Customer support assistants
Answer product and account questions from approved knowledge and customer-specific context, with citations and clear escalation paths.
Independent tool overview
Inkeep at a glance
Inkeep is an AI-agent platform for customer experience and operations. Its current product is broader than the original 'turn your content into a chatbot' positioning: teams can build assistants and multi-agent workflows in a no-code canvas or TypeScript SDK, connect tools through MCP and APIs, embed custom chat interfaces, trace runs, and deploy on their own infrastructure. A managed Enterprise offer adds unified search, support-channel integrations, governance, and implementation help. The self-hosted framework is free but uses an Elastic License 2.0 fair-code model with supplemental terms, so it should not be described as unrestricted open source.
Visit the official Inkeep site ↗
- Product type
- AI agent builder, customer assistants, internal copilots, and workflow automation
- Build modes
- No-code visual builder and TypeScript SDK with two-way sync
- Self-hosting
- Docker or Vercel, with operator-managed infrastructure and providers
- Protocols
- MCP, A2A, webhooks, and Vercel AI SDK formats
- License
- Elastic License 2.0 plus Inkeep supplemental terms; fair-code and source-available
- Managed plan
- Enterprise with quote-based pricing
- Enterprise data options
- Cloud, hybrid, or self-hosted with configurable providers
- Reviewed
- August 31, 2026 from the live site, current docs, repository, security, legal, and subprocessor pages
Overview
What Inkeep is
Inkeep now combines an agent framework, visual builder, developer SDK, UI components, protocols, and observability. Configurations can move in both directions between the no-code interface and TypeScript, giving product, support, operations, and engineering teams a shared way to define agents and workflows.
Customer-facing assistants can answer from product documentation and customer-specific systems, while internal coworkers can gather context from support, CRM, and billing tools and propose actions for an employee to approve. Automation agents can respond to webhooks, schedules, or external events and use MCP tools to update systems or create content.
The self-hosted framework includes multi-agent orchestration, credentials management, inline citations, chat components, A2A and MCP access, webhooks, Vercel AI SDK compatibility, traces, and OpenTelemetry. It supports multiple model providers and deployment through Docker or Vercel, but the operator owns infrastructure, providers, secrets, databases, upgrades, and incident response.
Enterprise adds managed ingestion and retrieval for public and private sources, Slack and support-platform integrations, PII removal, cloud or hybrid hosting, SSO, role-based access, audit logs, SLAs, security review materials, and a dedicated forward-deployed engineer. Pricing is quote-based and the exact data path depends on the contracted configuration.
Because Inkeep agents can read private context and take real actions, the core risk is not only hallucination. Prompt injection, excessive permissions, stale knowledge, confused tenant boundaries, unsafe tool arguments, retries, and silent automation can turn a wrong answer into a changed CRM record, exposed customer data, or published documentation. Production use needs least privilege, deterministic validation, test suites, human approvals, and a reversible operating model.
Use cases
Who Inkeep is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Support-agent copilots
Draft responses, collect context, and propose ticket or CRM actions for an employee to review and approve.
Documentation maintenance
Turn resolved tickets and product changes into draft docs or pull requests rather than publishing directly.
Mixed no-code and engineering teams
Let operational owners edit agents visually while developers use TypeScript, version control, testing, and deployment tooling.
Self-hosted agent platforms
Run a source-available framework with chosen model providers when the organization is prepared to operate its databases, credentials, tracing, upgrades, and security.
Managed enterprise retrieval
Use Inkeep's ingestion, managed RAG, channel integrations, governance, and forward-deployed engineering for a supported rollout.
Capabilities
Core Inkeep features
No-code visual builder
Provides a drag-and-drop canvas for defining agents, subagents, tools, triggers, prompts, and workflows.
TypeScript Agents SDK
Lets developers define the same agent system in code with typing, CI/CD, version control, and programmatic tests.
Two-way configuration sync
Supports moving changes between the visual builder and SDK so technical and non-technical contributors can collaborate.
