diff --git a/.gitignore b/.gitignore index 7ac257f..ba3cdc6 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,9 @@ cdk.out/ .DS_Store *.log .env + +# Python +__pycache__/ +*.pyc +.venv/ +.pytest_cache/ diff --git a/README.md b/README.md index d5aa4ee..3455c6e 100644 --- a/README.md +++ b/README.md @@ -120,6 +120,19 @@ GitHub Actions workflows are defined in: Deployment uses OIDC role assumption (no long-lived AWS keys). +## Demo Agents + +The `demo/agents/` directory contains working AI tutors (LangGraph-based) that demonstrate TLCS integration for learner memory. These agents run locally and connect to deployed TLCS infrastructure. + +```bash +cd demo/agents +uv sync +export OPENAI_API_KEY=your-key +uv run python scripts/demo_alice_conversation.py --mock # Full demo with AI conversation +``` + +See [demo/agents/README.md](demo/agents/README.md) for full documentation. + ## Account Bootstrap See [`docs/bootstrap-and-operations.md`](docs/bootstrap-and-operations.md) for bootstrap, deploy, environment teardown, observability, and idempotency guidance. diff --git a/demo/agents/.env.example b/demo/agents/.env.example new file mode 100644 index 0000000..e57462a --- /dev/null +++ b/demo/agents/.env.example @@ -0,0 +1,23 @@ +# Model Provider: "openai" or "bedrock" +MODEL_PROVIDER=openai + +# OpenAI (if MODEL_PROVIDER=openai) +OPENAI_API_KEY=sk-... + +# AWS Bedrock (if MODEL_PROVIDER=bedrock) +AWS_REGION=us-east-1 +AWS_ACCESS_KEY_ID=... +AWS_SECRET_ACCESS_KEY=... + +# TLCS Integration +TLCS_ENDPOINT=https://your-tlcs-api.amazonaws.com +TLCS_CLIENT_ID=your-cognito-client-id +TLCS_CLIENT_SECRET=your-cognito-client-secret +TLCS_TOKEN_URL=https://your-cognito-domain/oauth2/token + +# Storage Backend: "memory" or "dynamodb" +STORAGE_BACKEND=memory + +# DynamoDB (if STORAGE_BACKEND=dynamodb) +DYNAMODB_ENDPOINT=http://localhost:8000 +MEMORIES_TABLE=demo-agents-memories diff --git a/demo/agents/ARCHITECTURE.md b/demo/agents/ARCHITECTURE.md new file mode 100644 index 0000000..64f069e --- /dev/null +++ b/demo/agents/ARCHITECTURE.md @@ -0,0 +1,163 @@ +# Demo Agents Architecture + +## Component Overview + +### Memory Service (`memory/service.py`) + +Central service implementing Pre-read 2.3 five-component architecture: + +1. **Memory Store**: In-memory or DynamoDB-backed storage for MemoryItems +2. **Policy & AuthZ**: Scope-based access control via Grants +3. **Context Router**: Scoped retrieval with minimization +4. **Audit Subsystem**: Append-only operation log +5. **Interoperability**: Caliper export, TLCS sync + +### TLCS Client (`memory/tlcs/`) + +HTTP client for TLCS integration: + +- OAuth2 client credentials flow (Cognito) +- Token caching with auto-refresh +- `push_caliper()`: POST to /ingest/caliper +- `pull_context()`: GET from /learners/{id}/context + +### Scope System (`memory/scopes.py`) + +Multi-dimensional access control: + +- **Subject areas**: math, writing, general +- **Categories**: mastery, goal, preference, constraint +- **Source types**: user_declared, model_inferred +- **Purposes**: tutoring, assessment, college_apps + +### LangGraph Agents (`graph/agents/`) + +AI tutors built with LangGraph: + +- Memory extraction from conversations +- Personalized responses using learner context +- Automatic memory updates based on interactions + +## Data Flow + +``` +1. User → Agent + Natural language conversation + +2. Agent → Memory Service + Extract and store memories + +3. Memory Service → TLCS + Push Caliper events + +4. TLCS → Memory Service + Pull learner context + +5. Memory Service → Agent + Provide scoped context + +6. Agent → User + Personalized response +``` + +## Schema Mapping + +### MemoryItem → Caliper Event + +| MemoryItem Field | Caliper Field | Notes | +|------------------|---------------|-------| +| `id` | `generated.@id` | As URN | +| `category=MASTERY` | `@type: "AssessmentEvent"` | Event type | +| `category=GOAL` | `@type: "AssignableEvent"` | With action "Activated" | +| `confidence` | `extensions.scoreGiven` | Numeric 0-1 | +| `subject_area` | `object.name` | Subject context | +| `source_type` | `extensions.sourceType` | "user_declared" / "model_inferred" | +| `content` | `extensions.description` | Human-readable | +| `learner_id` | `actor.@id` | As URN | + +### TLCS Context → MemoryItem + +| TLCS Field | MemoryItem Field | Notes | +|------------|------------------|-------| +| `session.credentialId` | `original_id` | Round-trip fidelity | +| `session.issuerId` | `metadata["issuer_id"]` | Store in metadata | +| `trust.issuerKnown` | `metadata["issuer_known"]` | Trust indicator | +| `context.course` | `subject_area` | Direct mapping | +| `claim.category` | `category` | See mapping below | + +**Claim Category Mapping:** + +| TLCS Category | MemoryCategory | +|---------------|----------------| +| `achievement` | `MASTERY` | +| `competency` | `MASTERY` | +| `activity` | `FACT` | +| `participation` | `FACT` | + +## Security Model + +### Scope-Based Access + +Every memory operation validates: + +1. **Grant validity**: Is the grant active and not expired? +2. **Subject scope**: Does the memory's subject match allowed subjects? +3. **Category scope**: Is this category accessible to the agent? +4. **Purpose scope**: Is the requested purpose allowed? +5. **Source type**: Should model-inferred memories be included? + +### Trust Indicators + +Memories from TLCS include trust metadata: + +- `issuer_known`: Is the issuer in TLCS's trusted list? +- `source_type`: Was this user-declared or model-inferred? +- `provenance`: Full chain of custody + +## Deployment Options + +### Local Development + +``` +┌─────────────────────────────────────────────┐ +│ Local Machine │ +│ │ +│ ┌─────────────┐ ┌─────────────────────┐ │ +│ │ LangGraph │ │ Memory Service │ │ +│ │ Studio │───>│ (in-memory) │ │ +│ └─────────────┘ └─────────────────────┘ │ +│ │ │ +│ v │ +│ ┌─────────────────────┐ │ +│ │ Mock TLCS Client │ │ +│ └─────────────────────┘ │ +└─────────────────────────────────────────────┘ +``` + +### AWS Deployment + +``` +┌────────────────────────────────────────────────────────────┐ +│ AWS Account │ +│ │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ Demo Agents (ECS) │ │ +│ │ ┌─────────────┐ ┌─────────────────────┐ │ │ +│ │ │ LangGraph │ │ Memory Service │ │ │ +│ │ │ Container │───>│ (DynamoDB) │ │ │ +│ │ └─────────────┘ └─────────────────────┘ │ │ +│ └────────────────────────────│─────────────────────────┘ │ +│ │ │ +│ v │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ TLCS (Lambda) │ │ +│ │ POST /ingest/caliper GET /learners/{id}/context │ │ +│ └──────────────────────────────────────────────────────┘ │ +│ │ │ +│ v │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ Cognito (OAuth2) │ │ +│ │ Token endpoint for client credentials flow │ │ +│ └──────────────────────────────────────────────────────┘ │ +└────────────────────────────────────────────────────────────┘ +``` diff --git a/demo/agents/Dockerfile b/demo/agents/Dockerfile new file mode 100644 index 0000000..d753c6f --- /dev/null +++ b/demo/agents/Dockerfile @@ -0,0 +1,29 @@ +FROM python:3.12-slim + +WORKDIR /app + +# Install uv for fast dependency management +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +# Copy project files +COPY pyproject.toml . +COPY graph/ ./graph/ +COPY memory/ ./memory/ +COPY langgraph.json . + +# Install dependencies with AWS extras +RUN uv pip install --system -e ".[aws,api]" + +# Environment defaults +ENV MODEL_PROVIDER=bedrock +ENV STORAGE_BACKEND=memory +ENV PORT=8000 + +EXPOSE 8000 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ + CMD python -c "import httpx; httpx.get('http://localhost:8000/health')" || exit 1 + +# Run LangGraph API server +CMD ["langgraph", "up", "--host", "0.0.0.0", "--port", "8000"] diff --git a/demo/agents/README.md b/demo/agents/README.md new file mode 100644 index 0000000..59d7d0b --- /dev/null +++ b/demo/agents/README.md @@ -0,0 +1,185 @@ +# TLCS Demo Agents + +AI tutoring agents that demonstrate TLCS learner memory capabilities. + +## Overview + +This demo provides working LangGraph-based AI tutors (Math Tutor, Writing Coach) +that use TLCS to store and retrieve learner context. It showcases: + +- **Caliper ingestion**: Tutors push learning events to TLCS +- **Context retrieval**: Tutors pull learner profiles from TLCS +- **Cross-agent sharing**: Scoped access between different tutoring tools +- **Memory lifecycle**: Corrections, versioning, audit trails + +## Architecture + +``` +┌─────────────────┐ Caliper Events ┌─────────────────┐ +│ Math Tutor │ ───────────────────────>│ TLCS │ +│ (LangGraph) │ │ /ingest/* │ +└─────────────────┘ └─────────────────┘ + │ │ + │ GET /learners/{id}/context │ + │<──────────────────────────────────────────│ + v +┌─────────────────┐ +│ Memory Service │ ← Stores memories locally + syncs with TLCS +│ (Python) │ +└─────────────────┘ +``` + +## Running Demos + +### Main Demo: Alice Story with AI + TLCS (Recommended) + +This is the primary demo showing real AI conversation with TLCS integration: + +```bash +cd demo/agents +uv sync +export OPENAI_API_KEY=your-key # Required for AI conversation +uv run python scripts/demo_alice_conversation.py --mock +``` + +This demo shows: +1. Alice talks to Math Tutor (real LLM