Microsoft Research拥有开源 Agensh，为大型编译团队agents的放管。 Agensh多agent拉带去除大部分agent框架所依赖的中心管弦。 每个工人都选择自己的子任务，分享发现，并合并代码为共同的Git repo. 在"程序奔驰"(Programme Bench)上，队伍将设置由1个agent升级为1,024个agents.
TL; DR 数据
- A Harness:1至1,024 agents测试。 每个工人得到27.2万个托肯输入和12.8万个托肯输出限制。
- 运行在：Linux与Docker编曲。 默认的单一节点(8名工人). 1,024-agent跑道使用16个节点，每节64个agents. 工人拥有64个GiB记忆天花板。
- 性能：在5个最困难的程序Bench任务上，agents 融资得到更多的分数，并更快地到达它们。
- 最佳：79.59%的泛管通过率，GPT-6.1-索尔上为32个agents. GPT-5.6-索尔命中55.06%,1024个agents.
最糟糕的是：5个任务在128个agents时只达到28.78%. 从1到8 agents加分1.37分。
阿让什是什么？
Agensh是一款来自Microsoft Research的多agent牵引电动机，它协调了许多LLM agents而无铅agent.
目标问题在于管弦乐队的天花板。 在管弦乐-工作-设计中，一个领跑的agent计划，分配并合并了所有的工作。 研究论文以Claude Code agent团队，Codex子agents,Copilot分舰队和Kimi,Agent等为例。 Agensh让工人自己来协调。
Agensh合作如何循环？
每个工人同时并同步地运行着同样的5个步骤循环：
- 收集背景：阅读目标、同行进展、信息和调查结果。
- 索赔一项分任务：在索赔后提交索赔，然后通过直接信息解决重叠问题。
- 采取行动：在当地利用工具开展工作并及早公布有用的调查结果。
- 核查结果：对照分任务的接受标准检查工作。
- 合并进度：合并到共享Repo中，发布更新，然后重复。
三个服务连接工人。 Gitea托管共享的工作空间，有分支，PR和合并冲突。 Matter Most 携带一个团队通道，加上紧急相撞的直接消息。 根据DeLM改编的共享上下文板存储了指出：OBSERVED,FACT,FAIL，索赔和PATCH SUMARI。
循环生活在即时，而不是运行时代码中。 除了身份证之外，每个工人都会得到同样的迅速。 阿根什坐在单agent吊带上。 实验使用了GitHub Copilot CLI. 研究论文指出薄的适配器可以连接到其他的取接器，如Claude Code.
Agensh在Bench节目上的表现如何？
Microsoft团队测试了最难的5个程序Bench任务：FFmpeg,gromacs,pandoc,PHP-src和ctags. Agents从其所观察到的行为重建出每个程序。 他们在6小时内下线工作。 参考重置范围从2,767个文件(pandoc)到26,266个文件(PHP-src).
GPT-5.6-Sol在高推理能力下，平均最终测试-通过率移动如下：
- 1个agent:19.31%
- 8个agents:20.68%
- 32 agents: 26.52%
- 128 agents: 28.78%
这是9.47分的收益，大约49%的相对。 单是胰岛素的得分是33.89%(1)、41.75%(8)、43.11%(32)、50.94%(128)和55.06%(1 024)。
速度也得到了改进：在泛斗上，128agents在30分钟的检查站通过30%. 32和8 agents需要60和90分钟。
README 新增是一个更新的模型。 以GPT-6.1-sol为单位，泛管从56.19%(1agent)上升到79.59%(32agents). 这是41.64%的相对收益。 这些GPT-6-Sol数字不在v1文件中。
随着团队的成长，出现了什么行为？
研究小组追踪每个尺度的工人轨迹：
- 8 agents：同行同意接口，并自行解决了重叠的声称。
32 agents：同行审查共享工作；在反例后撤回一项批准。
- 128 agents：工人重新使用可信赖的审查员，并使一体化协议标准化。
- 1 024 agents：几名工人担任整顿员，同龄人恢复了失败的工作。
这些角色都没有硬编码。
Agensh如何与其他多agent相提并论？
你怎么管理阿根什？
发布内容包括一单节点Docker编译团队和一单agent基线。 两者都使用原生的Copilot非加太会。 您需要使用 Docker 引擎的 Linux, Compose v2，系统化，cgroup v2 和 Python 3. 11+. 默认为8个工人，每个任务6小时，gpt-5.6-Sol高功率。 您还需要程序 Bench (v1.2.5 被验证) 和 API 模型令牌。 完整的台阶坐在复制指南中。
关键外卖
- Agensh坐标为1,024 agents，没有中央管弦乐器。
- 工人通过Gitea、Matellimest和共同调查委员会进行自我申报。
- 128 agents将5个任务方案Bench从19.31%提高到28.78%。
- GPT-6.1-溶液，有32个agents击出79.59%的胰腺。
- 代码是麻省理工学院许可的，但象征性成本不披露。
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Microsoft Research has open-sourced Agensh, a decentralized harness for large teams of coding agents. The Agensh multi-agent harness removes the central orchestrator that most agent frameworks depend on. Every worker picks its own sub-task, shares findings and merges code into a common Git repo. On ProgramBench, the team scaled the setup from 1 agent to 1,024 agents.
