Alternatives
Products that do what Agent Forge, an agent framework, heuristic routing and graph execution does
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…
- 1
- 2
- 3
- 4
- 5AC
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…
Jan 2026 · github.com
- 6AL
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
- 7UO
Hey HN, In the months since we initially released Burr (https://news.ycombinator.com/item?id=39917364), we have been hard at work. We wanted to share some of the most exciting changes we’ve made to build Burr out as a full-stack development framework for AI agents. In case you don’t recall, Burr is an open-source python library that makes it easier to build and debug GenAI applications & agents by representing them as graphs of simple python objects/functions. Burr only abstracts away system-level concerns (state persistence, debugging, observability), and does not…
2024 · burr.dagworks.io
- 8AR
If you're interested in exploring what LLM-based agent systems these days actually do to solve certain benchmarks such as SWEBench or WebArena, we created a small leaderboard with our team, that allows to view a lot of public and OSS agent results including all the runtime traces (the step-by-step reasoning behind the scenes). Looking at traces is actually quite interesting, as they reveal a lot about the inner working and shortcomings of current agent system, e.g. see https://explorer.invariantlabs.ai/u/invariant/webarena--SteP... for an example trace.
2024 · explorer.invariantlabs.ai
- 9AA
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
- 10AF
Hey HN, Claude Code is powerful, but its execution is a black box. You see the final result, not the journey. Agent Flow makes the invisible visible in realtime: - Understand agent behavior: See how Claude breaks down problems, which tools it reaches for, and how subagents coordinate - Debug tool call chains: When something goes wrong, trace the exact sequence of decisions and tool calls that led there - See where time is spent: Identify slow tool calls, unnecessary branching, or redundant work at a glance - Learn by watching: Build intuition for how to write better prompts by observing how…
Mar 2026 · github.com
- 11GA
Hello! Introducing geniusrise, an agent framework and component ecosystem for building AI agent networks that are as flexible as your team. landing page: https://geniusrise.ai (fancy but useless) docs: https://docs.geniusrise.ai (please check this out) github: https://github.com/geniusrise (for dear devs) ## Thought process Since the ChatGPT disruption, I've been pondering on what the tooling layer is going to look like for building LLM-interfacing agents. Saw a plethora of tools coming out as we witness here every week. I'd broadly categorize them into the…
2023 · github.com
- 12HA
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
- 13OA
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
- 14SR
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
- 15AA
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
- 16AA
Hey HN! I am super excited (and slightly nervous) to introduce AgentServe! AgentServe is a framework to make hosting scalable AI agents as easy as possible. With 4 lines of code AS wraps your agent (any framework) in a FastAPI and connects it to a Task Queue (celery or redis). Why Should You Care? Standardized Communication Pattern: AgentServe proposes that all agents should communicate with each other and the outside world with “Tasks” that can be submitted in a sync or async way. This simple API wil enable Framework Agnostic: No favorites. OpenAI, LangChain, LlamaIndex, CrewAI are all…
2024 · github.com
- 17BC
We are a small group of undergrads interested in building human in the loop coding agents. We dream of a world where building complex agent workflows feels as simple and creative as playing with legos. When we were building stuff we needed a tool that made it easy to try out different code embedding models so that we could see which ones worked best in different scenarios and understand their strengths and weaknesses. So to speed that process up we made PurpleSearch an 'instant' search engine for your local codebases. This tool lets you quickly deploy any open source embedding model on…
2025
- 18RA
Hi HN! Sean from MindStudio here. I wanted to share something we've been working on that I think introduces some new ideas into the "AI coding agent" space. Remy is an AI agent that builds full-stack TypeScript apps from a spec written in a new flavor of annotated markdown. The spec has two layers: prose describing what the app does, and annotations that carry the technical precision (data types, edge cases, validation rules, code snippets). The agent then "compiles" this into code: backend methods, typed schemas, frontends, test scenarios, and everything else are derived artifacts of the…
Apr 2026 · remy.msagent.ai
- 19TA
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
- 20AF
Hi HN — I’m Abhi. We built Agint so PMs and engineers can design and edit software as a graph — architecture first — iterate with fast visual feedback, then generate deployable code from it when it’s ready. We presented underlying approach at NeurIPS (Deep Learning for Codegen) as an Agentic Graph Compiler: The graph (structure + types + semantic annotations) is the source of truth, and code is a compilation/export target. Paper: Agentic Graph Compilation for Software Engineering Agents: https://arxiv.org/abs/2511.19635 Live Demo: https://flow.agintai.com…
Jan 2026 · flow.agintai.com
- 21AA
Hey HN! We've just open-sourced Agent, our framework for running computer-use workflows across multiple apps in isolated macOS/Linux sandboxes. After launching Computer a few weeks ago, we realized many of you wanted to run complex workflows that span multiple applications. Agent builds on Computer to make this possible. It works with local Ollama models (if you're privacy-minded) or cloud providers like OpenAI, Anthropic, and others. Why we built this: We kept hitting the same problems when building multi-app AI agents - they'd break in unpredictable ways, work inconsistently across…
2025 · github.com
- 22RM
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…
2025 · github.com
- 23JL
I’ve been working on a multi-agent academic research lab, and I wanted to share it here today primarily to give a massive shoutout to the developers behind ZeroClaw. When designing the architecture for this, I needed an autonomous agent runtime that was lightweight, entirely agnostic, and highly secure for local execution. ZeroClaw’s pure Rust implementation provided exactly the zero-overhead foundation the project required. Because they solved the core runtime execution so elegantly, I was able to spend my time building the higher-level orchestration on top of it—like the retrieval graph…
Mar 2026 · rainlabteam.vercel.app
- 242O
Hi HN, We're the engineering team at Peakflo (B2B fintech). We built 20x internally because we kept copy-pasting Linear tickets into Claude, manually setting up branches, and babysitting agent output across terminals. Eventually we just built the infrastructure to connect task systems to agents directly — and decided to open source it. 20x is an open-source desktop app (macOS only — Linux and Windows on the roadmap) that orchestrates AI coding agents against your existing task systems. In practice: a Linear ticket gets pulled in → the triage agent assigns Claude Code + relevant skills → a…
Feb 2026 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →