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runagent-dev
runagent
Python2025–2025
runagent-dev / runagent

runagent

RunAgent simplifies serverless deployment of your AI agents. With a powerful CLI, multi-language SDK support, built-in agent invocation & streaming suppprt.

Command LineAI & Machine LearningSystems & InfrastructureDevOps & CloudLibraries & SDKs
Explore on GitHubRead Hacker News discussions
3Distinct people
8Mentions
0Explicit recommenders
482GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

RunAgent simplifies serverless deployment of your AI agents. With a powerful CLI, multi-language SDK support, built-in agent invocation & streaming suppprt.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 12:45:45 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week8 dated mentions

Observed mentions: 2025-07-17 16:04:42 UTC to 2025-11-15 13:24:23 UTC. Weeks start Monday, UTC.

0122025-07-14 00:00:00 UTC to 2025-07-21 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 commentsJul 142025-07-21 00:00:00 UTC to 2025-07-28 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-28 00:00:00 UTC to 2025-08-04 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2025-08-04 00:00:00 UTC to 2025-08-11 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2025-08-11 00:00:00 UTC to 2025-08-18 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2025-08-18 00:00:00 UTC to 2025-08-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-25 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-09-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-08 00:00:00 UTC to 2025-09-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsSep 82025-09-15 00:00:00 UTC to 2025-09-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-22 00:00:00 UTC to 2025-09-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-29 00:00:00 UTC to 2025-10-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-06 00:00:00 UTC to 2025-10-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-13 00:00:00 UTC to 2025-10-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-20 00:00:00 UTC to 2025-10-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-27 00:00:00 UTC to 2025-11-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-03 00:00:00 UTC to 2025-11-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-10 00:00:00 UTC to 2025-11-17 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsNov 10

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jul 142025-07-14 00:00:00 UTC2025-07-21 00:00:00 UTC220
Jul 212025-07-21 00:00:00 UTC2025-07-28 00:00:00 UTC000
Jul 282025-07-28 00:00:00 UTC2025-08-04 00:00:00 UTC220
Aug 42025-08-04 00:00:00 UTC2025-08-11 00:00:00 UTC110
Aug 112025-08-11 00:00:00 UTC2025-08-18 00:00:00 UTC220
Aug 182025-08-18 00:00:00 UTC2025-08-25 00:00:00 UTC000
Aug 252025-08-25 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 12025-09-01 00:00:00 UTC2025-09-08 00:00:00 UTC000
Sep 82025-09-08 00:00:00 UTC2025-09-15 00:00:00 UTC000
Sep 152025-09-15 00:00:00 UTC2025-09-22 00:00:00 UTC000
Sep 222025-09-22 00:00:00 UTC2025-09-29 00:00:00 UTC000
Sep 292025-09-29 00:00:00 UTC2025-10-06 00:00:00 UTC000
Oct 62025-10-06 00:00:00 UTC2025-10-13 00:00:00 UTC000
Oct 132025-10-13 00:00:00 UTC2025-10-20 00:00:00 UTC000
Oct 202025-10-20 00:00:00 UTC2025-10-27 00:00:00 UTC000
Oct 272025-10-27 00:00:00 UTC2025-11-03 00:00:00 UTC000
Nov 32025-11-03 00:00:00 UTC2025-11-10 00:00:00 UTC000
Nov 102025-11-10 00:00:00 UTC2025-11-17 00:00:00 UTC110

What Hacker News says

8 sources
Radeen1·Nov 15, 2025Submission

Show HN: Why Current Agentic Frameworks Don't Work Well in Production

So, I've been noticing a few key problems with agent integration in current setups. 1. Most software backends are built in non-Pythonic languages (Rust, Golang, Java, etc.). But agents are mostly in Python, with some having JS support max. How do we integrate them efficiently? Basically, cross-language integration; right? But how to do that without major headaches? 2. If the agent developer creates an API wrapper for it, the same process has to be followed for integrating another agent. Then third, fourth etc etc. 3. Reliable agent integration in current software often requires not just open monitoring but also tracking how the agent responds in day-to-day scenarios. For this, we need memory, right? But not the typical memory that remembers specific moments. Instead, the agent should learn how to interact with the customer. This behaviora
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l improvement we're calling Action Memory, where instead of focusing on what to remember, it emphasizes how to remember. 4. If we accept that agents are the next big thing after LLMs, then we have to focus on efficient execution of those agents too. For this, we need to lean into the serverless side but ensure compatibility with persistent agents. Focusing on all these things, we're building RunAgent.
4 points · Open discussion
Radeen1·Aug 13, 2025Submission

