Langroid Alternatives

Similar projects and alternatives to langroid

  1. textgen

    887 langroid VS textgen

    Open-source desktop app for local LLMs. Text, vision, tool-calling, OpenAI/Anthropic-compatible API. 100% private.

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  3. ollama

    Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

  4. txtai

    394 langroid VS txtai

    đź’ˇ All-in-one AI framework for semantic search, LLM orchestration and language model workflows

  5. qdrant

    184 langroid VS qdrant

    Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

  6. gpt4all

    GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.

  7. langchain

    94 langroid VS langchain

    The agent engineering platform.

  8. CodeTriage

    Discover the best way to get started contributing to Open Source projects

  9. semantic-kernel

    Integrate cutting-edge LLM technology quickly and easily into your apps

  10. dspy

    58 langroid VS dspy

    DSPy: The framework for programming—not prompting—language models

  11. outlines

    50 langroid VS outlines

    Structured Outputs

  12. instructor

    32 langroid VS instructor

    structured outputs for llms

  13. claude-code-tools

    Practical productivity tools for Claude Code, Codex-CLI, and similar CLI coding agents.

  14. langchaingo

    LangChain for Go, the easiest way to write LLM-based programs in Go

  15. swarm

    16 langroid VS swarm

    Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.

  16. mesop

    12 langroid VS mesop

    Rapidly build AI apps in Python

  17. agentlib

    A simple framework for building agents.

  18. mini-swe-agent

    The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!

  19. llm

    100 langroid VS llm

    Access large language models from the command-line

  20. agency

    🕵️‍♂️ Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach. (by neurocult)

  21. autogen

    71 langroid VS autogen

    A programming framework for agentic AI

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better langroid alternative or higher similarity.

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langroid reviews and mentions

Posts with mentions or reviews of langroid. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2026-02-19.
  • Agent Framework Is Just a Bad Clone of Elixir
    2 projects | news.ycombinator.com | 19 Feb 2026
    I ran into this question when thinking about the approach for a recent project. Yes CLI coding tools are good agents for interactive use, but if you are building a product then you do need an agent abstraction.

    You could package Claude Code into the product (via agents-sdk or Claude -p) and have it use the API key (with metered billing) but in my case I didn’t find it ergonomic enough for my needs, so I ended up using my own agent framework Langroid for this.

    https://github.com/langroid/langroid

    (No it’s not based on that similarly named other framework, it’s a clean, minimal, extensible framework with good dx)

  • How to Code Claude Code in 200 Lines of Code
    17 projects | news.ycombinator.com | 8 Jan 2026
  • Publishing your work increases your luck
    3 projects | news.ycombinator.com | 26 Dec 2025
    https://github.com/langroid/langroid

    [B] With the explosion of Claude Code and similar CLI coding agents, there were several interesting problems to solve for myself, and I started collecting them here: https://github.com/pchalasani/claude-code-tools

  • Agent Design Is Still Hard
    14 projects | news.ycombinator.com | 22 Nov 2025
    Relatedly, I haven’t seen two issues discussed much in agent design, but repeatedly come up in real world use cases:

    (1) LLM forgets to call a tool (and instead outputs plain text). Contrary to some of the comments here saying that these concerns will disappear as frontier models improve, there will always be a need for having your agent scaffolding work well with weaker LLMs (cost, privacy, etc).

    (2) Determining when a task is finished. In some cases, we want the LLM to decide that (e.g search with different queries until desired info found), but in others, we want to specify deterministic task completion conditions (e.g., end the task immediately after structured info extraction, or after acting on such info, or after the LLM sees the result of that action etc).

    After repeatedly running into these types of issues in production agent systems, we’ve added mechanisms for these in the Langroid[1] agent framework (I’m the lead dev), which has blackboard-like loop architecture that makes it easy to incorporate these.

    For issue (1) we can configure an agent with a `handle_llm_no_tool` [2] set to a “nudge” that is sent back to the LLM when a non-tool response is detected (it could also be set as a lambda function to take other possible actions)

    For issue (2) Langroid has a DSL[3] for specifying task termination conditions. It lets you specify patterns that trigger task termination, e.g.

