OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime

Chun-Wah Hsu; Kai Gong; Yu Wu; Xianhe Chen; Mengyang Liu; Jie Li; Hanyu Li; Zhixuan Liu

cs.SESep 29, 2026

OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime

Authors: Chun-Wah Hsu, Kai Gong, Yu Wu, Xianhe Chen, Mengyang Liu, Jie Li, Hanyu Li, Zhixuan Liu, +7 more

Organizations: Shanghai Jiao Tong University · University of Cambridge · Nanyang Technological University · The University of Hong Kong · Imperial College London · Peking University · Tencent

Abstract

Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration. However, existing evaluations typically assume configured organizations are followed faithfully, whereas reality differs. This behavioral gap, combined with differences in underlying system components, prevents clear attribution of observed gains. To this end, we introduce OpenCollab, a multi-agent coding framework that provides a unified infrastructure for programmable collaboration and controllable runtime. Specifically, OpenCollab unifies organization design, enforces experimental control on a shared runtime, and tracks execution through fine-grained event streams. On this basis, we define Adherence to quantify whether the declared organization is actually realized. Our experiments reveal that agents collaborate very differently across configurations: changing any single dimension shifts Adherence, from 47.2% to as high as 97.2%. Furthermore, extensive agentic coding benchmarks show that a two-coder workflow built on OpenCollab establishes new SOTA performance compared to the mainstream harnesses such as Mini-SWE-agent, Codex CLI, and Claude Code, showing that a well-designed organization can outperform strong existing harnesses, while OpenCollab's single-agent configuration uses the fewest tokens across all evaluated suites. OpenCollab establishes a unified multi-agent infrastructure for easy programmable collaboration and controlled causal evaluation.

Figures & tables

Figure 1: Perspectives on multi-agent collaboration: (a) organization, (b) benefit and cost, (c) configured versus observed execution, and (d) collaboration auditing.

	Held: can the factor be set explicitly?					Verified: can the run be checked?
Artifact	Model	Tools	Budget	Context	Topology	Realized	Compared
Multi Agent frameworks
AutoGen ( Wu et al., 2024 )	✓	✓	–	✓	✓	$\times$	$\times$
AG2 ( ag2ai, 2024 )	–	–	$\times$	–	–	–	$\times$
LangGraph ( LangChain, 2023 )	–	–	$\times$	–	–	$\times$	$\times$
Coding agents

Table 1: Audit of seven controlled evaluation conditions. ✓: by the artifact’s own settings and records; –: only through the researcher’s code, or in part;

\times

: not met.

Figure 2: OpenCollab overview. Single, Team, and Workflow controllers share a session runtime. Configuration and execution traces support Adherence and resource costs auditing.

Benchmark	Harness	Pass@1 (%) $\uparrow$	Avg. tokens (M) $\downarrow$	Avg. cost ( $)$ \downarrow$	Cache hit (%) $\uparrow$
SWE-bench Pro	Mini-SWE-Agent	61.66	4.16	0.90	36.65
	Codex CLI	63.73	7.31	0.73	90.27
	Claude Code	58.03	9.38	2.54	12.50
	OpenCollab (Base)	63.21	3.89	0.41	88.03
	OpenCollab (Duo)	64.25	6.50	0.73	84.95
Terminal-Bench 2.1	Mini-SWE-Agent	76.40	2.97	0.45	67.36

Table 2: Cross-harness evaluation on SWE-bench Pro, Terminal-Bench 2.1, and DeepSWE.

Dimension	Variant	Adherence (%) $\uparrow$	95% CI	Unverified (%)	Pass@1 (%) $\uparrow$	Tokens (M) $\downarrow$
Model	Qwen3.8-Flash	47.2	[30.4, 64.5]	13.9	75.0	3.05
	DeepSeek-V4.1-Flash	66.7	[49.0, 81.4]	22.2	72.2	3.65
	GPT-5.6-Luna	97.2	[85.5, 99.9]	2.8	61.1	5.79
Tools	All tools	47.2	[30.4, 64.5]	13.9	75.0	3.05
	No edit tools	86.1	[70.5, 95.3]	0.0	66.7	3.24
	Read-only	94.4	[81.3, 99.3]	2.8	52.8	4.24

Table 3: Single-dimension ablations relative to the reference team. Unverified runs are those terminated before collaboration; brackets report 95% Clopper–Pearson confidence intervals.

