Free open-source AI that replaces paid tools: FrontierAgent
Python devs, look at FrontierAgent
🔍 Free open-source Python developer tool
🧩 FrontierAgent, our agent framework, open-sourced alongside it — native command-line TUI, ReAct and Agent Team modes, one command on macOS and Linux, no preinstall, no hard Docker dependency.
🔗 ApodexAI/FrontierAgent
📌 Save this for later
#GitGems #IndianDevelopers #Python #AI #OpenSource
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Apodex 1.1: Scaling Agentic Intelligence for Complex Work
📅 Publication Date: Aug 24, 2026
📄 Paper: arxiv.org
🔗 Code: ApodexAI/FrontierAgent
💻 Project Page: apodex.com
2 views
最近在找能在终端里跑长程研究的 Agent，看到 Apodex 开源的 FrontierAgent，先马克下来。
可以 ReAct 单兵，也能 Agent Team 拆任务、并行子代理再汇总；沙箱 /inputs 只读、/workspace 可写、/outputs 交交付物。Apache-2.0，5.2k⭐。
ApodexAI/FrontierAgent
17 views
FrontierAgent: A Chinese Translation of the Agent Runtime, and What It Actually Ships
FrontierAgent is an open-source agent runtime, terminal product, and evaluation suite aimed at long-horizon research and file-based work. The original repo, ApodexAI/FrontierAgent, sits at 5243 stars, is written in Python, and is licensed Apache-2.0.
There is now a Chinese translation of that documentation: yangshun2005/FrontierAgent-cn. It was translated on 2026-10-14. It is a fresh repo, so don't read anything into a low star count — the point of this article is what's inside it, not how popular it is.
I want to be precise about what the Chinese repo contains, because it's not just a translated README. It has two distinct layers, and the distinction matters if you're deciding whether to use it.
Two layers in the Chinese repo
The project explicitly separates them:
1. A Chinese quick-start guide at the top of the page. This is supplementary material, not part of the original README. It exists so you can get the project running without reading the whole document.
2. The translated README body below it. This is a faithful translation of the original English documentation.
The maintainers say translation inaccuracies should go through Issues or PRs. That's a reasonable setup — the quick-start is opinionated and can drift from upstream, while the translated body tracks the original.
If you've ever tried to onboard onto an agent framework where the only docs are English and the terminology is dense (task boards, sandboxes, fail-closed authorization, checkpointing), you know why the quick-start layer is the useful part. It front-loads the minimum you need.
What FrontierAgent actually is
The one-line description from the project: a native command-line TUI, ReAct and Agent Team modes, one command on macOS and Linux, no preinstall, no hard Docker dependency.
The TUI ships two native workflows:
- ReAct — a single stateful agent that researches, reads files, writes deliverables, runs commands, and iterates inside a task-level sandbox.
- Agent Team — a coordinator maintains a task board, delegates independent work to parallel sub-agents, collects their reports, and synthesizes the result.
The same workflow engine drives the benchmark runner used to evaluate Apodex models. The framework, tools, workflows, and evaluation layers are kept separate, so each can be reused independently. That separation is the part I'd actually care about if I were building on top of this rather than just running it.
The pieces worth knowing about
Task board. In Agent Team mode, add_task and update_task events render live in the TUI sidebar. States are pending, in progress, done, blocked, and cancelled. This is a small thing that makes a big difference in practice — you can see what the coordinator thinks it's doing.
Sandboxed file operations. Shell and file tools share one task-level filesystem: /inputs is read-only, /workspace is working state, /outputs holds persistent deliverables. Both authorization and sandbox failures are fail-closed, meaning a failure denies rather than allows.
Async intervention. You can type into the agent while it runs. The input queues and injects at the next safe turn boundary instead of killing the current run. In Agent Team mode it steers the coordinator; sub-agents already running are allowed to finish.
Transparent deliverables. On macOS and Docker, /outputs maps to .apodex/runs/<run>/outputs on the host. The same run directory holds checkpoints, traces, engine logs, and trajectories.
Approvals, tracing, resume. Mutating operations show a diff and require approval unless you pass --yes. Sessions checkpoint, every operation is traced locally, /revert restores session changes, and --resume continues a saved run.
Built-in evaluation. A subprocess runner supports research and file-based benchmarks, deterministic artifact collection, concurrency, and progress tracking.
Getting it running
Prerequisites per the original: Git, Python 3.12, uv, and an OpenAI-compatible model endpoint. Docker is optional.
git clone https://github.com/ApodexAI/FrontierAgent.git
cd FrontierAgent
uv sync --python 3.12 --extra dev
cp .env.example .env
Then fill in .env:
OPENAI_API_KEY=your-key
OPENAI_BASE_URL=https://your-openai-compatible-endpoint/v1
OPENAI_MODEL=your-model-name
One honest gap: the Chinese quick-start does not give the exact command to launch the TUI. The materials only mention the frontier-agent TUI as a product name. If you need the launch command, read the translated README body — the quick-start tells you so directly rather than inventing a command.