    - "T" to terminate immediately after a tool-call,

    - "T[X]" to terminate after calling the specific tool X,

    - "T,A" to terminate after a tool call, and agent handling (i.e. tool exec)

    - "T,A,L" to terminate after tool call, agent handling, and LLM response to that

    [1] Langroid https://github.com/langroid/langroid

  • Using Claude Code to modernize a forgotten Linux kernel driver
    7 projects | news.ycombinator.com | 7 Sep 2025
    > using these tools as a massive force multiplier…

    Even before tools like CC it was the case that LLMs enabled venturing into projects/areas that would be intimidating otherwise. But Claude-Code (and codex-cli as of late) has made this massively more true.

    For example I recently used CC to do a significant upgrade of the Langroid LLM-Agent framework from Pydantic V1 to V2, something I would not have dared to attempt before CC:

    https://github.com/langroid/langroid/releases/tag/0.59.0

    I also created nice collapsible html logs [2] for agent interactions and tool-calls, inspired by @badlogic/Zechner’s Claude-trace [3] (which incidentally is a fantastic tool!).

    [2] https://github.com/langroid/langroid/releases/tag/0.57.0

    [3] https://github.com/badlogic/lemmy/tree/main/apps/claude-trac...

    And added a DSL to specify agentic task termination conditions based on event-sequence patterns:

    https://langroid.github.io/langroid/notes/task-termination/

    Needless to say, the docs are also made with significant CC assistance.

  • 6 Weeks of Claude Code
    6 projects | news.ycombinator.com | 2 Aug 2025
    Agreed. CC lets you attempt things that you wouldn’t have dared to try. For example here are two things I recently added to the Langroid LLM agent framework with CC help:

    Nice collapsible HTML logs of agent conversations (inspired by Mario Zechner’s Claude-trace), which took a couple hours of iterations, involving HTML/js/CSS:

    https://langroid.github.io/langroid/notes/html-logger/

    A migration from Pydantic-v1 to v2, which took around 7 hours of iterations (would have taken a week at least if I even tried it manually and still probably):

    https://github.com/langroid/langroid/releases/tag/0.59.0-b3

  • We Revamped Our Docs for AI Driven Development
    2 projects | news.ycombinator.com | 1 Aug 2025
    I really like the repo-mix tool for generating targeted LLMs.txt files.

    https://github.com/yamadashy/repomix

    I use this in a pre-commit hook to generate a few versions of these (of various token lengths) in Langroid:

    https://github.com/langroid/langroid

    For example I can dump one of the LLMs.txt into Gemini 2.5 pro and ask it about how certain Langroid features work, or ask it to implement simple agent systems, and it does a good (not perfect) job. I find this to be a convenient way to onboard people to Langroid, since LLMs don’t (yet) “know” Langroid too well (they know a little and hallucinate quite a bit)

  • Jules: An Asynchronous Coding Agent
    4 projects | news.ycombinator.com | 19 May 2025
  • 10 open-source MCPs that make your AI agents smarter than your team lead
    14 projects | dev.to | 9 May 2025
    github.com/langroid/langroid
  • Understanding the BM25 full text search algorithm
    5 projects | news.ycombinator.com | 19 Nov 2024
    In the Langroid[1] LLM library we have a clean, extensible RAG implementation in the DocChatAgent[2] -- it uses several retrieval techniques, including lexical (bm25, fuzzy search) and semantic (embeddings), and re-ranking (using cross-encoder, reciprocal-rank-fusion) and also re-ranking for diversity and lost-in-the-middle mitigation:

    [1] Langroid - a multi-agent LLM framework from CMU/UW-Madison researchers https://github.com/langroid/langroid

    [2] DocChatAgent Implementation -

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    www.saashub.com | 14 Jun 2026
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langroid/langroid is an open source project licensed under MIT License which is an OSI approved license.

The primary programming language of langroid is Python.


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