Alternatives

Products that do what LlamaGym – fine-tune LLM agents with online reinforcement learning does

1
Predibase Reinforcement Fine-Tuning▲175
LLM reinforcement fine-tuning platform to improve LLM output
2025
2
Taylor AI▲118
Fine-tune open source LLMs in minutes
2023
3
Fine-Tuner.ai▲218
The ultimate no-code solution for building AI agents
2023
4
Nebius AI Studio Fine-Tuning▲44
Transform generic AI models into specialized solutions
2025
5AP
A Platform for Many-Agent Reinforcement Learning▲18
2017 · github.com
6OS
Open Source Reinforcement Fine-Tuning for Your Agents▲5
2025 · augento.ai
7
FinetuneDB▲52
AI fine-tuning platform to create custom LLMs
2024
8CM
Composable middleware for LLM inference Optimization Passes▲7
Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
9
Promptly▲13
An AI Cost Optimization Infrastructure for LLM Applications
Mar 2026
10RA
Run an Agent Council of LLMs that debate and synthesize answers▲8
I built a local-first UI that adds two reasoning architectures on top of small models like Qwen, Llama and Mistral: a sequential Thinking Pipeline (Plan → Execute → Critique) and a parallel Agent Council where multiple expert models debate in parallel and a Judge synthesizes the best answer. No API keys, zero .env setup — just pip install multimind. Benchmark on GSM8K shows measurable accuracy gains vs. single-model inference.
Mar 2026 · github.com
11MF
Mixpanel for Voice AI Agents▲5
Hi, I’m Tom Shapland, the cofounder of Canonical AI. LLMs have changed the paradigm for Voice AI. Compared to rule-based systems (Siri, Alexa, Amazon Polly), LLM-based Voice AI agents understand the intent of the caller and can more often resolve the issue without escalation to a human agent. Moreover, with LLM-based Voice AI agents, developers can build a Voice AI agent more quickly, onboard customers quicker, and iterate on the product faster. Our customers’ Voice AI agents are doing amazing things. It’s so much fun to see the agents achieve the caller’s objective, even in the face of…
2024 · voice.canonical.chat
12GA
Galdor – a Go LLM agent framework with built-in tracing and replay▲7
Jun 2026 · github.com
13RL
Reinforcement Learning – DQN Tutorial▲6
2019 · youtube.com
14DO
Dynamiq – orchestration framework for agentic AI and LLM applications▲7
Dynamiq is an orchestration framework for agentic AI and LLM applications
2024 · github.com
15LF
LMM for LLMs – A mental model for building LLM apps▲6
I've been building agentic apps for some large Fortune 500 companies (T-Mobile, Twilio, etc.) and developed a mental model that serves as a practical guide in building agentic apps: separate the high-level agent specific logic from low-level platform capabilities. I call it the L-MM: the Logical Mental Model for LLM applications. This mental model has not only been tremendously helpful in building agents but also helping customers think about the development process - so when I am done with a consulting engagement they can move faster across the stack and enable engineers and platform teams…
2025
16CL
Chaos Llama – Chaos Monkey Build on AWS Lambda▲12
2016 · github.com
17LA
LLM agents to extract web data▲8
2024 · twitter.com
18LC
Local Coding Agent with LLMs to Delegate Tool Calls to Small AI Models▲9
May 2026 · github.com
19GW
Goku – WASM (wllama)-powered LLM inference and model manager▲9
Jul 2026 · userfrom1995.github.io
20LA
LLM agents that write Python to analyze execution traces at scale▲5
We combined Stanford's ACE (agents learning from execution feedback) with the Reflective Language Model pattern. Instead of reading traces in a single pass, an LLM writes and runs Python in a sandbox to programmatically explore them - finding cross-trace patterns that single-pass analysis misses. The framework achieved 2x consistency improvement on τ2-bench.
Mar 2026 · github.com
21AA
Axe – A CLI for running single-purpose LLM agents▲6
Mar 2026 · github.com
22HP
Horizon – Programmatic Prompt Generation and LLM Configurations▲7
Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…
2023 · gethorizon.ai
23AA
An all-in-one blog for learning Large Language Models (LLMs)▲5
An all-in-one blog for learning LLM ins and outs: tokenize, attention, PE, and more Project I've been diving deep into the internals of Large Language Models (LLMs) and started documenting my findings. My blog covers topics like: Tokenization techniques (e.g., BBPE) Attention mechanism (e.g. MHA, MQA, MLA) Positional encoding and extrapolation (e.g. RoPE, NTK-aware interpolation, YaRN) Architecture details of models like QWen, LLaMA Training methods including SFT and Reinforcement Learning If you're interested in the nuts and bolts of LLMs, feel free to check it out:…
2025 · comfyai.app
24RL
Reinforcement Learning from Scratch with TypeScript▲7
2023 · github.com

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