Multi-agent orchestration
Coordinator agents can route work to specialized subagents with bounded tools and responsibilities.
MCP and function tools
Connects agents to MCP servers, applications, APIs, and custom functions with credential and permission management.
Triggers and interoperable APIs
Agents can run from chat, webhooks, schedules, MCP, A2A, events, and Vercel AI SDK-compatible interfaces.
Agent UI components
Includes React and JavaScript chat components plus custom message interfaces such as forms and cards.
Traces and OpenTelemetry
Records agent execution traces and can export observability data through OpenTelemetry for debugging and evaluation.
Model-provider choice
The framework can be configured with supported providers such as Anthropic, OpenAI, Google, Azure, or Bedrock rather than requiring one model.
Unified AI Search
Enterprise manages ingestion, semantic retrieval, and citations across public documentation and private systems such as Notion or Confluence.
Support-channel integrations
Enterprise connects agents to Slack, Teams, Discord, Zendesk, Salesforce, and other customer-service surfaces.
Enterprise governance
Adds SSO, role-based access, audit logs, PII removal, deployment choices, security evidence, SLAs, and implementation support.
Process
How the Inkeep workflow works
Step 1
Define the job and harm boundary
Specify the user, allowed decisions, systems touched, data classes, escalation path, unacceptable outcomes, and actions that always require a person.
Step 2
Map the full data path
List every source, connector, model provider, database, trace store, integration, destination, region, retention rule, and subprocessor for the selected deployment.
Step 3
Choose self-hosted or Enterprise
Compare source-available licensing, infrastructure and support costs, managed RAG, governance, SLAs, channel integrations, and internal operating capacity.
Step 4
Complete contract and license review
Review Elastic License 2.0 and Inkeep's supplemental terms for self-hosting, or execute the appropriate order form, DPA, security terms, and any required regulated-workload agreement for Enterprise.
Step 5
Prepare trusted knowledge
Ingest authoritative, current, permissioned sources with owners, versions, freshness dates, tenant labels, and explicit exclusions; remove duplicates and stale documents.
Step 6
Configure identity and tenancy
Use organization and project roles, SSO where available, tenant-scoped retrieval, short-lived sessions, secure invite and offboarding flows, and test that one tenant cannot retrieve another tenant's data.
Step 7
Give every tool least privilege
Use separate credentials per environment and agent, narrow scopes, allowlisted endpoints and fields, read-only access by default, secret managers, rotation, and explicit approval for writes.
Step 8
Design for hostile inputs
Treat documents, tickets, webpages, tool results, and user prompts as untrusted. Isolate instructions from content, sanitize retrieved data, validate schemas, and reject commands embedded in sources.
Step 9
Build deterministic guardrails
Validate tool arguments, customer and tenant IDs, amounts, destinations, state preconditions, rate limits, idempotency keys, and postconditions outside the language model.
Step 10
Create a representative evaluation set
Test known answers, missing knowledge, conflicting sources, prompt injection, unsafe actions, PII, multilingual requests, escalation, connector failures, latency, and adversarial tool use.
Step 11
Require human approval for impact
Keep refunds, account changes, external messages, document publishing, CRM writes, deletions, and consequential decisions behind a review interface that shows sources and exact proposed changes.
Step 12
Deploy with production controls
For self-hosting, harden Docker or Vercel, databases, backups, TLS, network egress, secrets, patches, quotas, and disaster recovery. For managed use, confirm the contracted configuration and shared-responsibility boundary.
Step 13
Observe outcomes, not just runs
Monitor source coverage, citation support, containment, escalation, bad actions, retries, duplicates, latency, model and tool cost, customer corrections, and business impact with privacy-aware traces.
Step 14
Operate a kill switch and rollback
Be able to disable one connector, tool, workflow, model, or tenant quickly; preserve idempotent recovery and audit evidence for every action.
