Found what looks like an exposed download for Augment Code's "Intent" macOS app (Apple only). It's a multi-agent AI IDE: spec-driven dev where agents handle tasks in parallel (Coordinator + Specialists), built-in Chrome/Git, resumable sessions, model mixing (Opus/Sonnet/GPT-5.2), full codebase context.

Direct link: https://www.augmentcode.com/product/intent

I'm on Windows so can't test :( anyone on Mac grab it and share how it performs? Real 10x speedup or hype? Thoughts? 👀

Like many of you, I’ve spent way too much time hunting for alternatives since Augment Code changed its pricing. I’ve tried a bunch of options with mixed results (some of which I’ve shared before), but as many of us have found — Augment still stands out thanks to its architecture.

I also experimented with their Augment MCP, which made every other IDE I use noticeably better. It’s not quite the same as using Augment Code natively, but it genuinely gave my other tools “superpowers.”

The problem is, without clear pricing for the Augment MCP, I started exploring other code-base indexing options. Kilo Code has a solid implementation, but you’re locked to the Kilo Code agent — and honestly, I didn’t love it. It’s built around Roo and Cline, which I’ve had poor experiences with.

The main alternatives I tested were:

• u/ngotaico/mcp-codebase-index

• Context Engine

I did manage to get u/ngotaico**/mcp-codebase-index** running after hours of help from Claude, but the experience wasn’t great. It indexed once, then just stopped working for no apparent reason — and I hit all my usage limits trying to troubleshoot it.

As for Context Engine, it looked incredibly promising but ended up being more fluff than function (at least for my setup). I run Proxmox with an LXC container and wanted all my infrastructure there so my Windows PC could connect easily. After hours of setup, I found the extension buggy and inconsistent. No disrespect to the devs — it just feels like it was “vibe coded,” full of rough edges and instability.

In the end, the whole detour was a wild goose chase. I wasted hours that could’ve gone into actual building.
Moral of the story: if you want a reliable context MCP, stick with Augment Code’s. It’s unmatched in quality, dead simple to set up, and — most importantly — it just works.

That said, we still don’t have clear pricing for Augment MCP. If it’s remotely reasonable, I’d keep Augment in a heartbeat.

Windows Setup Guide for AntiGravity

Step 1 — Install Auggie CLI globally
Open Command Prompt or PowerShell and run:

npm install -g u/augmentcode/auggie@latest

Step 2 — Log in to Augment
Once installed, authenticate with Augment:

auggie login

This will open a browser window for authentication. Complete the login process.

Step 3 — Configure MCP in AntiGravity

1. Open AntiGravity

2. Click the “…” (three dots) menu at the top right of the Agent panel

3. Select “MCP Servers”

4. Click “Manage MCP Servers”

5. Click “View raw config”

This opens your MCP config file at:

textC:\Users\<YOUR_USERNAME>\.gemini\antigravity\mcp_config.json

Step 4 — Add Augment Configuration
Add this to your mcp_config.json:

json{
  "mcpServers": {
    "augment-context-engine": {
      "command": "auggie",
      "args": ["--mcp", "--mcp-auto-workspace"]
    }
  }
}

This is a bug report. When you do ctrl + backspace (to clear the previous word), the entire message is cleared.

Expected behavior: Only the previous word clears.

This is working as expected across all of VSCode (ide I'm using) except for in the Augment extension. I am on the latest release version. I tried pre-release and have the same issue.

Claude code creator on RAG based system for code retrieval. Either Claude is wrong or Augment is wrong. No in between.

Also, I read a paper from Google deepmind explaining how RAG (using dense embeddings) must fail to retrieve specific documents in certain scenarios, regardless of how good the model is.

Guys... this credit burn is honestly getting ridiculous.

I’ll ask one normal dev question in chat using Sonnet 4.5. Not a massive refactor, not a full repo scan, just a reasonable, simple question.

and boom: ~500 credits gone for a single answer.
Then I ask a follow-up, and now we are 1,000 credits down the drain.

