Decentralized Multi-Agent Systems with Shared Context

Parallel agents coordinate asynchronously through a shared context and task queue to solve tasks faster and more accurately.

Paper · Project Website · Trajectories · Quickstart · Results · CLI Reference

Open trajectories: We also release all 720 DeLM agent trajectories from our Terminal-Bench 4.0 and DeepSWE v1.1 evaluations on Hugging Face.

**DeLM runs agents in parallel and lets them coordinate through a shared context and task queue.** Agents claim work, publish findings, and reuse each other's files asynchronously. Each agent keeps its own workspace and produces a complete solution.

• Decentralized coordination: peers share progress through the shared context, without a main agent relaying their work.
• Higher accuracy with lower latency: on the paper's 10-task Terminal-Bench 4.0 subset, DeLM with two agents averages +12.5 percentage points and 1.78× speedup over the base harnesses across two models.
• Two harnesses, one engine: this branch runs n=2 or n=4 with Codex or Claude Code, using independent Docker images and a shared evaluation pipeline.

Installation

Use Python 3.13+, Git, Linux Docker Engine with BuildKit, and Docker Compose for tasks that include services. Agent CLIs and the Humanize driver are installed inside the images by the Dockerfiles. DeLM uses Humanize to run Codex and Claude Code sessions and consume their streaming events.

git clone --branch main https://github.com/yuzhenmao/DeLM.git
cd DeLM

Use an idle host with capacity for all workers: each worker receives the official task's full CPU, memory, and storage allocation. The runtime additionally reserves 4 GiB of host RAM. Service resources are reserved per worker's service group.

Quickstart

1. Fetch a task and build an image

python3.13 fetch_task.py retro-console-soc \
  --revision 452bf305c6daa62fc59061d22133a7cbc7c1572e
Choose the harness you will run:
python3.13 build_task.py tasks/terminal-bench-4.0/retro-console-soc --harness codex
Or:
python3.13 build_task.py tasks/terminal-bench-4.0/retro-console-soc --harness claude-code

Each build runs in the background and prints its output folder. Wait for result.json; confirm returncode is 0 before launching. The stages field records the base, runtime, verifier, and service build outcomes; a failure stops the build and returns a nonzero code.

| Harness | Dockerfile | Task image | | --- | --- | --- | | Codex | Dockerfile.codex | delm-workspace-:local | | Claude Code | Dockerfile.claude | delm-:claude-2.1.280 |

Both builds start from the unchanged official task image, delm-task-:base. They are independent: building Claude does not require building Codex. Each image supports both worker counts.

Runtime versions and model defaults are defined in runtime/config.py. The build script supplies those versions to both Dockerfiles. Locally built images are checked against the task source before a run; rebuild older images with build_task.py to add this source metadata. Rebuilding uses Docker's layer cache.

2. Choose authentication and launch

Use one of the following commands after building its matching image. API keys must be raw, single-line files with mode 600 or stricter, stored outside the checkout or inside ignored .local/. Replace the example credential paths with your own. Launch from a clean, committed checkout.

Codex with an API key

python3.13 -B delm.py retro-console-soc --n 2 --harness codex \
  --auth-mode api-key --api-key-file /secure/openai.key \
  --series astra-api-n2 --repeat 1 --model gpt-6-astra --effort xhigh

Codex with a coding plan

Use an existing Codex ChatGPT login file, also private with mode 600 or stricter:

python3.13 -B delm.py retro-console-soc --n 2 --harness codex \
  --auth-mode coding-plan --auth-file ~/.codex/auth.json \
  --series astra-plan-n2 --repeat 1 --model gpt-6-astra --effort xhigh

Claude Code with an API key

python3.13 -B delm.py retro-console-soc --n 2 --harness claude-code \
  --auth-mode api-key --api-key-file /secure/anthropic.key \
  --series opus-api-n2 --repeat 1 --model claude-opus-5-5 --effort xhigh

Claude Code with a coding plan

Run claude setup-token on a machine where you can complete the subscription login, then save only the returned OAuth token in a private file such as /secure/claude.token (mode 600). This is a raw token file, not a Claude login JSON file or an API key. See the official subscription token instructions.

python3.13 -B delm.py retro-console-soc --n 2 --harness claude-code \
  --auth-mode coding-plan --auth-file /secure/claude.token \
  --series opus-plan-n2 --repeat 1 --model claude-opus-5-5 --effort xhigh

The token file is mounted read-only. The worker loads it into its own environment, checks that Claude selected subscription OAuth, and omits the API-key helper. Host logins and settings are not imported. An expired or invalid token fails the run without switching to API billing; replace the token file when it expires. Tokens are not included in command arguments or run metadata. CLI-reported cost fields are not a subscription bill.

For four workers, change --n 2 to --n 4 and use a new --series name. Add --dry-run to inspect the resolved plan without reading credentials, starting containers, or calling a model. For tasks with services, supply positive --service-cpus and --service-memory-mb reservations per worker.

3. Inspect trajectories and results

python3.13 inspect_run.py .local/runs/astra-api-n2/retro-console-soc/repeat-1
python3.13 -m reporting.report

The report is written to .local/reports/results.md. The reader supports both worker counts, retains failed or incomplete runs, and leaves missing measurements unknown. Use --runs-root and --output to select another collection or output path. Reporting never modifies run records or invokes grading.

Results

Accuracy averages independently graded agent submissions; it is not best-of-n success. See the paper's metric definitions and full results for costs and other baselines.