Step 15
Re-evaluate continuously
Run the test suite on prompt, model, source, tool, permission, dependency, and policy changes, and keep the framework and infrastructure patched.
Cost
Inkeep pricing and free plan
Inkeep publishes two paths: a free-forever self-hosted framework and a quote-based Enterprise platform. Free refers to the Inkeep software license, not total cost—operators still pay for model usage, hosting, databases, search, observability, email, connector infrastructure, engineering, and support. The repository uses Elastic License 2.0 plus supplemental terms, so confirm that the intended commercial use is permitted.
Self-hosted framework
$0 software price
A source-available, fair-code package for teams that operate the full stack themselves.
- Visual builder and TypeScript SDK
- MCP servers and tools
- Agent UI library and observability
- Deploy to Docker or Vercel
- Community support
- Model, infrastructure, database, search, connector, and operations costs are separate
Enterprise
Custom quote
A managed platform and implementation program for production customer and operations agents.
- Managed unified search and RAG
- Public and private source ingestion
- Slack and support-platform integrations
- Cloud, hybrid, or self-hosted deployment
- SSO, role-based access, audit logs, PII removal, and SLAs
- Dedicated forward-deployed engineer, training, and support
Pricing checked . Check current pricing at the source ↗
Assessment
Inkeep strengths and limitations
Where it stands out
- Bridges no-code agent ownership and code-first engineering through two-way sync
- Covers agent logic, tools, UI, protocols, deployment, testing, and observability in one framework
- Supports multiple model providers and standard interfaces instead of forcing a single closed runtime
- Self-hosting provides substantial control over infrastructure and data flow
- Enterprise combines managed retrieval, channel integrations, governance, and hands-on implementation
- Inline citations and human-approval patterns fit support and documentation use cases
- Source-available code and active documentation make the architecture inspectable
- Published subprocessor and security materials provide a useful starting point for enterprise review
What to consider
- Inkeep's 'Open Source' plan uses Elastic License 2.0 plus supplemental terms, which is fair-code and source-available rather than an unrestricted OSI-approved open-source license
- Self-hosting transfers model selection, infrastructure, secrets, identity, databases, backups, patching, observability, availability, and incident response to the operator
- A multi-agent design can add latency, cost, hidden state, delegation errors, and debugging complexity without improving the underlying task
- Retrieval can surface stale, irrelevant, cross-tenant, manipulated, or prompt-injected content even when answers contain citations
- Tool-using agents can make irreversible or externally visible changes if credentials, arguments, approvals, retries, or idempotency are poorly designed
- Inline citations show what was retrieved, not that a claim is correct, complete, current, or authorized for that user
- The public Enterprise price and usage model is not disclosed, so buyers need a workload-specific quote covering models, ingestion, storage, support, and overages
- Enterprise feature labels such as HIPAA review do not establish that a specific deployment is suitable for regulated data; the public terms tell users not to submit sensitive personal data without an appropriate arrangement
- The privacy policy is dated October 2023 and the terms November 2024, while the subprocessor list was updated in March 2026; a current order form and DPA should control material production use
- The terms grant Inkeep a broad perpetual license to use User Content to develop and improve its Services and AI Functions, while stating that it does not use the content to train third-party foundation models; customers should negotiate the exact boundary they require
- The subprocessor list contains many optional infrastructure, model, database, analytics, and integration vendors, so the actual enabled set must be documented rather than inferred from the list
- Building reliable customer-facing automation requires a maintained evaluation set, content operations, security engineering, and ongoing human ownership; the builder does not remove that work
- Agent traces can contain sensitive prompts, retrieved context, tool inputs, and outputs unless logging, redaction, retention, and access controls are designed carefully
- Support and documentation drafts can still contain hallucinations, unsupported policy promises, unsafe troubleshooting steps, or cross-customer details
Compare
Inkeep alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Business Operations
Voiceflow
A visual platform focused on designing, testing, and deploying conversational voice and chat agents.
