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eliza

An open-source TypeScript framework for building autonomous AI agents that chat, remember, and act across chat platforms and blockchains.

TypeScriptMITAgent
โญ GitHubhttps://github.com/elizaOS/eliza
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Aug 5, 2026
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1. Project Overview

eliza is an open-source TypeScript framework for building autonomous AI agents โ€” software entities that can chat, remember context, use tools, and act across platforms like Discord, Telegram, and blockchains without requiring developers to write integration code from scratch.

2. Background & Positioning

elizaOS was created to give developers an accessible, extensible foundation for building AI agents that operate consistently across many surfaces โ€” web, desktop, mobile, and messaging platforms โ€” while preserving user autonomy and, where desired, privacy through local, on-device inference. The core mission is to make agent development as approachable as writing a plugin, rather than as building a bespoke integration for every platform and model provider.

Unlike single-purpose chatbot SDKs or closed agent platforms, elizaOS is:

  • Runtime-centric, not app-centric โ€” a shared @elizaos/core runtime handles memory, the message loop, and agent state, so the same agent definition can run as a Discord bot, a web app, or a CLI process.
  • Model-agnostic โ€” agents can call cloud model providers or run local models (2Bโ€“27B parameters) through the Eliza-1 model registry, giving teams a choice between convenience and full local control.
  • Deeply pluggable โ€” nearly all agent capability (platform connectors, blockchain wallets, tools) is delivered through a large, community-maintained plugin ecosystem rather than being hard-coded into the framework.

3. Feature Categories

  • ๐Ÿ”Œ Platform Connectors โ€” 90+ plugins, examples: Discord, Telegram, Slack, Twitter/X, Farcaster. Purpose: let a single agent definition operate natively across chat and social platforms.
  • โ›“๏ธ Blockchain & Wallets โ€” examples: Ethereum plugin, Solana plugin, non-custodial wallet actions, transaction approval flows. Purpose: allow agents to hold and use crypto wallets under explicit, bounded user approval.
  • ๐Ÿง  Model & Inference โ€” examples: Eliza-1 local model registry, cloud model provider adapters, Eliza Cloud model routing. Purpose: give developers a choice between local, private inference and hosted, cloud-routed inference.
  • ๐Ÿงฉ Agent Runtime Primitives โ€” examples: Actions, Providers, Evaluators, Services, Characters. Purpose: the composable building blocks every plugin registers into, forming the agent's behavior and personality.
  • ๐Ÿ—“๏ธ Productivity & Utility Integrations โ€” examples: calendar access, reminders, document/knowledge processing, automation tools. Purpose: extend agents beyond conversation into practical daily-task assistance.

4. Key Highlights

  • Character-driven personalities โ€” agent behavior and tone are defined declaratively through "character" files, separating personality configuration from runtime code.
  • Actions, Providers, and Evaluators model โ€” a clear separation between what an agent can do (actions), what context it can pull in (providers), and how it assesses its own responses (evaluators), making plugin behavior predictable and composable.
  • Local-first inference option โ€” the Eliza-1 model registry lets agents run entirely on-device, which matters for teams with privacy or offline requirements, alongside an optional cloud path.
  • Non-custodial blockchain support โ€” wallet operations on EVM and Solana chains are scoped with explicit approval boundaries rather than unrestricted transaction signing.
  • Multi-platform application suite โ€” beyond the framework itself, elizaOS ships web, desktop, mobile, and even bootable OS distributions, so the same agent can be deployed to end users, not just developers.
  • Large first-party plugin ecosystem โ€” over 90 plugins cover messaging platforms, blockchains, and device bridges, reducing the amount of custom integration code most projects need to write.

