AI operating layer

Build, deploy, monitor, and scale LLM-powered software

LLMLab wraps model calls with context, APIs, prompt-contracts, monitoring, and deployment layers a team needs to ship LLM-powered software.

Keep pipelines connected to changing documents, codebases, APIs, prompts, permissions, logs, and deployment channels without rebuilding the runtime for every agent.

Open LLMLab Explore Platform
Build Design visual pipelines instead of loose prompt chains
Deploy Expose pipelines through widgets, Slack, Discord, webhooks, and schedules
Monitor Track runtime output, usage, logs, storage, permissions, and billing caps
Scale Reuse prompts, memory, APIs, context, and governed infrastructure across teams
Platform layers

The operating system around the model call

LLMLab organizes production LLM software into six connected layers.

Layer 01

Pipeline Builder

Build agents as structured pipelines with routing, retrieval, actions, validation, memory, and runtime debugging.

Visual canvas Routing Runtime debugging
Includes
  • Rules and LLM routers
  • Knowledge and codebase nodes
  • Action, validation, and clarify paths
  • Memory reuse
  • Streaming runtime debugger
Click a layer to jump to its section.
Pipeline Builder

Build agents as inspectable pipelines

LLMLab gives teams a visual runtime for designing how requests move through context, routing, tools, validation, memory, and deployment. Each node has a job, each branch is inspectable, and each run can be tested before it reaches users.

Design Every node has a job

Inputs, actions, routers, retrieval, validation, memory, clarification, failure, and skip paths stay visible.

Operate Every run is inspectable

Follow touched nodes, live output, runtime state, node results, stop controls, logs, and saved run history.

Ship Every trigger is governed

Use presets, runtime fields, schedules, secure webhooks, widgets, Slack, Discord, and per-pipeline permissions.

Pipeline Orchestration Production execution graph
Streaming debugger
Input Request
Route Rule-based/ LLM
Retrieve Context Knowledge/ codebase
Act API or document
Validate JSON contract
Log Run history
Streaming runtime Clarify requests Use stored answers Validate + retry Flag for human review Log failures and perform follow-up
Selected capability

Structured pipeline graph

Compose inputs, rules routers, LLM routers, retrieval, actions, validation, clarification, memory, failure, and skip paths in one visible runtime.

Context Layer

Keep pipelines connected to live context

LLMLab connects pipelines to live cloud documents, websites, GitHub repositories, internal documents, parsed routes, schemas, UI artifacts, and reusable answer memory. When knowledge or code changes, pipelines can refresh the context they use instead of drifting away from the real product.

Living Context Layer Source updates become pipeline context
Auto-refresh ready
Cloud docs Websites GitHub codebases LLMLab documents
Live Context Layer Connected, code-aware context
that stays up to date
Parse Ingest Embed Rerank Retrieve
Automatic updates from Githhub triggers Website updates Cloud doc changes Public API changes
Source updates Auto-ingest Refreshed context Pipeline execution
Freshness Source updates can refresh runtime context

Auto-ingest changed documents, websites, and source items; block stale retrieval when current context matters.

Product grounding Agents understand the running product

Use docs, screens, forms, buttons, routes, schemas, API calls, and source anchors in support and operation pipelines.

Cost control Memory routes reuse trusted answers

Store validated responses in private memory documents and memory KBs before spending on another full run. Memory automatically updates or marks itself stale if source documents change.

01

Live Knowledge Bases

Link Google Drive and OneDrive sources, crawl websites and docs portals, and connect source items so knowledge bases stay aligned as documents and published knowledge change.

  • Auto-ingest updated documents, websites, and source items.
  • Use hybrid vector and BM25-style retrieval with reranking.
  • Block pipelines until ingestion finishes when freshness matters.
02

Codebase Intelligence

Connect GitHub repositories so pipelines and support agents can stay grounded in the code your product actually runs.

  • Parse backend routes, handlers, request schemas, and response schemas.
  • Map screens, forms, handlers, API calls, routes, and schemas into a UI graph for ranked LLM context.
  • Refresh parsed context as connected repositories and branches change, automatically updating pipelines.
03

Pipeline Documents

Use LLMLab documents as living pipeline state: editable by humans, writable by pipelines, versioned, and retrievable through knowledge bases.