Environment details
- Python: requires-python = ">=3.12"; the quick-start says Python 3.12.
- Tools: Git and uv required; Docker optional.
- Model endpoint: an OpenAI-compatible endpoint via OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL. Without all three it won't run.
- Optional dependency groups (from pyproject.toml): document-readers, plugins, sandbox, eval, dev.
- OS: not explicitly constrained in the docs. The Dockerfile is based on python:3.12-slim and installs bubblewrap, libreoffice-core, and libreoffice-impress inside the image.
Traps the Chinese docs call out
These are the ones that will cost you an afternoon if you miss them.
You must configure the model endpoint. No key, no base URL, no model name means no run. The project also offers a free Apodex API trial — two weeks, key from platform.apodex.ai — if you don't want to host a model yourself.
Sandbox and authorization behavior. Mutating operations show a diff and ask for approval unless --yes is set. Authorization and sandbox failures are fail-closed. In the Docker image SANDBOX_BACKEND=bwrap; if the runtime doesn't grant bubblewrap namespaces, it falls back to container mode.
Optional web tools need extra keys. Web research tools require SERPER_API_KEY with WEB_SEARCH_PROVIDER=serper. The materials leave JINA_API_KEY unset.
Version pinning. apodex-agent-core==0.14.1 is pinned exactly. The comment notes that 0.x MINOR upgrades can be breaking, which is why the pin is tight.
Network mirrors. The materials don't mention domestic mirrors or download accelerators. If you're behind a slow connection to PyPI, that's on you to solve — the docs don't address it.
Why the Chinese version is worth having
The value here isn't exotic. It's that the barrier for Chinese-speaking developers drops. Agent frameworks are dense with terms that don't translate cleanly — "fail-closed authorization," "turn boundary," "task board," "checkpoint" — and reading a translated version first means you spend your attention on the architecture instead of on vocabulary.
The two-layer structure is the right call. A quick-start that gets you to a running process, plus a faithful translation of the full documentation underneath. The maintainers are upfront that the quick-start is supplementary and the translation aims for fidelity, and they invite corrections.
If you're evaluating FrontierAgent and English docs are friction, start with the Chinese repo. Read the quick-start, get the endpoint configured, run it. Then go back to the translated body when you need to understand Agent Team's task board semantics or how /outputs maps on your platform.
Repo: yangshun2005/FrontierAgent-cn
Upstream: ApodexAI/FrontierAgent
---
*Chinese documentation: yangshun2005/FrontierAgent-cn*
I'm an independent enterprise AI delivery consultant — private LLM deployment, RAG, agent workflows, AIOps. If your team is stuck somewhere in that stack, my inbox is open.
Why Apodex 1.1 Mini Does the Job Instead of Just Answering
What if your AI stopped giving you answers and started finishing the whole job?
That's the promise behind Apodex 1.1 Mini, which went live on OpenRouter on 1 October 2026 with a 262,144-token memory and a free endpoint you can test today.
It reads your files, runs code, uses tools, and checks its own work before it hands anything back.
🔥 Want the exact setup for testing Apodex 1.1 Mini on your own files? Inside the AI Profit Boardroom, I've got a 30-day roadmap for long-memory AI research with step-by-step video tutorials, plus weekly coaching calls with 3,600+ members building real automations.
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Most AI tools work the same way.
You ask, they think, and they answer.
Apodex is trying to do something different, and for business owners sitting on piles of emails, call notes and customer feedback, that difference matters.
Here's the full video breakdown.
youtube.com
The quick facts on Apodex 1.1 Mini
Before I get into why it matters, here's what you're actually working with.
- It's free to try. OpenRouter lists it as apodex/apodex-1.1-mini:free, at zero cost per token.
- It has a huge memory. The context window is 262,144 tokens.
- It's open. The Mini has 35 billion parameters and uses the Apache 2.0 licence, so developers can download it and run it themselves.
- It's built for real work. OpenRouter says it supports files, data, code, tool calling and structured outputs, and describes it as reasoning-first.
- It has a bigger sibling. The full Apodex 1.1 was released on 24 August 2026, and the Mini followed in October.
Think of 35 billion parameters as the size of its brain.
That's small next to the giant models, but it's big enough to be useful, and small enough that a developer can actually run it.
Why a "Mini" model is worth your attention
A model with "Mini" in the name sounds like a cut-down toy.
This one isn't, because it was built for long work.
You give it one big task, and it keeps going through files, steps and checks along the way.
That's a very different tool from a chatbot you type one question into at a time.
What 262K tokens actually means for your business
A token is a small piece of a word, about three-quarters of a word on average.
So 262,144 tokens works out at roughly 190,000 words, which is several full books in one go.
The context window is how much the model can keep in front of it at once.
I like to picture a desk.
A small desk holds one sheet of paper, while a huge desk holds your whole project spread out side by side.
Apodex 1.1 Mini has a huge desk.
That matters when your task needs a lot of papers, notes and data in view at the same time.
