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License: License: unverified
Local-run terms: Clone the repository, configure environment, and run via Docker Compose or Bun with a local or external Postgres database.

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nanochat

FreeOpen SourceAPISelf-HostedAgentic

Pricing

Model
Free

Summary

Every add-on you need — search, memory, image generation, voice — usually means five separate SaaS subscriptions and five separate places your conversation data lives. Nanochat collapses that into a single self-hosted deploy.

The project is a self-hostable chat client that routes through the Nano-GPT API, which means access to frontier models sits behind one API key rather than a per-provider credential juggle. Docker Compose brings its own Postgres instance; data writes to a local volume and never touches a vendor's cloud. Web search, URL scraping, inline image and video generation, cross-conversation memory, voice I/O, and MCP server connections are all included in the base install — not gated behind a paid tier. The ceiling arrives when you push past Nano-GPT's own API constraints: model availability and rate limits are whatever Nano-GPT exposes, and swapping to a provider outside that ecosystem requires changes the current architecture does not support.

Bottom line: Pick nanochat if your team needs a privacy-first, all-features-included chat client running on your own hardware; plan around it if your architecture depends on direct API access to providers outside the Nano-GPT ecosystem.

Community Performance Report Card

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Best For: Users wanting full control over Nano-GPT chat data, Deployments requiring integrated search and generation tools, Projects needing MCP server tool integration, Privacy-focused persistent conversation memory, Scripting against a self-hosted REST API
  • Single-command Docker Compose deploy with bundled Postgres, so you are not manually provisioning a database or wiring connection strings before the first conversation.
  • Cross-conversation persistent memory included at the base install, which means context from last week's thread is available today without a separate vector database or memory service.
  • Web search, deep search, and URL-to-context scraping built in, so you avoid the per-API-call billing and credential management that comes with bolting on a search provider separately.
  • MCP server connections expose external tools to the agent, so automations that need real-world actions — file writes, API calls, service integrations — do not require a separate orchestration layer.
  • Documented REST API on your own instance, so scripts and CI pipelines call the same backend your browser uses rather than a separate service running different state.
  • Every model request routes through the Nano-GPT API, which means model availability, rate limits, and outages are inherited from that single dependency — teams that need direct provider access or want to run local Ollama models hit an architectural dead end and switch to multi-provider front-ends like LibreChat or Open WebUI.
  • MCP server integration requires the operator to configure and maintain MCP server processes alongside the nanochat instance; teams unfamiliar with the MCP ecosystem face a non-trivial setup surface before any agentic tool use is functional.
  • The iOS native client is in TestFlight beta per the project page, so mobile deployments targeting iOS users carry the stability expectations of pre-release software rather than a shipping product.

About

Platforms
Docker, Bun, Android, iOS, Linux desktop
API Available
Yes
Self-Hosted
Yes
Last Updated
2026-08-14T07:05:14.120Z

Best For

Who it's for

  • Users wanting full control over Nano-GPT chat data
  • Deployments requiring integrated search and generation tools
  • Projects needing MCP server tool integration
  • Privacy-focused persistent conversation memory
  • Scripting against a self-hosted REST API

What it does well

  • Self-hosted multi-model chat with persistent memory
  • Inline image and video generation within conversations
  • Web search and URL scraping for context enrichment
  • Connecting MCP tools for agentic capabilities
  • Voice dictation and text-to-speech in private deployments

Integrations

Nano-GPT APIMCP serversartificialanalysis.ai benchmarks
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Frequently Asked Questions

Is nanochat free?
Yes — nanochat is fully free to use. There is no paid tier.
Is nanochat open source?
Yes. nanochat is open source.
Does nanochat have an API?
Yes. nanochat exposes a developer API. See the official documentation at https://nanochat.app for details.
Can I self-host nanochat?
Yes. nanochat supports self-hosting on your own infrastructure.
What platforms does nanochat support?
nanochat is available on: Docker, Bun, Android, iOS, Linux desktop.

Separate subscriptions and split data stores

Every add-on you need — search, memory, image generation, voice — usually means five separate SaaS subscriptions and five separate places your conversation data lives. Nanochat collapses that into a single self-hosted deploy.

Core setup

The project is a self-hostable chat client that routes through the Nano-GPT API, which means access to frontier models sits behind one API key rather than a per-provider credential juggle. Docker Compose brings its own Postgres instance; data writes to a local volume and never touches a vendor’s cloud.

Built-in capabilities

Web search, URL scraping, inline image and video generation, cross-conversation memory, voice I/O, and MCP server connections are all included in the base install — not gated behind a paid tier. The ceiling arrives when you push past Nano-GPT’s own API constraints: model availability and rate limits.

Who it is for / who should skip it

Best for users wanting full control over Nano-GPT chat data, deployments requiring integrated search and generation tools, projects needing MCP server tool integration, privacy-focused persistent conversation memory, and scripting against a self-hosted REST API. Skip it if every model request must route through the Nano-GPT API, since model availability, rate limits, and outages are inherited from that single dependency; teams that need direct provider access or want to run local Ollama models hit an architectural dead end.