TL;DR
- A Harness: Tested from 1 to 1,024 agents. Each worker gets a 272,000-token input and 128,000-token output limit.
- Runs on: Linux with Docker Compose. Single node by default (8 workers). The 1,024-agent run used 16 nodes, 64 agents each. Workers have a 64 GiB memory ceiling.
- Performance: more agents raised scores on all 5 hardest ProgramBench tasks, and reached them sooner.
- Best: 79.59% pandoc pass rate with 32 agents on GPT-6.1-sol. GPT-5.6-sol hit 55.06% with 1,024 agents.
- Worst: the 5-task mean reached only 28.78% at 128 agents. Going from 1 to 8 agents added 1.37 points.
What is Agensh?
Agensh is a multi-agent harness from Microsoft Research that coordinates many LLM agents without a lead agent.
The target problem is the orchestrator ceiling. In orchestrator-worker designs, a lead agent plans, assigns and merges all work. The research paper cites Claude Code agent teams, Codex sub-agents, Copilot fleet and Kimi Agent Swarm as examples. Agensh lets the workers do that coordination themselves.
How does the Agensh cooperation loop work?
Each worker runs the same 5-step loop, concurrently and asynchronously:
- Gather context: read the goal, peer progress, messages and findings.
- Claim a sub-task: post a CLAIM, then settle overlaps by direct message.
- Take action: work locally with tools and post useful findings early.
- Verify results: check the work against the sub-task’s acceptance criteria.
- Merge progress: merge into the shared repo, post an update, then repeat.
Three services connect the workers. Gitea hosts the shared workspace, with branches, PRs and merge conflicts. Mattermost carries a team channel plus direct messages for urgent collisions. A shared context board, adapted from DeLM, stores typed notes: OBSERVED, FACT, FAIL, CLAIM and PATCH_SUMMARY.
The loop lives in the prompt, not in runtime code. Every worker gets the same prompt except its ID. Agensh sits on top of a single-agent harness. The experiments used GitHub Copilot CLI. The research paper states thin adapters can connect other harnesses, such as Claude Code.
How well does Agensh scale on ProgramBench?
Microsoft team tested the 5 hardest ProgramBench tasks: FFmpeg, gromacs, pandoc, PHP-src and ctags. Agents rebuild each program from its observed behavior. They work offline, within 6 hours. Reference repos range from 2,767 files (pandoc) to 26,266 files (PHP-src).
With GPT-5.6-sol at high reasoning effort, the mean final test-pass rate moved as follows:
- 1 agent: 19.31%
- 8 agents: 20.68%
- 32 agents: 26.52%
- 128 agents: 28.78%
That is a 9.47-point gain, about 49% relative. On pandoc alone, scores were 33.89% (1), 41.75% (8), 43.11% (32), 50.94% (128) and 55.06% (1,024).
Speed improved too: on pandoc, 128 agents passed 30% at the 30-minute checkpoint. 32 and 8 agents needed 60 and 90 minutes.
The README adds a newer model. With GPT-6.1-sol, pandoc rose from 56.19% (1 agent) to 79.59% (32 agents). That is a 41.64% relative gain. These GPT-6.1-sol figures are not in the v1 paper.
What behaviors emerged as the team grew?
The research team traced worker trajectories at each scale:
- 8 agents: peers agreed on interfaces and resolved overlapping claims themselves.
- 32 agents: peers reviewed shared work; one approval was withdrawn after a counterexample.
- 128 agents: workers reused trusted reviewers and standardized an integration protocol.
- 1,024 agents: several workers acted as integrators, and peers recovered failed work.
None of these roles was hard-coded.
How does Agensh compare with other multi-agent harnesses?
How do you run Agensh?
The release includes a single-node Docker Compose team and a single-agent baseline. Both use native Copilot ACP sessions. You need Linux with Docker Engine, Compose v2, systemd, cgroup v2 and Python 3.11+. Defaults are 8 workers, 6 hours per task and gpt-5.6-sol at high effort. You also need ProgramBench (v1.2.5 was validated) and a model API token. Full steps sit in the reproduction guide.
Key Takeaways
- Agensh coordinates up to 1,024 agents with no central orchestrator.
- Workers self-claim tasks via Gitea, Mattermost and a shared findings board.
- 128 agents lifted the 5-task ProgramBench mean from 19.31% to 28.78%.
- GPT-6.1-sol with 32 agents hit 79.59% on pandoc.
- Code is MIT licensed, but token cost is not disclosed.
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