The honest warning about big context windows
A big desk doesn't mean the model understands every page perfectly.
Nobody should claim that, and I won't.
What I can say is that Apodex was designed around long work, and the desk is big enough for it.
OpenRouter also shows speed numbers.
When I looked, the best provider showed about 1.09 seconds to the first response and about 146 tokens per second after that.
The endpoint is brand new, so treat those numbers as early, because they're already moving.
The free endpoint also has rate limits, so you can't hammer it all day.
>Plan your tests before you start, and give it one real problem rather than fifty random ones.
How Apodex works as an agent, not a chatbot
Here's the big idea.
You give Apodex a goal, and it makes a plan.
Then it searches, reads, analyses, and writes code to test things.
It checks the results and changes the plan if something new shows up.
Only then does it hand you the finished work.
The Apodex team calls this working capability.
Their argument is that hard real-world tasks need more than good language skills.
The model has to handle files, use tools, remember where it is, and recover when something fails.
How the Apodex team trained it
The team describes two ways they trained for this kind of work.
The first is called environment scaling, which means training the model inside lots of realistic work settings with files and tools.
The second is called agentic coordination scaling, which means training it to work with other agents like a team.
In plain words, environment scaling is practice on real jobs, and agentic coordination scaling is practice working with co-workers.
What a real business task looks like
Say you run a community and you want to know what to teach next.
I'd use the AI Profit Boardroom as my own example here.
You hand the model all your member questions and call notes, and you ask it for the five biggest problems.
That's exactly the kind of job Apodex was made for.
Or give it a folder of past emails from your community and ask which questions keep coming back.
A normal chat tool might skim a few emails and guess.
A model built for long work is meant to read them all and show its evidence.
🔥 If you want the prompts I'd use for this kind of file-based research, the AI Profit Boardroom has a prompt library full of them, plus four coaching calls every week where you can bring your Apodex and OpenRouter setup and we work through it live.
skool.com
Frontier Agent: the feature that makes Apodex 1.1 Mini feel different
Apodex ships with an environment called Frontier Agent, and it's free on GitHub.
ApodexAI/FrontierAgent
It gives you two ways of working.
ReAct mode is one worker doing the whole job
The first mode is called ReAct.
One agent does the task from start to finish.
It researches, analyses, writes code, checks, and delivers, so you get one worker following one line of steps.
Agent Team mode is where it gets interesting
The second mode is called Agent Team.
A coordinator takes the big task and splits it up, then sends helpers off to work at the same time.
- One helper searches for information.
- One helper looks at the data.
- One helper writes and runs code.
- One helper checks the claims.
The coordinator then collects every report and puts them together into one answer.
Think about a group school project where one kid does the reading, one does the numbers, one checks the facts, and one puts the poster together.
It's the same idea, except the kids are AI.
Here's how I'd use it.
Ask the agent team to study why people leave a community, with one helper reading member feedback and another checking the call notes.
A third helper then matches the two and lists the fixes.
What the Apodex 1.1 benchmarks show (and what they don't)
These numbers come from the official Frontier Agent page on GitHub.
The Apodex team ran and reported them themselves, and nobody outside has confirmed them yet, so keep that in mind.
They also cover the Apodex 1.1 family rather than the Mini on its own.
There are three setups being compared, which are Apodex 1.0, Apodex 1.1 in ReAct mode, and Apodex 1.1 in Agent Team mode.
- On APEX-Agents, the scores go from 16.5 to 34.4 to 38.5, which is more than double from the old version to the new one with a team.
- On FrontierScience Research, the scores go from 28.3 to 55.0 to 63.3.
That second one is a research test, and research is what this model is all about.
The pattern is the interesting part.
The jump from 1.0 to 1.1 is big, and switching on the team adds even more on top.
Why Apodex 1.1 Mini matters right now
Here's where I've landed.
Apodex 1.1 Mini is on OpenRouter with a 262,144-token memory, plus tools, files and code.
The big idea is a model that works through a whole project with a team of helpers, and a way to check its own claims.
Right now, there's a window where most business owners haven't figured out long-memory AI like this.
They're still typing one question at a time into a chat box.
That window won't stay open forever.
My advice is simple.
Give Apodex a real problem, give it your own files, and see where it holds up and where it breaks.
FAQ
What is Apodex 1.1 Mini?
Apodex 1.1 Mini is an open, reasoning-first AI model from Apodex, built for long research tasks that use files, data, code and tools.
Is Apodex 1.1 Mini free?
Yes, there's a free endpoint on OpenRouter, although it has rate limits, so you'll need to plan your tests.
How big is the Apodex 1.1 Mini context window?
It's 262,144 tokens, which is roughly 190,000 words, or several full books in one go.
What is the difference between ReAct and Agent Team?
ReAct uses one agent that does the whole task step by step, while Agent Team uses a coordinator that splits the work across parallel helpers and combines their reports.
Can I run Apodex 1.1 Mini myself?
