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A local control plane for AI agents.

Plan work, run agents, and turn repeatable delivery into visual workflows with human approvals. Valdr keeps run history, Workspace Knowledge, and Agent Memory Notebooks in your local workspace, with the evidence to review what happened and improve what comes next.

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Outcomes

What changes when agent work is structured, scored, and reviewed.

Prepare work before agents run

Give planned work an owner, acceptance criteria, and the approval policy your process requires.

Agents stay inside their boundaries

Scope agents to selected projects and tools, then add human gates before the sensitive steps you want reviewed.

An audit trail you can inspect

Session prompts, tool actions, changes, and available seven-dimension scorecards stay together for review.

Reviewers get evidence with the work

The session transcript and any available scorecard stay with the output, so reviewers don't have to reconstruct context.

Scoring catches problems before you ship

Run a seven-dimension score on any session before approving it. Low scores surface issues so reviewers know where to focus.

Context survives across projects

Workspace Knowledge, capabilities, and Agent Memory Notebooks keep standards, dependencies, decisions, and lessons available without re-explaining them every session.

Platform features

What this looks like in practice

A task goes from idea to shipped code with full traceability at every step.

The shift

From ad-hoc prompting to structured delivery

Most agent workflows are a loop: prompt, hope, manually verify. Valdr replaces that with a defined path — plan the work, run agents within boundaries, score the output, and place human approval at the points your team chooses.

Before

  • You re-explain project conventions every time you start a new agent session.
  • Agent output lands with no record of what it was told to do or why it made certain choices.
  • Cross-project dependencies require manual spelunking before an agent can make a useful change.
  • Important lessons sit in chat history instead of becoming reusable project memory.

After

  • Agents inherit task context, acceptance criteria, coding standards, and cross-project Workspace Knowledge — no re-explaining.
  • Session records collect prompts, tool calls, changes, and available scores.
  • Code map queries let agents trace definitions, callers, references, docs, and related code across attached projects.
  • Agent Memory Notebooks turn hard-won context into scoped memory the next session can retrieve.

How it works

  1. 1

    Define the work

    Set goals, acceptance criteria, and owners before agents start.

  2. 2

    Run with context

    Agents execute within approved scopes, use Workspace Knowledge for cross-project deep dives, and pause when they need human input.

  3. 3

    Review and ship

    Check the scorecards and evidence, then approve or block.

How Valdr stays honest

Local records, visible evidence, and review controls you can place before sensitive actions.

Local control plane

Keep Valdr's execution records and audit history in your environment. Connected tools and model providers receive content when you use them.

  • Local records you can inspect
  • Offline-friendly runtime

Review where it matters

Add explicit approval checkpoints so the workflows you govern pause before sensitive actions.

  • Approval gates at selected steps
  • Clear ownership and handoff

Evidence-backed governance

Score sessions with inspectable context so teams can trace why outcomes passed or failed.

  • Seven-dimension scorecards
  • Actionable audit trail for remediation

Pricing

Snapshot of the three tiers with a clear path to the full comparison.

Raider

Free forever. Full UI and local runtime for hands-on operation.

Who this is for: Individual builders evaluating Valdr or running project and agent operations from the UI.

$0 / month

  • Full Valdr UI — dashboard, tasks, sprints, reviews
  • Prompt and capability library
  • Local control plane, offline-friendly with local models
  • No MCP tool access — UI driven only
  • +1 more in full comparison
Use Raider (Free)

Vanguard

MCP access to PM tools. Automate tasks, sprints, and reviews.

Who this is for: Builders who want agents to read and write project data through MCP tools.

Most popular

$25 / month

  • Everything in Raider
  • Seven-dimension session scoring
  • MCP access to Tasks, Sprints, Reviews, and Projects
  • MCP access to Agents, Capabilities, and Prompts
  • +2 more in full comparison
Subscribe to Vanguard

Sovereign

Workspace Knowledge, durable Workflows, Agent Memory Notebooks, and session orchestration.

Who this is for: Power users building agents that deep-dive across projects, retain memory, chain reviews, and orchestrate delivery.

$50 / month

  • Everything in Vanguard
  • Workspace Knowledge MCP — source-aware context across projects
  • Agent Memory Notebooks scoped by project or workspace
  • MCP access to Sessions — spawn and manage agents
  • +5 more in full comparison
Subscribe to Sovereign

Raider requires no credit card. Paid tiers are monthly subscriptions with cancel-anytime billing.

View full pricing details

Need every capability side-by-side? Open the full `/pricing/` breakdown.

FAQ

What is Valdr?
A desktop control plane that plans, runs, and scores AI agent work from your machine. It adds structure — tasks, owners, acceptance criteria, scoring, and human review gates — so agent output is traceable and reviewable before it ships.
Where is my data stored?
Valdr stores its control-plane records on your machine or private network. Connected tools and model providers receive the content you send to them.
Can agents remember project context?
On Sovereign, agents can use Workspace Knowledge to search across projects, then preserve reusable findings in Agent Memory Notebooks. They can retrieve attached docs, runbooks, code context, references, and prior memory instead of starting from scratch every time.
Does Valdr send data to outside services?
Valdr keeps its control-plane records in your environment. Configured tools and model providers receive the content you send when you use them.
Can we run Valdr offline?
Valdr is offline‑friendly and well suited to private-network or high‑sensitivity environments.
Do you collect telemetry or analytics?
No background telemetry — diagnostics are opt‑in only.
How do approvals work?
Add human gates to the workflow steps that need sign-off. Each gate records who may decide, the allowed outcomes, and the evidence to review.
Will this slow us down?
Use review rules for sensitive work; let trusted steps run automatically.
Local records
No background telemetry
Human review
Offline‑friendly

Security & Privacy

Keep Valdr records local and choose which services receive agent context.

  • Local control plane: Keep Valdr records on your machine or private network.
  • No background telemetry: Diagnostics are opt-in.
  • Human review: Add approval gates before the sensitive steps your team chooses.
  • Clear boundaries: You decide which projects, files, and agents have access.
  • Easy to verify: Local deployment with clear boundaries for high‑sensitivity work.

Get started with Valdr

Raider is free forever. Paid tiers unlock MCP access and advanced orchestration.

Start with Raider (Free)View full pricing