  • Write procedures, summaries, decisions, state, and structured outputs.
  • Attach documents back into knowledge bases as LLMLab-native sources.
  • Use document contracts to turn pipeline output into predictable fields.
04

Public API Pipelines

Watch public API contracts for changes and automatically refresh affected pipeline prompts, actions, and tool links.

  • API watchers detect schema updates and trigger pipeline regeneration only where behavior is impacted.
  • Required JSON fields are auto-parsed from responses and normalized into pipeline context.
  • Output contracts enforce field presence, shape, and data types before downstream routing.
Retrieval quality

Use vector search, lexical search, rerankers, source metadata, and citations to pull higher-signal context into each pipeline step.

Governed storage

Protect sensitive context with encrypted chunks, encrypted code/source text, private DEK support, and role-governed access.

Context cleanup

Review chunks, vector counts, source metadata, stale context, model footprint, and remove embeddings that should not accumulate unchecked.

APIs & Actions

Turn API docs into pipeline tools

LLMLab turns public APIs, internal routes, and human-readable API documentation into governed pipeline actions. Parse the API, connect org-specific credentials, map runtime fields, and call endpoints from pipelines with request schemas, response contracts, validation, and logs.

API operating layer

Shared definitions become org-safe pipeline actions

Keep the endpoint map reusable while credentials, runtime defaults, permissions, and execution logs stay scoped to each organization and pipeline run.

Version pinned Schema aware Observable
Definition sources
Catalog Public API database

Shared pipeline-ready endpoint definitions for common APIs.

Specs OpenAPI + Postman

Parse structured specs into endpoints, parameters, bodies, and schemas.

Docs Human API docs

Use LLM-assisted extraction when teams only have messy documentation.

Code Internal routes

Backend routes parsed from codebases become internal action context.

Reusable API definition
Parse + normalize Endpoint contract
Endpoint

method, path, source page, version, deprecation state

Auth

bearer, Basic, API-key header, custom header, query param

Request

path params, query params, headers, request body schema

Response

response schema, output fields, downstream mappings

Organization runtime
Connection Org-specific API Connection

Credentials, base URLs, runtime defaults, permissions, and stale-source behavior.

Secrets Encrypted org-secret binding

Auth fields resolve from governed secrets instead of prompts or node text.

Action node Schema-aware execution

Runtime fields map into requests; responses validate against contracts.

Run record Logs + review state

Request mappings, responses, errors, validation states, and outputs stay attached.

Public API database

Start from shared endpoint definitions

LLMLab keeps reusable API definitions in a shared catalog so organizations do not rebuild endpoint maps for common integrations.

Jira GitHub Bitbucket Microsoft Graph Gmail Google Drive Google Docs Asana Intercom Pipedrive Stripe Xero Plaid SendGrid Webflow OpenAI
API docs parser

Parse docs into structured pipeline tools

Parse OpenAPI specs, Postman collections, and human-readable API docs into endpoints, auth fields, parameters, request bodies, and response schemas.

  • OpenAPI, Swagger, Postman, and provider discovery formats
  • LLM-assisted extraction when no clean spec exists
  • Source pages, parser versions, warnings, errors, and schema counts
Secure API connections

Separate shared definitions from org credentials

The API Definition describes what endpoints exist. The API Connection describes how one organization authenticates, which defaults it uses, and who can use it.

  • Bearer token, Basic Auth, API-key header, custom header, query param, and provider-specific auth modes
  • Auth fields bound to encrypted org secrets or approved runtime values
  • Org-scoped base URLs, permissions, runtime defaults, and stale-source blocking
Runtime field mapping

Map pipeline data into requests without hardcoding prompts

Parsed schemas tell pipelines what an endpoint expects, what it returns, and how downstream nodes can use the result.

  • Map pipeline data into path params, query params, headers, request bodies, and custom inputs
  • Expose request fields, response fields, output fields, and downstream mappings
  • Validate generated request payloads before the call is sent
Schema-aware Action nodes

Call public APIs, internal routes, HTTP endpoints, and webhooks

Select an API definition, API connection, and endpoint, then LLMLab can populate request mappings, response schemas, output fields, webhook schemas, and validation contracts.

