
Meta-Reasoning
Your LLM doesn't think. We make sure of it.
Meta-Reasoning launched on April 15, 2026 with 4 votes, #2,155 of 21,248 launches that month and more than 75% of that year's launches.
What it does
Meta-Reasoning is an SDK for governing how LLMs reason. Instead of treating models as agents, it externalizes cognition into a controllable architecture: observe reasoning structure, mutate it with constraints, and record trajectories in an epistemic ledger. Enable debugging, replay, benchmarking, and CI/CD for reasoning. Make LLM behavior testable, deterministic, and governed. Already available on 🦞OpenClaw, 🟠ClaudeCode and 🤖Codex!
LLMs don't reason. They replay patterns. Meta-Reasoning takes control away from the model and gives it to you. An external controller observes how the model reasons, bans its dominant strategies, forces it into unexplored territory, and records every failure. The model executes. You govern. A standard LLM collapses into a single strategy. Meta-Reasoning forces exploration. The standard LLM repeats its dominant pattern until it stalls. Meta-Reasoning bans that pattern and forces the model into unexplored cognitive space. Every other framework lets the LLM choose its own reasoning strategy. Meta-Reasoning doesn't. The model is stateless and cognitively passive. It receives instructions,…from tictacguy.github.io
Does a similar job
all alternatives →- ABAutoThink – Boosts local LLM performance with adaptive reasoning2025 · ▲397
I built AutoThink, a technique that makes local LLMs reason more efficiently by adaptively allocating computational resources based on query complexity. The core idea: instead of giving every query the same "thinking time," classify queries as HIGH or LOW complexity and allocate thinking tokens accordingly. Complex reasoning gets 70-90% of tokens, simple queries get 20-40%. I also implemented steering vectors derived from Pivotal Token Search (originally from Microsoft's Phi-4 paper) that guide the model's reasoning patterns during generation. These vectors encourage behaviors like numerical…
TrueFoundry AI GatewayDec 2025 · truefoundry.com · ▲315Many teams connecting LLMs to external tools eventually encounter the same architectural issue: as more tools and agents are added, the integration pattern becomes an N×M mesh of direct connections. Each agent implements its own auth, retries, rate limiting, and logging; each tool needs credentials distributed to multiple places and observability becomes fragmented. We built LLM gateway with this goal to provide a single place to manage authentication, authorization, routing, and observability for MCP servers, with a path toward a more general agent-gateway architecture in the future. The…

- SASteiner – An open-source reasoning model inspired by OpenAI o12024 · medium.com · ▲83
Steiner is a series of reasoning models trained on synthetic data using reinforcement learning. These models can explore multiple reasoning paths in an autoregressive manner during inference and autonomously verify or backtrack when necessary, enabling a linear traversal of the implicit search tree. Blog: https://medium.com/@peakji/a-small-step-towards-reproducing-... Hugging Face: https://huggingface.co/collections/peakji/steiner-preview-67...
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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…
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