chat-recall vs Harden: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of chat-recall and Harden — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

chat-recall

Freemium

Makes every conversation your team has had with AI coding assistants searchable, and flags secrets leaked into those chats.

Unified Conversation Search: Full-text search across the chats, plans, task lists and notes written by five supported AI coding tools, searchable the moment they arrive.
Local Secret Redaction: Passwords and API keys are stripped on your own computer before anything is uploaded; only the last few characters are ever received.
Leaked Key Reporting: Shows every key found, whether it is still live, and how many conversations it appeared in, with support for custom in-house key formats you register.
MCP Server and Recall Tools: Exposes the history to your assistants through an MCP server so they can query past work directly rather than starting cold.
Ranked Action Plan: Derives code findings and a prioritized list of what to fix next, written out as CODE_TASKS.md.
Self-Closing Bug Tasks: Each detected bug becomes a task with a sketched fix, and closes itself once the problem is actually gone.
Config Distribution: Skills and MCP configuration follow you to every machine and to whichever assistant you pick up next, with a per-machine view of what is missing.
Per-Project Rules: Mark a project as a prototype or a live product once, and every assistant that opens it plays by the matching rules.

Credential Incident Response: Find which keys were pasted into assistant conversations, whether they are still valid, and where they spread.
Recovering Past Decisions: Search months of AI conversations to recover a plan or rationale instead of asking the same question again.
Onboarding a New Machine: Sign in on a new laptop and get the full conversation history and every accumulated skill without copying files by hand.
Switching Assistants: Try a different AI coding tool without losing the add-ons and context built up in the previous one.
Team Knowledge Sharing: Share project history selectively with teammates and assign follow-up work from a shared task board.
Security Review Before Shipping: Run the secret monitor and code findings over accumulated history as a pre-release check.

Free

Local security layer for AI coding agents that inspects every tool call before it runs and can block, rewrite, or pause the action.

Pre-Execution Tool Call Checking: Every command, file edit, tool call, and outbound request is evaluated before it is allowed to run, not audited afterwards.
Intent-Aware Decisions: The check compares the proposed action against the task the developer actually requested plus session context, rather than matching a static rule list.
On-Device Model: A post-trained 8B-parameter model runs locally and is reported to beat frontier models on agent-security benchmarks while remaining fast enough to sit inline.
Graduated Responses: Actions can be allowed, blocked, redacted, paused for a human answer, or simply logged, and a blocked action does not stop the agent's other work.
Secret and Exfiltration Protection: Catches credential leaks, data exfiltration attempts, and destructive infrastructure operations before they execute.
Local Review Dashboard: Every decision is logged and reviewable through a dashboard that runs on the developer's own machine.
Broad Agent Compatibility: Works with Claude Code, Cursor, Codex, Hermes, OpenClaw, Kiro, and Antigravity.
Privacy by Design: Repository contents and tool output stay on the local machine because inference happens there.

Guarding Autonomous Coding Sessions: Let a coding agent run with fewer manual approvals while a local check still catches destructive commands.
Preventing Secret Leakage: Stop an agent from pasting API keys or credentials into an outbound request or a committed file.
Blocking Prompt-Injection Damage: Catch actions an agent was steered into by malicious content in a repository, issue, or web page it read.
Protecting Production Infrastructure: Intercept destructive infrastructure operations before they reach cloud resources or databases.
Agent Behaviour Auditing: Review a logged trail of what an agent tried to do and which actions were blocked or redacted.
Team Policy Enforcement: Apply consistent guardrails across developers using different coding agents on the same codebase.