Yes, it uses the Apache 2.0 licence, so developers can download it and run it on their own setup.
About Julian
I'm Julian Goldie — AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom. I help business owners scale with AI agents, automation and SEO. I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers. I share daily AI training inside the Boardroom.
📺 Video notes + links to the tools 👉
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🎥 Learn how I make these videos 👉
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🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉
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1 point
FrontierAgent pairs a terminal agent that has ReAct and Agent Team modes with a benchmark runner that, per its README, is powered by the same workflow engine: ApodexAI/FrontierAgent
#AIAgents #MultiAgent #Python
FrontierAgent Runs Its Terminal Agent on the Same Engine as Its Benchmarks
My reading of the FrontierAgent README is that its interesting claim is structural rather than a feature list: the workflow engine behind the frontier-agent terminal app is, according to the README, the same one that powers the benchmark runner used to evaluate Apodex models. That makes the terminal product feel closer to a published test harness. If you want to see how these agent workflows behave on long-horizon research and file work, you can run them yourself, on your own tasks, against your own OpenAI-compatible endpoint.
The README describes the project as an open-source agent runtime, terminal product, and evaluation suite. It also says the framework, tools, workflows, and evaluation layer remain separate so each can be reused independently. The repository layout reflects that with distinct directories: frontier_agent/ for the generic loop, scheduling, registries, AgentBus, and observers; plugins/tools/ for tool implementations; workflows/ for the ReAct and Agent Team pipelines; apodex/ for the terminal CLI and TUI; and benchmarks/ for the public harness plus bundled FrontierSearchBench and FrontierChallenge.
Two workflows, two execution models
The TUI ships two native workflows, and the README scopes them differently.
In react mode, one stateful agent researches, reads files, writes deliverables, runs commands, and iterates in a task-scoped sandbox. The README pitches it for focused research, repository analysis, and document or file work, using the tui workflow profile.
In agent_team mode, a coordinator maintains a task board, delegates independent work to parallel sub-agents, collects their reports, and synthesizes the result. The README adds that the coordinator dispatches bounded parallel assignments, receives structured reports, and can use an optional fast reporter for final evidence review. The task board is visible: add_task and update_task events show up live in the TUI sidebar with pending, active, completed, blocked, and cancelled states.
Picking a mode is a flag:
uv run frontier-agent --mode react --cwd /path/to/project
uv run frontier-agent --mode agent_team --cwd /path/to/project
One behavior differs between the modes in a way that matters mid-run. You can type while an agent works, and the instruction is queued and injected at the next safe turn boundary without discarding the active run. In Agent Team mode, that input steers the coordinator, while sub-agents that are already running are allowed to finish.
The sandbox is the contract
The part I would read first as an operator is the filesystem. Shell and file tools share one task-scoped filesystem: /inputs is read-only, /workspace holds working state, and /outputs holds persistent deliverables. The README states that authorization and sandbox failures are fail-closed. On macOS and Docker, /outputs maps to .apodex/runs/<session-id>/outputs on the host, and that run directory also contains the checkpoint, trace, engine log, and trajectories.
That layout gives you a place to look after the agent finishes. Supplied documents can go in through --input, which the README shows attaching files read-only before the TUI starts. Deliverables come out in a known directory next to the trace of how they were produced.
Mutating operations show a diff and require approval unless --yes is enabled. Sessions are checkpointed, every action is traced locally, /revert restores session changes, and --resume continues a saved run.
Running it as an evaluation harness
The subprocess runner, per the README, supports research and file-grounded benchmarks, deterministic artifact collection, concurrency, progress inspection, and rerunning individual failures. The README also includes a chart it labels as Apodex-1.1 benchmark results across professional work, finance, scientific research, and general reasoning tasks. Treat those as the project's own reported results for its own model.
There is a load warning to read before you scale anything. Agent Team parallelism is additional to benchmark concurrency, so the README recommends starting with --concurrency 1, because total simultaneous model calls can approach runner concurrency multiplied by the team spawn limit. If your endpoint bills or rate-limits per call, that product is the figure to plan around. Setting SWARM_NO_WEB=1 disables Agent Team web tools, and REACT_NO_WEB=1 is the setting for closed-book ReAct tasks.
Getting a first run
Requirements are Git, Python 3.12, uv, and an OpenAI-compatible model endpoint; Docker is optional. After uv sync --python 3.12 --extra dev, you copy .env.example to .env and set OPENAI_API_KEY, OPENAI_BASE_URL, and OPENAI_MODEL, with optional SERPER_API_KEY and JINA_API_KEY for web research tools. The README notes that scientific and document packages are optional in native mode, and the agent installs only what a task needs into <project>/.apodex/runtime/native.
Pre-built linux/amd64 and linux/arm64 images on the GitHub Container Registry cover the path without a local Python environment, through docker compose run --rm agent. For local model serving, the README points NVIDIA users to a GPU compatibility matrix and warns that a driver mismatch surfaces late as opaque CUDA or Triton kernel errors during model load. The GPU helper picks a reviewed userspace track from the host driver but never installs or replaces the driver itself.