conversation) +2. Memories are automatically extracted by the AI +3. Memories are pushed to TLCS as Caliper events +4. Writing Coach pulls from TLCS (with scoped access) +5. Writing Coach talks to Alice (personalized from TLCS context) + +**Demo Flags:** + +| Flag | Description | +|------|-------------| +| `--mock` | Use simulated TLCS (default, no server needed) | +| `--real` | Use deployed AWS TLCS (requires env vars below) | +| `--quiet`, `-q` | Hide detailed output (Caliper JSON, filtered memories, injected context) | + +**Verbose output is on by default** for presentations - it shows evidence that: +- Memories were extracted and converted to Caliper format +- Scope filtering excluded math-specific memories from Writing Coach +- The AI received learner context in its prompt + +### Other Demos (No API key needed) + +```bash +uv run python scripts/demo_alice.py # Scoping demo (programmatic) +uv run python scripts/demo_tlcs_sync.py --mock # TLCS sync demo +``` + +### With Deployed TLCS + +Run the main demo against real AWS infrastructure: + +```bash +# Get these values from CDK deploy output: +# npx cdk deploy TrustedLearningContextServiceStack-dev --context stage=dev +export TLCS_ENDPOINT=https://your-api.execute-api.region.amazonaws.com +export TLCS_TOKEN_URL=https://your-pool.auth.region.amazoncognito.com/oauth2/token +export TLCS_CLIENT_ID=your-client-id +export TLCS_CLIENT_SECRET=your-client-secret # From Cognito console +export OPENAI_API_KEY=your-key + +uv run python scripts/demo_alice_conversation.py --real --verbose +``` + +The `--real` flag validates all required environment variables before running. + +### LangGraph Studio + +```bash +cd demo/agents +langgraph dev +``` + +## Demo Scenarios + +### Scenario 1: Alice Full Story (`demo_alice_conversation.py`) ⭐ MAIN DEMO + +The complete Pre-read Section 3 scenario with **real AI conversations**: + +1. Alice has a real conversation with Math Tutor (LLM responds) +2. AI automatically extracts memories (goals, preferences, mastery) +3. Memories pushed to TLCS as Caliper events +4. Writing Coach pulls from TLCS with scoped access +5. Writing Coach has personalized conversation with Alice + +**What the audience sees:** +- Actual AI tutor responses (not scripted) +- Memories being extracted in real-time +- TLCS integration via Caliper +- Cross-tool scoping (math mastery hidden from writing coach) + +### Scenario 2: Scoping Demo (`demo_alice.py`) + +Programmatic demo (no LLM needed) showing scope filtering: + +1. Pre-defined memories added for Alice +2. Math Tutor sees all 7 memories +3. Writing Coach sees only 3 (general preferences, not math) +4. Shows filtering rules in action + +### Scenario 3: TLCS Sync (`demo_tlcs_sync.py`) + +Shows bidirectional sync with TLCS: + +1. Add local memories to portable memory store +2. **Push to TLCS**: Export as Caliper events → POST /ingest/caliper +3. **Pull from TLCS**: GET /learners/{id}/context → convert to MemoryItems +4. Merge strategies: add_new, replace, skip_all + +## Key Files + +| File | Purpose | +|------|---------| +| `graph/agents/math_tutor.py` | LangGraph Math Tutor agent | +| `graph/agents/writing_coach.py` | LangGraph Writing Coach agent | +| `memory/service.py` | Central memory service with TLCS sync | +| `memory/tlcs/client.py` | TLCS HTTP client (OAuth2) | +| `memory/tlcs/mapper.py` | Caliper ↔ MemoryItem conversion | +| `memory/scopes.py` | Access scope definitions | + +## Scope Filtering Architecture + +Currently, **scope-based filtering happens client-side** in the demo agents code: + +1. Agent pulls all learner context from TLCS (`GET /learners/{id}/context`) +2. Demo's `PortableMemoryService` applies scope filtering locally +3. Filtered memories are used for personalization + +The filtering logic lives in: +- `memory/scopes.py` - `Scope.allows()` method checks subject_area, category, purpose +- `memory/service.py` - `get_memories()` applies the scope filter + +**Future TLCS Enhancement**: This filtering could move server-side to TLCS core. The `GET /learners/{id}/context` endpoint could accept scope parameters: + +``` +GET /learners/{id}/context?subjects=writing,general&categories=preference,constraint +``` + +This would enable TLCS to enforce data minimization at the API level, rather than trusting clients to filter appropriately. The `Scope` dataclass in `memory/scopes.py` defines the contract that could become query parameters or a request body for server-side filtering. + +## Environment Variables + +| Variable | Required | Description | +|----------|----------|-------------| +| `TLCS_ENDPOINT` | Yes* | TLCS API URL | +| `TLCS_CLIENT_ID` | Yes* | Cognito client ID | +| `TLCS_CLIENT_SECRET` | Yes* | Cognito client secret | +| `TLCS_TOKEN_URL` | Yes* | Cognito token endpoint | +| `MODEL_PROVIDER` | No | `openai` or `bedrock` (default: openai) | +| `STORAGE_BACKEND` | No | `memory` or `dynamodb` (default: memory) | + +*Required for TLCS integration demos, optional for local-only demos. + +## Pre-read References + +This demo implements concepts from the "Portable Learner Memory" pre-read: + +- **Section 2.3**: Five-component architecture (memory store, policy, context router, audit, interop) +- **Section 2.4**: User-declared vs model-inferred distinction +- **Section 3**: Alice story (cross-tool memory sharing) +- **Section 4.1**: Memory lifecycle with versioning +- **Section 4.4**: Correction pathways, provenance metadata diff --git a/demo/agents/graph/__init__.py b/demo/agents/graph/__init__.py new file mode 100644 index 0000000..d1670f3 --- /dev/null +++ b/demo/agents/graph/__init__.py @@ -0,0 +1,15 @@ +"""Graph module for TLCS demo agents.""" + +from graph.agents.math_tutor import build_math_tutor_graph +from graph.agents.writing_coach import build_writing_coach_graph +from graph.main import build_graph +from graph.state import PortableMemoryState +from graph.config import PortableMemoryConfig + +__all__ = [ + "build_graph", + "build_math_tutor_graph", + "build_writing_coach_graph", + "PortableMemoryState", + "PortableMemoryConfig", +] diff --git a/demo/agents/graph/agents/__init__.py b/demo/agents/graph/agents/__init__.py new file mode 100644 index 0000000..e8f0b21 --- /dev/null +++ b/demo/agents/graph/agents/__init__.py @@ -0,0 +1,12 @@ +"""Agent definitions for TLCS demo.""" + +from graph.agents.base import get_memory_service, reset_memory_service +from graph.agents.math_tutor import build_math_tutor_graph +from graph.agents.writing_coach import build_writing_coach_graph + +__all__ = [ + "get_memory_service", + "reset_memory_service", + "build_math_tutor_graph", + "build_writing_coach_graph", +] diff --git a/demo/agents/graph/agents/base.py b/demo/agents/graph/agents/base.py new file mode 100644 index 0000000..9212266 --- /dev/null +++ b/demo/agents/graph/agents/base.py @@ -0,0 +1,154 @@ +"""Shared agent factory with memory service integration. + +Pre-read References: +- Section 2.3: Five-component architecture +- Section 2.5: Enforceable operations (all via service layer) + +Each agent: +1. Receives a grant with its scope at startup +2. Fetches scoped memories via the service +3. Generates response with memory context +4. All operations are audited +""" + +from typing import Callable + +from langchain_core.language_models import BaseChatModel +from langchain_core.messages import SystemMessage +from langgraph.graph import StateGraph, START, END +from langgraph.types import RunnableConfig + +from graph.state import PortableMemoryState +from graph.config import PortableMemoryConfig +from graph.models import get_chat_model +from memory.service import PortableMemoryService +from memory.scopes import Scope +from memory.grants import Grant, create_grant + + +# Global service instance (shared across agents) +_memory_service: PortableMemoryService | None = None + + +def get_memory_service() -> PortableMemoryService: + """Get or create the global memory service instance.""" + global _memory_service + if _memory_service is None: + _memory_service = PortableMemoryService() + return _memory_service + + +def reset_memory_service() -> None: + """Reset the memory service. For testing only.""" + global _memory_service + _memory_service = None + + +def _get_config_value(config: RunnableConfig, key: str, default): + """Get a value from the configurable dict.""" + configurable = config.get("configurable", {}) + return configurable.get(key, default) + + +def build_subject_agent( + name: str, + subject_area: str, + scope: Scope, + format_prompt: Callable[[list[dict]], str], + tutor_model: str | BaseChatModel = "gpt-4o", + extraction_model: str | BaseChatModel = "gpt-4o-mini", + model_kwargs: dict | None = None, +) -> StateGraph: + """Factory for subject-specific agents. + + Creates an agent that: + 1. Fetches memories scoped to its subject area + 2. Uses a subject-specific prompt + 3. All operations go through the memory service (Pre-read 2.5) + + Args: + name: Agent name (e.g., "math_tutor") + subject_area: Subject this agent focuses on (e.g., "math") + scope: The scope this agent operates under + format_prompt: Function to format system prompt with memories + tutor_model: Model for tutoring responses + extraction_model: Model for memory extraction (not used in simplified