What is Agentweaver?
Run a team of AI agents for any scenario you can describe.
Agentweaver is a self-hosted, multi-agent orchestration platform. Submit a goal — software delivery, content authoring, PM discovery, incident response, or anything else knowledge-work — and a team of named specialist agents delivers it in isolated sandboxes, with you in control at every gate that matters.
How it works
Coordinator orchestration
Submit a goal. The coordinator:
- Drafts an OutcomeSpec — goal, desired outcome, scope, assumptions
- Selects the best-fit workflow for your task via an LLM pass over available workflows and team roles — surfacing the choice and rationale. You can override from the Start task dialog or by typing
use {workflow-id}in the coordinator chat. - Asks for your confirmation before any work starts
- Decomposes the confirmed spec into a WorkPlan — subtasks arranged in a dependency graph
- Dispatches child agents in parallel, each in their own sandbox
- Shows a live topology graph of every agent and its status
- Lets you steer mid-run — send a directive, redirect a child, amend the plan, or stop
- Assembles all results into one combined diff
- Routes through a single review gate (RAI + human approval)
- Runs a Scribe pass after merge to record what the team learned
Key concepts
Projects
A Project is the top-level container — a git working directory bound to an AI configuration (provider and default model). Every run, every agent, and all team memory live inside a project. Create from scratch or clone from GitHub.
Blueprints and casting
A Blueprint is a reusable team definition: roles, workflows, review policy, and sandbox policy. When you instantiate a Blueprint into a project, the casting algorithm assigns named agents to each role — drawn from thematic universes like The Matrix or Star Wars. Five predefined Blueprints ship with Agentweaver.
Workflows
Workflows are YAML-defined multi-role pipelines that define the scenario. Agentweaver ships seven built-in workflows:
| Workflow | Use it for |
|---|---|
software-delivery | Code changes, features, refactors |
bug-fix | Targeted bug investigations and fixes |
code-review | Automated review of a diff or branch |
content-authoring | Drafting docs, blog posts, articles |
pm-discovery | Product discovery, research, specs |
incident-response | Live incidents and postmortems |
agent-evaluation | Testing and evaluating agent outputs |
When you submit a task, an LLM pass automatically matches it to the best-fit workflow. You can also author your own or generate one from a description.
The board
Every project has a Kanban board with six columns:
| Column | Owned by |
|---|---|
| Backlog | You — capture tasks here |
| Ready | You — drag tasks here when ready to run |
| Problems | Coordinator — failed runs land here with reason |
| Human Review | Coordinator — runs awaiting your approval |
| Active | Coordinator — currently running orchestrations |
| Done | Coordinator — completed, merged runs |
A heartbeat periodically promotes Ready tasks and starts coordinator runs up to a configurable limit.
Runs
A Run is a unit of execution. Each run works inside an isolated git worktree, streams every event live, and requires your explicit approval before anything merges.
→ Submitting and Watching Runs
Review & Merge
All work passes through a review pipeline before merging: RAI safety check → human approval gate. For coordinator orchestrations, this happens once over the assembled output of all agents.
Team Memory
Agents build on prior work through four memory layers compiled into every agent's context:
- Active Decisions — hard constraints (architectural and scope decisions)
- Core context — project-level standing context
- Learnings and patterns — top high-importance entries from prior runs
- Open session — current run context
Agents submit entries to a Decision Inbox typed as learning, pattern, update, architectural, or scope. After each run, a Scribe pass merges the inbox into the shared decisions ledger.
→ Agent Teams & Blueprints — Memory
MCP server
The full Agentweaver feature set is available programmatically through an MCP server. Any MCP-compatible client (including Copilot CLI) can drive the complete lifecycle — projects, runs, board, workflows, blueprints, casting, memory, decisions, sandbox, and diagnostics.
Why Agentweaver
| Other tools | Agentweaver |
|---|---|
| Orchestration primitives you wire up yourself | Full-stack, scenario-flexible experience out of the box |
| Optional HITL through workflow patterns | Mandatory OutcomeSpec gate + mandatory human review — enforced by the platform |
| State in opaque managed stores | File-native, repo-resident, inspectable team/memory/decisions |
| One review gate per agent | Single collective review over all assembled work |
| Platform-specific | Self-hosted, no platform-level cloud dependency |
| Code-only scenarios | Any knowledge-work scenario via the workflow system |