---
GitHub: ApodexAI/FrontierAgent
---
Curated by Agent Palisade — practical AI for small and mid-sized businesses.
Mentions
Free open-source AI that replaces paid tools: FrontierAgent Python devs, look at FrontierAgent 🔍 Free open-source Python developer tool 🧩 FrontierAgent, our agent framework, open-sourced alongside it — native command-line TUI, ReAct and Agent Team modes, one command on macOS and Linux, no preinstall, no hard Docker dependency. 🔗 ApodexAI/FrontierAgent 📌 Save this for later #GitGems #IndianDevelopers #Python #AI #OpenSource
Apodex 1.1: Scaling Agentic Intelligence for Complex Work 📅 Publication Date: Aug 24, 2026 📄 Paper: arxiv.org 🔗 Code: ApodexAI/FrontierAgent 💻 Project Page: apodex.com
最近在找能在终端里跑长程研究的 Agent，看到 Apodex 开源的 FrontierAgent，先马克下来。 可以 ReAct 单兵，也能 Agent Team 拆任务、并行子代理再汇总；沙箱 /inputs 只读、/workspace 可写、/outputs 交交付物。Apache-2.0，5.2k⭐。 ApodexAI/FrontierAgent
FrontierAgent: A Chinese Translation of the Agent Runtime, and What It Actually Ships FrontierAgent is an open-source agent runtime, terminal product, and evaluation suite aimed at long-horizon research and file-based work. The original repo, ApodexAI/FrontierAgent, sits at 5243 stars, is written in Python, and is licensed Apache-2.0. There is now a Chinese translation of that documentation: yangshun2005/FrontierAgent-cn. It was translated on 2026-10-14. It is a fresh repo, so don't read anything into a low star count — the point of this article is what's inside it, not how popular it is. I want to be precise about what the Chinese repo contains, because it's not just a translated README. It has two distinct layers, and the distinction matters if you're deciding whether to use it. Two layers in the Chinese repo The project explicitly separates them: 1. A Chinese quick-start guide at the top of the page. This is supplementary material, not part of the original README. It exists so you can get the project running without reading the whole document. 2. The translated README body below it. This is a faithful translation of the original English documentation. The maintainers say translation inaccuracies should go through Issues or PRs. That's a reasonable setup — the quick-start is opinionated and can drift from upstream, while the translated body tracks the original. If you've ever tried to onboard onto an agent framework where the only docs are English and the terminology is dense (task boards, sandboxes, fail-closed authorization, checkpointing), you know why the quick-start layer is the useful part. It front-loads the minimum you need. What FrontierAgent actually is The one-line description from the project: a native command-line TUI, ReAct and Agent Team modes, one command on macOS and Linux, no preinstall, no hard Docker dependency. The TUI ships two native workflows: - ReAct — a single stateful agent that researches, reads files, writes deliverables, runs commands, and iterates inside a task-level sandbox. - Agent Team — a coordinator maintains a task board, delegates independent work to parallel sub-agents, collects their reports, and synthesizes the result. The same workflow engine drives the benchmark runner used to evaluate Apodex models. The framework, tools, workflows, and evaluation layers are kept separate, so each can be reused independently. That separation is the part I'd actually care about if I were building on top of this rather than just running it. The pieces worth knowing about Task board. In Agent Team mode, add_task and update_task events render live in the TUI sidebar. States are pending, in progress, done, blocked, and cancelled. This is a small thing that makes a big difference in practice — you can see what the coordinator thinks it's doing. Sandboxed file operations. Shell and file tools share one task-level filesystem: /inputs is read-only, /workspace is working state, /outputs holds persistent deliverables. Both authorization and sandbox failures are fail-closed, meaning a failure denies rather than allows. Async intervention. You can type into the agent while it runs. The input queues and injects at the next safe turn boundary instead of killing the current run. In Agent Team mode it steers the coordinator; sub-agents already running are allowed to finish. Transparent deliverables. On macOS and Docker, /outputs maps to .apodex/runs/<run>/outputs on the host. The same run directory holds checkpoints, traces, engine logs, and trajectories. Approvals, tracing, resume. Mutating operations show a diff and require approval unless you pass --yes. Sessions checkpoint, every operation is traced locally, /revert restores session changes, and --resume continues a saved run. Built-in evaluation. A subprocess runner supports research and file-based benchmarks, deterministic artifact collection, concurrency, and progress tracking. Getting it running Prerequisites per the original: Git, Python 3.12, uv, and an OpenAI-compatible model endpoint. Docker is optional. git clone https://github.com/ApodexAI/FrontierAgent.git cd FrontierAgent uv sync --python 3.12 --extra dev cp .env.example .env Then fill in .env: OPENAI_API_KEY=your-key OPENAI_BASE_URL=https://your-openai-compatible-endpoint/v1 OPENAI_MODEL=your-model-name One honest gap: the Chinese quick-start does not give the exact command to launch the TUI. The materials only mention the frontier-agent TUI as a product name. If you need the launch command, read the translated README body — the quick-start tells you so directly rather than inventing a command. Environment details - Python: requires-python = ">=3.12"; the quick-start says Python 3.12. - Tools: Git and uv required; Docker optional. - Model endpoint: an OpenAI-compatible endpoint via OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL. Without all three it won't run. - Optional dependency