[HN Gopher] Show HN: Dexto - Connect your AI Agents with real-wo...
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Show HN: Dexto - Connect your AI Agents with real-world tools and
data
Hi HN, we're the team at Truffle AI (YC W25), and we've been
working on Dexto (https://www.dexto.ai/), a runtime and
orchestration layer for AI Agents that lets you turn any app,
service or tool into an AI assistant that can reason, think and
act. Here's a video walkthrough -
https://www.youtube.com/watch?v=WJ1qbI6MU6g We started working on
Dexto after helping clients setup agents for everyday marketing
tasks like posting on LinkedIn, running Reddit searches, generating
ad creatives, etc. We realized that the LLMs weren't the issue. The
real drag was the repetitive orchestration around them: - wiring
LLMs to tools - managing context and persistence - adding memory
and approval flows - tailoring behavior per client/use case Each
small project quietly ballooned into weeks of plumbing where each
customer had mostly the same, but slightly custom requirement. So
instead of another framework where you write orchestration logic
yourself, we built Dexto as a top-level orchestration layer where
you declare an agent's capabilities and behavior: - which tools or
MCPs the agent can use - which LLM powers it - how it should behave
(system prompt, tone, approval rules) Once configured, the agent
runs as an event-driven loop - reasoning through steps, invoking
tools, handling retries, and maintaining its own state and memory.
Your app doesn't manage orchestration, it just triggers and
subscribes to the agent's events and decides how to render or
approve outcomes. Agents can run locally, in the cloud, or hybrid.
Dexto ships with a CLI, a web UI, and a few sample agents to get
started. To show its flexibility, we wrapped some OpenCV functions
into an MCP server and connected it to Dexto
(https://youtu.be/A0j61EIgWdI). Now, a non-technical user could
detect faces in images or create custom photo collages by talking
to the agent. The same approach works for coding agents, browser
agents, multi-speaker podcast agents, and marketing assistants
tuned to your data. https://docs.dexto.ai/examples/category/agent-
examples Dexto is modular, composable and portable allowing you to
plug in new tools or even re-expose an entire Dexto agent as an MCP
Server and consume it from other apps like Cursor
(https://www.youtube.com/watch?v=_hZMFIO8KZM). Because agents are
defined through config and powered by a consistent runtime, they
can run anywhere without code changes making cross-agent (A2A)
interactions and reuse effortless. In a way, we like to think of
Dexto as a "meta-agent" or "agent harness" that can be customized
into a specialized agent depending on its tools, data, and
platform. For the time being, we have opted for an Elastic V2
license to give maximum flexibility for the community to build with
Dexto while preventing bigger players from taking over and
monetizing our work. We'd love your feedback: - Try the
quickstart and tell us what breaks - Share a use case you want to
ship in a day, and we'll suggest a minimal config Repo:
https://github.com/truffle-ai/dexto Docs:
https://docs.dexto.ai/docs/category/getting-started Quickstart:
npm i -g dexto
Author : shaunaks
Score : 25 points
Date : 2025-10-28 16:07 UTC (6 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| boxerab wrote:
| From the site: "Join developers building intelligent applications
| with Dexto. Open source, local-first, and ready for production."
|
| Note that this code is licensed under "Elastic License 2.0
| (ELv2)", so not open source according to OSI.
| mrdarkie wrote:
| does anyone have a Mumbai-based SaaS orchestrator for my
| orchestrators?
| ra wrote:
| What's your pricing model?
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