AI operating layer

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

LLMLab wraps model calls with the workflow, context, API, prompt-contract, monitoring, and deployment layer teams need to ship LLM-powered software.

Keep workflows 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 workflow systems instead of loose prompt chains
Deploy Expose workflows 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

Workflow Builder

Build agents as structured workflows 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.
Workflow Builder

Build agents as inspectable workflows.

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-workflow permissions.

Workflow Runtime Production execution graph
Streaming debugger
Input Request
Route Rules or LLM
Retrieve Context Knowledge + code
Act API or document
Validate JSON contract
Log Run history
Clarify Memory Failure path API action Codebase context Output contract
Live stream Touched nodes Stop control Run history
Selected capability

Structured workflow graph

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

Context Layer

Keep workflows connect to live context

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

Living Context Layer Source updates become workflow context
Auto-refresh ready
Cloud docs Websites GitHub codebase LLMLab documents Memory
Context Layer Connected retrieval plus product graph
Ingest Chunk Embed Rerank Retrieve
Parse Routes Schemas UI graph
Workflow nodes Support agents API actions Document writes
Source updates Auto-ingest Refreshed context Workflow runtime
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 operations workflows.

Cost control Memory routes reuse trusted answers.

Store validated responses in private memory documents and memory KBs before spending on another full run.

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 workflows until ingestion finishes when freshness matters.
02

Codebase Intelligence

Connect GitHub repositories so workflows 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.
  • Refresh parsed context as connected repositories and branches change.
03

Workflow Documents

Use LLMLab documents as living workflow state: editable by humans, writable by workflows, 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 workflow output into predictable fields.
04

Reusable Memory

Store validated answers as private memory so repeated questions can reuse trusted responses and reduce model spend.

  • Memory nodes write reusable answers into private documents and memory KBs.
  • Similar requests can route to saved answers before another full retrieval run.
  • Memory stays reviewable, governable, and connected to workflow execution.
Retrieval quality

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

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

API operating layer

Shared definitions become org-safe workflow actions.

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

Version pinned Schema aware Observable
Definition sources
Catalog Public API database

Shared workflow-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 workflow 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 workflow data into requests without hardcoding prompts.

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

  • Map workflow 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 workflow nodes
Production runtime

Pin, log, validate, and review every API step.

Workflow 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 workflow run
Prompt Contracts

Version prompts. Enforce contracts. Keep workflows maintainable.

LLMLab manages prompts as runtime assets. Version node prompts, layer system and organization behavior, regenerate prompts when workflows 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 workflow role
Prompt Library Active version

Selected components are assembled with workflow context and contract rules.

Regenerate Affected prompts

Workflow 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 workflow 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 workflow 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, workflow 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 workflow paths to turn actions into structured data producers.

Controls & Monitoring

Govern workflows like production infrastructure.

LLMLab gives teams production controls around LLM workflows: members, roles, permissions, external users, encrypted secrets, privacy settings, usage and billing caps, workflow 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 workflow 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 workflows, API connections, models, documents, codebases, and integrations.

Members
24
Custom roles
6
Pending approvals
2
Last change
manage.workflows updated
External ID
ext_user_2049
Runtime role
widget_runtime_role
Workflow 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 workflows 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 workflows, model calls, retrieval, API actions, integrations, storage, and hosted infrastructure.

Review month-to-date, daily, and lifetime spend, event counts, usage breakdowns, workflow run costs, billing caps, Stripe setup, payment state, invoices, GPU billing, and worker queue state so production workflows cannot 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 workflow runs node by node, from input and routing to retrieval, API calls, validation, review states, and output.

Watch workflows 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 workflow 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 workflows where people and systems already work.

Deploy the same governed workflow runtime 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 workflow 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, workflow and trigger-node selection, allowed workflows, 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 workflow-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 workflows faster.

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

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