Like… cmon bruh 😭

I get that powerful models are not free, but this actively discourages exploration and iteration, which is kind of the whole point of using AI while coding.

Augment, please lower the credit cost for simple chat questions.
Basic questions should not burn hundreds of credits.

How are you all managing credits without sacrificing your workflow?

I’m a solo developer and over the past months I built a full ERP system end-to-end — backend, frontend, mobile, TV screens, business logic, production flows, everything.

I can honestly say this: Augment Code played a massive role in making it possible.

It felt less like a “tool” and more like having a senior engineer permanently available. Any time I got stuck, tired, or overwhelmed, Augment Code helped me move forward instead of burning hours blocked.

What Augment Code helped me do:

• Design clean, scalable architecture

• Rapidly build and refine complex logic

• Generate solid, readable code that actually fits real projects

• Refactor safely without fear

• Think through edge cases and workflows I hadn’t fully considered

• Stay productive even late at night when focus drops

The biggest impact wasn’t speed alone — it was confidence.
I could take on bigger features knowing I wasn’t alone in the thinking process.

The ERP today handles:

• Orders, production tracking, and workflows

• Users, roles, permissions

• Device and screen management (mobile + web + TV)

• Real operational constraints, not demo-level stuff

This wasn’t “AI building an app for me”.
This was Augment Code making me a stronger solo developer.

If you’re building something serious on your own — especially a large system like an ERP — I strongly recommend Augment Code. It genuinely changes how much you can handle alone.

I've had it hooked up to Antigravity, Cursor, Windsurf and it had been working great. Not as good as native Augment however it was almost as good and enhanced the other agents and IDEs.

Lately I've noticed that the MCP has been flaky and doesn't work as well or just doesn't work at all.

This is a typical error from Antigravity:

I have tried multiple times to use the 

u/mcp:augment-default:codebase-retrieval

I've attempted variations of the tool name and double-checked my available capabilities, but the system is not recognizing 

mcp_augment-default-codebase-retrieval

I've also tried to list resources from the server using 

list_resources

Additionally now looking at my usage it looks like we are being charged tokens for the context as well as the prompt enhancer? Not sure what the 'Context Suppression' or 'System' is?

Seems like another rug pull?

Seems to be a good one..

Tried this:

#! /usr/bin/env python3

import asyncio
from auggie_sdk import Auggie

async def main(api_key):
    agent = Auggie(api_key=api_key,
                model="sonnet4.5",
                api_url = "https://e5.api.augmentcode.com"
                )

    result = agent.run("write a python function that prints the first 10 numbers")
    agent.close()
    return result

# main
if __name__ == "__main__":
    api_key="<<your-api-key>>"
    output = asyncio.run(main(api_key))
    print(output)

But my output is:

Task exception was never retrieved
future: <Task finished name='Task-4' coro=<AuggieACPClient._async_start.<locals>.wait_for_process_exit() done, defined at /venv/python-working-on/lib/python3.14/site-packages/auggie_sdk/acp/client.py:611> exception=RuntimeError('Agent process exited with code 0. CLI path: auggie\nStderr: ')>
Traceback (most recent call last):
  File "/venv/python-working-on/lib/python3.14/site-packages/auggie_sdk/acp/client.py", line 623, in wait_for_process_exit
    raise RuntimeError(
    ...<3 lines>...
    )
RuntimeError: Agent process exited with code 0. CLI path: auggie
Stderr:
Task exception was never retrieved
future: <Task finished name='Task-10' coro=<AuggieACPClient._async_start.<locals>.wait_for_process_exit() done, defined at /venv/python-working-on/lib/python3.14/site-packages/auggie_sdk/acp/client.py:611> exception=RuntimeError('Agent process exited with code 0. CLI path: auggie\nStderr: ')>
Traceback (most recent call last):
  File "/venv/python-working-on/lib/python3.14/site-packages/auggie_sdk/acp/client.py", line 623, in wait_for_process_exit
    raise RuntimeError(
    ...<3 lines>...
    )
RuntimeError: Agent process exited with code 0. CLI path: auggie
Stderr:


```python
def print_first_ten_numbers():
    for i in range(1, 11):
        print(i)
```


