Alternatives
Products that do what Vantage does
YAML based Agentic Framework
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Hello! We just released freeact (https://github.com/gradion-ai/freeact), a lightweight agent library that empowers language models to act as autonomous agents through executable code actions. By enabling agents to express their actions directly in code rather than through constrained formats like JSON, freeact provides a flexible and powerful approach to solving complex, open-ended problems that require dynamic solution paths. * Supports dynamic installation and utilization of Python packages at runtime * Agents learn from feedback and store successful code actions as…
2025 · github.com
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Hey HN, I've been working on an open-source framework for creating AI agents that evolve, communicate, and collaborate to solve complex tasks. The Evolving Agents Framework allows agents to: Reuse, evolve, or create new agents dynamically based on semantic similarity Communicate and delegate tasks to other specialized agents Continuously improve by learning from past executions Define workflows in YAML, making it easy to orchestrate agent interactions Search for relevant tools and agents using OpenAI embeddings Support multiple AI frameworks (BeeAI, etc.) Current Status & Roadmap This is…
2025 · github.com
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2025 · ashenfad.github.io
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We recently started to use agents to update some documentation across our codebase on a weekly basis, and everything quickly turned into cron jobs, logs, and terminal output. it worked, but was hard to tell what agents were doing, why something failed, or whether a workflow was actually progressing. We thought it would be more interesting to treat agents as long-lived workers with state and responsibilities and explicit handoffs. Something you can actually see and reason about, instead of just tailing logs. So we built Clawe, a small coordination layer on top of OpenClaw that lets agent…
Feb 2026 · github.com
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Oct 2025 · agentml.dev
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2025 · spiceai.org
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Nov 2025 · github.com
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I'm Josh, founder of Synth. We've been working on coding agent optimization with method like GEPA and MIPRO (the latter of which, I helped to originally develop), agent evaluation via methods like RLMs, and large scale deployment for training and inference. We've also worked on patterns for memory, processing live context, and managing agent actions, combining it all in a single stack called Horizons. With the release of OpenAI's Frontier and the consumer excitement around OpenClaw, we think the timing is right to release a v0. It integrates with our sdk for evaluation and optimization but…
Feb 2026 · github.com
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2025 · github.com
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I'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts. The architecture aims to solve critical gaps in deterministic orchestration identified by *Prof. Claudionor Coelho Jr. (Stanford alum, ML/DL Faculty at Santa Clara Univ., and Senior Fellow for AI at Majestic Labs)* during our work on the Kiroku project. *Key Technical Features:* * *Neurosymbolic Native:* We integrated Prolog to logically validate LLM outputs. This combines neural…
Jan 2026 · fabceolin.github.io
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The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes…
Jun 2026
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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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2015 · bitbucket.org
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Go-based, zero third-party framework dependencies
Mar 2026 · github.com
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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
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We implemented Stanford's Agentic Context Engineering paper which shows agents can improve their performance just by evolving their own context. How it works: Agents execute tasks, reflect on what worked/failed, and curate a "playbook" of strategies. All from execution feedback - no training data needed. Happy to answer questions about the implementation or the research!
Oct 2025 · github.com
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