SpineCodex

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An enhanced Codex CLI for complex, long-running software engineering tasks—resolving 89% more tasks than Codex at 27% lower cost, with up to 10× the effective context length.

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<h1 align="center"><img src="./.github/assets/spinecodex-tree.svg" width="56" alt="SpineCodex tree mark" /> SpineCodex</h1>

Life begins with division and differentiation. An agent owns its own morphogenesis.

Based on <a href="https://github.com/openai/codex">OpenAI Codex</a>. Maintained by <a href="https://ghabix.github.io">Jiahong Xiang</a> and <a href="https://camsyn.github.io">Kunqiu Chen</a>.

English · <a href="./README.zh-CN.md">简体中文</a>

## Why SpineCodex

SpineCodex is an enhanced, independently maintained version of the [OpenAI
Codex CLI](https://github.com/openai/codex) for complex, long-running software
engineering tasks. It inherits your existing Codex configuration and works out
of the box. Compared with Codex, it resolves **89% more tasks at 27% lower total
cost** on [SWE-Milestone](https://github.com/DeepCommit-ai/SWE-Milestone) and
extends the effective working context by up to **10×**. It also improves the
average score by **10.8 points** on [ProgramBench](https://programbench.com) and
the mean score by **9.2 points** on [FrontierSWE](https://www.frontierswe.com).

| Linear context | SpineCodex |
| -------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| ❌**Run out of context?** | ✅**256K → 2.5M Effective Working Context** SpineJIT compiles completed branches into semantic Node Memory, extending effective working context beyond the native window. |
| ❌**Drift after repeated compaction?** | ✅**Minimum Effective Context. Maximum Focus.** Through the SpineTree, the agent manages tasks and context as one unified system, staying focused on the minimum context required by the current task. |
| ❌**Lose patience and focus on long tasks?** | ✅**Recursive Subagent Scaling on Demand.** SpineJIT lets the agent recursively unfold into specialized subagents on demand, bringing divide-and-conquer structure and greater reasoning depth to complex problems. |

## Get started

Just install and run—SpineCodex automatically inherits your existing Codex configuration and works out of the box.

```bash
npm install -g @spinejit/spine-codex@latest
spine-codex
```

SpineJIT transparently manages context with no user intervention; after sending your first task, run `/spine-tree` to confirm it is working. Experimental Spine Spawn (`spine_spawn`) and Memory Projection (`spinetree_memory_projection`) can be enabled via `/experimental`.

### Codex Desktop

To use SpineCodex with the official Codex Desktop app, first quit Codex Desktop,
then download the launcher for your platform:

- **Windows:** Keep [`start-spinecodex-desktop.cmd`](./scripts/codex-desktop/start-spinecodex-desktop.cmd)
  and [`start-spinecodex-desktop.ps1`](./scripts/codex-desktop/start-spinecodex-desktop.ps1)
  in the same folder, then run the `.cmd` file.
- **macOS:** Run [`start-spinecodex-desktop.command`](./scripts/codex-desktop/start-spinecodex-desktop.command).
  If needed, make it executable with `chmod +x start-spinecodex-desktop.command`.

The launchers use the native SpineCodex backend installed by npm and enable the
Spine tree UI automatically.

## Experimental features

| Feature                                                       | Purpose                                                                                                                                                                     |
| ------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Spine Spawn** (`spine_spawn`)                       | At any node, concurrently spawn multiple differentiated branch agents that inherit its history, recursively collaborate, and converge through cache-friendly context reuse. |
| **Memory Projection** (`spinetree_memory_projection`) | Project compiled Node Memory into inspectable Markdown.                                                                                                                     |

Run `/experimental` to enable Spine Spawn or Memory Projection, then save and
start a new conversation.

<a href="./.github/assets/spinecodex-loop.webp">
 <img src="./.github/assets/spinecodex-loop.webp" width="1200" alt="SpineCodex context tree growing through recursive agent spawning" />
 </a>
 
 Click to view the full animation.

## Long-horizon performance

Across three long-horizon coding benchmarks, SpineCodex delivers stronger
outcomes: **1.89× resolved tasks at 27% lower total cost** on SWE-Milestone,
**+10.80pp average score** on ProgramBench, and **+9.2pp mean score** on
FrontierSWE.

### SWE-Milestone (ICML 2026)

*Long-horizon software development · 80 milestones · GPT-5.6 · sol high*

| System               |     Resolved |        Total cost |
| -------------------- | -----------: | ----------------: |
| BaseCodex            |            9 |           $764.18 |
| **SpineCodex** | **17** | **$556.46** |

**1.89× resolved tasks at 27% lower total cost.**

### ProgramBench

*Whole-repo program reconstruction · Random sample: 50 of 200 tasks · GPT-5.6 · Sol high · conservative cost estimate*

| System               |       Avg. score | Tasks scoring >95% |              Cost |
| -------------------- | ---------------: | -----------------: | ----------------: |
| BaseCodex            |           62.55% |               2/50 |           $188.12 |
| **SpineCodex** | **73.35%** |     **7/50** | **$475.10** |

**+10.80pp average score and 3.5× high-scoring tasks.**

### FrontierSWE

*Ultra-long-horizon coding · 9-task evaluation · GPT-5.6 · high · estimated API cost per trial*

| System               |     Mean score |     Best score |             Cost |
| -------------------- | -------------: | -------------: | ---------------: |
| BaseCodex            |           33.5 |           37.9 |           $20.16 |
| **SpineCodex** | **42.7** | **46.8** | **$37.29** |

**+9.2pp mean score and +8.9pp best score.**

## How SpineJIT works

**Agent Morphogenesis:** Each task shapes its own context and execution through
just-in-time context-tree compilation and recursive subagent scaling.