5. Use Cases by Role

  • General developers โ€” build a custom chat or automation agent by composing existing plugins (Discord, Telegram, calendar) instead of writing platform SDKs by hand.
  • Data / research scientists โ€” use knowledge and document-processing workflows combined with local model inference to experiment with retrieval and memory-augmented agents without sending data to third-party APIs.
  • Project managers โ€” deploy an agent for calendar management, reminders, and cross-platform coordination (e.g., a Discord/Slack assistant) with minimal custom code.

6. Getting Started

Find what you need โ€” browse the plugin registry and documentation:

https://docs.elizaos.ai/

Install / integrate โ€” clone the repository and start the development environment:

git clone --filter=blob:none https://github.com/elizaos/eliza.git
cd eliza
bun install
bun run dev

Contribute โ€” open an issue to discuss non-trivial changes, then submit a pull request against the develop branch:

git clone https://github.com/elizaos/eliza.git

See CONTRIBUTING.md in the repository for full requirements.

7. Project Structure

eliza/
โ”œโ”€โ”€ packages/
โ”‚   โ”œโ”€โ”€ core/          # @elizaos/core โ€” runtime, types, message loop, memory primitives
โ”‚   โ”œโ”€โ”€ agent/          # @elizaos/agent โ€” standalone agent process and HTTP backend
โ”‚   โ”œโ”€โ”€ app-core/        # @elizaos/app-core โ€” application hosting and orchestration
โ”‚   โ”œโ”€โ”€ ui/              # @elizaos/ui โ€” shared React components
โ”‚   โ””โ”€โ”€ cli/              # command-line tooling for scaffolding and running agents
โ”œโ”€โ”€ plugins/               # first-party plugins (platform connectors, blockchains, models)
โ”œโ”€โ”€ scripts/                # repository maintenance utilities
โ””โ”€โ”€ patches/                 # dependency patches

The packages/core runtime is the shared foundation every other package and plugin builds on; plugins/ is the primary place developers add or discover new agent capabilities.

8. Related Ecosystem

  • Eliza Cloud โ€” an optional hosted platform providing authentication, model routing, and deployment for agents built with the open-source framework.
  • Eliza-1 model registry โ€” a set of local models (2Bโ€“27B parameters) that plug into the runtime's model-handler interface for on-device inference.
  • Model providers and messaging platforms โ€” the plugin system connects to third-party services such as Discord, Telegram, Slack, and blockchain networks (Ethereum, Solana), which remain independently operated services outside the elizaOS project.

9. License

  • โœ… Use, modify, and distribute eliza, including for commercial purposes, under the MIT license.
  • โœ… Fork the project and build proprietary agents or products on top of it.
  • โŒ Do not remove the original copyright and license notice from copies or substantial portions of the software.
  • โ„น๏ธ MIT provides no warranty; review the license text before relying on eliza in production, and check individual plugin licenses if they connect to third-party paid services.

10. FAQ

Q: Do I need to use Eliza Cloud to run an agent?
A: No. Eliza Cloud is optional; agents can run fully locally or against your own configured model providers.

Q: Can agents run without sending data to any cloud model provider?
A: Yes, via the Eliza-1 local model registry, which supports on-device inference for models in the 2Bโ€“27B parameter range.

Q: How do I add support for a new chat platform?
A: Build or install a plugin exporting a Plugin object that registers the relevant actions, providers, and services โ€” see the plugin documentation at docs.elizaos.ai.

Q: What blockchains does eliza support out of the box?
A: First-party plugins currently cover EVM-compatible chains and Solana, with non-custodial wallet operations subject to explicit approval boundaries.

Q: Where do I report a bug or propose a feature?
A: Open an issue in the GitHub repository before submitting non-trivial pull requests, per CONTRIBUTING.md.

11. Quick Links

12. Summary

eliza gives developers a model-agnostic, plugin-driven runtime for building AI agents that work consistently across chat platforms, blockchains, and local or cloud inference backends. It is most valuable for teams that want to ship a multi-platform agent quickly without hand-rolling integrations for every messaging service or model provider, and for developers who want the option of fully local, private inference.