Alternatives
Products that do what Agent-contracts, contract-based LangGraph agents does
Hi HN, I’m the author of agent-contracts, a Python library that explores a contract-based approach to structuring LangGraph agents. When building larger LangGraph-based systems, I kept running into the same issues: - node responsibilities becoming implicit - state dependencies spreading across the graph - routing logic getting harder to reason about - refactoring feeling increasingly risky agent-contracts is an attempt to make these boundaries explicit. Each node declares a contract that describes: - which parts of the state it reads and writes - what external services it depends on - when…
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- 2NO
Hello HN! The day has finally come to stop adding features and start sharing what I've been building the last 5-6 months. It's a bit of CrewAI, OpenDevon, LangFuse/Cloud all in one, providing devs who prefer TypeScript an integrated framework thats provides a lot out of the box to start experimenting and building agents with. It started after peeking at the LangChain docs a few times and never liking the example code. I began experimenting with automating a simple Jira request from the engineering team to add an index to one of our Google Spanner databases (for context I'm the…
2024 · github.com
- 3HA
Hi HN, I am Umer. I recently built an experimental framework called HyperFlow to explore the idea of self-improving AI agents. Usually, when an agent fails a task, we developers step in to manually tweak the prompt or adjust the code logic. I wanted to see if an agent could automate its own improvement loop. Built on LangChain and LangGraph, HyperFlow uses two agents: - A TaskAgent that solves the domain problem. - A MetaAgent that acts as the improver. The MetaAgent looks at the TaskAgent's evaluation logs, rewrites the underlying Python code, tools, and prompt files, and then tests the new…
Apr 2026
- 4AA
I’ve been experimenting with infrastructure for multi-agent systems. I built a small project called AgentLog. The core idea is very simple, topics are just append-only JSONL files. Agents publish events over HTTP and subscribe to streams using SSE. The system is intentionally single-node and minimal for now. Future ideas I’m exploring: - replayable agent workflows - tracing reasoning across agents - visualizing event timelines - distributed/federated agent logs Curious if others building agent systems have run into similar needs.
Mar 2026 · github.com
- 5AA
We’ve published a set of open-source reference implementations on how to build production-grade Agentic AI applications on AWS. What’s in the repo: • Agentic RAG, memory, and planning workflows with LangGraph & CrewAI • Strands-based flows with observability using OTEL & Arize • Evaluation with LLM-as-judge and cost/performance regressions • Built with Bedrock, S3, Step Functions, and more GitHub: https://github.com/aws-samples/sample-agentic-frameworks-on-... Would love your thoughts — feedback, issues, and stars welcome!
2025 · github.com
- 6AF
I’ve been working on a temporal database for agents that combines graphs, tables, and compute. While building it, I ended up needing an agent framework that could handle both simple tool-use tasks and more graph-based execution, so I pulled that out into a separate project, Agent Forge. Agent Forge uses a two-tier execution model: * a heuristic router decides whether a request is simple or complex * simple requests go through a lightweight agent loop with a single system prompt and tool-calling loop * more complex requests can use memory retrieval, reflection constraints, tree search, and…
Mar 2026 · github.com
- 7AL
Hi HN, I built this to address what I see as the fundamental problem with ReAct-style agents: compounding errors. Even a small mistake made early enough in the loop can snowball and ruin the final output. But with search, agents can look multiple steps ahead and backtrack before committing to a particular trajectory. This has already been shown in a few papers to help agents avoid mistakes and boost overall task performance, but there's no easy way to actually build these kinds of agents. So that's why I made this framework. I believe search will eventually become table stakes for building…
2024 · github.com
- 8SR
Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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hello everyone, my first post! AA here, founder of ⌘ Langbase.com — we are a developer platform for building and scaling serverless AI memory agents. I know surveys can be boring, but this one’s different—it’s interactive! That's very much intentional. My team and I have been up for the last 21 hours putting together this report. This was a looot of work, so I hope y'all like it. Introducing … State of AI Agents 2024 report On Langbase, we processed 184 billion tokens and handled 786 million AI agent runs from 36K developers. From all that data plus insights from 3.4K builders who filled out…
2024 · langbase.com
- 10HG
Most AI applications are built for individuals but work happens in groups and humans want to collaborate with both agentic AI and other teammates in the same session. We created Hybrid Groups for that purpose. In Hybrid Groups, agents join group chats as virtual team members in Slack and GitHub. They participate in group conversations, proactively contribute when needed and perform actions on behalf of individual users, like managing your calendar for meeting suggestions or updating your todo list without sharing access to your private resources to the group. The project is open-source at…
2025 · youtube.com
- 11AD
While reading Agentic Design Patterns by Antonio Gulli, I wanted to see how these patterns look in real code. I cloned the OpenAI Codex repo (the open-source AI coding assistant that recently trended on HN) — but it was in Rust. So, I used an Cursor to help me extract and translate 18+ agentic patterns from Codex’s codebase into Python. That small experiment turned into a full open-source guide: GitHub: Codex Agentic Patterns https://github.com/artvandelay/codex-agentic-patterns Each pattern comes with: A short explanation and code sample A runnable exercise and agent…
Oct 2025 · artvandelay.github.io
- 12TA
We’ve been seeing more and more developers use AI coding agents directly in their GraphQL workflows. The problem is the agents tend to fall back to generic or outdated GraphQL patterns. After correcting the same issues over and over, we ended up packaging the GraphQL best practices and conventions we actually want agents to follow as reusable “Skills,” and open-sourced them here: https://github.com/apollographql/skills Install with `npx skills add apollographql/skills` and the agent starts producing named operations with variables, `[Post!]!` list patterns, and more…