Configuration	Adherence (%) $\uparrow$	Pass@1 (%) $\uparrow$	ITT (%) $\uparrow$	CACE (%) $\uparrow$
Single	–	69.4	–	–
Open card (reference)	47.2 [47.2, 61.1]	75.0	$+5.6$	$+11.8$ [ $+9.1$ , $+11.8$ ]
Mandatory card	91.7 [91.7, 100.0]	72.2	$+2.8$	$+3.0$ [ $+2.8$ , $+3.0$ ]
Budget stated, 2M	75.0 [75.0, 83.3]	69.4	$0.0$	$0.0$ [ $0.0$ , $0.0$ ]
Read-only	94.4 [94.4, 97.2]	52.8	$-16.7$	$-17.6$ [ $-17.6$ , $-17.1$ ]

Table 4: Deployment contrasts and adherence-adjusted estimates across configurations on SWE-bench Pro. ITT is the Pass@1 difference from Single. Adherence is the share of runs with

A_{r}=1

. CACE is ITT divided by Adherence. Brackets show sensitivity to the labeling rule for unverifiable verdicts and are not confidence intervals.

Figure 3: Two execution trajectories from Table 3 on a shared time axis. Lanes denote agents; blocks represent model calls colored by tool usage.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Configuration	Adherence (%)	Tasks $A_{r}{=}1$ / $0$	ITT	Wald	Paired, $A_{r}{=}1$	Paired, $A_{r}{=}0$
Open card	47.2	17 / 19	$+5.6$	$+11.8$	$-5.9$	$+15.8$
Budget stated, 2M	75.0	27 / 9	$0.0$	$0.0$	$-3.7$	$+11.1$
Mandatory card	91.7	33 / 3	$+2.8$	$+3.0$	$+3.0$	$0.0$
Read-only	94.4	34 / 2	$-16.7$	$-17.6$	$-14.7$	$-50.0$

Appendix

Table 6: Wald ratio and paired contrasts against the Single agent on SWE-bench Pro. Paired,

A_{r}=1

is the mean Team-minus-Single difference over the tasks whose Team run is adherent, the direct estimate of Eq. ( 10 ) under the product coupling above; Paired,

A_{r}=0

is the same difference over the remaining tasks, the estimate of

\delta_{0}

. By construction

\widehat{\mathrm{ITT}}=\widehat{\alpha}_{\mathrm{adh}}\cdot(\text{Paired},A_{r}{=}1)+(1-\widehat{\alpha}_{\mathrm{adh}})\cdot(\text{Paired},A_{r}{=}0)

. Differences in percentage points.

Code	Table 2 row	Card	Model	Entry tools	Topology
S	–	–	Qwen3.8-Flash	all	–
S ′	–	–	Qwen3.8-Flash	all	–
R	reference	Open	Qwen3.8-Flash	all	Star
M1	Model	Open	DeepSeek-V4.1-Flash	all	Star
M2	Model	Open	GPT-5.6-Luna	all	Star
T1	Tools	Open	Qwen3.8-Flash	no edit	Star

Appendix

Table 7: The configurations of the adherence experiment. Each row but S ′ is a row of Table 3 , or the single agent it is compared with in Table 4 ; S ′ reruns S unchanged. Every agent has a 2M-token allowance except in B1 and B3. Each configuration runs the 36 tasks once. S and S ′ are the single agent; Star has an Adopter and two Coders, and both rings use the Ring roster (an analyst, a coder and a tester).

Card	Closing section
Open	What is yours to judge. Nothing above tells you what order to do things in, whom to talk to, or how much of the work to do yourself. Keeping all of it and handing parts of it over are both open to you, and neither one is what you are expected to do. Those are your calls, and you make them the way you would judge any piece of work: what the request actually needs, what is worth another agent’s attention, what it costs to describe a piece of it well enough to hand over, and what it costs to carry all of it in one budget and one conversation.
Optional	What this run asks of you. Implementation is the Coders’. There are two of them and neither can see what the other is doing, so ask them one at a time: send the first a message with message_agent describing the problem and let it work out its own fix, and when its answer is back, write the second its own brief and let it work out its own. What goes into that second brief is yours – what you learned from the first is yours to use or to leave out. Which of the two this run delivers is yours to choose. You may depart from this and do the implementation yourself; that is your call, and there are runs where it is the better one. Use team_status first if you want to see who is live. Everything else about how you work – what you read, how you diagnose, what you put in each message – is still yours.
Mandatory	What this run asks of you. This run is not asking you to decide how to divide the work. It is asking you to divide it a particular way, so do that. Implementation is the Coders’. There are two of them and neither can see what the other is doing, so ask them one at a time: send the first a message with message_agent describing the problem and let it work out its own fix, and when its answer is back, write the second its own brief and let it work out its own. What goes into that second brief is yours – what you learned from the first is yours to use or to leave out. Which of the two this run delivers is yours to choose. Use team_status first if you want to see who is live. Everything else about how you work – what you read, how you diagnose, what you put in each message – is still yours.