groups (from pyproject.toml): document-readers, plugins, sandbox, eval, dev. - OS: not explicitly constrained in the docs. The Dockerfile is based on python:3.12-slim and installs bubblewrap, libreoffice-core, and libreoffice-impress inside the image. Traps the Chinese docs call out These are the ones that will cost you an afternoon if you miss them. You must configure the model endpoint. No key, no base URL, no model name means no run. The project also offers a free Apodex API trial — two weeks, key from platform.apodex.ai — if you don't want to host a model yourself. Sandbox and authorization behavior. Mutating operations show a diff and ask for approval unless --yes is set. Authorization and sandbox failures are fail-closed. In the Docker image SANDBOX_BACKEND=bwrap; if the runtime doesn't grant bubblewrap namespaces, it falls back to container mode. Optional web tools need extra keys. Web research tools require SERPER_API_KEY with WEB_SEARCH_PROVIDER=serper. The materials leave JINA_API_KEY unset. Version pinning. apodex-agent-core==0.14.1 is pinned exactly. The comment notes that 0.x MINOR upgrades can be breaking, which is why the pin is tight. Network mirrors. The materials don't mention domestic mirrors or download accelerators. If you're behind a slow connection to PyPI, that's on you to solve — the docs don't address it. Why the Chinese version is worth having The value here isn't exotic. It's that the barrier for Chinese-speaking developers drops. Agent frameworks are dense with terms that don't translate cleanly — "fail-closed authorization," "turn boundary," "task board," "checkpoint" — and reading a translated version first means you spend your attention on the architecture instead of on vocabulary. The two-layer structure is the right call. A quick-start that gets you to a running process, plus a faithful translation of the full documentation underneath. The maintainers are upfront that the quick-start is supplementary and the translation aims for fidelity, and they invite corrections. If you're evaluating FrontierAgent and English docs are friction, start with the Chinese repo. Read the quick-start, get the endpoint configured, run it. Then go back to the translated body when you need to understand Agent Team's task board semantics or how /outputs maps on your platform. Repo: yangshun2005/FrontierAgent-cn Upstream: ApodexAI/FrontierAgent --- *Chinese documentation: yangshun2005/FrontierAgent-cn* I'm an independent enterprise AI delivery consultant — private LLM deployment, RAG, agent workflows, AIOps. If your team is stuck somewhere in that stack, my inbox is open.
Why Apodex 1.1 Mini Does the Job Instead of Just Answering What if your AI stopped giving you answers and started finishing the whole job? That's the promise behind Apodex 1.1 Mini, which went live on OpenRouter on 1 October 2026 with a 262,144-token memory and a free endpoint you can test today. It reads your files, runs code, uses tools, and checks its own work before it hands anything back. 🔥 Want the exact setup for testing Apodex 1.1 Mini on your own files? Inside the AI Profit Boardroom, I've got a 30-day roadmap for long-memory AI research with step-by-step video tutorials, plus weekly coaching calls with 3,600+ members building real automations. skool.com Most AI tools work the same way. You ask, they think, and they answer. Apodex is trying to do something different, and for business owners sitting on piles of emails, call notes and customer feedback, that difference matters. Here's the full video breakdown. youtube.com The quick facts on Apodex 1.1 Mini Before I get into why it matters, here's what you're actually working with. - It's free to try. OpenRouter lists it as apodex/apodex-1.1-mini:free, at zero cost per token. - It has a huge memory. The context window is 262,144 tokens. - It's open. The Mini has 35 billion parameters and uses the Apache 2.0 licence, so developers can download it and run it themselves. - It's built for real work. OpenRouter says it supports files, data, code, tool calling and structured outputs, and describes it as reasoning-first. - It has a bigger sibling. The full Apodex 1.1 was released on 24 August 2026, and the Mini followed in October. Think of 35 billion parameters as the size of its brain. That's small next to the giant models, but it's big enough to be useful, and small enough that a developer can actually run it. Why a "Mini" model is worth your attention A model with "Mini" in the name sounds like a cut-down toy. This one isn't, because it was built for long work. You give it one big task, and it keeps going through files, steps and checks along the way. That's a very different tool from a chatbot you type one question into at a time. What 262K tokens actually means for your business A token is a small piece of a word, about three-quarters of a word on average. So 262,144 tokens works out at roughly 190,000 words, which is several full books in one go. The context window is how much the model can keep in front of it at once. I like to picture a desk. A small desk holds one sheet of paper, while a huge desk holds your whole project spread out side by side. Apodex 1.1 Mini has a huge desk. That matters when your task needs a lot of papers, notes and data in view at the same time. The honest warning about big context windows A big desk doesn't mean the model understands every page perfectly. Nobody should claim that, and I won't. What I can say is that Apodex was designed around long work, and the desk is big enough for it. OpenRouter also shows speed numbers. When I looked, the best provider showed about 1.09 seconds to the first response and about 146 tokens per second after that. The endpoint is brand new, so treat those numbers as early, because they're already moving. The