fast-agent vs Hexabot
LLM agent frameworks and workflow platforms compared
fast-agent is a Python framework and CLI for building, evaluating, and running LLM agents and multi-agent workflows. Hexabot is a TypeScript platform for YAML-defined automations that combine actions, memory, tools, and conversational channels.
fast-agent: Build and Run LLM Agents and Workflows
A Python framework and CLI for building, testing, and running LLM agents that use MCP tools and compose into workflows. It suits developers who want both interactive agent development and a programmable framework.
Hexabot: Build AI Workflows for Conversational Channels
Hexabot is a TypeScript platform for building and running agentic workflows, actions, and conversational channels in one runtime. It suits teams that want to connect AI tools and bots through configurable workflows rather than build a runtime from scratch.
| fast-agent | Hexabot | |
|---|---|---|
| Language | Python | TypeScript |
| License | Apache-2.0 | — |
| Stars | 3.9k | 1.1k |
| Forks | 446 | 240 |
| Last analyzed | Oct 7, 2026 | Oct 10, 2026 |
Key differences
- fast-agent defines agents and their configuration in Python; Hexabot defines workflows in YAML with typed runtime contracts.
- fast-agent focuses on agent composition, model-provider options, and MCP connections; Hexabot combines workflow automation with conversational channels, memory, and schema-validated actions.
- fast-agent offers native Anthropic, OpenAI, and Google providers, plus access to other providers through TensorZero; Hexabot's listed integrations emphasize MCP and channel support.
- fast-agent is licensed Apache-2.0; Hexabot's metadata says NOASSERTION, while its README states FCL-1.0-ALv2, so review its license file before adoption.
- Hexabot includes a TypeORM data layer, with SQLite as the local default and Postgres support; fast-agent's description centers on agent and workflow configuration rather than a database layer.
- fast-agent has 3.9k stars and 447 forks; Hexabot has 1.3k stars and 246 forks.
Choose fast-agent if you…
- need to define agents in Python and compose them into multi-agent workflows.
- want an interactive terminal CLI or to run agent applications as MCP servers or through ACP clients.
- need native Anthropic, OpenAI, or Google provider options and configurable MCP connections.
Choose Hexabot if you…
- want YAML-defined workflows with schema-validated actions and reusable bindings.
- need to connect workflow automations with conversational channels and runtime memory.
- want a platform with a TypeORM data layer and options for local SQLite or Postgres.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.