Feb 2026 · skills.sh
- 13IB
Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
Jan 2026 · github.com
- 14AI
The goal of Agentic is to create a set of standard AI functions / tools which are optimized for both normal TS-usage as well as LLM-based apps. It's designed to work with all of the major TS AI SDKs (LangChain, LlamaIndex, Vercel AI SDK, OpenAI SDK, Firebase Genkit, etc) via adaptors. Would love feedback from the HN community :)
2024 · github.com
- 15AS
Hey HN, We’ve been experimenting with how to make AI agents more deterministic, observable, and production-safe, and that led us to build AgentML — an open-source language for defining agent behavior as state machines, not prompt chains. My co-founder posted before but linked to the project website instead of the repo, so resharing here. AgentML lets you describe your agent’s reasoning and actions as a finite-state model (think SCXML for agents). Each state, transition, and tool call is explicit and machine-verifiable. That means you can: - Reproduce any decision path deterministically -…
Nov 2025 · github.com
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Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate code and move on. You explore data, train models, evaluate results, adjust assumptions, rerun experiments, compare metrics, generate artifacts, and iterate; often over hours or days. Most modern coding agents already go beyond single prompts. They can plan steps, write files, run…
Jan 2026 · marketplace.visualstudio.com
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Hey HN, we’re Ross and Javier, co-founders of Engraph (www.engraph.ai). Our goal is to completely automate the process of building ETL pipelines, from ad hoc pipelines for question answering to fully fledged ETL pipelines within large organisations: For ad hoc pipelines, a question answering platform which enables users to ask questions in natural language about their organisation's data. Traditionally, access to data within organisations is limited to a handful of data-engineers. This means that if an employee needs access to some data, they have to go through a lengthy process of…
2023
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Hi HN. I'm the founder of Phoenix Labs (ex TikTok, Applied AI) and we're open sourcing our internal tooling today which is like a toolchain / meta-harness for CLI agents useful for really scaling eng and creative work. We are a very small team who's building a very ambitious product so we had to find ways to squeeze every ounce of efficiency that we could get our hands on. Harness strengths of different models (Claude, GPTs) and CLI-harnesses (Claude Code, Codex), safe/robust browser integration to speed up UX/QA testing, teams cli to speed up security reviews and parallelize…
May 2026 · agents-cli.sh
- 19RA
Hi HN, I recently open sourced a small tool (Remuda) that I built at work to remove some of the friction of launching and managing agents and figured HN might be interested. I also wrote a post on the company blog that goes into more detail about why I built it and showcases its features: https://www.yendo.com/blog/remuda-an-agent-orchestrator
Jun 2026 · github.com
- 20MA
This weekend I built a multi-agent coding system which, quite unexpectedly, beat Claude Code on Stanford's Terminal Bench! The architecture is straightforward, consisting of an orchestrator agent that deploys explorer & coder subagents to complete complex terminal based tasks, utilising an intelligent context sharing mechanism along the way which makes it all work. The repo has a lot of technical details, and all the code and prompts for you to play around with if you'd like! I had a lot of fun making this, I hope you have fun reading the README, using it yourself, or even extending it! As…
2025 · github.com
- 21UA
Three months ago, we started developing an open source agent framework. We previously tried existing frameworks in our enterprise product but faced challenges in certain areas. Problems we experienced: * We risked our stateless architecture when we wanted to add an agented feature to our existing system. Current frameworks lack server-client architecture, requiring significant effort to maintain statelessness when adding an agent framework to your application. * Scaling problem - needed to write Docker configurations as existing frameworks lack official Docker support. Each agent in my…
2025 · github.com
- 22OA
Hi HN, I’m Mike, the founder of OpenRig. I built this because my Claude Code + Codex setup kept forming little "topologies" of long-lived agents that worked well together, but the terminal sprawl was intense. So I built a primitive the agents could intuitively reach for to save and recreate these setups on the fly. This then led to more agent-first primitives like coordination, declarative workflow patterns, workspaces, etc. Several months in and these "rigs" I manage with openrig require a lot less babysitting and I can manage more projects at once without getting overwhelmed. The short…
May 2026 · openrig.dev
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Hey HN, Staff Engineer at Ably here. Over the past few months I've been speaking to engineers building AI assistants, copilots, and agentic workflows (over 40 companies at this point), with particular focus on cloud-hosted agents. I expected the hard problems to be in model selection, prompt engineering, and orchestration. Instead, the same infrastructure challenges kept coming up: realtime sync between agents and end clients is surprisingly painful to get right. - Managing and scaling WebSocket or SSE connections between agents and clients - Buffering messages server-side and implementing…
Jan 2026 · ably.com
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I built Hermes, an open-source Python framework for multi-agent financial research. Most AI “equity research” demos stop at generating text. In practice, real workflows require pulling structured XBRL financials from SEC filings, extracting labeled sections like MD&A and Risk Factors, merging macro and market data, building actual Excel models with formulas, and generating investment memos in Word or PDF. Hermes is designed to handle that full pipeline end to end. It includes 35 financial data tools covering SEC EDGAR (via edgartools), FRED, Yahoo Finance market data, and RSS-based financial…
Feb 2026 · github.com
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