Tempera

Name: Tempera
Author: anvanster

by anvansterversion 0.4.29i★ –iRuns on your computer

Registry nameio.github.anvanster/tempera

Install

Overview

Description from the maker (original English)

Persistent episodic memory for AI coding: capture, retrieve, brief, dream cycle, cross-project.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "tempera": {
      "command": "npx",
      "args": [
        "-y",
        "@astudioplus/tempera-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add tempera -- npx -y @astudioplus/tempera-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "tempera": {
      "command": "npx",
      "args": [
        "-y",
        "@astudioplus/tempera-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "tempera": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@astudioplus/tempera-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account anvanster, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7249 servers in this category
Published to the registry more recently than 14% of the other 7249 servers in this category
Basic checks equal to or better than 100% of the other 7249 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Tempera safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.4.29) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

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Codebase Memory

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Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

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Exa

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Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Tempera - Persistent Memory for Claude Code

Tempera gives Claude Code a persistent memory that learns from experience. Instead of starting fresh each session, Claude can recall past solutions, learn what works, and get smarter over time.

Why Tempera?

The Problem: Claude Code forgets everything between sessions. You solve the same problems repeatedly, and Claude can't learn from past successes or failures.

The Solution: Tempera captures coding sessions as "episodes", indexes them for semantic search, and uses reinforcement learning to surface the most valuable memories when relevant.

Without Tempera:                    With Tempera:

┌─────────────┐ ┌─────────────┐ │ Session 1 │ ──forgotten──> │ Session 1 │ ──captured──┐ └─────────────┘ └─────────────┘ │ ┌─────────────┐ ┌─────────────┐ ▼ │ Session 2 │ ──forgotten──> │ Session 2 │ ◄──recalls──┤ └─────────────┘ └─────────────┘ │ ┌─────────────┐ ┌─────────────┐ │ │ Session 3 │ ──forgotten──> │ Session 3 │ ◄──recalls──┘ └─────────────┘ └─────────────┘ │ │ ▼ ▼ No learning Continuous improvement

How It Works

The Learning Loop

┌────────────────────────────────────────────────────────────────┐

│ 1. START TASK │ │ User: "Fix the login redirect bug" │ └────────────────────────────────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────────────┐ │ 2. RETRIEVE MEMORIES │ │ Claude searches: "login redirect bug" │ │ Finds: "Fixed similar issue by sanitizing return URLs" │ │ + Session context: related episodes from the same task │ └────────────────────────────────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────────────┐ │ 3. SOLVE FASTER │ │ Claude uses past experience to solve the problem │ └────────────────────────────────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────────────┐ │ 4. CAPTURE SESSION │ │ Claude saves: what was done, what worked, what failed │ │ Auto-links to current session for multi-step tasks │ └────────────────────────────────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────────────┐ │ 5. LEARN FROM FEEDBACK │ │ User: "That memory was helpful!" │ │ → Episode utility increases │ │ → Multi-hop Bellman propagation spreads value │ │ → Session-linked episodes get boosted │ │ → Unhelpful memories fade over time │ └────────────────────────────────────────────────────────────────┘

What Makes It "Learn"

Mechanism	What It Does
Feedback	Helpful episodes gain utility score
Multi-hop Bellman Propagation	Value spreads through the similarity graph across multiple hops
Session Chaining	Related episodes in multi-step tasks are linked and boost each other
Temporal Credit	Episodes before successes get credit (even across session boundaries)
Recency Boost	Fresh episodes can be weighted higher in retrieval (opt-in)
Scope-aware Decay	Project-bound claims fade in ~70 days; language-level facts last ~3 years; universal truths never decay
Verification State	Captures advance from `Untested` → `TestsPass` → `Merged` → `StableNoRevert`; later states weigh more
Calibration	Per-(task, project) verified vs. declared ratio surfaces overconfidence
Dream Cycle	Nightly reflection, pattern detection, contradiction probing, and template extraction
Self-Improvement Log	Tracks corrections, missed questions, and queues clarifying questions for next session
Cross-Project Transfer	Claims marked language / crate / domain / forever-scoped surface across projects

Over time, frequently helpful knowledge rises to the top, while stale or unhelpful memories fade away — and the system itself accumulates a per-project picture of where it tends to be wrong.

The bigger surfaces (v0.6 onward)

Beyond the basic capture/retrieve loop, Tempera ships several higher-order surfaces. Each is opt-in but all flow through the same MCP tools — Claude can use them without any custom client code.

Grounded capture (v0.6): Every captured claim carries a falsifiability score, a category, and a ValidityScope (Forever / Language / Crate / Domain / Workaround / Project). Decay rates are per-scope — universal truths never expire, project-specific conventions fade in months, workarounds expire when the underlying issue closes.
Dream cycle (v0.7): A budgeted nightly pipeline that runs verify_advance → decay → reflect → patterns → contradict → templates. Reflections turn high-signal days into prose; patterns surface themes that keep recurring; contradict probes pairs of frequently-retrieved episodes for factual disagreements; templates extract reusable step sequences from successful task clusters.
Self-improvement (v0.8): Calibration tracks the ratio of declared vs. verified successes per (task, project). Mistakes log records corrections the agent made. Should-have-asked log records questions it realized it should have asked first. Ask-backs are clarifying questions the *system itself* drafts via Haiku when a capture ends in failure with vague intent — queued for the next session in that project.
Brief surface (v0.9): One MCP call joins all of the above against the file set the agent is about to touch. tempera_brief(files, task_type?, domain?) returns pending ask-backs, the matching reasoning template, top correction categories for those files, should-have-asked triggers, and a calibration warning if the agent's track record on this kind of task is shaky.
Cross-project learning (v0.10): tempera_retrieve and tempera_brief both accept cross_project=true. Transferable claims (anything not project-scoped) surface across projects; Project-scoped knowledge stays bound to its codebase. Legacy captures default to non-transferable until reclassified.

