly what has and hasn't been proven, see docs/known-limitations.md.

---

Architecture at a glance

flowchart TD
    U[User / API / CLI / App] --> C[CLI or SDK]
    C --> A[Agent Runtime]
    A --> W[Workflow Orchestrator]
    A --> T[Tool Registry]
    A --> M[Memory Layer]
    A --> R[RAG Layer]
    A --> G[Guardrails]
    W --> S[State / Checkpoints]
    T --> I[Integrations]
    R --> V[Vector Stores / Retrievers / Rerankers]
    G --> E[Enterprise Policies]
    A --> O[Observability]
    O --> D[Dashboard / Metrics / Traces]
    E --> AUD[Audit / RBAC / Tenant Controls]
    C --> DEP[Docker / Compose / Helm]

---

Repository map

Path	Purpose
largestack/_core	Main agent/tool/runtime primitives
largestack/_workflow	Workflow graph, checkpoints, interrupts, subgraphs
largestack/_rag	RAG query engines, eval, summary index
largestack/_memory	Memory stores and memory tools
largestack/_guard	Provider/tool guardrail policies
largestack/_security	Sandbox, permissions, vault, encryption, network controls
largestack/_enterprise	RBAC, audit, tenant, SSO/session, billing/payment modules
largestack/_observe	Cost, traces, OTEL, telemetry helpers
largestack/_dashboard	Dashboard app and APIs
largestack/_integrations	Provider/tool integrations
largestack/_templates	Project starter templates
examples/	Runnable examples
tests/	Unit, integration, security, RAG eval tests
scripts/	Certification, smoke, scenario, and live DeepSeek validation scripts
deploy/	Docker, Compose, Helm, monitoring assets
release_evidence/	Internal smoke/soak logs from the maintainer's local runs

---

Production-positioning honesty

Largestack is strong for:

• developer demos,
• investor demos,
• internal AI platform experiments,
• controlled pilots,
• agentic framework portfolio proof,
• private beta deployments.

Largestack should not yet be marketed as:

• fully BFSI-certified,
• SOC2/ISO-certified,
• a complete coverage match for the LangChain/LangGraph ecosystem,
• public SaaS production platform without load tests, external VAPT, and real Kubernetes install proof.

Known limitations are tracked in docs/known-limitations.md. Review that file before publishing release, SaaS, BFSI, or regulated-enterprise claims.

---

Roadmap

Priority	Work
P0	Add load/concurrency evidence after completed 24h soak
P0	Queue/backpressure for high traffic
P0	Distributed workers and job leasing
P0	Durable replay/checkpoint recovery
P1	Real Kubernetes cluster install test
P1	Observability UI polish and replay debugger
P1	More beginner templates and tutorials
P2	Public docs website
P2	Community examples and plugin ecosystem
P3	Enterprise certifications, VAPT, compliance evidence

---

Search and discovery

If you are looking for this project, search for:

• Largestack AI
• largestack Python framework
• RivaiLabs Largestack
• pip install largestack
• Largestack agents RAG guardrails observability

Official links:

• Website: https://largestack.ai
• PyPI: https://pypi.org/project/largestack/
• GitHub: https://github.com/Rivailabs/largestack
• Docs: https://docs.largestack.ai

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License

Apache-2.0.