version) + model_kwargs: Additional model kwargs + + Returns: + Compiled StateGraph + """ + # Initialize models if strings (uses MODEL_PROVIDER env var) + if isinstance(tutor_model, str): + tutor_model = get_chat_model(tutor_model, model_kwargs) + + def get_or_create_grant( + learner_id: str, + config: RunnableConfig, + ) -> Grant: + """Get or create a grant for this agent and learner.""" + service = get_memory_service() + + # Check for existing grant + existing = service.grant_store.get_for_agent(name, learner_id) + if existing: + return existing + + # Create new grant + grant = create_grant( + learner_id=learner_id, + agent_id=name, + scope=scope, + ) + service.grant_store.store(grant) + return grant + + def tutor_node( + state: PortableMemoryState, + config: RunnableConfig, + ) -> dict: + """Generate a tutoring response using memory context.""" + service = get_memory_service() + learner_id = state.get("learner_id", "default_learner") + + # Get grant for this agent + grant = get_or_create_grant(learner_id, config) + + # Fetch scoped memories + max_memories = _get_config_value(config, "max_memories_in_context", 20) + memories = service.search_memories( + query="", # Get all relevant memories + learner_id=learner_id, + grant=grant, + limit=max_memories, + purpose="tutoring", + ) + + # Convert to dicts for prompt formatting + memory_dicts = [m.to_dict() for m in memories] + + # Format system prompt with memory context + system_prompt = format_prompt(memory_dicts) + + # Build messages with system prompt + messages = [SystemMessage(content=system_prompt)] + state["messages"] + + # Generate response + response = tutor_model.invoke(messages) + + return {"messages": [response]} + + # Build graph + builder = StateGraph( + PortableMemoryState, + config_schema=PortableMemoryConfig, + ) + + # Add nodes + builder.add_node("tutor", tutor_node) + + # Add edges: START -> tutor -> END + builder.add_edge(START, "tutor") + builder.add_edge("tutor", END) + + return builder.compile() diff --git a/demo/agents/graph/agents/math_tutor.py b/demo/agents/graph/agents/math_tutor.py new file mode 100644 index 0000000..443cab9 --- /dev/null +++ b/demo/agents/graph/agents/math_tutor.py @@ -0,0 +1,45 @@ +"""Math Tutor agent. + +A subject-specific agent for math tutoring that: +- Reads math + general memories +- Writes mastery, preference, goal, constraint memories for math +- All operations scoped and audited +""" + +from langchain_core.language_models import BaseChatModel +from langgraph.graph import StateGraph + +from graph.agents.base import build_subject_agent +from graph.prompts.math_tutor import format_math_tutor_prompt +from memory.scopes import create_math_tutor_scope + + +def build_math_tutor_graph( + tutor_model: str | BaseChatModel = "gpt-4o", + extraction_model: str | BaseChatModel = "gpt-4o-mini", + model_kwargs: dict | None = None, +) -> StateGraph: + """Build the Math Tutor agent graph. + + The math tutor: + - Can read math and general memories + - Can write mastery, preference, goal, constraint memories + - All operations scoped to math subject area + + Args: + tutor_model: Model for tutoring responses + extraction_model: Model for memory extraction + model_kwargs: Additional model kwargs + + Returns: + Compiled StateGraph + """ + return build_subject_agent( + name="math_tutor", + subject_area="math", + scope=create_math_tutor_scope(), + format_prompt=format_math_tutor_prompt, + tutor_model=tutor_model, + extraction_model=extraction_model, + model_kwargs=model_kwargs, + ) diff --git a/demo/agents/graph/agents/writing_coach.py b/demo/agents/graph/agents/writing_coach.py new file mode 100644 index 0000000..9f86ffd --- /dev/null +++ b/demo/agents/graph/agents/writing_coach.py @@ -0,0 +1,45 @@ +"""Writing Coach agent. + +A subject-specific agent for writing coaching that: +- Reads writing + general memories +- Writes mastery, preference, goal, constraint memories for writing +- All operations scoped and audited +""" + +from langchain_core.language_models import BaseChatModel +from langgraph.graph import StateGraph + +from graph.agents.base import build_subject_agent +from graph.prompts.writing_coach import format_writing_coach_prompt +from memory.scopes import create_writing_coach_scope + + +def build_writing_coach_graph( + tutor_model: str | BaseChatModel = "gpt-4o", + extraction_model: str | BaseChatModel = "gpt-4o-mini", + model_kwargs: dict | None = None, +) -> StateGraph: + """Build the Writing Coach agent graph. + + The writing coach: + - Can read writing and general memories + - Can write mastery, preference, goal, constraint memories + - All operations scoped to writing subject area + + Args: + tutor_model: Model for tutoring responses + extraction_model: Model for memory extraction + model_kwargs: Additional model kwargs + + Returns: + Compiled StateGraph + """ + return build_subject_agent( + name="writing_coach", + subject_area="writing", + scope=create_writing_coach_scope(), + format_prompt=format_writing_coach_prompt, + tutor_model=tutor_model, + extraction_model=extraction_model, + model_kwargs=model_kwargs, + ) diff --git a/demo/agents/graph/config.py b/demo/agents/graph/config.py new file mode 100644 index 0000000..428b176 --- /dev/null +++ b/demo/agents/graph/config.py @@ -0,0 +1,34 @@ +"""Configuration schema for Portable Memory experiment. + +These settings appear in LangGraph Studio's sidebar for runtime adjustment. +""" + +from dataclasses import dataclass, field + + +@dataclass(kw_only=True) +class PortableMemoryConfig: + """Runtime configuration for the Portable Memory agents. + + Shared configuration used by both Math Tutor and Writing Coach. + """ + + tutor_model: str = field( + default="gpt-4o", + metadata={"description": "Model for tutoring responses (quality matters)"} + ) + + extraction_model: str = field( + default="gpt-4o-mini", + metadata={"description": "Model for memory extraction (speed/cost optimized)"} + ) + + max_memories_in_context: int = field( + default=20, + metadata={"description": "Maximum memories to include in tutor context"} + ) + + extract_memories: bool = field( + default=True, + metadata={"description": "Whether to extract memories each turn"} + ) diff --git a/demo/agents/graph/main.py b/demo/agents/graph/main.py new file mode 100644 index 0000000..07c6fc5 --- /dev/null +++ b/demo/agents/graph/main.py @@ -0,0 +1,132 @@ +"""Main graph definition for TLCS Demo Agents. + +This module contains build_graph() which constructs the LangGraph for +interactive tutoring with memory extraction. +""" + +from typing import Literal + +from langchain_core.language_models import BaseChatModel +from langchain_core.messages import SystemMessage, AIMessage +from langgraph.graph import StateGraph, START, END +from langgraph.types import RunnableConfig + +from graph.state import PortableMemoryState, MemoryDict +from graph.config import PortableMemoryConfig +from graph.models import get_chat_model +from graph.prompts.tutor import format_tutor_prompt +from memory.schema import MemoryItem +from memory.extraction import extract_memories + + +def _get_config_value(config: RunnableConfig, key: str, default): + """Get a value from the configurable dict.""" + configurable = config.get("configurable", {}) + return configurable.get(key, default) + + +def create_tutor_node(tutor_model: BaseChatModel): + """Create the tutor node that generates responses.""" + + def tutor_node( + state: PortableMemoryState, + config: RunnableConfig, + ) -> dict: + """Generate a tutoring response using memory context.""" + # Get memories for context + memories = state.get("memories", []) + max_memories = _get_config_value(config, "max_memories_in_context", 20) + + # Use most recent memories up to limit + context_memories = memories[-max_memories:] if len(memories) > max_memories else memories + + # Format system prompt with memory context + system_prompt = format_tutor_prompt(context_memories) + + # Build messages with system prompt + messages = [SystemMessage(content=system_prompt)] + state["messages"] + + # Generate response + response = tutor_model.invoke(messages) + + return {"messages": [response]} + + return tutor_node + + +def create_extractor_node(extraction_model: BaseChatModel): + """Create the extractor node that identifies new memories.""" + + def extractor_node( + state: PortableMemoryState, + config: RunnableConfig, + ) -> dict: + """Extract new memories from the conversation.""" + # Check if extraction is enabled + extract_enabled = _get_config_value(config, "extract_memories", True) + if not extract_enabled: + return {"memories": []} + + # Get existing memories as MemoryItem objects + existing_dicts = state.get("memories", []) + existing_memories = [MemoryItem.from_dict(m) for m in existing_dicts] + + # Extract new memories from messages + new_memories = extract_memories( + messages=state["messages"], + existing_memories=existing_memories, + model=extraction_model, + ) + + # Convert to dicts for state storage + new_memory_dicts: list[MemoryDict] = [m.to_dict() for m in new_memories] + + return {"memories": new_memory_dicts} + + return extractor_node + + +def build_graph( + tutor_model: str | BaseChatModel = "gpt-4o", + extraction_model: str | BaseChatModel = "gpt-4o-mini", + tutor_model_kwargs: dict | None = None, + extraction_model_kwargs: dict | None = None, +) -> StateGraph: + """Build the interactive tutor graph with memory extraction. + + Args: + tutor_model: Model for tutoring (string name or model instance) + extraction_model: Model for extraction (string name or model instance) + tutor_model_kwargs: Additional kwargs for tutor model init + extraction_model_kwargs: Additional kwargs for extraction model init + + Returns: + Compiled StateGraph + """ + # Initialize models if strings (uses MODEL_PROVIDER env var) + if isinstance(tutor_model, str): + tutor_model = get_chat_model(tutor_model, tutor_model_kwargs) + + if isinstance(extraction_model, str): + extraction_model = get_chat_model(extraction_model, extraction_model_kwargs) + + # Create nodes + tutor_node = create_tutor_node(tutor_model) + extractor_node = create_extractor_node(extraction_model) + + # Build graph + builder = StateGraph( + PortableMemoryState, + config_schema=PortableMemoryConfig, + ) + + # Add nodes + builder.add_node("tutor", tutor_node) + builder.add_node("extractor", extractor_node) + + # Add edges: START -> tutor -> extractor -> END + builder.add_edge(START, "tutor") + builder.add_edge("tutor", "extractor") + builder.add_edge("extractor", END) + + return builder.compile() diff --git a/demo/agents/graph/models.py b/demo/agents/graph/models.py new file mode 100644 index 0000000..e07394a --- /dev/null +++ b/demo/agents/graph/models.py @@ -0,0 +1,113 @@ +"""Model provider abstraction for portable memory. + +This module provides a factory function to create chat models based on +the MODEL_PROVIDER environment variable. Supports: +- OpenAI (default): For local development +- Bedrock: For AWS AgentCore deployment + +Environment variables: + MODEL_PROVIDER: "openai" (default) or "bedrock" + AWS_REGION: AWS region for Bedrock (default: us-east-1) +""" + +import os +from typing import Any + +from langchain_core.language_models import BaseChatModel + + +# Model name mappings for cross-provider compatibility +BEDROCK_MODEL_MAP = { + # OpenAI model names -> Bedrock model IDs + "gpt-4o": "anthropic.claude-3-5-sonnet-20241022-v2:0", + "gpt-4o-mini": "anthropic.claude-3-haiku-20240307-v1:0", + "gpt-4": "anthropic.claude-3-5-sonnet-20241022-v2:0", + "gpt-3.5-turbo": "anthropic.claude-3-haiku-20240307-v1:0", + # Claude model names (for explicit Bedrock usage) + "claude-sonnet": "anthropic.claude-3-5-sonnet-20241022-v2:0", + "claude-haiku": "anthropic.claude-3-haiku-20240307-v1:0", + "claude-3-5-sonnet": "anthropic.claude-3-5-sonnet-20241022-v2:0", + "claude-3-haiku": "anthropic.claude-3-haiku-20240307-v1:0", +} + + +def get_chat_model( + model_name: str | None = None, + model_kwargs: dict[str, Any] | None = None, +) -> BaseChatModel: + """Get a chat model based on the MODEL_PROVIDER environment variable. + + Args: + model_name: Model name/ID. For OpenAI, use OpenAI model names. + For Bedrock, can use OpenAI names (will be mapped) or + Bedrock model IDs directly. + model_kwargs: Additional kwargs to pass to the model constructor. + + Returns: + A BaseChatModel instance. + + Environment: + MODEL_PROVIDER: "openai" (default) or "bedrock" + AWS_REGION: AWS region for Bedrock (default: us-east-1) + """ + provider = os.getenv("MODEL_PROVIDER", "openai").lower() + kwargs = model_kwargs or {} + + if provider == "bedrock": + return _get_bedrock_model(model_name, kwargs) + else: + return _get_openai_model(model_name, kwargs) + + +def _get_openai_model(model_name: str | None, kwargs: dict[str, Any]) -> BaseChatModel: + """Get an OpenAI chat model.""" + from langchain_openai import ChatOpenAI + + return ChatOpenAI( + model=model_name or "gpt-4o", + **kwargs, + ) + + +def _get_bedrock_model(model_name: str | None, kwargs: dict[str, Any]) -> BaseChatModel: + """Get an AWS Bedrock chat model.""" + try: + from langchain_aws import ChatBedrockConverse + except ImportError: + raise ImportError( + "langchain-aws is required for Bedrock models. " + "Install with: uv pip install langchain-aws" + ) + + # Map model name to Bedrock model ID + default_model = "anthropic.claude-3-5-sonnet-20241022-v2:0" + model_id = model_name or default_model + + # If it's an OpenAI or Claude friendly name, map it + if model_id in BEDROCK_MODEL_MAP: + model_id = BEDROCK_MODEL_MAP[model_id] + + return ChatBedrockConverse( + model=model_id, + region_name=os.getenv("AWS_REGION", "us-east-1"), + **kwargs, + ) + + +def get_model_provider() -> str: + """Get the current model provider. + + Returns: + "openai" or "bedrock" + """ + return os.getenv("MODEL_PROVIDER", "openai").lower() + + +def is_bedrock() -> bool: + """Check if using Bedrock models.""" + return get_model_provider() == "bedrock" + + +def is_openai() -> bool: + """Check if using OpenAI models.""" + return get_model_provider() == "openai" diff --git a/demo/agents/graph/prompts/__init__.py b/demo/agents/graph/prompts/__init__.py new file mode 100644 index 0000000..40703d5 --- /dev/null +++ b/demo/agents/graph/prompts/__init__.py @@ -0,0 +1,11 @@ +"""Prompts for TLCS demo agents.""" + +from graph.prompts.math_tutor import format_math_tutor_prompt +from graph.prompts.writing_coach import format_writing_coach_prompt +from graph.prompts.tutor import format_tutor_prompt + +__all__ = [ + "format_math_tutor_prompt", + "format_writing_coach_prompt", + "format_tutor_prompt", +] diff --git a/demo/agents/graph/prompts/math_tutor.py b/demo/agents/graph/prompts/math_tutor.py new file mode 100644 index 0000000..7ad1212 --- /dev/null +++ b/demo/agents/graph/prompts/math_tutor.py @@ -0,0 +1,111 @@ +"""Math Tutor system prompt with memory injection. + +The math tutor uses extracted memories to personalize responses. +Memories are formatted into the system prompt to provide context +about the learner's background, goals, preferences, and mastery level. +""" + +MATH_TUTOR_SYSTEM_PROMPT = """\ +You are a supportive, adaptive math tutor helping a learner. Your goal is to: +1. Understand what the learner is trying to accomplish in mathematics +2. Adapt your explanations to their level and preferences +3. Build on what they already know mathematically +4. Be encouraging while maintaining mathematical rigor + +{memory_context} + +Math-Specific Guidelines: +- Start with concrete examples before abstract concepts +- Use visual representations (describe diagrams, graphs) when helpful +- Connect new concepts to previously mastered skills +- Identify and address common mathematical misconceptions +- Celebrate progress on difficult problems + +General Guidelines: +- Ask clarifying questions when the learner's needs are unclear +- Provide examples that connect to their background when possible +- Adjust your language complexity based on their demonstrated mastery +- Acknowledge and work within any constraints they have +- Keep responses focused - one concept at a time + +Respond naturally as a tutor would in conversation. +""" + + +def format_memory_context(memories: list[dict]) -> str: + """Format memories into context for the system prompt. + + Args: + memories: List of memory dicts with category, content, source_type, etc. + + Returns: + Formatted context string + """ + if not memories: + return "You don't yet know much about this learner. Take time to understand their math background and goals." + + sections = { + "fact": [], + "goal": [], + "preference": [], + "mastery": [], + "constraint": [], + "relationship": [], + } + + for memory in memories: + category = memory.get("category", "fact") + content = memory.get("content", "") + confidence = memory.get("confidence", 1.0) + source_type = memory.get("source_type", "model_inferred") + + # Add qualifier for lower-confidence or inferred memories + qualifiers = [] + if confidence < 0.7: + qualifiers.append("uncertain") + if source_type == "model_inferred": + qualifiers.append("inferred") + + if qualifiers: + content = f"{content} ({', '.join(qualifiers)})" + + if category in sections: + sections[category].append(content) + + context_parts = [] + + if sections["fact"]: + context_parts.append("About this learner:\n- " + "\n- ".join(sections["fact"])) + + if sections["goal"]: + context_parts.append("Their math goals:\n- " + "\n- ".join(sections["goal"])) + + if sections["preference"]: + context_parts.append("Learning preferences:\n- " + "\n- ".join(sections["preference"])) + + if sections["mastery"]: + context_parts.append("Math skills/knowledge:\n- " + "\n- ".join(sections["mastery"])) + + if sections["constraint"]: + context_parts.append("Constraints to respect:\n- " + "\n- ".join(sections["constraint"])) + + if sections["relationship"]: + context_parts.append("Key relationships:\n- " + "\n- ".join(sections["relationship"])) + + if context_parts: + return "What you know about this learner:\n\n" + "\n\n".join(context_parts) + else: + return "You don't yet know much about this learner. Take time to understand their math background and goals." + + +def format_math_tutor_prompt(memories: list[dict]) -> str: + """Format the complete math tutor system prompt with memory context. + + Args: + memories: List of memory dicts + + Returns: + Complete system prompt + """ + memory_context = format_memory_context(memories) + return