TIMO Labs

Tools We Build

Open-source and proprietary tools for AI-managed development. Built from real enterprise engagements.

Ember AgentOS
01 — Ember

Persistent Memory for AI Agents

AI agents forget everything between sessions. Every new conversation starts from zero. Your engineers repeat the same context, the same corrections, the same preferences. Ember fixes that.

Ember is a persistent, file-based memory system. It stores decisions, preferences, project context, and corrections. When an agent starts a new session, it recalls what matters. The agent remembers.

$ ember recall "authentication refactor decisions" → Session 34: Decided to use JWT over session tokens (2026-02-14) → Session 37: Added refresh token rotation per security review → Session 41: Migrated all endpoints, backward-compat removed 3 related memories found. Context loaded. $ ember store "Switched to RS256 signing for JWT tokens" ✓ Stored. Tagged: authentication, security, jwt
01

Persistent Storage

Decisions, preferences, corrections, project context. Stored across sessions. Nothing gets lost.

02

Intelligent Recall

Agents pull relevant context automatically when a new session starts. No manual re-prompting.

03

Deep Context Tracing

Follow the chain of reasoning across sessions. Understand why a decision was made three weeks ago.

04

Multi-Agent Memory

Multiple agents share a common memory layer. One agent learns something, the others know it too.

Compatibility

Available as an MCP server. Works with Claude Code, Cursor, and any MCP-compatible tool. Standard protocol, no vendor lock-in.

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02 — AgentOS

Agent Orchestration for Engineering Teams

Using AI tools individually is ad-hoc. There is no structure, no review, no accountability. AgentOS turns AI-assisted development into a managed workflow.

Create tickets. Assign them to agents with scope, acceptance criteria, and review gates. Run multiple agents in parallel. Track everything. No unreviewed AI code reaches production.

$ agentOS assign --task "Refactor payment module" \ --agent claude-code \ --scope src/payments/ \ --review-gate required \ --acceptance "all tests pass, no public API changes" ✓ Task TKT-047 assigned. Agent working. $ agentOS status TKT-045 ✓ Complete — Auth middleware refactor (reviewed, merged) TKT-046 ● In review — API rate limiting (4 files changed) TKT-047 ◔ Working — Payment module refactor (ETA: 12 min)
01

Structured Tasks

Scope, acceptance criteria, review gates. Every agent gets a clear assignment with defined boundaries.

02

Multi-Agent Orchestration

Run multiple agents on independent tasks in parallel. Coordinate their work automatically.

03

Quality Gates

Every agent output goes through review before merge. Approval, rejection, escalation. No exceptions.

04

Observable

Track what agents are doing, what they changed, what got reviewed. Full visibility into AI adoption across the team.

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The Stack

How They Work Together

Ember — Memory Layer
  • Agents remember context across sessions
  • Decisions, preferences, corrections persist
  • Multi-agent shared knowledge
  • Deep tracing across session history
AgentOS — Orchestration Layer
  • Agents work on structured tasks
  • Quality gates enforce review
  • Parallel execution across teams
  • Full observability and tracking
Together

Ember provides the memory. AgentOS provides the structure. Together they turn ad-hoc AI tool usage into managed AI development — repeatable, auditable, scalable.

This is the infrastructure behind Timo's methodology.

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poorna@timolabs.dev