Appendix

Table 8: The closing section of the Adopter’s card, the only text that differs between the Open, Optional and Mandatory cards; the rest of the card is identical. Text the Optional and Mandatory cards share is in gray; what distinguishes each card is in black.

Panel of Figure 4 — Figure 4: The four organizations. Left: Duo, a Workflow, OpenCollab (Duo) of Table 2 ; every arrow is a handoff that code makes on every run, and the dashed one runs only when the selection rule does not decide. The other three are the topologies of Table 3 , run as Teams: an arrow means the agent at its tail can message the agent at its head, and whether it does is the model’s choice.

Benchmark	Both pass	Only Duo	Only Base	Both fail	$p$
Terminal-Bench 2.1 (89)	68	6	3	12	0.51
DeepSWE (113)	58	21	5	29	0.0025

Appendix

Table 9: OpenCollab (Duo) against OpenCollab (Base), paired by task.

p

is the exact two-sided sign test on the two middle columns.

	Adherence			Pass@1
Code	% [95% CI]	Unverified %	vs reference	%	vs reference	vs S
S	–	–	–	69.4	–	–
S ′	–	–	–	66.7	2:3 (1.00)	–
R	47.2 [30.4, 64.5]	13.9	–	75.0	–	3:1 (0.63)
M1	66.7 [49.0, 81.4]	22.2	13:6 (0.17)	72.2	2:3 (1.00)	–
M2	97.2 [85.5, 99.9]	2.8	19:1 ( $<$ 0.001)	61.1	2:7 (0.18)	–

Appendix

Table 10: Adherence and Pass@1 of every configuration, with paired comparisons (only this configuration succeeded : only the other did, exact sign test

p

). Adherence for a Star run counts both Coders; for a Ring run, the coder and the tester.

Figure 5: How runs end, as a share of each configuration’s 36 runs (Appendix B.1 ). Completed: the entry agent submitted. Token budget: the entry agent used up its allowance before submitting. Wall clock: the run reached 5,400 s. Failed: no result after the run was launched again. Configuration codes as in Table 7 .

Figure 6: Tokens per run. (a) Each dot is a run; the bar is the mean and the red tick is the run’s allowance (each agent’s allowance times the number of agents), with “mean / allowance” in millions at the right. (b) The mean split across agents, in percent. In a Ring the three agents are the analyst, the coder and the tester.

Figure 7: Wall clock per run. (a) Each dot is a run; the bar is the mean and the red tick is the 5,400 s limit, with “mean / limit” in minutes at the right. (b) The mean split across agents, where an agent’s time is the duration of its own model calls and tool calls; grey is the harness’s time between turns, a thin sliver at the right of each bar.

Figure 8: Share of tokens by agent in each tenth of a run, from its start to its last model call, averaged over the configuration’s runs. One panel per team configuration of Table 7 with Qwen3.8-Flash.

Figure 9: Read-only variant (the adopter can only read files and adopt a commit), resolved: two coders fix the task and the adopter adopts Coder A’s commit. A SWE-bench Pro task from the teleport repository.

Figure 12: The session state machine that every session runs. Solid edges are the checked transition table; a transition not in the table raises an error instead of being taken. Two escapes bypass the table from any state: an unhandled fault ends the session in ERROR , and a cancellation from outside the loop ends it in STOPPED . STOPPED is a controlled stop at a limit or a refusal, with the reason recorded (Table 14 ), not a fault. Each end state returns to IDLE only when a new turn starts.

Figure 13: Two controllers over one session runtime. The Team controller opens a session for every declared role, each with its own token allowance; the model decides whether to hand work on with message_agent , and a message along an undeclared edge is refused and recorded. The Workflow controller is a Python script that fixes the order of work and opens one session per step; a single-agent run is one session with no controller above it. All sessions run the same code, which checks every limit before a model call, acts in the role’s workspace, and writes to one ordered event stream per run (Appendix C.5 ).