MATH_TUTOR_SYSTEM_PROMPT.format(memory_context=memory_context) diff --git a/demo/agents/graph/prompts/tutor.py b/demo/agents/graph/prompts/tutor.py new file mode 100644 index 0000000..323cf5c --- /dev/null +++ b/demo/agents/graph/prompts/tutor.py @@ -0,0 +1,80 @@ +"""Tutor system prompt with memory injection. + +The tutor uses extracted memories to personalize responses. +Memories are formatted into the system prompt to provide context +about the learner's background, goals, preferences, and mastery level. +""" + +TUTOR_SYSTEM_PROMPT = """\ +You are a supportive, adaptive tutor helping a learner. Your goal is to: +1. Understand what the learner is trying to accomplish +2. Adapt your explanations to their level and preferences +3. Build on what they already know +4. Be encouraging while maintaining academic rigor + +{memory_context} + +Guidelines: +- Ask clarifying questions when the learner's needs are unclear +- Provide examples that connect to their background when possible +- Adjust your language complexity based on their demonstrated mastery +- Acknowledge and work within any constraints they have +- Celebrate progress and effort, not just correct answers + +Respond naturally as a tutor would in conversation. Keep responses focused +and avoid overwhelming the learner with too much information at once. +""" + + +def format_memory_context(memories: list[dict]) -> str: + """Format memories into context for the system prompt.""" + if not memories: + return "You don't yet know much about this learner. Take time to understand their needs." + + sections = { + "fact": [], + "goal": [], + "preference": [], + "mastery": [], + "constraint": [], + } + + for memory in memories: + category = memory.get("category", "fact") + content = memory.get("content", "") + confidence = memory.get("confidence", 1.0) + + # Add confidence qualifier for lower-confidence memories + if confidence < 0.7: + content = f"{content} (uncertain)" + + if category in sections: + sections[category].append(content) + + context_parts = [] + + if sections["fact"]: + context_parts.append("About this learner:\n- " + "\n- ".join(sections["fact"])) + + if sections["goal"]: + context_parts.append("Their goals:\n- " + "\n- ".join(sections["goal"])) + + if sections["preference"]: + context_parts.append("Learning preferences:\n- " + "\n- ".join(sections["preference"])) + + if sections["mastery"]: + context_parts.append("What they know/can do:\n- " + "\n- ".join(sections["mastery"])) + + if sections["constraint"]: + context_parts.append("Constraints to respect:\n- " + "\n- ".join(sections["constraint"])) + + if context_parts: + return "What you know about this learner:\n\n" + "\n\n".join(context_parts) + else: + return "You don't yet know much about this learner. Take time to understand their needs." + + +def format_tutor_prompt(memories: list[dict]) -> str: + """Format the complete tutor system prompt with memory context.""" + memory_context = format_memory_context(memories) + return TUTOR_SYSTEM_PROMPT.format(memory_context=memory_context) diff --git a/demo/agents/graph/prompts/writing_coach.py b/demo/agents/graph/prompts/writing_coach.py new file mode 100644 index 0000000..24fbd4b --- /dev/null +++ b/demo/agents/graph/prompts/writing_coach.py @@ -0,0 +1,111 @@ +"""Writing Coach system prompt with memory injection. + +The writing coach uses extracted memories to personalize responses. +Memories are formatted into the system prompt to provide context +about the learner's background, goals, preferences, and writing level. +""" + +WRITING_COACH_SYSTEM_PROMPT = """\ +You are a supportive, adaptive writing coach helping a learner. Your goal is to: +1. Understand what the learner is trying to accomplish with their writing +2. Adapt your feedback to their level and preferences +3. Build on their existing writing strengths +4. Be encouraging while maintaining high standards + +{memory_context} + +Writing-Specific Guidelines: +- Focus on one or two improvement areas at a time +- Give specific, actionable feedback +- Praise effective writing choices, not just effort +- Help learners develop their own voice +- Connect writing skills to their goals (academic, professional, creative) + +General Guidelines: +- Ask clarifying questions about purpose and audience +- Provide examples when helpful +- Adjust feedback depth based on their level +- Acknowledge time and other constraints +- Keep responses focused and constructive + +Respond naturally as a writing coach would in conversation. +""" + + +def format_memory_context(memories: list[dict]) -> str: + """Format memories into context for the system prompt. + + Args: + memories: List of memory dicts with category, content, source_type, etc. + + Returns: + Formatted context string + """ + if not memories: + return "You don't yet know much about this learner. Take time to understand their writing background and goals." + + sections = { + "fact": [], + "goal": [], + "preference": [], + "mastery": [], + "constraint": [], + "relationship": [], + } + + for memory in memories: + category = memory.get("category", "fact") + content = memory.get("content", "") + confidence = memory.get("confidence", 1.0) + source_type = memory.get("source_type", "model_inferred") + + # Add qualifier for lower-confidence or inferred memories + qualifiers = [] + if confidence < 0.7: + qualifiers.append("uncertain") + if source_type == "model_inferred": + qualifiers.append("inferred") + + if qualifiers: + content = f"{content} ({', '.join(qualifiers)})" + + if category in sections: + sections[category].append(content) + + context_parts = [] + + if sections["fact"]: + context_parts.append("About this learner:\n- " + "\n- ".join(sections["fact"])) + + if sections["goal"]: + context_parts.append("Their writing goals:\n- " + "\n- ".join(sections["goal"])) + + if sections["preference"]: + context_parts.append("Learning preferences:\n- " + "\n- ".join(sections["preference"])) + + if sections["mastery"]: + context_parts.append("Writing skills/strengths:\n- " + "\n- ".join(sections["mastery"])) + + if sections["constraint"]: + context_parts.append("Constraints to respect:\n- " + "\n- ".join(sections["constraint"])) + + if sections["relationship"]: + context_parts.append("Key relationships:\n- " + "\n- ".join(sections["relationship"])) + + if context_parts: + return "What you know about this learner:\n\n" + "\n\n".join(context_parts) + else: + return "You don't yet know much about this learner. Take time to understand their writing background and goals." + + +def format_writing_coach_prompt(memories: list[dict]) -> str: + """Format the complete writing coach system prompt with memory context. + + Args: + memories: List of memory dicts + + Returns: + Complete system prompt + """ + memory_context = format_memory_context(memories) + return WRITING_COACH_SYSTEM_PROMPT.format(memory_context=memory_context) diff --git a/demo/agents/graph/server.py b/demo/agents/graph/server.py new file mode 100644 index 0000000..2de0b65 --- /dev/null +++ b/demo/agents/graph/server.py @@ -0,0 +1,45 @@ +"""Production entry point for LangGraph server. + +This module instantiates the graphs with real models for production use. +langgraph.json references this file for both math_tutor and writing_coach. +""" + +import os + +from graph.agents.math_tutor import build_math_tutor_graph +from graph.agents.writing_coach import build_writing_coach_graph +from graph.main import build_graph + +# Get model configuration from environment +TUTOR_MODEL = os.environ.get("TUTOR_MODEL", "gpt-4o") +EXTRACTION_MODEL = os.environ.get("EXTRACTION_MODEL", "gpt-4o-mini") + +# Model kwargs from environment +model_kwargs = {} +if os.environ.get("LLM_TEMPERATURE"): + model_kwargs["temperature"] = float(os.environ["LLM_TEMPERATURE"]) + +# Build the general tutor graph (with memory extraction) +tutor = build_graph( + tutor_model=TUTOR_MODEL, + extraction_model=EXTRACTION_MODEL, + tutor_model_kwargs=model_kwargs, + extraction_model_kwargs=model_kwargs, +) + +# Build the Math Tutor graph +math_tutor = build_math_tutor_graph( + tutor_model=TUTOR_MODEL, + extraction_model=EXTRACTION_MODEL, + model_kwargs=model_kwargs, +) + +# Build the Writing Coach graph +writing_coach = build_writing_coach_graph( + tutor_model=TUTOR_MODEL, + extraction_model=EXTRACTION_MODEL, + model_kwargs=model_kwargs, +) + +# Legacy single-graph export (for backwards compatibility) +graph = tutor diff --git a/demo/agents/graph/state.py b/demo/agents/graph/state.py new file mode 100644 index 0000000..6f3d623 --- /dev/null +++ b/demo/agents/graph/state.py @@ -0,0 +1,55 @@ +"""State schema for Portable Memory experiment. + +The state includes: +- messages: Conversation history (via MessagesState) +- memories: Extracted learner memories (portable) +- learner_id: Identifier for the learner +""" + +from typing import Annotated, TypedDict +from langgraph.graph import MessagesState + + +class MemoryDict(TypedDict, total=False): + """Serialized memory item for state storage.""" + id: str + category: str + content: str + source: str + confidence: float + source_type: str # "user_declared" | "model_inferred" + status: str # "candidate" | "confirmed" + created_at: str + valid_until: str | None + subject_area: str | None + version: int + previous_version_id: str | None + correction_reason: str | None + purposes: list[str] + original_format: str | None + original_id: str | None + + +def merge_memories(existing: list[MemoryDict], new: list[MemoryDict]) -> list[MemoryDict]: + """Merge new memories with existing, avoiding duplicates by ID.""" + existing_ids = {m["id"] for m in existing} + merged = list(existing) + for memory in new: + if memory["id"] not in existing_ids: + merged.append(memory) + existing_ids.add(memory["id"]) + return merged + + +class PortableMemoryState(MessagesState): + """State for the Portable Memory agent. + + Extends MessagesState which provides: + - messages: list of chat messages with add_messages reducer + + Adds: + - memories: list of extracted memories (with merge reducer) + - learner_id: identifier for the learner (for export) + """ + memories: Annotated[list[MemoryDict], merge_memories] + learner_id: str diff --git a/demo/agents/langgraph.json b/demo/agents/langgraph.json new file mode 100644 index 0000000..51c9484 --- /dev/null +++ b/demo/agents/langgraph.json @@ -0,0 +1,9 @@ +{ + "dependencies": ["."], + "graphs": { + "tutor": "./graph/server.py:tutor", + "math_tutor": "./graph/server.py:math_tutor", + "writing_coach": "./graph/server.py:writing_coach" + }, + "env": ".env" +} diff --git a/demo/agents/memory/__init__.py b/demo/agents/memory/__init__.py new file mode 100644 index 0000000..63de109 --- /dev/null +++ b/demo/agents/memory/__init__.py @@ -0,0 +1,22 @@ +"""Memory module for TLCS demo agents. + +This module provides portable learner memory with TLCS integration. +""" + +from memory.schema import MemoryItem, MemoryCategory, MemoryVersion, LearnerProfile +from memory.scopes import Scope +from memory.grants import Grant, create_grant +from memory.service import PortableMemoryService +from memory.extraction import extract_memories + +__all__ = [ + "MemoryItem", + "MemoryCategory", + "MemoryVersion", + "LearnerProfile", + "Scope", + "Grant", + "create_grant", + "PortableMemoryService", + "extract_memories", +] diff --git a/demo/agents/memory/audit.py b/demo/agents/memory/audit.py new file mode 100644 index 0000000..c04d04c --- /dev/null +++ b/demo/agents/memory/audit.py @@ -0,0 +1,214 @@ +"""Append-only audit log for memory operations. + +Pre-read References: +- Section 2.3: "append-only (or tamper-evident) log of memory accesses + and mutations, enabling user inspection" +""" + +from dataclasses import dataclass, field +from datetime import datetime, UTC +from typing import Literal, TYPE_CHECKING +import uuid + +if TYPE_CHECKING: + from memory.scopes import Scope + + +@dataclass +class AuditEntry: + """Audit log entry for memory operations.""" + + entry_id: str + timestamp: datetime + learner_id: str + + # Operation details + operation: Literal[ + "read", "search", "add", "update", "correct", + "delete", "export", "import", "grant_created", "grant_revoked" + ] + + # Agent/grant info + agent_id: str + grant_id: str | None = None + scope_used: dict = field(default_factory=dict) + purpose: str | None = None + + # What was accessed/modified + memory_ids: list[str] = field(default_factory=list) + memory_count: int = 0 + categories_accessed: list[str] = field(default_factory=list) + subjects_accessed: list[str] = field(default_factory=list) + + # For mutations + previous_values: dict | None = None + new_values: dict | None = None + reason: str | None = None + + # Minimization tracking (Pre-read 2.3) + total_available: int = 0 + returned_after_scope: int = 0 + returned_after_minimization: int = 0 + + def to_dict(self) -> dict: + return { + "entry_id": self.entry_id, + "timestamp": self.timestamp.isoformat(), + "learner_id": self.learner_id, + "operation": self.operation, + "agent_id": self.agent_id, + "grant_id": self.grant_id, + "scope_used": self.scope_used, + "purpose": self.purpose, + "memory_ids": self.memory_ids, + "memory_count": self.memory_count, + "categories_accessed": self.categories_accessed, + "subjects_accessed": self.subjects_accessed, + "previous_values": self.previous_values, + "new_values": self.new_values, + "reason": self.reason, + "total_available": self.total_available, + "returned_after_scope": self.returned_after_scope, + "returned_after_minimization": self.returned_after_minimization, + } + + @classmethod + def from_dict(cls, data: dict) -> "AuditEntry": + return cls( + entry_id=data["entry_id"], + timestamp=datetime.fromisoformat(data["timestamp"]), + learner_id=data["learner_id"], + operation=data["operation"], + agent_id=data["agent_id"], + grant_id=data.get("grant_id"), + scope_used=data.get("scope_used", {}), + purpose=data.get("purpose"), + memory_ids=data.get("memory_ids", []), + memory_count=data.get("memory_count", 0), + categories_accessed=data.get("categories_accessed", []), + subjects_accessed=data.get("subjects_accessed", []), + previous_values=data.get("previous_values"), + new_values=data.get("new_values"), + reason=data.get("reason"), + total_available=data.get("total_available", 0), + returned_after_scope=data.get("returned_after_scope", 0), + returned_after_minimization=data.get("returned_after_minimization", 0), + ) + + +class AuditLog: + """Append-only audit log with query capabilities.""" + + def __init__(self, storage=None): + from memory.persistence import get_storage_backend + self._storage = storage or get_storage_backend() + + def log( + self, + learner_id: str, + operation: str, + agent_id: str, + grant_id: str | None = None, + scope: "Scope | None" = None, + purpose: str | None = None, + memory_ids: list[str] | None = None, + categories: list[str] | None = None, + subjects: list[str] | None = None, + previous_values: dict | None = None, + new_values: dict | None = None, + reason: str | None = None, + total_available: int = 0, + returned_after_scope: int = 0, + returned_after_minimization: int = 0, + ) -> AuditEntry: + """Log an operation to the audit log.""" + entry = AuditEntry( + entry_id=str(uuid.uuid4()), + timestamp=datetime.now(UTC), + learner_id=learner_id, + operation=operation, + agent_id=agent_id, + grant_id=grant_id, + scope_used=scope.to_dict() if scope else {}, + purpose=purpose, + memory_ids=memory_ids or [], + memory_count=len(memory_ids) if memory_ids else 0, + categories_accessed=categories or [], + subjects_accessed=subjects or [], + previous_values=previous_values, + new_values=new_values, + reason=reason, + total_available=total_available, + returned_after_scope=returned_after_scope, + returned_after_minimization=returned_after_minimization, + ) + + self._storage.store_audit_entry(entry.to_dict()) + return entry + + def query( + self, + learner_id: str, + agent_id: str | None = None, + operation: str | None = None, + since: datetime | None = None, + until: datetime | None = None, + limit: int | None = None, + ) -> list[AuditEntry]: + """Query audit log entries.""" + entry_dicts = self._storage.query_audit( + learner_id=learner_id, + agent_id=agent_id, + operation=operation, + limit=None, + ) + + results = [] + for entry_dict in entry_dicts: + entry = AuditEntry.from_dict(entry_dict) + + if since and entry.timestamp < since: + continue + if until and entry.timestamp > until: + continue + + results.append(entry) + + if limit and len(results) >= limit: + break + + return results + + def get_recent(self, learner_id: str, count: int = 10) -> list[AuditEntry]: + """Get most recent entries for a learner.""" + all_entries = self.query(learner_id) + sorted_entries = sorted(all_entries, key=lambda e: e.timestamp, reverse=True) + return sorted_entries[:count] + + def get_summary(self, learner_id: str) -> dict: + """Get a summary of audit activity for display.""" + entries = self.query(learner_id) + + if not entries: + return { + "total_operations": 0, + "operations_by_type": {}, + "agents": [], + "first_operation": None, + "last_operation": None, + } + + ops_by_type: dict[str, int] = {} + agents: set[str] = set() + + for entry in entries: + ops_by_type[entry.operation] = ops_by_type.get(entry.operation, 0) + 1 + agents.add(entry.agent_id) + + return { + "total_operations": len(entries), + "operations_by_type": ops_by_type, + "agents": list(agents), + "first_operation": entries[0].timestamp.isoformat() if entries else None, + "last_operation": entries[-1].timestamp.isoformat() if entries else None, + } diff --git a/demo/agents/memory/extraction.py b/demo/agents/memory/extraction.py new file mode 100644 index 0000000..66b3a8e --- /dev/null +++ b/demo/agents/memory/extraction.py @@ -0,0 +1,154 @@ +"""LLM-based memory extraction from conversations. + +This module uses an LLM to extract structured memories from conversation +messages. The extraction happens every turn, identifying new facts, goals, +preferences, mastery indicators, and constraints about the learner. + +Pre-read References: +- Section 2.4: User-declared vs model-inferred distinction +""" + +import json +from typing import Sequence + +from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage +from langchain_core.language_models import BaseChatModel + +from memory.schema import MemoryItem, MemoryCategory, SourceType + + +EXTRACTION_SYSTEM_PROMPT = """\ +You are a memory extraction system for an educational AI tutor. Your job is to identify +and extract learnable