You fix a tone. You rewrite a regex. You re-explain how your team formats PRs. Then the AI forgets, and you do it all again next session.

Gradata turns every correction into a rule your AI carries forward. Not a longer prompt. Not a bigger context window. A behavioral rule that graduates from instinct → pattern → rule the more it proves itself — and dies the moment it stops.

pip install gradata

One-command setup for Claude Code, Cursor, Windsurf, or any MCP-compatible IDE:

npx gradata-install install --ide=claude-code

Works with any LLM. Python 3.11+. Zero required dependencies. Local-first. Apache-2.0.

Memory systems remember what you said. Gradata learns how you think.

• vs fine-tuning — no training run, no model lock-in, no GPU. Adapts at inference time.
• vs system prompts — static rules you hand-write vs dynamic rules the model earns.
• vs Mem0 / Letta — they store context; Gradata evolves behavior. Use both.

Not generally smarter. Calibrated to you.

flowchart LR
    A["You correct your AI"] --> B["brain.correct(draft, final)"]
    B --> C["Behavioral rule extracted"]
    C --> D["Confidence grows with reinforcement"]
    D --> E["INSTINCT → PATTERN → RULE"]
    E --> F["Related rules cluster → META-RULE"]
    F --> G["Your AI converges on YOUR judgment"]

    style A fill:#6366f1,stroke:#4f46e5,color:#fff
    style E fill:#8b5cf6,stroke:#7c3aed,color:#fff
    style G fill:#10b981,stroke:#059669,color:#fff

Every correction creates a lesson. Lessons compete. Contradicted rules lose confidence and die. Idle rules decay. Only rules that survive real-world application get promoted into your AI's behavior.

This is evolution, not configuration.

stateDiagram-v2
    [*] --> INSTINCT: Correction captured
    INSTINCT --> PATTERN: Reinforced across sessions
    PATTERN --> RULE: Proven + passes adversarial gates
    RULE --> META_RULE: 3+ related rules cluster

    INSTINCT --> KILLED: Contradicted or idle
    PATTERN --> INSTINCT: Confidence dropped
    RULE --> ARCHIVE: Graduated (reference only)

Ablation v4 — 4 models × 6 conditions × 16 tasks × 3 iterations = 432 trials, blind-judged by Haiku 4.5.

The rules aren't just a format trick. We ran the Min et al. (2022) random-label control — plausible-but-unrelated rule text in the same envelope. Three of four models regress by 3–10%. Content is doing the work, not XML structure.

Smaller/local models benefit most. Frontier models get calibrated faster. The curve is the product demo: corrections-per-session drops monotonically as the brain converges.

from gradata import Brain

brain = Brain.init("./my-brain")

# Your AI produces output. You fix it. The brain learns.
brain.correct(
    draft="We are pleased to inform you of our new product offering.",
    final="Hey, check out what we just shipped.",
)
# → Extracts: "Write in a casual, direct tone, avoid formal business language"

# Next session, learned rules are injected automatically:
rules = brain.apply_brain_rules("write an email")
# → [RULE] TONE: Write in a casual, direct tone...

# Prove the brain is converging:
brain.manifest()   # Mathematical proof of convergence
brain.prove()      # Paired t-test on correction rate

/plugin marketplace add Gradata/gradata
/plugin install gradata

Prereq: pipx install gradata. See .claude-plugin/README.md.

pipx install gradata
gradata install-hook --ide=claude-code

The @gradata/cli npm package talks to a local Gradata daemon — no Python required at call time:

npm i @gradata/cli

import { GradataClient } from "@gradata/cli";

const client = new GradataClient({ endpoint: "http://127.0.0.1:8765" });
await client.correct({
  draft: "We are pleased to inform you of our new product offering.",
  final: "Hey, check out what we just shipped.",
  outputType: "email",
});

Full API in packages/npm/README.md.

docker run --rm -p 8765:8765 -v $(pwd)/brain:/brain \
  ghcr.io/gradata/gradata/daemon:latest \
  daemon --brain-dir /brain --port 8765

Or docker build -t gradata/daemon:dev . from the repo root. docker-compose.yml included for local dev.

gradata init ./my-brain        # create a brain
gradata demo ./eval-brain      # try a pre-trained one
gradata convergence            # ASCII chart of correction trend
gradata manifest --json        # mathematical convergence proof
gradata review                 # approve/reject pending promotions
gradata stats                  # brain health metrics
gradata doctor                 # diagnose issues

Core learning loop

• brain.correct(draft, final) — captures corrections, extracts behavioral instructions
• brain.apply_brain_rules(task) — injects graduated rules into prompts
• brain.manifest() / brain.prove() — convergence proof, not vanity metrics
• Event bus: brain.bus.on("correction.created" | "lesson.graduated" | "meta_rule.created" | "session.ended", handler)

Meta-rules. When 3+ rules cluster, an LLM synthesizes a scoped meta-rule with applies_when / never_when conditions.

