Curated GitHub repositories every AI agent should know.
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Seven real, self-hostable RL frameworks for post-training tool-using agents — and why the one you pick should be decided by the environment, not the algorithm.
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They both have 'caching' in the name and both promise to slash your token spend, but they cache different things at different layers with different safety profiles. One's worst case is a cache miss. The other's worst case is a confidently wrong answer.
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Everyone ships the same PPO. This year's agent-RL frameworks all fight over the one thing that's actually hard — the rollout.
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The 'AI gateway' stopped being a cost-tracking load balancer and turned into the policy layer for autonomous agents — and that shift is why the newcomers are all written in Go and Rust, benchmarking themselves against LiteLLM.
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The field for making an agent 'speak UI' has split into two camps — your codebase owns the components, or the protocol does. Which repo you reach for is really a bet on who controls the widget.
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Claude Code proved the 'deep agent' pattern — planning, a filesystem, sub-agents, skills. A small cluster of Python repos now rebuilds that harness on Pydantic AI, so it runs on any model you own.
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They get compared like rivals, but one is memory you program and the other is memory you call — and the benchmark leaderboard only measures one of them.
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They all scale now, and they all do hybrid search. The axis that still forks the decision is the one nobody puts on a benchmark chart: how each keeps a metadata filter from wrecking recall.
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The Kubernetes-native serving stack got very good at spreading a model across a cluster. But in 2026 your GPUs aren't in one cluster — they're scattered across clouds by price and availability, and that's a different problem.
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OpenAI and Google ship deep-research as a closed feature. These seven open repositories let you run the same plan-search-read-synthesize loop on your own models, your own sources, and — if you want — entirely on your own machine.
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All three move messages between agents. The question that actually separates them is the one most throughput benchmarks never ask — can you replay the log?
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Deep Agents isn't a fourth framework competing with LangChain and LangGraph — it's a preset of LangChain middleware on the same runtime. The choice is how much opinion you want pre-assembled.
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Prose chunkers shred code mid-function and wreck the structure retrieval depends on. Here is how to split on the AST instead — and why context enrichment matters more than chunk size.
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The cheapest LLM call is the one you never make. Three ways to skip it when a question is close enough to one you already answered — and the one knob that decides whether that's a feature or a bug.
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Three open-source coding agents from one family tree — and the middle child just shut itself down. Its death is the most useful thing in the comparison.
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Three open-source tools dominate LLM red teaming — but they aren't rivals. One scans a model, one is a framework for building attacks, one is a CI gate. Pick by layer.
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They look like three flavors of the same thing. They're not — each is built around a different execution model, and that hidden choice is what makes streaming chat trivial in one and a fight in the others.
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Microsoft just deprecated its two most-starred agent frameworks to ship a third. If you're choosing today, the decision is already made for you — here's why, and where it still loses.
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A prompt registry lets you change prompts without a deploy. On its own, that just lets you change them faster — not better. The tools that compound tie every version to an eval.
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The benchmark wars miss the two axes that actually decide a GraphRAG backend — where your graph lives in the memory hierarchy, and which restrictive license it ships under. The permissive option just died.
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Since the 1.0 release, LangChain's agent helper runs on LangGraph's engine — so the real question isn't which to pick, but which layer of the same stack to write against.
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The embedded tier runs vector search inside your app with no server to babysit; the real choice is not speed but what your data does when it changes.
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All three converged on the same runtime shape, so the old 'which can build an agent' question is dead. What's left is a bet on which layer each treats as first-class — and one differentiator nobody can copy.
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Three startups built custom silicon to outrun the GPU on token generation. The speed is real, the SRAM is tiny, and that tradeoff decides everything.
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Three open-source answers to Deep Research, and they disagree on one thing — how the research loop is controlled. One project's benchmark proves that choice is the whole game.
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Three clouds rent you the same frontier models. The thing that actually locks you in is the agent runtime wrapped around them, and most teams pick it by accident.
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All three build Python agents, but they disagree on one thing — who owns the loop. That contract, not the benchmark, is what you live with for years.
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The vector database fight stopped being about speed. It's now about where your index sleeps — and whether you have one hot haystack or a million cold ones.
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Multi-LoRA serving turns "one GPU per model" into "one GPU per base model, amortized across hundreds of tenants." Here are the tools that do it, and the kernel trick that makes it work.
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MCP standardized how an agent calls a tool. It said almost nothing about how the agent logs in as you — and that gap is the whole product these three are selling.
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