AllBrain

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 save_event()
Agent B starts the same project	Fresh context — no knowledge of Agent A's work	resume_project() returns full event history
Two agents write conflicting changes	Silent overwrite, no one knows	detect_conflicts() surfaces both versions
Handoff between agents	Manual copy-paste of context	list_events() with agent filter shows handoff trail
Debugging state drift	"It worked in my session"	Deterministic replay from raw events, exactly reproducible

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_project across any MCP client
• Agent 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 --codex

This configures Codex to start AllBrain automatically. Replace --codex with the client name:

Client	Flag
Codex	--codex
Claude Code	--claude
OpenCode	--opencode
Cursor	--cursor
VS Code	--vscode
Zed	--zed
Gemini CLI	--gemini
Kiro	--kiro
Windsurf	--windsurf
Antigravity	--antigravity
Claude Desktop	--claude-desktop

Use --all to configure every supported client at once.

Verify it works

uvx allbrain-agent-runtime install --codex --verify

The --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
minimal	save_event, list_events, resume_project	Getting started
memory	minimal + retrieve_memory	Need recall
collaboration	memory + task/conflict/resolution tools	Multi-agent handoff
reasoning	memory + decision pipeline tools	Planning and analysis
core	save_event, list_events, retrieve_memory, git_info, create_task, get_task_graph, orchestrate_project, run_decision_pipeline, create_snapshot, resume_project	Essential workflow
full	50 tools	Everything

uv run allbrain start --project . --agent my-agent --tool-profile memory

Glama 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 full

Alternatif 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 --verify

Or run the guided onboarding wizard:

uv run allbrain onboard

It 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 core profile. 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 tools
• minimal profile: 3 tools (save_event, list_events, resume_project)
• core profile: 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%)