Terminal-Bench 4.0

Evaluation uses 10 selected long-horizon tasks, chosen by the latency of the Codex baseline (see the paper's Appendix B). The table shows the single-agent harness and DeLM configurations; the paper and website also include native-subagent and AOrchestra baselines. Values are mean ± standard deviation over three runs, and speedup is relative to the corresponding single-agent harness.

DeepSWE v1.1

Evaluation uses 10 selected long-horizon tasks, chosen by the latency of the Codex baseline (see the paper's Appendix B). As in the Terminal-Bench table, the rows compare each single-agent harness with DeLM at n=2 and n=4. Values are mean ± standard deviation over three runs; latency is in minutes, and speedup is relative to the corresponding single-agent harness.

SWE-bench Verified

All methods use Gemini 3 Flash. For this benchmark, DeLM is built on AOrchestra's harness rather than Codex or Claude Code. Values are mean ± standard deviation over three runs. Latency is in seconds, and speedup is relative to Claude Code.

ProgramBench

Claude Opus 5.5 with Claude Code as the harness, on ctags and pandoc under a 120-minute budget. The metric is the hidden-test pass rate, tracked as development progresses; the table reports the pass rate at the end of the budget.

How it works

1. Claim work. Agents claim open tasks from a common task queue, and any agent can add new tasks. The queue shows who owns each task, so agents can choose complementary work.
2. Publish progress. Findings, failed approaches, and completed subtasks are added to an append-only shared context, with files attached by reference.
3. Reuse and correct. Peers read relevant entries and import shared files into their own workspaces while continuing their work. Defects are reported as FAIL entries and fixed by new entries rather than edits.
4. Submit independently. Each worker produces a complete solution for the official verifier.

Shared runtime for n=2 and n=4

| Setting | Codex | Claude Code | | --- | --- | --- | | Pinned CLI | 0.153.4 | 2.1.280 | | Default model | gpt-6-astra | claude-opus-5-5 | | Reasoning effort | xhigh | xhigh | | Authentication | API key or coding plan | API key or coding plan | | Workers | 2 or 4 | 2 or 4 |

Both harnesses select authentication with --auth-mode api-key|coding-plan. Coding-plan mode takes a Codex ChatGPT auth.json or a Claude setup-token file, respectively, through --auth-file.

For either harness, --model MODEL_ID overrides the default model. Runtime and trajectory checks verify the selected model. Reasoning effort remains xhigh; there is no automatic model fallback or effort downgrade.

Both use Humanize commit 413d02e44d0cc0514b9f5bd3fcefea156b047a49 and the common runtime/runner.py. Authentication, overwrite protection, collection, and grading use the same implementation for both worker counts. The roster and prompt depend on n: the n=2 and n=4 prompts in prompts/ assign different initial roles (see the paper's Appendix E), so changing n is not a count-only prompt ablation. The n=2 prompt uses the same apply description as the n=4 prompt.

Official task instructions, resources, verifiers, and model time limits are preserved. After workers finish, the runner captures every submission, stops the task environments, and grades serially. The extra 600-second process cleanup guard does not extend model time. Failed and incomplete attempts remain in the records and are not automatically retried or overwritten.

Directory Structure

.
├── delm.py                  # Public launcher and immutable series settings
├── fetch_task.py            # Download an official task at a pinned revision
├── build_task.py            # Build the task and selected harness image
├── inspect_run.py           # Inspect one saved run
├── Dockerfile.codex         # Independent Codex runtime
├── Dockerfile.claude        # Independent Claude Code runtime
├── runtime/                 # Shared engine, provider adapters, and native workers
├── board/                   # Shared context, task queue, and file protection
├── prompts/                 # n=2 and n=4 prompts and initial roles
├── benchmark/               # Task validation, services, collection, and grading
├── reporting/               # Read-only run readers and Markdown reports
├── LICENSE                  # MIT license
├── index.html               # Project website
└── website/                 # Project website assets

Downloaded tasks and runtime outputs are ignored by Git. Credentials, trajectories, benchmark datasets, and archives are not committed.

CLI Reference

Launch options

python3.13 delm.py --help
python3.13 build_task.py --help

| Option | Meaning | | --- | --- | | task | Fetched Terminal-Bench task name, such as retro-console-soc | | --n 2\|4 | Number of DeLM workers; default 2 | | --harness codex\|claude-code | Agent harness; default codex | | --series NAME | Required experiment series with immutable settings | | --repeat N | One repeat number, starting at 1; does not launch a loop | | --model MODEL | Defaults to gpt-6-astra or claude-opus-5-5 | | --effort xhigh | Reasoning effort; xhigh is the only supported value and the default | | --auth-mode api-key\|coding-plan | Explicit authentication choice; default api-key | | --api-key-file PATH | Private raw key file for API-key mode | | --auth-file PATH | Private Codex auth.json or raw Claude setup-token file for coding-plan mode | | --service-cpus N | Additional CPU reservation per worker's service group | | --service-memory-mb N | Additional RAM reservation per worker's service group | | --dry-run | Print the resolved plan without launching |

A dry run still requires a credential path, but the file need not exist. Use separate invocations with --repeat 2, --repeat 3, and so on for more repeats. Changes to code, worker count, harness, authentication, model, service budgets, or task image require a new series. Existing attempts are never overwritten.

Citation

@misc{mao2026delm,
  title         = {Decentralized Multi-Agent Systems with Shared Context},
  author        = {Yuzhen Mao and Jerry Gu and Aadi Chauhan and
                   Qizheng Zhang and Hangoo Kang and Azalia Mirhoseini},
  year          = {2026},
  eprint        = {2606.10662},
  archivePrefix = {arXiv},
  primaryClass  = {cs.MA},
  url           = {https://arxiv.org/abs/2606.10662}
}