axiom-perception-mcp 2.1.0

Empirical memory for AI agents — patterns learned from real executions, not hand-written rules. Workflow checkpointing, failure knowledge base, multi-agent coordination. Zero API keys required.

axiom-perception-mcp

Persistent memory and pattern learning for AI agents.

Claude forgets how to use your computer between sessions. axiom-perception-mcp fixes that — it gives Claude a long-term memory for multi-step workflows so it never re-learns the same thing twice.

claude: recall_pattern("post tweet")
→ "Found pattern v3 (97% success rate, 42 executions):
    1. Navigate to x.com
    2. Click 'Post' in the left sidebar
    3. ..."

Zero API keys. No setup. Just install and Claude starts remembering.

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Install

# Claude Desktop / Claude Code
uvx axiom-perception-mcp

Add to your MCP client config:

{
  "mcpServers": {
    "perception": {
      "command": "uvx",
      "args": ["axiom-perception-mcp"]
    }
  }
}

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The problem it solves

When Claude tries to do something in your browser — post a tweet, create a PR, fill a form — it often spends 5-10 minutes figuring it out through trial and error. Every session, from scratch.

axiom-perception-mcp stores what worked. Next time, Claude skips straight to the answer.

Works alongside any automation tool: Playwright MCP, Computer Use, browser tools. This MCP handles what to do, your automation tool handles how to do it.

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8 tools

Tool	What it does
recall_pattern(task, app?)	Get the best known workflow before starting a task
save_pattern(task, steps, app, category)	Save a workflow that worked
update_pattern(id, steps, reason?)	Improve a pattern with a better approach
record_outcome(id, success, time_ms?)	Track executions to build success rate
list_patterns(app?, category?)	Browse all known workflows
search_patterns(query)	Search across all patterns
export_pattern(id)	Export as JSON to share with the community
fetch_community_patterns(app?)	Import proven patterns from the shared database

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Community patterns — no cold start

On first use, run fetch_community_patterns() to load proven workflows contributed by users worldwide:

• Twitter/X: post tweet, post thread, reply, follow user, like
• LinkedIn: publish post, comment
• GitHub: create PR, create issue, comment on PR
• DEV.to: publish article (browser + API)
• Bluesky: post via AT Protocol API
• Hacker News: search, submit Show HN
• Generic: login form, screenshot, copy page content
• Dev tools: deploy to MCPize, publish to PyPI, claim on Glama

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How the collective intelligence works

1. You discover that a 3-step approach works where the community uses 8 steps
2. Call update_pattern() — the pattern upgrades to your faster version
3. Call export_pattern() — share it in a GitHub issue
4. Everyone who installs axiom-perception-mcp gets your improvement via fetch_community_patterns()

Patterns are ranked by success rate. Better solutions automatically get promoted. The more users contribute, the smarter every agent gets.

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Usage pattern

# Before starting any multi-step task:
recall_pattern("create github PR")
→ Follow the steps

# After completing (success or failure):
record_outcome("a3f9b2c1", success=True, time_ms=4200)

# If you find a faster way:
update_pattern("a3f9b2c1", new_steps=[...], reason="2 fewer clicks")

# Share your improvement:
export_pattern("a3f9b2c1")
→ Paste the JSON in a GitHub issue

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Platforms

Platform	Link
PyPI	pip install axiom-perception-mcp
GitHub	vdalhambra/axiom-perception-mcp
Smithery	vdalhambra/axiom-perception
Glama	Auto-indexed

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Data storage

Patterns are stored locally in ~/.axiom/perception/patterns.db (SQLite). Nothing leaves your machine unless you explicitly call fetch_community_patterns() or export_pattern().

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License

MIT — by Axiom