AI-Team-Team - A generic dynamic multi-agent collaboration framework

#agents #ai #llm #showdev

With ATT (AI-Team-Team), AI can freely form teams, define how they interact, how teams collaborate, and create all sorts of incredibly complex hierarchical (or dynamic) relationships.

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👉 Project Repository：https://github.com/AI-Team-Team/AI-Team-Team

🧬 Topology & Lineage Control

Tree-like Lineage Spawning: Spawns recursive child agent teams (AgentTeam) at runtime to arbitrary depths, strictly bounded by depth limits to prevent stack overflow.
Autonomous Member Configs: Defines dynamic child memberships mapping role presets, custom system instructions, and LLM aliases to shape custom agent personalities.
Dynamic Lineage Migration: Permits active teams to request parent-hierarchy migrations, arbitrated by modular strategies with loop/cycle detection and parent notification logs.
Hierarchical Topology Map: Injects an ASCII-drawn indented tree map of active teams (displaying purposes, status, and progress metrics in real-time) directly into the agent prompt context.
Global Expert Discovery: Automatically appends a directory of all active system experts (names, roles, and profiles) into the agent's identity context to facilitate peer discovery.

🧠 ReAct Loops & Execution Engine

Bounded ReAct Loops: Executes standard Thought/Action/Observation reasoning cycles, capped by max steps to prevent runaway API tokens.
Robust Argument Parser: A safe literal lexical parser (ast.literal_eval) with multiline XML support, code block stripping, and a comma-merging heuristic to handle unquoted complex strings (like SQL queries).
Dialogue Memory Compression: Automates memory pruning by summarizing early conversation turns using the agent's LLM while preserving the latest high-fidelity messages.
Model Registry & Callbacks: Delegates all LLM generation logic to a single global callback handler (generator_handler), keeping the framework lightweight and vendor-independent.

🗳️ Governance & Inter-Team Communication

Democratic Voting System: Features an asynchronous voting pipeline to add or remove members, requiring unanimous participation and a $\ge 2/3$ agreement majority.
Anonymous Voting: Enforces voter anonymity via cast_vote(..., public=False) which masks voter names as "Anonymous Voter" in the team prompt context.
Negotiation Broker: Regulates P2P messaging and agreement tunnels through dynamic parent rules (allowing regex purpose/lineage checks) or interactive LLM leader proxies.
Collaborative DocLib Storage: Equips teams with built-in document libraries. Access is governed by prefix path ACL permissions (READ/WRITE) that inherit recursively downward to subdirectories.

🔒 Context Protection & Safety Gates

Gated Context Protection: Restricts direct large file reads; falls back to Outline Warnings with a 5-line sample of files exceeding 50 KB, prompting agents to make paginated, sliced chunk requests.
Tool Auditor Interception: Registers pre-execution interception hooks to audit, vet, approve, or reject specific tool calls (e.g. database safety query check).

💾 Persistence & Diagnostics

SQLite State Snapshots: Automatically serializes topologies, lineages, memory logs, DocLib files, and active voting proposals to SQLite on state changes, enabling crash-recovery.
Supervisory Dialogue Audits: A non-participating 3-AI Supervisory Team (Integrity, Continuity, Deadlock) reviews round transcripts, recursively escalating anomalies up the tree lineage.
Decoupled Dashboards: Exposes clear runtime callback event hooks (on_status_change, on_activity_added, on_log_append) to update console UIs without codebase pollution.

(All the features mentioned above have been substantially implemented.)

🛠️ Current Status & Future Plans:

The project is currently a fundamentally complete Python package, through which you can observe some fascinating AI sociological behaviors (it has already helped me brainstorm quite a few great ideas). In the near future, I might launch a simple application for end-users based on ATT.

Many details are still rough around the edges, and some ideas are still being rolled out step by step. However, I personally believe the core concept is highly intriguing, and I warmly welcome your feedback, suggestions, and contributions 👍

Thanks for reading, and have a great one! Bye!