Active Source of Truth for Your Coding Agents

Active Source of Truth for Your Coding Agents

Meetless (mla) is an open-source control layer that captures decisions made by AI coding agents, routes unresolved conflicts to humans, and injects human-approved project decisions into agent prompts so agents act on the current, governed project truth.

Active Source of Truth for Your Coding Agents is ai agents software teams evaluate for ai agents. Use this page to review pricing, integration signals, and the best alternatives before you commit.

Freemium Enterprise 70/100
One of 615 tools in AI Agents
Added 1 month ago
Data reviewed Aug 24, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: AI Agents

What it does

AI Agents software for decision-makers comparing workflow fit and alternatives.

Best fit

AI Agents

Pricing snapshot

Freemium from Free during beta (no credit card required)

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Meetless (mla) is a governance/control layer for AI coding agents that ensures every agent acts on the project's current, human-approved decisions. It observes chosen coding-agent sessions, captures decisions, presents captured items for human approval or rejection, preserves provenance and history, and prepends the approved governing context to prompts before agents act. The product is positioned for individual builders and software teams, supports Claude Code and Codex, is open source (Apache-2.0), and is free during beta.

MLA watches your Claude Code and Codex sessions, captures decisions as work happens, detects stale or conflicting instructions, and steers each agent before it acts. You review only the changes that require human judgment.

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Key Features

Session Capture

MLA observes chosen coding-agent sessions and identifies decisions, constraints, and assumptions worth preserving so they can be governed.

Human Approval Workflow

Captured material waits for human review — accept, reject, or leave pending — and an agent cannot promote its own proposal into approved project truth.

Decision Reconciliation & Provenance

When new decisions replace or conflict with accepted ones, MLA preserves history, marks which decision is current, and records provenance such as who approved a decision and when.

Prompt-time Steering

Before an agent acts, MLA supplies the approved decisions and guardrails (prepended to the prompt) so the agent begins with the governing context instead of reconstructing it from potentially stale files.

Local Prompt-time Injection

Approved decisions are injected into the agent's context via a local read of local state; the prompt-time injection path does not require a network request or a model call.

Open Source & Inspectable

The mla client is open source and licensed under Apache-2.0; the project emphasizes inspectable telemetry, documented data handling, and an uninstall that removes local state and hooks.

Pricing

Free Tier Available

Free during beta; no credit card required.

Beta

Free during beta (no credit card required)
  • Claude Code and Codex support
  • Open-source client (Apache-2.0)
  • Free during beta

Use Cases

Single developer long-running projects

Keep architectural and project decisions alive across long-running projects and compressed sessions so coding agents do not revive discarded approaches.

Team-wide decision consistency

Give every engineer and coding agent the exact same human-approved decisions to prevent stale local context from creating incompatible implementations.

Conflict detection and escalation

Detect contradictory decisions across sessions before they land and route unresolved conflicts to a person for resolution rather than letting an agent pick arbitrarily.

Multi-model agent workflows

Maintain a single governed project truth while moving between Claude Code and Codex without maintaining separate project memories.

Integrations

Claude Code

Works across Claude Code agents to provide governed context and steering.

Codex

Works across Codex agents to provide governed context and steering.

GitHub (source available)

Source is published on GitHub (mla client is open source) so teams can inspect and extend the client.

Benefits

Ensures agents act on the current, human-approved decisions rather than stale or conflicting instructions.
Reduces erroneous agent changes by surfacing conflicts and routing them to humans for judgment.
Provides provenance and accountability for decisions (who approved what and when).
Operates locally at prompt time to limit unnecessary network calls and expose clear telemetry controls.
Open-source client allows inspection of behavior and data handling.

Limitations

Benchmarks published on the site are preliminary internal tests (noted as N=5 in several cases) and include withdrawn or corrected results.
MLA is not a zero-network product: governed session capture sends session material (prompts, decisions, tool calls, documents) required to provide the service.
Telemetry includes one content-bearing exception (evidence behind a governance block) which is sent to the configured backend; telemetry can be disabled but some features require captured session data.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Install MLA into your coding-agent workflow (Install MLA call-to-action on the site).
  2. 2 Select the coding sessions you want MLA to govern so it can capture decisions and evidence.
  3. 3 Review captured items and approve, reject, or leave them pending to establish project truth.
  4. 4 Use agents (Claude Code or Codex) with MLA so approved decisions are prepended to prompts before the agent acts.
  5. 5 Configure telemetry and uninstall behavior if desired (telemetry can be disabled; mla uninstall clears local state).

Support

Docs

Product documentation and research pages referenced on the site (Docs, Research, TELEMETRY.md).

Walkthrough / Sales

Request a walkthrough (site offers 'Request a walkthrough').

Community / Chat

Join the project's Discord (Join our Discord link on the site).

Source / Issue tracker

Read the source on GitHub to inspect code and open issues.

API

Available: No

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