  • Use the same action layer for public APIs and internal backend routes parsed from codebases
  • Use quick HTTP actions for one-off calls, then promote repeated integrations into reusable definitions
  • Carry response contracts into downstream pipeline nodes
Production runtime

Pin, log, validate, and review every API step

Pipeline actions can pin API definition versions so changing docs or endpoint schemas do not silently break production behavior.

  • Request and response logs, redacted headers, response payloads, errors, and review states
  • Runtime validation for request mappings and response schemas
  • Downstream output contracts attached to each pipeline run
Prompt Contracts

Version prompts. Enforce contracts. Keep pipelines maintainable

LLMLab manages prompts as runtime assets. Version node prompts, layer system and organization behavior, regenerate prompts when pipelines change, and use output contracts to turn model responses into data that routes, validators, documents, and API actions can use.

Prompt behavior stack Managed runtime behavior
Contract-aware
Behavior layers
System defaults LLMLab-maintained baseline behavior
Organization behavior Team-specific instructions and versions
Node component Focused behavior for each pipeline role
Prompt Library Active version

Selected components are assembled with pipeline context and contract rules.

Regenerate Affected prompts

Pipeline paths, routes, APIs, schemas, documents, and contracts can refresh the prompts they touch.

Output contract
Required fields Route decision API response fields Document write fields Validation result Review / escalation metadata
Versioned prompts System + org layers Node components Regeneration Output fields Document contracts API schemas Route-aware contracts
01

Prompt Library

Manage prompts as first-class pipeline assets instead of hidden strings inside nodes. Inspect, edit, generate, organize, and control who can change production behavior.

02

Prompt Versions

Iterate on prompt behavior without losing prior versions. Promote the active version when a pipeline is ready because prompt changes are production behavior changes.

03

Node Components

Compose behavior from focused components for actions, LLM routing, trigger logic, knowledge context, codebase context, clarification, and validation.

04

Regeneration

When routes, pipeline branches, API endpoints, schemas, document contracts, codebase context, or output contracts change, regenerate affected prompts instead of hand-editing brittle instructions.

05

Output Contracts

Keep contract-driven output machine-readable. Routers return explicit branch decisions, validators can pass, rewrite, retry, fail, or review output, and uncertain runs can request review or escalation where configured.

06

Schema Sources

Use document contracts, public API schemas, internal route schemas, HTTP response schemas, webhook output schemas, generated contracts, and known pipeline paths to turn actions into structured data producers.

Controls & Monitoring

Govern pipelines like production infrastructure

LLMLab gives teams production controls around LLM pipelines: members, roles, permissions, external users, encrypted secrets, privacy settings, usage and billing caps, pipeline logs, streaming runtime debugging, admin activity history, vector storage review, and public abuse monitoring.

Who can use it What it can access What it costs What it stores What it ran What changed How public deployments are protected
Operations Console Governance, usage, runtime, storage, and public safety in one view
Production controls
Members 24 6 custom roles
MTD spend $184.27 $315.73 under cap
Runtime Running API Action node
Public safety 14 events 1 active ban
Runtime Observability Live stream
Run: running
Input Router KB API Action Validate
Current node
API Action
Touched nodes
4
Last validation
passed
Stop control
available
Usage Guardrail Billing
Daily spend
$12.43
Lifetime spend
$2,913
Events
1,284
Monthly cap
$500
Secrets & Privacy Encrypted
Org secrets
18
Private DEK
active
OAuth mode
protected
Log privacy
restricted
Storage Review Vectors
Estimated storage
2.8 GB
Chunks
184,203
Stale chunks
6,420
Cleanup selected
220 chunks
Public Safety Abuse
Actor/IP hash
ip_7f3a92c1
Public runs
231
Ban state
active
Last event
prompt spam
Admin Activity Diff available
Actor admin@company.com
Action role.updated
Category roles
Target Support Operator
Changed fields permissions
Diff JSON available
Dedicated workers: 2 Queue depth: 4 Provisioning: complete GPU plan: active
Access Control

Control who can build, run, edit, deploy, or manage every resource in the pipeline stack

Manage organization members, approve or deny requests, suspend access, assign roles, gate admin tabs with permission checks, map external IDs to scoped runtime roles, and set resource-level rules for pipelines, API connections, models, documents, codebases, and integrations.

