Tempera

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 or after request logging?"

        📋 REASONING TEMPLATE (bugfix / auth-middleware)
          url-redirect validation
            1. Find every redirect target read from request input
            2. Validate against allow-list or treat as untrusted
            3. Add regression test with adversarial input

        ⚠️  TOP CORRECTION CATEGORIES for these files
          - validation_skipped (3×)
          - host_header_trust (2×)

        Tempera says I've been wrong here before in specific ways.
        Let me ask: should auth run before or after logging?

User: Before — we need an auth'd subject for log lines.

Claude: [Implements with URL allow-list, following the template steps]

        Done. [Calls tempera_capture(
          summary: "Fixed login redirect by validating return URL against allow-list",
          outcome: "success",
          validity_scope: "domain:auth-middleware"
        )]
        ✅ Episode saved. (Cross-project — this is an auth-middleware pattern.)

User: That memory was helpful!

Claude: [Calls tempera_feedback(helpful: true, episodes: ["..."])]
        Marked helpful — future retrievals for similar tasks will rank this higher.

CLI Commands

The CLI mirrors the MCP tool surface so you can drive everything Claude does from a shell.

Basics

# Initialize Tempera
tempera init

# Capture an episode (from a session transcript or interactively)
tempera capture --session /path/to/transcript.md

# Index episodes for semantic search (or re-index)
tempera index [--reindex]

# Search memories — project-scoped by default
tempera retrieve "database connection issues"
tempera retrieve "sqlx pattern" --cross-project       # v0.10.1 — pull from other projects

# Provide feedback
tempera feedback helpful --episodes abc123,def456

The brief surface (v0.9)

# Joint summary of every self-improvement signal for these files
tempera brief --files src/auth.rs,src/router.rs \
              --task-type bugfix --domain auth-middleware

# Include rows from other projects (foreign rows are tagged [from <project>])
tempera brief --files src/store.rs --cross-project

Session warmup (v0.8.5)

# Show + clear the pending ask-back for this project (if any)
tempera session-start

# History of system-drafted clarifying questions
tempera ask-backs [--pending] [--project P]

Self-improvement surfaces (v0.8)

# Log a correction the user made
tempera log-correction --category "lifetime annotations" \
                       --description "I assumed &str when &'a str was needed" \
                       --correction "use named lifetime to match trait"

# View the correction log
tempera mistakes [--top 5]              # top categories
tempera mistakes --project tempera      # raw list filtered

# Log a question you should have asked up front
tempera log-should-have-asked --trigger "edit auth middleware" \
                              --question "Which auth provider is wired up?" \
                              --answer "No auth — internal-only service."

# View the should-have-asked log
tempera asks --top 5

Reasoning templates (v0.8.3)

# List stored templates
tempera templates list

# Fetch a specific template
tempera templates get --task-type bugfix --domain async-rust

# Manually trigger extraction (otherwise runs in dream cycle)
tempera templates extract --max-usd 0.20

Calibration (v0.8.1)

# Per-(task_type, project) verified vs declared rates
tempera calibration --project tempera --task-type bugfix

Dream cycle (v0.7)

# Run the full cycle with a budget cap (default $0.50)
tempera dream --max-usd 0.50

# Run one phase, or list available phases
tempera dream --phase reflect
tempera dream --list

# Plan only — show what would happen without making LLM calls
tempera dream --dry-run

# Author yesterday's reflection (Haiku triage + Sonnet authorship if score >= 0.5)
tempera reflect [--date 2026-05-26] [--dry-run]

# Surface active factual contradictions found during dream
tempera contradict --list

Verification (v0.6.1)

# Move an episode forward in the verification chain
tempera advance-verification --episode abc123 --to tests_pass --run-id <id>
tempera advance-verification --episode abc123 --to merged --commit <sha>
tempera advance-verification --episode abc123 --to stable_no_revert --days 30

Maintenance + analytics

# Multi-hop Bellman propagation (run weekly)
tempera propagate --temporal

# Prune old / low-value episodes
tempera prune --older-than 90 --min-utility 0.2 --execute

# Stats + trends
tempera stats
tempera trends --project tempera --bucket weekly

# Health check + remediation
tempera doctor [--remediate --yes --target-score 90]

# Eval harness (P@5, R@5, MRR, nDCG@5 against a fixture)
tempera eval run --fixture evals/fixtures/real.jsonl --mode hybrid

# Snapshot / restore the data dir
tempera backup
tempera backup --list
tempera backup --restore 20260524T123456Z

Data Storage

Tempera stores everything locally in ~/.tempera/ (shared across all projects). One memory pool serves every project; the project filter is applied at query time.