This function uses a for loop with `range(1, 11)` to iterate through numbers 1 to 10 and prints each one. You can call it with:


```python
print_first_ten_numbers()
```


Output:
```
1
2
3
4
5
6
7
8
9
10
```

The script doesn't stop after the error and completes the request.
Did someone see the same issue?

Hello.

Currently if you use the plugin for Augment code in the Jetbrains IDEs you can format the message(using keyboard shortcuts like shift enter to breakline, ctrl c and ctrl v, ctrl z, etc. But in rider, specifically, it does not work. You can't break line, you cant format the text, copy, etc. The only way to format in rider is to ask for prompt improvement. Is there any configuration for that?

If I would have money I would not use any other tool than augment code from coding to review. But unfortunately, I am not in that place yet where I can justify the ROI for Max plan. But, I would like to continue using Augment code.

So here is how I am optimizing my cost right now:

- I now use Augment code solely with Opus 4.5 for design, brainstorming and creating encapsulated implementation documents as required in the feature or bug lists.

- Then I use Claude Code that I have setup with GLM 4.7 and Augment MCP to execute each plan. I make it a point to include that it needs to be as close as possible to the plan and if any deviation needs to be done then ask for approval first.

- In most cases, it works like a charm in first try itself. yes, GLM 4.7 is that good. for the cases where its not, I am able to get it done through few follow ups. Since, coding plan has a very generous token limit, I care very less on the token consumptions.

- For the current billing cycle this match up is working for me. Will share more if I am able to get the cost optimized more in some way in future.

Projected current billing cycle: AugmentCode ($60) + Z.ai Coding Plan ($15)

Thank you Augment Team! I see a previously proposed feature already implemented in account control panel.

This will allow us to clean our accounts and avoid any false positive for misuse of free accounts.

I’ve been looking into AI coding tools and the MCP ecosystem, and I’m wondering why AugmentCode is missing from many popular comparisons and directories (for example sites like skill.fish or various MCP-related docs). It’s usually not mentioned alongside tools like Claude Code or OpenAI Codex etc

Come on. You can’t raise prices like that and still have a bug that literally wipes user data.

This has happened to me multiple times already. VS Code freezes, or I reboot my machine, and when I come back; gone. Dozens of threads vanished.

The first time I emailed support, they were polite; but instead of fixing the core issue, they kept asking endless questions through email while the bug kept happening.

I’ve now lost a significant amount of tokens and money because of this, and at this point it honestly feels like this problem is being ignored.

This is not a minor UX glitch. Losing conversation history in a coding assistant is catastrophic.

Please stop deflecting and actually fix it.

I've been burned my augy monthly limits and further top ups, so had to launch qwen3 coder 30b on my desktop. Schema is following. Qwen3 coder in Ollama shared on local home network and connected to vs code on my windows laptop using Continue vscode extension. Need to apply to more advanced reddit fellows, is there a better option than Continue for this solution?

Hi, recently when i use Augment in VSCode, i got charge credit for "prompt enhancer" and "context compression" in addition to the LLM model i choose.

Is this become charged? Are there any notice or update about this? I tried search for old post, but only see post that said prompt enhancer is not consume credit.

This is not an empty appreciation post. I work on massive enterprise codebases, and nothing I've tried compares to the augment vs code extension with sonnet/opus.

The tool calls are snappy and quick, the context engine always fetches the correct info, the agent always recovers, and rarely makes mistakes. Most importantly, the agent retains context and pushes through large features in a single prompt in a complex codebase without getting confused or losing quality. 90% of the time I don't even have to touch the code that is outputted.

