For engineers

Self-improving, portable context for AI agents

Alignbase gives your team one place to manage shared agent context. Host your AGENTS.md, Skills, and memory, then watch as every agent's learnings improve the whole fleet.

Get StartedLearn more

Works natively inside:

Codex(CLI + Desktop)
Claude Code(CLI + Desktop)
ChatGPT
Claude
More

AI is missing a shared context layer:

  1. Vendor lock-in

    Every vendor has its own context stack

    Teams configure AGENTS.md, Skills, Memory, MCPs, plugins, and other inputs for each agent vendor, then repeat the work when they change tools.

  2. Cold start

    Every session starts from scratch

    Agents begin without current priorities, project context, operating rules, Skills, or working Memory, so teams pay for the same learning again.

  3. Noncompliance

    Agent inputs lack governance

    Security cannot prove which policies, permissions, project context, and Skill versions reached an agent before it acted.

  4. Misaligned fleet

    Agents don't share context or learnings

    Teams cannot coordinate agents across branches, repos, users, models, and harnesses for strategic or tactical work.

EngineeringAGENTS.md
Code reviewSkill
ProjectMemory
Eve'sCodex
Eli'sClaude
Jake'sKimi

Manage context once. Use it with every agent.

Alignbase gives AGENTS.md, Skills, and Memory a portable source of truth with authentication, permissions, versions, and audits.

Context your agents should start with:

  • Strategy, KPIs, and customers
  • Security, privacy, and compliance
  • Cross-repo architecture and migrations
  • Engineering and review standards

Skills your agents should have available:

  • Test commands and fixtures
  • Release and rollback steps
  • Security review checklists
  • Scripts, templates, and references

Memory your agents should keep current:

  • Current working state
  • User corrections and preferences
  • Unfinished work and next steps
  • Failed approaches and known traps

One context layer for the full agent lifecycle:

  1. Portable
    73%
    of organizations use multiple AI vendorsIBM Institute for Business Value and Oxford Economics, “The Calculus of AI Sovereignty” (2026). In a survey of 1,000 senior executives, 73% described their AI environments as intentionally multi-vendor.Read the source

    Portable, agent-agnostic context & configuration

    Manage context, AGENTS.md, Skills, Memory, MCPs, and plugins once across multiple AI vendors, without lock-in.

  2. Aligned
    15%
    higher worker productivity with company contextBrynjolfsson, Li, and Raymond, “Generative AI at Work,” Quarterly Journal of Economics (2025). The study followed 5,172 customer-support agents using a company-specific AI assistant.Read the source

    Agent strategic alignment

    Fix cold-start by giving every agent current priorities, context, rules, and Skill access so it can deliver better work, faster and cheaper.

  3. Governed
    75%
    less privacy leakage with governed contextWang et al., “Privacy in Action,” Findings of EMNLP (2025). A contextual integrity-based checker reduced privacy leakage from 36.08% to 7.30% and from 33.06% to 8.32% across two models while preserving task helpfulness.Read the source

    Agent context governance

    Govern what reaches each agent to protect sensitive context, with an audit trail tied to approved versions.

  4. Self-Improving
    63%
    fewer tokens with managed contextLodha et al., “Less Context, Better Agents” (2026 preprint). Selective context and summaries raised completion from 71% to 91.6% while reducing token use by 62.6%.Read the source

    Self-improving, coordinated agent fleets

    Distill and share approved learnings to cut token use and coordinate the fleet across branches, repos, users, models, and harnesses.

Start improving agent context in minutes.

Connect the agents you and your team already use, then keep their AGENTS.md, Skills, and Memory current without changing your workflow.

  1. Step 1

    Create an Alignbase context Repository

    Open a free account to initialize your team-wide context repository.

  2. Step 2

    Connect an agent

    Connect an agent to read and write shared context.

  3. Step 3

    Host agent context

    Host your AGENTS.md, Skills, and Memory in Alignbase.

  4. Step 4

    Watch agents self-improve context

    Agents proactively suggest context improvements as they encounter gaps, errors, or speed bumps in their work.

CompanyStrategy
EngPolicies
SalesPolicies
Division 1KPIs
Division 2KPIs
Team AArchitecture
Team BArchitecture
Get Started
Questions engineers ask.

Short answers about how Alignbase fits with AGENTS.md, Skills, Memory, wikis, and the coding agents you already use.

What is AI coding agent context?
AI coding agent context is the product, policy, customer, architecture, and team knowledge an agent needs to implement work correctly. Repo-local commands and conventions still belong in repo AGENTS.md files. Alignbase is for team and cross-repo context those files cannot manage well.
How is Alignbase different from AGENTS.md?
AGENTS.md is useful for repo-local rules, commands, and conventions. Alignbase adds a managed AGENTS.md layer, governed Skills, and short-term Memory, with permissions, versions, and audit history for the whole agent fleet.
Does Alignbase replace repo-level AGENTS.md files?
No. Repo-level instructions are still useful for local commands and codebase rules. Alignbase adds the team context that lives outside one repo, then routes the right bundle and Skill access to each agent. A team can be a squad, function, or whole company.
How do Skills fit into Alignbase context?
Some context should be loaded when an agent starts. Other context should be available only when the task calls for it. Alignbase manages both, with tags, permissions, versions, and audit records.
What does self-improving, portable context mean?
It means useful agent learnings can become reviewed, versioned context or Skills, while each agent can maintain short-term working Memory between sessions. Alignbase keeps all three portable across agents and tools with the right permissions and audit history.
Why not just point agents at a knowledge base?
Knowledge bases are usually too broad, noisy, and stale for agent startup context. Alignbase gives agents approved context and Skills, plus short-term Memory for working recall. Memory stays concise and current instead of becoming another knowledge base.
Which engineering context should go into Alignbase?
Good starting points include product goals, customer constraints, security rules, cross-repo architecture decisions, active migrations, rollout plans, incidents, and review policy that applies beyond one repo.
What benefits should engineering teams expect?
Alignbase gives teams portable, agent-agnostic context and configuration without vendor lock-in; strategic alignment that fixes agent cold-start; context governance for compliant, auditable inputs; and fleet alignment for self-improving, coordinated agents.
How do teams start?
Start with one team, service area, or repeated workflow where coding agents miss team-level context. Connect an agent, host the AGENTS.md and Skills it should use, then assign a Memory for working recall across sessions. Monitor agent work and publish durable learnings back to the fleet.