Tempera

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾

No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for tempera, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.

Capabilities & Tool Schemas (12)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Tempera.

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 p…

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.

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.

Documentation Overview

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.

Code

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

Dockerfile

┌────────────────────────────────────────────────────────────────┐
│  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

bash

# 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

bash

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:

bash

# 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

Terminal

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

npm downloads

Package downloads in the last 30 days.

Last commit

3mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Tempera

We don't have a confirmed install command for tempera yet, so we don't publish a generated one — a guessed package name would point at the wrong package or none at all. Follow the project's own README or setup instructions (https://github.com/anvanster/tempera) for the current steps.

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Technical Specs & Signals

CategoryKnowledge & Memory

1/5 checks healthy over the last 45d

45Quality signal: Fair · 45/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools20/30

Adoption & activity4/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Tempera →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

Tempera

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Manual Client & Custom JSON ConfigExpand JSON ▾

Capabilities & Tool Schemas (12)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Tempera.

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

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.

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.

Documentation Overview

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.

Code

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

Dockerfile

┌────────────────────────────────────────────────────────────────┐
│  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

bash

# 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

bash

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:

bash

# 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

Terminal

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

npm downloads

Package downloads in the last 30 days.

Last commit

3mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Tempera

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/tempera?style=directory)](https://allmcps.com/mcp/tempera)

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<a href="https://allmcps.com/mcp/tempera"><img src="https://allmcps.com/api/badge/tempera?style=directory" alt="Tempera on AllMCPs" /></a>

Technical Specs & Signals

CategoryKnowledge & Memory

1/5 checks healthy over the last 45d

45Quality signal: Fair · 45/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools20/30

Adoption & activity4/15

Community engagement0/10

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.