Installation

Build from Source

# Clone and build

git clone https://github.com/anvanster/tempera.git cd tempera cargo build --release

Two binaries are created:

- target/release/tempera (CLI tool)

- target/release/tempera-mcp (MCP server for Claude Code)

Install from crates.io

cargo install tempera

First Run - Model Download

On first use, Tempera downloads the BGE-Small embedding model (~128MB) for semantic search. This happens automatically and only once:

# Initialize and trigger model download

tempera init

Output:

🔄 Loading embedding model (this may download the model on first run)...

✅ Embedding model loaded

The model is cached globally at ~/.tempera/models/ and shared across all projects.

Setup with Claude Code

1. Add the MCP Server

claude mcp add tempera --scope user -- /path/to/Tempera/target/release/tempera-mcp

The --scope user flag makes it available across all your projects.

2. Restart Claude Code

Exit and restart Claude Code to load the new MCP server.

3. Verify

Run /mcp in Claude Code. You should see tempera with 12 tools.

MCP Tools

Once connected, Claude has access to these 12 tools, grouped by purpose:

Session warmup (call at task start)

Tool	When to Use
`tempera_session_start`	Call ONCE at the very start. Returns any clarifying question tempera drafted after a previous failed/partial session in this project.
`tempera_brief`	Call once the file set is known. Joins pending ask-back, reasoning template, top correction categories for these files, should-have-asked triggers, and calibration warning into one response. Pass `task_type` + `domain` for richer output. Set `cross_project=true` to supplement with rows from other projects.
`tempera_retrieve`	Search for similar past episodes. Set `scope="cross-project"` to include transferable claims from other projects.
`tempera_template`	Pull the reasoning template stored for a `(task_type, domain)` pair. The step sequence past wins followed.

During task

Tool	When to Use
`tempera_log_correction`	When the user corrects an assumption / decision / piece of code. Categorized log; the brief surface uses it.
`tempera_log_should_have_asked`	When you realize mid-task you should have asked a question up front. Records the trigger context, the question, and the eventual answer.

End of task

Tool	When to Use
`tempera_capture`	Save session as an episode. Auto-detects session links and runs propagation. The intent-extraction LLM call also suggests a `ValidityScope` for cross-project routing.
`tempera_feedback`	Mark retrieved episodes as helpful or not. Drives the utility-learning loop.

Diagnostics + maintenance

Tool	When to Use
`tempera_status`	Per-project memory health snapshot.
`tempera_stats`	Statistics + trend analytics (helpfulness over time, domain growth, learning curve).
`tempera_propagate`	Multi-hop Bellman propagation with convergence tracking. Periodic maintenance.
`tempera_review`	Consolidate similar BKMs, cleanup. Run after related task series.

Standard warmup pattern

tempera_session_start          # is anything queued from last session?

tempera_brief(files, ...) # what does tempera know about this exact change? tempera_retrieve(query) # then deep-dive into specific episodes

Key Lifecycle Behaviors

Start of session: Call tempera_session_start first to consume any pending ask-back, then tempera_brief once the file set is known.

End of task: Capture successful sessions with tempera_capture — don't wait to be asked. Capture also drafts an ask-back via Haiku if the outcome was Failure/Partial with vague intent, so the next session in this project sees the gap.

Session chaining: Episodes captured within 2 hours of each other in the same project are automatically linked. Multi-step tasks share utility and context.

Cross-project routing: New captures get a ValidityScope suggestion from the intent-extraction LLM (v0.10.3). Project-scoped claims stay bound to one codebase; Language / Crate / Domain / Workaround / Forever claims surface across projects when retrieval opts in.

tempera_retrieve Modes

Search:           tempera_retrieve(query: "authentication bug")

List all: tempera_retrieve(all: true) Show details: tempera_retrieve(query: "abc12345") # episode ID Cross-project: tempera_retrieve(query: "sqlx ordering", scope: "cross-project")

Example Conversation

User: Fix the login redirect vulnerability

Claude: [Calls tempera_session_start()] No pending ask-back — proceeding.

[Calls tempera_brief(files: ["src/auth.rs", "src/router.rs"], task_type: "bugfix", domain: "auth-middleware")]

🔴 PENDING ASK-BACK Ask the user first: "Should auth run before o

Only the beginning is shown; read the rest in the repo

Tempera

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Tempera - Persistent Memory for Claude Code

Why Tempera?

How It Works

The Learning Loop

What Makes It "Learn"

The bigger surfaces (v0.6 onward)

Installation

Build from Source

Two binaries are created:

- target/release/tempera (CLI tool)

- target/release/tempera-mcp (MCP server for Claude Code)

Install from crates.io

First Run - Model Download

Output:

🔄 Loading embedding model (this may download the model on first run)...

✅ Embedding model loaded

Setup with Claude Code

1. Add the MCP Server

2. Restart Claude Code

3. Verify

MCP Tools

Session warmup (call at task start)

During task

End of task

Diagnostics + maintenance

Standard warmup pattern

Key Lifecycle Behaviors

tempera_retrieve Modes

Example Conversation