Show HN: Build and Deploy Custom AI Agents with a Simple Prompt with GPT-5-Mini

I created a tool that lets you write a prompt like “build a research report agent in LlamaIndex” or in any framework and automatically generates and deploys the agent locally, complete with a testing page. Powered by OpenAI’s GPT-5-mini, it’s stable (98% success rate in my tests) and works with frameworks like Agno and LlamaIndex. Previous GPT models struggled with this kind of tasks, but this handles them well.
2 points · Open discussion
Radeen1·Aug 6, 2025Submission

Show HN: Built an Agentic Gaming PC Builder

I built a small project showing how to build an agentic PC builder in Golang using the Agno framework and RunAgent. The agent takes a user’s budget and performance needs, pulls real-time pricing data, and streams personalized build recommendations—live as it thinks. The cool part? You can write your AI logic in Python and consume it natively in Go with full streaming support. It is a great example of cross-language agent interoperability.
1 points · Open discussion
sawradip·Jul 28, 2025Submission

Show HN: AI code reviewer with Python agent called from Rust, streaming works

Built an AI code reviewer using Letta (Python) that I can call natively from Rust applications. The interesting part: real-time streaming works perfectly across the language boundary with zero hassle using RunAgent. The agent runs in Python with persistent memory, leverages the best in house agentic memory management with Letta (Pythonic AI agent framework), and my rust code just uses it (kinda) natively, though Letta has no Rust bindings. And, streaming works like magic. No FFI, no complex bridges - just native async/streaming that feels like calling any Rust librar, but without writing a single line of websocket code. The AI agent remembers coding patterns and learns from previous reviews, but the deployment abstraction is what made this possible. Code: https://github.com/runagent-dev/runagent/tree/main/examples/...
6 points · Open discussion
adewba·Jul 18, 2025Submission

Show HN: RunAgent: Model Context Protocol (MCP) and Vercel but for AI Agents

RunAgent is a universal AI agent deployment platform that solves the language barrier and deployment complexity in AI development. While most AI frameworks like LangChain, LangGraph, CrewAI, Letta etc only provide Python SDKs and have fragmented deployment processes, RunAgent allows developers to write agents once in Python using any framework and access them natively from Rust, JavaScript, Go, or any other language through comprehensive SDKs. The platform works through a framework-agnostic entrypoint system where developers simply provide a config file and their agent code, then deploy with a single terminal command to get an agent ID and endpoint. The magic happens in the SDKs - developers can call these deployed agents like native functions in their preferred programming language, with full support for both synchronous and streaming re
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sponses. RunAgent is open-sourced to foster ecosystem growth similar to MCP's success. By standardizing the Python agent structure through simple config files, the platform enables community-driven development where Python developers can create agents that any developer in any language can instantly discover and deploy. For example, when a Rust developer needs a specific agent functionality, they can find community-contributed agents and hook into them with a single command deployment, eliminating language barriers entirely. The open-source foundation handles local development and the multi-language SDK ecosystem, while the upcoming cloud platform promises Vercel-like simplicity for AI agents with serverless infrastructure that can deploy 2000 agents in under 10 seconds. RunAgent aims to democratize AI agent access across all programming languages and eliminate the current deployment friction that forces developers into Python/Js/Ts (max)-only workflows, positioning itself as "MCP for Agent Deployment" by providing the same standardization that MCP brought to model context, but for agent deployment and cross-language accessibility.
2 points · Open discussion
sawradip·Jul 17, 2025Submission

Show HN: RunAgent; Multi-Framework Agent Deployment and Rust,Go,JS SDKs(+others)

RunAgent eliminates the complexity of AI agent deployment across different frameworks and languages. Today's developers face deployment nightmares with fragmented frameworks (LlamaIndex, LangChain, LangGraph, CrewAI, Letta, Agno, etc.) each requiring different deployment processes, creating unnecessary friction. The Solution: Like MCP (Model Context Protocol), RunAgent provides a standardized approach to agent deployment. Developers simply provide a config file and their agent code - RunAgent handles the rest with REST API and WebSocket (Streaming and non streaming). Our open-source platform supports any framework through our agentic framework agnostic deployment approach, accessible via SDKs (Python, JavaScript/TypeScript, Go, Rust and many more coming soon). So our goal is the AI agents for every developer, in every language. RunAgent i
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s MCP for Agent Deployment.
6 points · Open discussion

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