Research prototype · Multi-agent collaboration

EvoTeam

Team project · repository authorRepository created Sep 2026Ongoing

An experience-driven system for evolving multi-agent organisations, built on openJiuwen Core. It runs tasks with the current Strategy and only proposes candidate strategies offline when evidence such as repeated failures crosses a threshold; a candidate changes the default organisation for later tasks only after independent validation.

Role: This is a team project; the README lists the members and their tasks. Only the repository-author role is stated here, which does not mean the work was done alone.

  • Python
  • Pydantic
  • openJiuwen Core
  • SQLite

Problem

Context

Roles and collaboration strategies in multi-agent teams are usually fixed by developers. EvoTeam makes constrained changes to a versioned Strategy based on cross-task execution evidence, and a candidate must pass a validation gate before it is promoted. The main scenario is generating and checking complex project plans.

Approach

Context

The initial Strategy v0 is Planner → Executor → Critic, with a developer-defined role pool. The system runs the current strategy; repeated failures, performance drift, cost anomalies or low-contribution evidence above a threshold trigger offline evolution; once stable, the strategy enters STABLE and the system stops exploring but keeps monitoring.

Online:  Task → Current Strategy → Orchestrator → Team / AgentInstance
         → Run → Evaluator → SealedRun → Experience → StrategyMonitor

Offline: Trigger → EvolutionManager → Attribution → Mutation → Candidate
         → Validator → Improvement Attribution → Validation Gate
         → Promote / Reject → later tasks use the validated Strategy

Source: repository README, read on 28 Oct 2026

Implemented

Implemented
  • Integration of the real ReActAgent from openJiuwen 0.1.17.post1.
  • init / run / observe / evolve entry points, SQLite traces and snapshots.
  • Repeated-failure monitoring; two candidate types (prompt changes and an added Verifier) and multi-candidate comparison.
  • Regression runs with DeepSeek covering complex tasks, bounded rework and real gate rejections of multiple candidates.

Source: repository README, read on 28 Oct 2026

Not finished yet

Not finished / planned
  • Drift, cost and contribution monitoring.
  • Tool / skill policies and more general structural mutations.
  • Experiment thresholds still need baseline calibration.

Source: repository README, read on 28 Oct 2026

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