Explore Voiceflow →Coding
LangChain
A code-first ecosystem for developers who want lower-level control over agent graphs, retrieval, tools, and observability.
Explore LangChain →Business Operations
n8n
A workflow-automation platform with AI nodes for teams that prioritize broad integration coverage and deterministic orchestration.
Explore n8n →Business Operations
Intercom Copilot
A managed support-agent assistant for organizations already centered on Intercom rather than building a general agent platform.
Explore Intercom Copilot →Business Operations
Dust
An enterprise assistant platform for teams that want managed access to company knowledge and workplace tools.
Explore Dust →Questions
Inkeep FAQs
What is Inkeep?
Inkeep is a platform for building customer-facing assistants, internal copilots, and multi-agent workflows with a visual builder or TypeScript SDK. It also offers managed enterprise retrieval, integrations, governance, and implementation support.
Is Inkeep only a documentation chatbot?
No. That describes its earlier positioning. The current platform can answer questions, retrieve customer context, propose or execute tool actions, automate support and documentation workflows, and expose agents through multiple protocols and interfaces.
Is Inkeep free?
The self-hosted framework has a $0 software price. You still pay for model APIs, hosting, databases, search, observability, connectors, email, engineering, and operations. Enterprise pricing is available by quote.
Is Inkeep open source?
Inkeep calls the free plan Open Source, but its repository states that the framework uses Elastic License 2.0 plus Inkeep supplemental terms. That is a source-available fair-code license with restrictions, so review the license before commercial deployment or redistribution.
Can I self-host Inkeep?
Yes. The documentation supports Docker and Vercel deployment and includes authentication, authorization, database, connector, and observability services. Self-hosting means your team is responsible for secure configuration, providers, upgrades, backups, monitoring, and recovery.
Which models does Inkeep support?
The framework is designed to work with multiple providers. Current deployment examples include Anthropic, OpenAI, Google, Azure, and Bedrock credentials. Verify feature and tool compatibility for the exact model you select.
What does Inkeep Enterprise add?
Enterprise adds managed public and private source ingestion, optimized retrieval, semantic search, Slack and support integrations, PII controls, SSO, RBAC, audit logs, hosting choices, security reviews, SLAs, and a dedicated engineering program.
Does Inkeep train models on customer data?
Its terms say Inkeep may use User Content to develop and improve its Services and AI Functions, but will not use the content to train third-party large language or foundation models and does not allow AI subprocessors to do so. Customers needing a narrower rule should confirm it in their contract and DPA.
Can Inkeep handle sensitive or regulated data?
Do not assume so from a feature table. The public terms ask users not to submit sensitive personal data and make customers responsible for suitability. Complete a current security and subprocessor review and execute any required DPA, BAA, residency, or regulated-workload agreement before use.
How do I prevent an Inkeep agent from taking a harmful action?
Give it minimal credentials, allowlist tools and fields, validate every argument and tenant outside the model, use idempotency and limits, require human approval for impactful actions, test prompt injection and failures, log safely, and maintain an immediate kill switch.
Are Inkeep citations proof that an answer is correct?
No. A citation can still point to a stale, irrelevant, incomplete, or unauthorized passage. Evaluate claim support, source freshness, retrieval recall, tenant isolation, and whether the answer omits conflicting material.
What should an Inkeep pilot measure?
Measure grounded-answer accuracy, source coverage, citation support, containment, escalation quality, unsafe action rate, tenant isolation, latency, cost, human correction time, customer outcome, and recovery from connector or model failures.
Bottom line
Our Inkeep verdict
Inkeep is compelling for organizations that want one agent definition to serve no-code operators, TypeScript developers, customer interfaces, and automated workflows. The strongest differentiator is the bridge between a source-available self-hosted framework and a managed enterprise retrieval and CX platform. Adopt it only with clear license and contract terms, a complete data map, least-privilege tools, adversarial evaluations, human approval for meaningful actions, and an owner responsible for the system after launch.
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