smhanov

smhanov / laconic

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An agentic research orchestrator for Go that is optimized to use free search & low-cost limited context window llms.

16
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100% credibility
Found Feb 07, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Go
AI Summary

A lightweight Go library for creating efficient AI research agents that answer questions by searching the web and compressing information to work with small AI models.

How It Works

1
🔍 Discover Laconic

You stumble upon this handy tool while looking for ways to make AI helpers research tough questions without getting overwhelmed.

2
💻 Start the demo

You grab the ready-to-run example program and launch it on your computer with a simple command.

3
🤖 Connect a thinking brain

You link it to a local AI thinker or an online service so your helper can plan and reason like a pro.

4
📋 Pick your research style

You choose between quick summaries or deep fact-gathering adventures to match your question.

5
❓ Ask your question

You type in what you want to know, like the latest news or a tricky fact.

6
🔄 Watch it research

Your helper automatically searches the web, collects key facts, and builds knowledge without wasting space.

✅ Get smart answers

You receive a clear, reliable response grounded in real web info, saving you hours of searching.

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AI-Generated Review

What is laconic?

Laconic is a Go library for building agentic research AI agents that pair low-cost LLMs with free search engines to answer questions beyond the model's training data. It keeps prompts lean—via rolling summaries or atomic fact notebooks—so even 4B-parameter models like qwen2.5 handle real research on 4k/8k contexts without overflowing. Wire up your LLM provider (Ollama, OpenAI), pick a search like DuckDuckGo or Brave, and call Answer() for grounded responses with total cost tracking.

Why is it gaining traction?

Unlike bloated ReAct agents that explode context, laconic compresses state at every step, slashing costs on cheap backends while enforcing web-grounding—no hallucinated answers. Dual strategies shine: scratchpad for quick facts (2-4 LLM calls), graph-reader for multi-hop queries with page fetching and early stopping. The CLI demo lets you test agentic research workflows instantly with mistral on localhost or gpt-4o, debug prompts included.

Who should use this?

Go devs prototyping agentic research tools, like github agentic ai assistants or agentic research labs querying live data. Backend teams building agentic research prototypes for reports, fact-checking, or RAG knowledge graphs. Anyone tired of ungrounded LLM chats needing an agentic research assistant that stays cheap and verifiable.

Verdict

Grab it for agentic research ai experiments—the CLI and examples get you answers in minutes, docs cover prompts and costs clearly, tests pass offline. With 13 stars and 1.0% credibility, it's an early agentic research prototype, but mature enough for side projects if you're in Go.

(198 words)

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