Security. PII redaction before storage • HMAC-SHA256 provenance on every correction • score obfuscation so confidence never leaks to the LLM • per-brain salt on graduation thresholds.

Integrations. OpenAI · Anthropic · LangChain · CrewAI adapters · MCP server for Claude Code / Cursor / Windsurf · Claude Code hooks that auto-capture corrections · custom providers via GRADATA_LLM_PROVIDER=openai (or any OpenAI-compatible endpoint).

Every graduated rule can be traced back to the corrections that created it. No opaque behavior. Git diff for AI preferences.

from gradata import Brain

brain = Brain("./my-brain")

# List graduated rules (optionally filter)
brain.rules()
brain.rules(include_all=True, category="tone")

# Trace a rule to the corrections that created it
brain.explain("rule_abc123")
# → {"rule_id": ..., "description": ..., "source_corrections": [...], "sessions": [...]}

# Full provenance chain (rule → lesson → corrections → events)
brain.trace("rule_abc123")

# Export for review, diffing, or sharing
brain.export_data(output_format="json")           # or "yaml"
brain.export_rules(min_state="PATTERN")           # OpenSpace-compatible SKILL.md
brain.export_rules_json(min_state="RULE")         # flat sorted array
brain.export_skill(output_dir="./skills")         # full skill directory
brain.export_tree(format="obsidian", path="./vault")

# Human veto
brain.pending_promotions()
brain.approve_promotion("rule_abc123")
brain.reject_promotion("rule_abc123")

Full signatures in docs/sdk/brain.md.

graph TB
    subgraph Public API
        Brain["brain.py"]
        CLI["cli.py"]
    end

    subgraph Core Pipeline
        Core["_core.py"]
        EventBus["events_bus.py"]
        EventLog["_events.py"]
    end

    subgraph Enhancements
        Classifier["edit_classifier.py"]
        Cache["instruction_cache.py"]
        Graduation["self_improvement.py"]
        Meta["meta_rules.py"]
    end

    subgraph Security
        PII["safety.py"]
        Prov["correction_provenance.py"]
    end

    Brain --> Core
    Core --> Classifier --> Cache
    Core --> Graduation --> Meta
    Core --> EventBus
    Core --> EventLog
    Core --> PII
    Core --> Prov

    style Brain fill:#6366f1,stroke:#4f46e5,color:#fff
    style Core fill:#8b5cf6,stroke:#7c3aed,color:#fff
    style EventBus fill:#f59e0b,stroke:#d97706,color:#fff

• src/gradata/ — the Python SDK (correction → rules → graduation pipeline)
• tests/ — SDK tests (pytest)
• docs/ — mkdocs site sources (published to gradata.ai/docs)
• examples/ — SDK usage examples
• packages/npm/ — @gradata/cli JS client
• gradata-install/ — npm wrapper for one-command IDE setup
• .claude-plugin/ + hooks/ — Claude Code plugin manifest
• brain/ — research scripts (benchmarks, simulations)

• GitHub Issues — bugs, features, questions
• GitHub Discussions — ideas, show & tell
• Docs — guides, API reference

Built on Constitutional AI (Anthropic, 2022), Duolingo's half-life regression (Settles & Meeder, ACL 2016), the Copilot RCT (Peng et al., 2023), SuperMemo's two-component memory model (Wozniak, 1995), and MT-Bench LLM-as-judge (Zheng et al., NeurIPS 2023). Sits alongside Mem0, Letta, and EverMind — with one difference: Gradata learns from your corrections, not just recalls facts. Full credits in CREDITS.md.

See CONTRIBUTING.md.

Apache-2.0. The full SDK — rules, hooks, graduation, meta-synthesis, scoring, profiling — is permissively open. Use it anywhere: commercial, proprietary, SaaS, internal tooling. No copyleft, no linking obligations, no commercial-license upsell.

Gradata Cloud is an optional hosted service (team brain, corrections corpus, brain marketplace, managed LLM). The SDK does not require it — everything works locally with your own LLM key.