Arm	Definition	Differs from Single in	Realized
Single	one agent works the task to completion	—	trivially
Workflow	shared role cards; a script decides the order of work, what each role receives, and when to stop	organization, enforced	by code
Team	shared role cards; the model decides whether to delegate, what to send, and when to stop	organization, offered	measured, $\widehat{\alpha}_{\mathrm{adh}}$

Appendix

Table 11: The three arms. The last column says how a run is known to have realized its arm. Only in the Team is it a measurement of the model’s choice. In the Workflow the script, not the model, issues every handoff it reaches.

Figure 14: The runtime’s four layers as a clean architecture. Every import points inward, from bootstrap to adapters to application to domain; the application declares ports, interfaces that the adapters implement, so it never imports a model provider, a tool or a workspace directly. Figure 15 gives the files in each layer.

Layer	Files	Lines	What it holds
Public surface	9	894	The client ( sdk/client.py ); the modules for tools, environments, team files and workflows
Bootstrap	22	6,690	Loading a team file ( team_config.py , Appendix C.3 ); resolving tool names ( tool_registry.py ); assembling a session, a team or a workflow run
Adapters	72	16,171	Model providers and the per-model capability table ( llm/ ); the tools, among them the patch tool, the shell and message_agent ( tools/ ); local, container and worktree workspaces; the sandbox policy ( safety.py ); the per-run event stream ( trace.py , Appendix C.5 )
Application	51	15,202	The session loop and its limits ( session_run.py ); the Team controller ( application/scheduler.py and its scheduler_* and scheduler* modules); the Workflow controller ( workflow.py and its workflow_* modules); context compaction ( shaping/ )
Domain	13	1,820	The ten-state session machine and its legal transitions ( session.py , Appendix C.1 ); the team topology ( team.py ); the table of sessions that the Team controller keeps, as plain data ( domain/scheduler.py ); agents, events and token estimates

Appendix

Table 12: The runtime’s layers, top to bottom. Apart from five statements in the command-line interface (see text), no module imports from a layer above its own. Lines counts every line of the layer’s Python files.

Figure 15: The two code bases. The runtime’s layers run top to bottom, each with its file and line counts (Table 12 ), and a module imports only from its own layer or the ones below; the one exception is five imports from the command-line interface into the bootstrap layer (dashed), which the experiments do not use. The evaluator reaches the runtime only through its public surface.

Event type	What it adds to the common fields	What it supports
Model call	Token counts and latency of the step	Per-role spend; the organizational overhead of Appendix C.5
Tool execution	Tool name, arguments and result	Who briefed whom; which edges were addressed. Refused messages and refused tool calls are written as separate events
Agent finish	Whether the role produced content	Whether a teammate worked, read together with its model calls
Session termination	The role’s termination reason (Table 14 )	Which runs were cut off by the allowance after delegation began
Refused spawn	The reason for the refusal	That the roster cannot change within a run (Section 3.2 ); that a single-agent run cannot coordinate
Worktree change	Commit sha	Which role’s commits reached which worktree (Section 3.4 )

Appendix

Table 13: The seven event types. Model calls and agent finishes settle whether a teammate worked; tool executions of message_agent show who briefed whom; a session termination, read with the token counts of the model calls, identifies the runs whose decision the allowance cut short.

State	Recorded reason	When it is recorded
DONE	completed ; submitted	The role gave a final answer, or called the submit tool.
STOPPED	budget exceeded	At PRECHECK , the role had already spent its allowance.
	budget exhausted before model call	The next call, priced before it was sent, would not fit in what remained.
	budget exceeded after model call	The last call cost more than its estimate and took the role past its allowance.
	step limit reached	The session reached its step limit.
	loop block limit reached	The role kept repeating a tool call without progress.

Appendix

Table 14: The end states of a session (Figure 12 ) and the reasons recorded with them. Each reason is a string whose fixed prefix is shown; some continue with a count, such as the tokens used. The wall-clock limit is not among them: the evaluator stops a run that reaches it from outside the runtime and records that in the run’s metrics row.

Axis	Realized value, read from	Deviation
Participation	the teammates that spent tokens and produced at least one model output	a declared teammate did no work
Delegation	a teammate that spent tokens and produced at least one model output	no such teammate, where the assigned regime offers or requires delegation
Role boundary	the tool calls attributed to each agent	a call outside the agent’s assigned tool set is executed
Budget sharing	each role’s token draw against its own allowance	a role reached its allowance and no refusal was recorded
Information flow	message_agent calls in the event log, in every arm	a message travels along an edge the topology does not declare
Context policy	the compaction level applied on each turn	a level other than the assigned one fires

Appendix

Table 15: The six adherence axes. The assigned value is written before the run, and the realized value is read from the event log.

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