**TL;DR:** SpineJIT replaces the live suffix of a context with shorter memory,
while keeping the prefix unchanged so it can continue to hit the prompt cache.

To control this suffix replacement precisely, SpineJIT is implemented as a
just-in-time compilation and context-mapping pipeline:

$$
\text{context messages}
\rightarrow \text{Spine tokens}
\rightarrow \text{SpineTree (ParseStack)}
\rightarrow \text{new context}
$$

The pipeline has two main stages.

### 1. JIT-compile context into a SpineTree

SpineJIT treats a context $C$---a message list, or simply a sentence whose characters are messages---as a stream to compile.

At each sampling boundary, it turns newly appended messages and control events into **Spine tokens** and updates a live LR(0) ParseStack:

SpineJIT uses four token kinds:

$$
\Sigma_{\mathrm{Spine}} = \{\mathrm{Message},\ \mathrm{Open},\ \mathrm{Close},\ \mathrm{SpineSpawnNode}\}
$$

`Message` represents a raw context item. `Open`, `Close`, and
`SpineSpawnNode` are special tokens emitted by SpineJIT at the corresponding
sampling boundaries.

$$
\begin{aligned}
\mathrm{SpineTree} &\to \mathrm{Nodes}\ \mathrm{End} \\
\mathrm{Nodes} &\to \mathrm{Node} \mid \mathrm{Nodes}\ \mathrm{Node} \\
\mathrm{Node} &\to \mathrm{Message} \mid \mathrm{SpineTreeNode} \\
\mathrm{SpineTreeNode} &\to \mathrm{Open}\ \mathrm{Nodes}\ \mathrm{Close} \mid \mathrm{SpineSpawnNode}
\end{aligned}
$$

`End` is only the logical end of a session; a live session never emits it.
Therefore, the ParseStack is the live SpineTree, and the reduction `Open Nodes Close -> SpineTreeNode` turns a closed subtree into one node.

In short, SpineJIT uses LR(0) JIT compilation to map context $C$ to a Spine Tree $PS$:

$$
PS = \mathrm{compile}(C)
$$

### 2. Map the SpineTree into a new context

The structured SpineTree can now be mapped into a shorter context while preserving its stable prefix. For ParseStack $PS$, define:

$$
C' = f(PS) = \prod_{i=0}^{n} h(PS[i])
$$

$$
h(X) =
\begin{cases}
\prod_{x \in X} h(x), & X = \mathrm{Nodes} \\
\mathrm{raw}(X), & X = \mathrm{Message} \\
\mathrm{memory}(X), & X = \mathrm{SpineTreeNode} \\
\mathrm{spine\\_node\\_desc}(X), & X = \mathrm{Open}
\end{cases}
$$

Here, $\prod$ means ordered concatenation.

The mapping is deliberately small:

- `Message` keeps its original content through $\mathrm{raw}(X)$.
- A closed `SpineTreeNode` is replaced by its shorter $\mathrm{memory}(X)$.
- An unmatched `Open` is represented by a concise
  $\mathrm{spine\\_node\\_desc}(X)$, helping the LLM delimit the currently live
  Spine node.

As parsing progresses, completed work in the context suffix is reduced into a `SpineTreeNode` and then projected as memory. Earlier context remains unchanged:

$$
\mathrm{prefix} \cdot \mathrm{suffix}
\longrightarrow
\mathrm{prefix} \cdot \mathrm{memory}
$$

This is the central idea of SpineJIT: compress the context where work has finished, without invalidating the reusable prefix.

### 3. How SpineJIT inserts Spine control tokens

The LLM decides when to open or close a `SpineTreeNode` from the current context. The guiding objective is to maximize the average relevance of the remaining context to the current task.

Here, a **sampling** means one complete processing cycle for a model response: the response itself together with any tool calls it produces.

SpineJIT exposes Spine tools to let the LLM express these decisions. After a successful tool call in a sampling step, SpineJIT inserts the corresponding control token at a precise boundary:

| Tool call       | Inserted token      | Position      |
| --------------- | ------------------- | ------------- |
| `spine.open`  | `Open`            | Pre-sampling  |
| `spine.close` | `Close`           | Pre-sampling  |
| `spine.next`  | `Close Open`      | Pre-sampling  |
| `spine.spawn` | `SpineSpawnNode`s | Post-sampling |

These tokens connect the model's task-boundary decisions to the LR(0) parser, which continuously updates the ParseStack and therefore the context seen by the next sampling step.

## Citation

A technical report on SpineJIT will be released soon.

If you use SpineCodex in your research, please cite this repository:

```bibtex
@software{xiang2026spinecodex,
  title = {Agent Morphogenesis: Just-in-Time Context Tree Compilation for Cost-Efficient Recursive Subagent Scaling},
  author = {Jiahong Xiang and Kunqiu Chen and Yuqun Zhang},
  year = {2026},
  url = {https://github.com/GhabiX/SpineCodex}
}
```

## Project

SpineCodex is an independently maintained fork based on and derived from
[OpenAI Codex](https://github.com/openai/codex). It is not the official OpenAI
Codex CLI or the official `@openai/codex` npm package.

- [Source](https://github.com/GhabiX/SpineCodex)
- [Releases](https://github.com/GhabiX/SpineCodex/releases)
- [Issues](https://github.com/GhabiX/SpineCodex/issues)
- [Contributing](./docs/contributing.md)
- [Installing and building from source](./docs/install.md)
- [Spine feedback and privacy](./docs/spine-feedback.md)
- [Upstream Codex documentation](https://developers.openai.com/codex)

SpineCodex is licensed under the [Apache-2.0 License](LICENSE). OpenAI Codex
and other derived components retain their attribution in [NOTICE](NOTICE).

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