free endpoint also has rate limits, so you can't hammer it all day. >Plan your tests before you start, and give it one real problem rather than fifty random ones. How Apodex works as an agent, not a chatbot Here's the big idea. You give Apodex a goal, and it makes a plan. Then it searches, reads, analyses, and writes code to test things. It checks the results and changes the plan if something new shows up. Only then does it hand you the finished work. The Apodex team calls this working capability. Their argument is that hard real-world tasks need more than good language skills. The model has to handle files, use tools, remember where it is, and recover when something fails. How the Apodex team trained it The team describes two ways they trained for this kind of work. The first is called environment scaling, which means training the model inside lots of realistic work settings with files and tools. The second is called agentic coordination scaling, which means training it to work with other agents like a team. In plain words, environment scaling is practice on real jobs, and agentic coordination scaling is practice working with co-workers. What a real business task looks like Say you run a community and you want to know what to teach next. I'd use the AI Profit Boardroom as my own example here. You hand the model all your member questions and call notes, and you ask it for the five biggest problems. That's exactly the kind of job Apodex was made for. Or give it a folder of past emails from your community and ask which questions keep coming back. A normal chat tool might skim a few emails and guess. A model built for long work is meant to read them all and show its evidence. 🔥 If you want the prompts I'd use for this kind of file-based research, the AI Profit Boardroom has a prompt library full of them, plus four coaching calls every week where you can bring your Apodex and OpenRouter setup and we work through it live. skool.com Frontier Agent: the feature that makes Apodex 1.1 Mini feel different Apodex ships with an environment called Frontier Agent, and it's free on GitHub. ApodexAI/FrontierAgent It gives you two ways of working. ReAct mode is one worker doing the whole job The first mode is called ReAct. One agent does the task from start to finish. It researches, analyses, writes code, checks, and delivers, so you get one worker following one line of steps. Agent Team mode is where it gets interesting The second mode is called Agent Team. A coordinator takes the big task and splits it up, then sends helpers off to work at the same time. - One helper searches for information. - One helper looks at the data. - One helper writes and runs code. - One helper checks the claims. The coordinator then collects every report and puts them together into one answer. Think about a group school project where one kid does the reading, one does the numbers, one checks the facts, and one puts the poster together. It's the same idea, except the kids are AI. Here's how I'd use it. Ask the agent team to study why people leave a community, with one helper reading member feedback and another checking the call notes. A third helper then matches the two and lists the fixes. What the Apodex 1.1 benchmarks show (and what they don't) These numbers come from the official Frontier Agent page on GitHub. The Apodex team ran and reported them themselves, and nobody outside has confirmed them yet, so keep that in mind. They also cover the Apodex 1.1 family rather than the Mini on its own. There are three setups being compared, which are Apodex 1.0, Apodex 1.1 in ReAct mode, and Apodex 1.1 in Agent Team mode. - On APEX-Agents, the scores go from 16.5 to 34.4 to 38.5, which is more than double from the old version to the new one with a team. - On FrontierScience Research, the scores go from 28.3 to 55.0 to 63.3. That second one is a research test, and research is what this model is all about. The pattern is the interesting part. The jump from 1.0 to 1.1 is big, and switching on the team adds even more on top. Why Apodex 1.1 Mini matters right now Here's where I've landed. Apodex 1.1 Mini is on OpenRouter with a 262,144-token memory, plus tools, files and code. The big idea is a model that works through a whole project with a team of helpers, and a way to check its own claims. Right now, there's a window where most business owners haven't figured out long-memory AI like this. They're still typing one question at a time into a chat box. That window won't stay open forever. My advice is simple. Give Apodex a real problem, give it your own files, and see where it holds up and where it breaks. FAQ What is Apodex 1.1 Mini? Apodex 1.1 Mini is an open, reasoning-first AI model from Apodex, built for long research tasks that use files, data, code and tools. Is Apodex 1.1 Mini free? Yes, there's a free endpoint on OpenRouter, although it has rate limits, so you'll need to plan your tests. How big is the Apodex 1.1 Mini context window? It's 262,144 tokens, which is roughly 190,000 words, or several full books in one go. What is the difference between ReAct and Agent Team? ReAct uses one agent that does the whole task step by step, while Agent Team uses a coordinator that splits the work across parallel helpers and combines their reports. Can I run Apodex 1.1 Mini myself? Yes, it uses the Apache 2.0 licence, so developers can download it and run it on their own setup. About Julian I'm Julian Goldie — AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom. I help business owners scale with AI agents, automation and SEO. I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers. I share daily AI training inside the Boardroom. 📺 Video notes + links to the tools 👉 skool.com 🎥 Learn how I make these videos 👉 aiprofitboardroom.com 🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉 skool.com