~/.tempera/
├── config.toml              # Configuration (all RL params configurable)
├── episodes/                # Canonical episode JSON
│   └── 2026-01-25/
│       └── <id>.json
├── jobs.sqlite              # SQLite for everything indexable (see below)
├── vectors/                 # Vector index (vectrust embeddings)
├── models/                  # BGE-Small embedding model (~128MB)
├── reflections/             # Daily reflection markdown (v0.7.3)
├── patterns/                # Cross-day pattern pages (v0.7.4)
└── templates/               # Reasoning templates (v0.8.3)

SQLite tables (in jobs.sqlite)

Everything that needs SQL lives here. Each store opens the DB on first use and runs its migration; migrations are in migrations/ and run in order.

Migration	Table	Purpose
0001	jobs	Background job queue with lease semantics
0002	error_fingerprints	blake3-hashed normalized error text
0003	dream_verdicts	Day-level Haiku triage cache
0004	reflections	Daily reflection records
0005	patterns	Cross-day theme clusters
0006	contradictions	Episode-pair disagreements + Wilson CI
0007	calibration_buckets	(task_type, project) declared vs verified counts
0008	mistakes	Anchored correction log
0009	reasoning_templates	Extracted reasoning step sequences
0010	should_have_asked	Questions the agent should have asked up front
0011	ask_backs	System-drafted clarifying questions for next session

All projects share the same pool. Cross-project routing is controlled by each episode's ValidityScope (see below) — not by separate storage.

Configuration

All knobs live in ~/.tempera/config.toml. The defaults are tuned to be useful out of the box; you only need to touch this if you want to change retrieval ranking, dream-cycle behavior, or per-phase budgets.

Retrieval + ranking

[retrieval]
mode = "hybrid"                  # vector | keyword | hybrid (BM25 + vector fusion)
similarity_weight = 0.3          # Weight for semantic similarity (project mode)
utility_weight = 0.7             # Weight for learned utility (project mode)
hybrid_similarity_weight = 0.85  # RRF-normalized retrieval (hybrid mode)
hybrid_utility_weight = 0.15
recency_weight = 0.0             # Recency (0 = off, opt-in)
recency_halflife_days = 30.0
mmr_lambda = 0.7                 # MMR diversity (0=diverse, 1=relevant)
min_similarity = 0.5             # Filter threshold

[bellman]
gamma = 0.9                      # Discount factor for Bellman updates
alpha = 0.1                      # Learning rate
propagation_threshold = 0.5      # Min similarity for propagation
max_propagation_depth = 2        # Multi-hop depth (hops)
temporal_credit_window_hours = 1

Capture + verification

[capture]
auto_capture = true
extract_intent_llm = true        # Use LLM to extract intent + claim + scope
capture_diffs = true
ask_back_on_failure = true       # Draft a clarifying question on Failure/Partial captures (v0.8.5)

Dream cycle (v0.7)

[dream]
default_max_usd = 0.50           # Per-cycle budget cap
stable_threshold_days = 30       # Days before Merged → StableNoRevert
triage_model = "claude-haiku-4-5-20251001"
reflect_model = "claude-sonnet-4-6"

# Patterns phase
patterns_lookback_days = 30
patterns_min_evidence = 3
patterns_cluster_threshold = 0.75

# Contradict phase
contradict_top_n = 50
contradict_min_similarity = 0.6
contradict_max_similarity = 0.95
contradict_max_pairs = 30
contradict_min_confidence = 0.7

# Templates phase (v0.8.3)
templates_min_evidence = 3
templates_min_verification_weight = 0.30  # 0.30 = Untested (lenient); 0.60 = Merged

Storage + maintenance

[storage]
max_age_days = 180               # Max episode age for pruning
min_utility_threshold = 0.05     # Min utility to keep
min_retrievals = 2               # Min retrievals before pruning allowed
consolidation_threshold = 0.85   # BKM merge threshold
cluster_threshold = 0.85
stale_age_days = 30
stale_utility_threshold = 0.2

Decay rates are scope-aware (per the ValidityScope on each episode's claim):

Scope	Decay/day	Half-life
Forever	0.000	∞
Language { name }	0.001	~3 years
Domain { tag }	0.005	~140 days
Project { name }	0.010	~70 days
Crate { name, version }	0.020	~35 days
Workaround { ref, expires }	0.050	~14 days
(no scope set, legacy)	0.010	~70 days

Under the Hood

Multi-hop Bellman Propagation

Value from helpful episodes spreads through the similarity graph in multiple hops:

Hop 0: Source episodes (high helpfulness, ≥2 retrievals)
  │
  ▼  γ¹ discount
Hop 1: Similar episodes updated
  │
  ▼  γ² discount
Hop 2: Episodes similar to hop-1 updated
  │
  ▼  Converges when no updates occur

Session Chaining

Episodes captured within 2 hours of each other in the same project are automatically linked:

