AllBrain MCP
AllBrain MCP is an event-sourced memory and orchestration server for multi-agent AI systems, enabling shared state management, conflict detection, decision reasoning, and deterministic replay.
Event Management
Record, query, and filter the shared event log; reconstruct and resume project state from events; create point-in-time snapshots
Git Integration
Retrieve git context, status, and recent file changes
Conflict & Contradiction Management
Detect and resolve conflicts between agents; identify contradictory states in the event log
Intent & Semantic Analysis
Extract semantic intents from agent actions; resume projects with intent-enriched context
Task Orchestration
Create, assign, prioritize, and hand off tasks; define dependencies; visualize task graphs; get a full project orchestration view
Decision Pipeline & Reasoning
Run full decision pipelines with foresight, scenario generation/evaluation, counterfactuals, uncertainty estimation, and meta-reasoning
Simulate actions, rank alternatives, generate future plans, explain decisions, and identify knowledge gaps and information needs
Memory
Build and query a semantic memory index from project events; get policy recommendations based on memory
Observability & Replay
Access observability dashboards, workflow traces, and system/reliability metrics
Deterministically replay workflows step-by-step; compare agent performance and behavior
UI Views
Provide UI-formatted endpoints for traces, replays, workflow graphs, and metrics dashboards
Provides tools to access Git commit history, diffs, and repository context for project awareness.
Integrates with Google's Gemini AI model for executing agent tasks.
Allows execution of subtasks using OpenAI models via the Agent Runtime adapter.
Used as an alternative distributed task queue backend for asynchronous execution of agent tasks.
Used as a distributed task queue backend for asynchronous execution of agent tasks.
Serves as the global database for storing agent events, snapshots, and project context.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AllBrain MCPorchestrate a multi-agent DAG workflow to implement OAuth login"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AllBrain Agent Runtime
One brain. Many agents. One shared memory.
Without AllBrain vs with AllBrain
Scenario | Without AllBrain | With AllBrain |
Agent A saves a plan | Written to local chat history, immediately lost when session ends | Appended to shared event store via |
Agent B starts the same project | Fresh context — no knowledge of Agent A's work |
|
Two agents write conflicting changes | Silent overwrite, no one knows |
|
Handoff between agents | Manual copy-paste of context |
|
Debugging state drift | "It worked in my session" | Deterministic replay from raw events, exactly reproducible |
Related MCP server: Enhanced Sequential Thinking MCP Server
The problem
Your AI coding agents don't talk to each other. Agent A saves a plan, Agent B starts fresh, Agent C has no idea what happened. Each agent works in isolation, repeating mistakes and missing context.
AllBrain gives every agent a shared workbench. Each tool call is recorded in an append-only event store. When the next agent arrives, it sees everything that happened before — events, sessions, conflicts, decisions — and picks up cleanly.
What AllBrain gives you
Shared memory —
save_event,list_events,resume_projectacross any MCP clientAgent attribution — every event is tagged with the agent that wrote it
Conflict detection — automatic surface of conflicting state updates
Decision pipelines — counterfactual reasoning, scenario planning, foresight
Deterministic replay — rebuild project state from raw events
50 tools in full profile across 18 domain modules (start with 3, enable more as needed)
30-second demo
# Agent A: save a plan
uv run allbrain start --project . --agent agent-a
# In Agent A's client, call:
# save_event(type="task_planned", payload={"task": "implement auth"})# Agent B: see what Agent A did
uv run allbrain start --project . --agent agent-b
# In Agent B's client, call:
# list_events()
# resume_project()See examples/two_agent_sqlite_pilot.py for a full two-agent workflow with conflict detection and replay verification.
Install for one client
The PyPI distribution is allbrain-agent-runtime. The canonical CLI command is
allbrain; allbrain-mcp and allbrain-agent-runtime remain compatibility
aliases for existing installations and scripts.
uvx allbrain-agent-runtime install --codexThis configures Codex to start AllBrain automatically. Replace --codex with the client name:
Client | Flag |
Codex |
|
Claude Code |
|
OpenCode |
|
Cursor |
|
VS Code |
|
Zed |
|
Gemini CLI |
|
Kiro |
|
Windsurf |
|
Antigravity |
|
Claude Desktop |
|
Use --all to configure every supported client at once.
Verify it works
uvx allbrain-agent-runtime install --codex --verifyThe --verify flag starts the server, saves a test event, reads it back, and confirms shared memory is working.
Tool profiles
Start with --tool-profile minimal (3 tools) and expand when needed:
Profile | Tools | Use when |
| save_event, list_events, resume_project | Getting started |
| minimal + retrieve_memory | Need recall |
| memory + task/conflict/resolution tools | Multi-agent handoff |
| memory + decision pipeline tools | Planning and analysis |
| save_event, list_events, retrieve_memory, git_info, create_task, get_task_graph, orchestrate_project, run_decision_pipeline, create_snapshot, resume_project | Essential workflow |
| 50 tools | Everything |
uv run allbrain start --project . --agent my-agent --tool-profile memoryGlama MCP Portal
Glama MCP evaluates this server with the balanced core tool profile
(--tool-profile core in glama.json). Its 10 public tools cover shared
memory, task orchestration, snapshots, Git context, and decision workflows
without exposing the entire development surface.
Yerel geliştirme veya tüm yetenekleri kullanmak için full profili kullanın:
uv run allbrain start --project . --agent claude-code --tool-profile fullAlternatif olarak .mcp.json (varsayılan full ile gelir) kullanılabilir.
From source
git clone https://github.com/Mustafa-Ali-Ertugrul/allbrain-mcp.git
cd allbrain-mcp
uv sync
./scripts/install-mcp.sh --all --isolate --verifyOr run the guided onboarding wizard:
uv run allbrain onboardIt walks you through client selection, install, verification, and your first event step by step.
See the full setup guide for manual config, troubleshooting, and shared-vs-isolated databases.
First memory save
Once AllBrain is installed and the client is restarted, call:
save_event(type="task_started", payload={"task": "implement auth", "agent": "codex"})Then verify it was recorded:
list_events()Switch to another client, call list_events() again — the same event appears.
Tool count and supported clients
Note: Glama evaluates the balanced 10-tool
coreprofile. The full profile remains available for local development.
50 tools in the full MCP profile across 18 domain tool modules
Default profile (
full) registers all toolsminimalprofile: 3 tools (save_event,list_events,resume_project)coreprofile: 10 tools (essential workflow + reasoning)
Data lifecycle and security
AllBrain stores events, sessions, and audit logs in local SQLite. Data never leaves your machine. Credential-like values are redacted before storage.
Data lifecycle — what is stored, retention, cleanup, restore
Uninstall guide — remove AllBrain from clients and delete data
Advanced docs
Full setup guide — all clients, shared vs isolated databases, troubleshooting
Custom agent integration — use AllBrain from any MCP client
Python SDK — typed async client (experimental)
Architecture — event sourcing, reducers, stream ordering
Storage backends — SQLite vs PostgreSQL vs queue adapters
Multi-agent pilot — two-agent workflow walkthrough
Upgrade guide — migrations, rollback, breaking changes
Community examples — real user setups, terminal output, workflows
Status
2567 tests collected
stdio MCP handshake verified
Python 3.12+ (CI at 3.13)
Coverage: 80.72% (enforced threshold 80%)
Maintenance
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