Members
24
Custom roles
6
Pending approvals
2
Last change
manage.pipelines updated
External ID
ext_user_2049
Runtime role
widget_runtime_role
Pipeline access
run by Support role
API connection
Stripe Production
Secrets & Privacy

Store provider keys, API credentials, OAuth tokens, and integration secrets as encrypted organization resources

Create, rotate, and delete secrets for model providers, API connections, public integrations, rerankers, and pipelines while protecting logs, OAuth tokens, stored text, source chunks, and codebase chunks with private DEK support.

Secrets
18
API secret users
7
Encrypted chunks
enabled
Last rotated
Stripe API key
Usage & Billing Guardrails

Track usage across pipelines, model calls, retrieval, API actions, integrations, storage, and hosted infrastructure

Review month-to-date, daily, and lifetime spend, event counts, usage breakdowns, pipeline run costs, billing caps, Stripe setup, payment state, invoices, GPU billing, and worker queue state so production pipelines can't quietly run past budget.

Month-to-date
$184.27
Daily spend
$12.43
Lifetime spend
$2,913
Events
1,284
Monthly cap
$500
Payment
configured
Runtime Observability

Inspect pipeline runs node by node, from input and routing to retrieval, API calls, validation, review states, and output

Watch pipelines execute live, stop runs, inspect touched nodes, read node output, track API calls and errors, debug before release, and review saved run history after deployment.

Run
completed
Touched nodes
5
Review
not required
Duration
1.8s
Storage Review

Inspect and clean up embedded chunks, stale context, encrypted previews, source metadata, and vector storage footprint

Review total chunks, vector counts, estimated bytes, stale chunks, last access, text previews where allowed, embedding models, source metadata, delete responses, and bulk cleanup.

Vector storage
2.8 GB
Vectors
184,203
Stale chunks
6,420
Top model
text-embedding-3-small
Public Safety

Monitor public pipeline traffic, actor/IP stats, abuse events, and ban states for exposed widgets, webhooks, Slack, and Discord flows

Run public integrations through scoped roles, watch rate and traffic patterns, ban or unban abusive actors, and clean up bindings, sessions, token nonces, credentials, and access grants when connections are removed.

Actor/IP hash
ip_7f3a92c1
Abuse events
14
Public runs
231
Ban state
active
Deployment Channels

Deploy pipelines where people and systems already work

Deploy the same governed pipeline through web integrations, Slack, Discord, secure webhooks, schedules, assistant surfaces, and public connection bindings. Each channel can pass runtime fields, use scoped roles, trigger specific pipeline paths, and feed back into the same logs and monitoring layer.

LLMLab One runtime everywhere
Governed runtime

Every surface uses the same runtime, contracts, permissions, usage tracking, logs, monitoring, history, and scoped execution.

Runtime fields

Map user, channel, message, payload, scheduled, and manual-run data into runtime fields for nodes and API actions.

Public connections

Manage provider installs, bindings, trigger-node selection, allowed pipelines, and generated roles.

Lifecycle security

External actors use scoped roles. Verified events and deletes clean up bindings, sessions, credentials, nonces, and grants.

Selected channel

Embed pipeline-backed assistants with hosted scripts, iframe delivery, theming, welcome messages, website access rules, widget settings, and generated runtime roles.

  • Hosted script + iframe
  • Theme, welcome, and widget controls
  • Website access rules
  • Generated widget runtime role
  • Runtime logs
Hosted script iframe delivery Widget theming Scoped public role
Connection binding Surface: Website assistant
Trigger
Visitor message
Role
Generated widget runtime role
Access
Website access rules
Logs
Runtime captured
Start building

Build production AI pipelines faster

Use LLMLab to compose pipelines, attach context, call APIs, enforce contracts, monitor execution, deploy across real channels, and explore the model options behind the platform.

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