Session abc123:
  ├── Episode 1: "Investigated auth bug" (debug)
  ├── Episode 2: "Found root cause in token validation" (research)
  └── Episode 3: "Fixed token expiry check" (bugfix, success)
       ↓
  Temporal credit flows back to episodes 1 & 2
  Session-linked propagation boosts all 3

The Dream Cycle (v0.7)

A budgeted background pipeline that runs nightly (or on demand). Each phase shares a CostBudget; free phases ignore it, paid phases check try_spend() before each LLM call.

verify_advance  →  decay  →  reflect  →  patterns  →  contradict  →  templates
   (free)         (free)   (Sonnet)    (Sonnet)    (Haiku)        (Sonnet)
                          ↓             ↓            ↓             ↓
                  reflections/  patterns/   contradictions  templates/

• verify_advance: bumps episodes from Merged to StableNoRevert after stable_threshold_days.
• decay: scope-aware utility decay (see table above).
• reflect: Haiku triage gates Sonnet authorship; high-signal days get a reflection page.
• patterns: agglomerative clustering on reflection embeddings → cross-day themes.
• contradict: pairs frequently-retrieved BKM episodes and asks Haiku whether they disagree on a factual claim; surfaces a Wilson 95% CI on the contradiction rate.
• templates: groups successful verified episodes by (task_type, domain), extracts reusable step sequences via Sonnet.

Worst case per full cycle: roughly $0.50 with default settings.

Scoring Formula

Retrieval ranking combines three signals with normalized weights:

score = (sim_w × similarity + util_w × utility + rec_w × recency) / (sim_w + util_w + rec_w)

Default in hybrid mode: 85% similarity (RRF-normalized over vector + BM25), 15% utility, 0% recency. The VerificationState of each episode multiplies into salience — well-verified successes weigh more.

Cross-project routing (v0.10)

Every claim carries a ValidityScope that determines:

• Decay rate (table above).
• Transferability: is_transferable() returns true for everything except Project { name }. The retrieve and brief surfaces use this to decide what surfaces when the agent opts into cross_project=true.

Legacy episodes captured before v0.6.4 don't have a scope set, so they stay project-bound by default. New captures (v0.10.3+) get a scope suggested automatically by the intent-extraction LLM call — using a colon-encoded format like language:rust, crate:[email protected], domain:async-rust, workaround:repo#123, or project. The default when in doubt is project, keeping the system conservative.

Maintenance

Run periodically to keep memory healthy:

# Nightly: dream cycle (verify_advance + decay + reflect + patterns + contradict + templates)
tempera dream --max-usd 0.50

# Weekly: Propagate utility values (multi-hop with convergence)
tempera propagate --temporal

# Monthly: Clean up old/useless episodes
tempera prune --older-than 90 --min-utility 0.2 --execute

# As needed: Check trends
tempera trends

# As needed: Review and consolidate
# (via MCP) tempera_review(action: "consolidate")

# As needed: health check + auto-remediate
tempera doctor --remediate --yes

The dream cycle is the load-bearing piece for long-running memory hygiene. It uses Haiku for cheap gating and Sonnet for authorship — the default $0.50 cap is the worst case across every phase.

Environment Variables

Variable	Description
ANTHROPIC_API_KEY	For LLM-based intent extraction (--extract-intent)
TEMPERA_DATA_DIR	Override default data directory
FASTEMBED_CACHE_DIR	Override embedding model cache location

Troubleshooting

MCP server not loading

1. Check path: ls /path/to/tempera-mcp
2. Check config: cat ~/.claude.json
3. Restart Claude Code completely
4. Run /mcp to verify

Embeddings slow on first run

The BGE-Small model (~128MB) downloads on first use from HuggingFace. This requires internet access. After download, the model is cached at ~/.tempera/models/ and works offline.

Vector search not finding anything

Run tempera index to create/update the vector database.

Model download fails

If behind a firewall or proxy, ensure access to huggingface.co. The model files are downloaded via HTTPS.

tempera_brief returns "nothing to surface"

This is normal early on — the brief joins against signal data (mistakes, asks, templates, calibration) that accrues over time. Specifically:

• The mistakes / should-have-asked sections only fire when the files you pass overlap with previously-logged rows.
• The template section only fires when at least 3 successful verified episodes share the (task_type, domain) pair (templates accrue during the dream cycle).
• The calibration warning needs ≥5 declared-success captures in the bucket before it surfaces.

Fall back to tempera_retrieve for episode-level recall.

tempera retrieve --cross-project finds nothing

Episodes captured before v0.6.4 don't have a ValidityScope set, and v0.10's cross-project filter treats unscoped claims as project-bound (conservative default). Either (a) capture new episodes with v0.10.3+, which auto-suggests a scope, or (b) manually classify legacy episodes via the MCP validity_scope parameter on capture.

License

Apache 2.0

Contributing

Contributions welcome! Please open an issue or PR.