FrontierAgent pairs a terminal agent that has ReAct and Agent Team modes with a benchmark runner that, per its README, is powered by the same workflow engine: ApodexAI/FrontierAgent #AIAgents #MultiAgent #Python
FrontierAgent Runs Its Terminal Agent on the Same Engine as Its Benchmarks My reading of the FrontierAgent README is that its interesting claim is structural rather than a feature list: the workflow engine behind the frontier-agent terminal app is, according to the README, the same one that powers the benchmark runner used to evaluate Apodex models. That makes the terminal product feel closer to a published test harness. If you want to see how these agent workflows behave on long-horizon research and file work, you can run them yourself, on your own tasks, against your own OpenAI-compatible endpoint. The README describes the project as an open-source agent runtime, terminal product, and evaluation suite. It also says the framework, tools, workflows, and evaluation layer remain separate so each can be reused independently. The repository layout reflects that with distinct directories: frontier_agent/ for the generic loop, scheduling, registries, AgentBus, and observers; plugins/tools/ for tool implementations; workflows/ for the ReAct and Agent Team pipelines; apodex/ for the terminal CLI and TUI; and benchmarks/ for the public harness plus bundled FrontierSearchBench and FrontierChallenge. Two workflows, two execution models The TUI ships two native workflows, and the README scopes them differently. In react mode, one stateful agent researches, reads files, writes deliverables, runs commands, and iterates in a task-scoped sandbox. The README pitches it for focused research, repository analysis, and document or file work, using the tui workflow profile. In agent_team mode, a coordinator maintains a task board, delegates independent work to parallel sub-agents, collects their reports, and synthesizes the result. The README adds that the coordinator dispatches bounded parallel assignments, receives structured reports, and can use an optional fast reporter for final evidence review. The task board is visible: add_task and update_task events show up live in the TUI sidebar with pending, active, completed, blocked, and cancelled states. Picking a mode is a flag: uv run frontier-agent --mode react --cwd /path/to/project uv run frontier-agent --mode agent_team --cwd /path/to/project One behavior differs between the modes in a way that matters mid-run. You can type while an agent works, and the instruction is queued and injected at the next safe turn boundary without discarding the active run. In Agent Team mode, that input steers the coordinator, while sub-agents that are already running are allowed to finish. The sandbox is the contract The part I would read first as an operator is the filesystem. Shell and file tools share one task-scoped filesystem: /inputs is read-only, /workspace holds working state, and /outputs holds persistent deliverables. The README states that authorization and sandbox failures are fail-closed. On macOS and Docker, /outputs maps to .apodex/runs/<session-id>/outputs on the host, and that run directory also contains the checkpoint, trace, engine log, and trajectories. That layout gives you a place to look after the agent finishes. Supplied documents can go in through --input, which the README shows attaching files read-only before the TUI starts. Deliverables come out in a known directory next to the trace of how they were produced. Mutating operations show a diff and require approval unless --yes is enabled. Sessions are checkpointed, every action is traced locally, /revert restores session changes, and --resume continues a saved run. Running it as an evaluation harness The subprocess runner, per the README, supports research and file-grounded benchmarks, deterministic artifact collection, concurrency, progress inspection, and rerunning individual failures. The README also includes a chart it labels as Apodex-1.1 benchmark results across professional work, finance, scientific research, and general reasoning tasks. Treat those as the project's own reported results for its own model. There is a load warning to read before you scale anything. Agent Team parallelism is additional to benchmark concurrency, so the README recommends starting with --concurrency 1, because total simultaneous model calls can approach runner concurrency multiplied by the team spawn limit. If your endpoint bills or rate-limits per call, that product is the figure to plan around. Setting SWARM_NO_WEB=1 disables Agent Team web tools, and REACT_NO_WEB=1 is the setting for closed-book ReAct tasks. Getting a first run Requirements are Git, Python 3.12, uv, and an OpenAI-compatible model endpoint; Docker is optional. After uv sync --python 3.12 --extra dev, you copy .env.example to .env and set OPENAI_API_KEY, OPENAI_BASE_URL, and OPENAI_MODEL, with optional SERPER_API_KEY and JINA_API_KEY for web research tools. The README notes that scientific and document packages are optional in native mode, and the agent installs only what a task needs into <project>/.apodex/runtime/native. Pre-built linux/amd64 and linux/arm64 images on the GitHub Container Registry cover the path without a local Python environment, through docker compose run --rm agent. For local model serving, the README points NVIDIA users to a GPU compatibility matrix and warns that a driver mismatch surfaces late as opaque CUDA or Triton kernel errors during model load. The GPU helper picks a reviewed userspace track from the host driver but never installs or replaces the driver itself. --- GitHub: ApodexAI/FrontierAgent --- Curated by Agent Palisade — practical AI for small and mid-sized businesses.