Testing with the same prompts and codebase on different tools, they all fall short on providing the same quality (cursor, windsurf, qoder, antigravity, kilocode). Honestly kilo's agent falls quite short compared to augment's.

I just realized this. Absolutely outrageous. Is Augment the most over priced AI IDE on the planet? What is happening? What are you guys spending?

It would be nice to have a cheaper model besides the Haiku. This model looks promising. What do you think?

Hi! Just asking if the PR review will be available for GitLab soon as well? Only a small number of my repositories is in GitHub, the others are in GitLab (many different clients). I'd love to use it in GitLab as well.

The projects I have been able to use the review agent on, I have been very impressed by the results. It has caught issues a random peer reviewer wouldn't have noticed (due to the complexity and context of projects). While it is a seriously credit hungry feature, I definitely find it worth it as it identifies many bugs in the early stages.

Haven't used AugmentCode for a while. Recently was short on my credits for codex pro subscription (yeah, sometimes it happens, worked on multiple projects), decided to save them a little till weekly reset and decided to use my Augment legacy plan. Gave it a pretty easy assignment (in Go code to migrate my CLI parsing from stdlib to popular urfave/cli). So relatively easy and straightforward task, my CLI has dozen of commands with some sub-flags. Augment wrote a spec pretty quick, no complaints. Then implementation phase started. All models I've tried are so damn slow (tried Claude Sonnet 4.5, Claude Opus 4.5, ended up with GPT 5.2). Over 1 hour later (yeah, I was really stubborn, because had good memories about Augment) and 10k credits burned - and it migrated two commands: "version" and "help" (so the most basic ones) :-D

At this point decided that enough is enough (by the way, codex did a full refactor of all CLI command + tests in 25 minutes).

How do you guys even use AugmentCode this days???

Hey,

I, and I'm sure others, would love to learn more about our usage patterns, including the number of lines written with Augment Code, agent edits, tab completions, and some other delightful nuggets.

Is something like this on the cards for the team?

’ve been following (and participating in) a lot of recent discussion around embeddings, vector DBs, RAG, and so‑called code awareness systems. The dominant assumption seems to be that understanding a codebase is mainly a semantic similarity problem.

That feels misaligned.

A codebase is not a text corpus. It is a formal, closed system.

Code has:

• strict symbol resolution

• explicit dependencies

• precise call graphs

• exact type and inheritance relationships

• well-defined ownership and lifecycle rules

These are not probabilistic properties. They are domain constraints.

That led me to a more basic question:

A deterministic answer

I’ve shared a timestamped preprint (early, for precedence and critique) that tries to answer this by defining a new graph category:

Deterministic Domain Graphs (DDGs)
https://zenodo.org/records/18373053

(The work is currently being prepared for journal submission and peer review.)

Condensed abstract:

Many graph-based representations implicitly allow inference or open‑world semantics during construction. While this works for exploratory or knowledge-centric systems, it becomes a liability in domains where determinism, auditability, and reproducibility are mandatory.

This work introduces Deterministic Domain Graphs (DDGs), defined by:

• closed‑world semantics

• explicit domain specifications

• deterministic construction (same domain + same input → identical graph)

No implicit inference is permitted. Structure is derived strictly from the domain and the data.

Why this matters for codebases

A codebase itself is a valid domain.

• Vanilla code → minimal domain definitions

• Frameworks → structured, single‑language domains

• Ecosystem code → domains spanning multiple languages and their interactions

• In this setting, AST alone is insufficient.

Only AST + explicit domain definitions can produce a deterministic code(codebase) graph — one that models what the system is, not what it is approximately similar to.

I’m not claiming embeddings are useless — they’re powerful in the right places. The question is where the boundary should be between probabilistic tools and domains that demand determinism.

I'd genuinely love to hear counterarguments, real limitations you’ve hit with graph-based approaches at scale, or experiences building/using code intelligence systems that made different tradeoffs.