information about the student from conversation messages. + +Extract memories in these categories: +- fact: Background information about the learner (e.g., "speaks Spanish at home", "works night shift", "has two kids") +- goal: What they're trying to achieve (e.g., "wants to pass algebra final", "preparing for GED") +- preference: How they like to learn (e.g., "prefers examples over theory", "learns better with visuals") +- mastery: Evidence of what they know or can do (e.g., "understands basic fractions", "struggles with word problems") +- constraint: Limitations on their learning (e.g., "only 30 minutes per day", "no access to video content") + +Rules: +1. Only extract NEW information not already captured in existing memories +2. Be specific and concrete - avoid vague statements +3. Include subject area when relevant (math, writing, science, etc.) +4. Set confidence lower (0.5-0.8) for inferred information, higher (0.9-1.0) for explicit statements +5. Return empty list if no new memories are found + +Return a JSON array of memory objects. Each object should have: +- category: one of "fact", "goal", "preference", "mastery", "constraint" +- content: the memory content as a clear statement +- confidence: 0.0-1.0 confidence score +- subject_area: subject if relevant, or null + +Example output: +[ + {"category": "fact", "content": "Works as a nurse with rotating shifts", "confidence": 0.95, "subject_area": null}, + {"category": "mastery", "content": "Can solve basic linear equations", "confidence": 0.8, "subject_area": "math"}, + {"category": "preference", "content": "Prefers step-by-step worked examples", "confidence": 0.9, "subject_area": null} +] +""" + + +def format_messages_for_extraction( + messages: Sequence[BaseMessage], + existing_memories: list[MemoryItem], +) -> list[BaseMessage]: + """Format messages for the extraction LLM.""" + # Summarize existing memories so LLM knows what's already captured + existing_summary = "" + if existing_memories: + memory_lines = [f"- [{m.category.value}] {m.content}" for m in existing_memories] + existing_summary = f"\n\nExisting memories (do not duplicate):\n" + "\n".join(memory_lines) + + # Format recent conversation + conversation_lines = [] + for msg in messages[-10:]: # Last 10 messages for context + if isinstance(msg, HumanMessage): + conversation_lines.append(f"Student: {msg.content}") + elif isinstance(msg, AIMessage): + conversation_lines.append(f"Tutor: {msg.content}") + + conversation_text = "\n".join(conversation_lines) + + return [ + SystemMessage(content=EXTRACTION_SYSTEM_PROMPT + existing_summary), + HumanMessage(content=f"Extract new memories from this conversation:\n\n{conversation_text}"), + ] + + +def parse_extraction_response(response: str) -> list[dict]: + """Parse the LLM response into memory dicts.""" + # Clean up response - sometimes LLMs add markdown code blocks + text = response.strip() + if text.startswith("```json"): + text = text[7:] + if text.startswith("```"): + text = text[3:] + if text.endswith("```"): + text = text[:-3] + text = text.strip() + + # Handle empty/no memories case + if not text or text == "[]": + return [] + + try: + data = json.loads(text) + if isinstance(data, list): + return data + return [] + except json.JSONDecodeError: + # If parsing fails, return empty list + return [] + + +def extract_memories( + messages: Sequence[BaseMessage], + existing_memories: list[MemoryItem], + model: BaseChatModel, +) -> list[MemoryItem]: + """Extract new memories from conversation messages. + + Args: + messages: Recent conversation messages + existing_memories: Already-extracted memories (to avoid duplicates) + model: LLM to use for extraction + + Returns: + List of newly extracted MemoryItem objects + """ + if not messages: + return [] + + # Only extract if there are human messages (learner input) + has_human_message = any(isinstance(m, HumanMessage) for m in messages) + if not has_human_message: + return [] + + # Format and call LLM + extraction_messages = format_messages_for_extraction(messages, existing_memories) + response = model.invoke(extraction_messages) + + # Parse response + memory_dicts = parse_extraction_response(response.content) + + # Convert to MemoryItem objects + new_memories = [] + for mem_dict in memory_dicts: + try: + category = MemoryCategory(mem_dict.get("category", "fact")) + memory = MemoryItem( + category=category, + content=mem_dict.get("content", ""), + source="conversation", + confidence=float(mem_dict.get("confidence", 0.8)), + subject_area=mem_dict.get("subject_area"), + source_type="model_inferred", # Default: LLM-extracted = inferred + ) + new_memories.append(memory) + except (ValueError, KeyError): + # Skip malformed memories + continue + + return new_memories diff --git a/demo/agents/memory/formats/__init__.py b/demo/agents/memory/formats/__init__.py new file mode 100644 index 0000000..8b93738 --- /dev/null +++ b/demo/agents/memory/formats/__init__.py @@ -0,0 +1,15 @@ +"""Interoperability formats for memory export/import.""" + +from memory.formats.caliper import ( + export_caliper, + export_caliper_json, + import_caliper, + import_caliper_json, +) + +__all__ = [ + "export_caliper", + "export_caliper_json", + "import_caliper", + "import_caliper_json", +] diff --git a/demo/agents/memory/formats/caliper.py b/demo/agents/memory/formats/caliper.py new file mode 100644 index 0000000..a516f34 --- /dev/null +++ b/demo/agents/memory/formats/caliper.py @@ -0,0 +1,248 @@ +"""Caliper Analytics export/import. + +Caliper is a 1EdTech standard for learning activity events. It uses +JSON-LD format with actor-action-object semantics. + +Pre-read References: +- Section 2.4: Source type (user_declared vs model_inferred) +- Section 4.1: Version information + +Reference: https://www.imsglobal.org/spec/caliper/v1p2 +""" + +import json +from datetime import datetime, UTC +from typing import Any +import uuid + +from memory.schema import MemoryItem, MemoryCategory + + +def _category_to_event_type(category: MemoryCategory) -> tuple[str, str]: + """Map memory category to Caliper event type and action. + + Returns (event_type, action) tuple. + """ + mapping = { + MemoryCategory.MASTERY: ("AssessmentEvent", "Completed"), + MemoryCategory.GOAL: ("AssignableEvent", "Activated"), + MemoryCategory.FACT: ("AnnotationEvent", "Tagged"), + MemoryCategory.PREFERENCE: ("AnnotationEvent", "Tagged"), + MemoryCategory.CONSTRAINT: ("AnnotationEvent", "Tagged"), + } + return mapping.get(category, ("Event", "Created")) + + +def _memory_to_event( + memory: MemoryItem, + learner_id: str, + app_id: str, +) -> dict[str, Any]: + """Convert a MemoryItem to a Caliper Event.""" + event_type, action = _category_to_event_type(memory.category) + event_id = f"urn:uuid:{uuid.uuid4()}" + + event = { + "id": event_id, + "type": event_type, + "@context": "http://purl.imsglobal.org/ctx/caliper/v1p2", + "actor": { + "id": learner_id, + "type": "Person", + }, + "action": action, + "object": { + "id": f"urn:memory:{memory.id}", + "type": _get_object_type(memory.category), + "name": memory.content[:100], + "description": memory.content, + }, + "eventTime": memory.created_at.isoformat() + "Z", + "edApp": { + "id": app_id, + "type": "SoftwareApplication", + "name": "Portable Memory Tutor", + }, + } + + # Add score for mastery events + if memory.category == MemoryCategory.MASTERY: + event["generated"] = { + "id": f"urn:score:{memory.id}", + "type": "Score", + "scoreGiven": memory.confidence, + "maxScore": 1.0, + } + + # Add subject area as group if present + if memory.subject_area: + event["group"] = { + "id": f"urn:subject:{memory.subject_area}", + "type": "CourseSection", + "name": memory.subject_area, + } + + # Store original memory data for round-trip + event["extensions"] = { + "portableMemory": { + "originalId": memory.id, + "category": memory.category.value, + "source": memory.source, + "confidence": memory.confidence, + "sourceType": memory.source_type, + "status": memory.status, + "validUntil": memory.valid_until.isoformat() if memory.valid_until else None, + "subjectArea": memory.subject_area, + "version": memory.version, + "previousVersionId": memory.previous_version_id, + "correctionReason": memory.correction_reason, + "purposes": memory.purposes, + } + } + + return event + + +def _get_object_type(category: MemoryCategory) -> str: + """Get Caliper object type for memory category.""" + mapping = { + MemoryCategory.MASTERY: "AssessmentItem", + MemoryCategory.GOAL: "AssignableDigitalResource", + MemoryCategory.FACT: "DigitalResource", + MemoryCategory.PREFERENCE: "DigitalResource", + MemoryCategory.CONSTRAINT: "DigitalResource", + } + return mapping.get(category, "DigitalResource") + + +def export_caliper( + memories: list[MemoryItem], + learner_id: str = "urn:uuid:learner", + sensor_id: str = "urn:uuid:portable-memory-sensor", + app_id: str = "urn:uuid:portable-memory-tutor", +) -> dict[str, Any]: + """Export memories as a Caliper envelope with events. + + Args: + memories: List of memories to export + learner_id: Identifier for the learner + sensor_id: Identifier for the sending sensor + app_id: Identifier for the application + + Returns: + Caliper envelope as a dictionary (JSON-serializable) + """ + now = datetime.now(UTC).isoformat() + "Z" + + events = [_memory_to_event(m, learner_id, app_id) for m in memories] + + envelope = { + "@context": "http://purl.imsglobal.org/ctx/caliper/v1p2", + "sensor": sensor_id, + "sendTime": now, + "data": events, + } + + return envelope + + +def export_caliper_json(memories: list[MemoryItem], **kwargs) -> str: + """Export memories as Caliper JSON string.""" + return json.dumps(export_caliper(memories, **kwargs), indent=2) + + +def _event_to_memory(event: dict[str, Any]) -> MemoryItem | None: + """Convert a Caliper Event back to a MemoryItem.""" + extensions = event.get("extensions", {}) + portable_memory = extensions.get("portableMemory", {}) + + obj = event.get("object", {}) + event_time = event.get("eventTime", datetime.now(UTC).isoformat()) + + if portable_memory: + try: + return MemoryItem( + id=portable_memory.get("originalId", str(uuid.uuid4())), + category=MemoryCategory(portable_memory["category"]), + content=obj.get("description", obj.get("name", "")), + source=portable_memory.get("source", "import"), + confidence=float(portable_memory.get("confidence", 0.8)), + source_type=portable_memory.get("sourceType", "model_inferred"), + status=portable_memory.get("status", "confirmed"), + created_at=datetime.fromisoformat(event_time.rstrip("Z")), + valid_until=datetime.fromisoformat(portable_memory["validUntil"]) if portable_memory.get("validUntil") else None, + subject_area=portable_memory.get("subjectArea"), + version=portable_memory.get("version", 1), + previous_version_id=portable_memory.get("previousVersionId"), + correction_reason=portable_memory.get("correctionReason"), + purposes=portable_memory.get("purposes", []), + original_format="caliper", + original_id=event.get("id"), + ) + except (KeyError, ValueError): + pass + + # Fresh import: infer from Caliper structure + event_type = event.get("type", "Event") + category_mapping = { + "AssessmentEvent": MemoryCategory.MASTERY, + "AssignableEvent": MemoryCategory.GOAL, + "AnnotationEvent": MemoryCategory.FACT, + } + category = category_mapping.get(event_type, MemoryCategory.FACT) + + confidence = 0.8 + generated = event.get("generated", {}) + if generated.get("scoreGiven") is not None: + try: + confidence = float(generated["scoreGiven"]) + except ValueError: + pass + + subject_area = None + group = event.get("group", {}) + if group.get("name"): + subject_area = group["name"] + + try: + created_at = datetime.fromisoformat(event_time.rstrip("Z")) + except ValueError: + created_at = datetime.now(UTC) + + return MemoryItem( + category=category, + content=obj.get("description", obj.get("name", "")), + source="import", + confidence=confidence, + created_at=created_at, + subject_area=subject_area, + original_format="caliper", + original_id=event.get("id"), + ) + + +def import_caliper(caliper_data: dict[str, Any]) -> list[MemoryItem]: + """Import memories from a Caliper envelope. + + Args: + caliper_data: Caliper envelope as a dictionary + + Returns: + List of MemoryItem objects + """ + memories = [] + + events = caliper_data.get("data", []) + + for event in events: + memory = _event_to_memory(event) + if memory: + memories.append(memory) + + return memories + + +def import_caliper_json(json_str: str) -> list[MemoryItem]: + """Import memories from Caliper JSON string.""" + caliper_data = json.loads(json_str) + return import_caliper(caliper_data) diff --git a/demo/agents/memory/grants.py b/demo/agents/memory/grants.py new file mode 100644 index 0000000..b73f113 --- /dev/null +++ b/demo/agents/memory/grants.py @@ -0,0 +1,243 @@ +"""Access grants for memory operations. + +Pre-read References: +- Section 2.5: OAuth-style authorization model +- Section 4.4: Revocation with downstream propagation +- Section 2.3: Temporary vs persistent grants +""" + +from dataclasses import dataclass, field +from datetime import datetime, UTC +from typing import TYPE_CHECKING +import uuid + +if TYPE_CHECKING: + from memory.schema import MemoryItem + from memory.scopes import Scope + + +@dataclass +class DownstreamUse: + """Record of data being sent to an external system (Pre-read 4.4). + + Tracks where exported data went so we can notify on revocation. + """ + + use_id: str + system_id: str # e.g., "tlcs" + memory_ids: list[str] = field(default_factory=list) + exported_at: datetime = field(default_factory=lambda: datetime.now(UTC)) + webhook_url: str | None = None + + def to_dict(self) -> dict: + return { + "use_id": self.use_id, + "system_id": self.system_id, + "memory_ids": self.memory_ids, + "exported_at": self.exported_at.isoformat(), + "webhook_url": self.webhook_url, + } + + @classmethod + def from_dict(cls, data: dict) -> "DownstreamUse": + return cls( + use_id=data["use_id"], + system_id=data["system_id"], + memory_ids=data.get("memory_ids", []), + exported_at=datetime.fromisoformat(data["exported_at"]), + webhook_url=data.get("webhook_url"), + ) + + +@dataclass +class Grant: + """Access grant for a specific agent/application.""" + + grant_id: str + learner_id: str + agent_id: str + scope: "Scope" + + # Grant metadata + granted_at: datetime = field(default_factory=lambda: datetime.now(UTC)) + expires_at: datetime | None = None + granted_by: str = "learner" + + # Grant type (Pre-read 2.3 - temporary vs persistent) + grant_type: str = "persistent" # "persistent", "temporary", "session" + session_id: str | None = None + + # Revocation (Pre-read 4.4) + revoked: bool = False + revoked_at: datetime | None = None + revocation_reason: str | None = None + + # For revocation propagation + downstream_uses: list[DownstreamUse] = field(default_factory=list) + + def is_valid(self, current_session_id: str | None = None) -> bool: + """Check if grant is currently valid.""" + if self.revoked: + return False + if self.expires_at and datetime.now(UTC) > self.expires_at: + return False + if self.grant_type == "session" and self.session_id != current_session_id: + return False + return True + + def add_downstream_use( + self, + system_id: str, + memory_ids: list[str], + webhook_url: str | None = None, + ) -> DownstreamUse: + """Record that data was exported to an external system.""" + use = DownstreamUse( + use_id=str(uuid.uuid4()), + system_id=system_id, + memory_ids=memory_ids, + webhook_url=webhook_url, + ) + self.downstream_uses.append(use) + return use + + def to_dict(self) -> dict: + return { + "grant_id": self.grant_id, + "learner_id": self.learner_id, + "agent_id": self.agent_id, + "scope": self.scope.to_dict(), + "granted_at": self.granted_at.isoformat(), + "expires_at": self.expires_at.isoformat() if self.expires_at else None, + "granted_by": self.granted_by, + "grant_type": self.grant_type, + "session_id": self.session_id, + "revoked": self.revoked, + "revoked_at": self.revoked_at.isoformat() if self.revoked_at else None, + "revocation_reason": self.revocation_reason, + "downstream_uses": [u.to_dict() for u in self.downstream_uses], + } + + +def validate_grant( + grant: Grant, + operation: str, + memory: "MemoryItem | None" = None, + purpose: str | None = None, + session_id: str | None = None, +) -> bool: + """Validate grant for an operation. + + Args: + grant: The access grant + operation: "read", "write", "export", etc. + memory: The memory being accessed (for scope validation) + purpose: The purpose of the access + session_id: Current session ID (for session-scoped grants) + + Returns: + True if the grant allows this operation + """ + # Check basic validity + if not grant.is_valid(session_id): + return False + + # Check operation permissions + if operation in ("read", "search", "export"): + if not grant.scope.can_read: + return False + elif operation in ("write", "add", "update", "correct", "delete"): + if not grant.scope.can_write: + return False + + # Check memory access if provided + if memory is not None: + if not grant.scope.allows(memory, purpose): + return False + + return True + + +def create_grant( + learner_id: str, + agent_id: str, + scope: "Scope", + grant_type: str = "persistent", + granted_by: str = "learner", + expires_at: datetime | None = None, + session_id: str | None = None, +) -> Grant: + """Create a new access grant.""" + return Grant( + grant_id=str(uuid.uuid4()), + learner_id=learner_id, + agent_id=agent_id, + scope=scope, + grant_type=grant_type, + granted_by=granted_by, + expires_at=expires_at, + session_id=session_id, + ) + + +class GrantStore: + """Grant storage with pluggable backend.""" + + def __init__(self, storage=None): + from memory.persistence import get_storage_backend + self._storage = storage or get_storage_backend() + self._scope_cache: dict[str, "Scope"] = {} + + def _grant_to_object(self, grant_dict: dict) -> Grant: + """Convert a grant dictionary to a Grant object.""" + from memory.scopes import Scope + + grant_id = grant_dict["grant_id"] + if grant_id in self._scope_cache: + scope = self._scope_cache[grant_id] + else: + scope = Scope.from_dict(grant_dict["scope"]) + self._scope_cache[grant_id] = scope + + return Grant( + grant_id=grant_dict["grant_id"], + learner_id=grant_dict["learner_id"], + agent_id=grant_dict["agent_id"], + scope=scope, + granted_at=datetime.fromisoformat(grant_dict["granted_at"]), + expires_at=datetime.fromisoformat(grant_dict["expires_at"]) if grant_dict.get("expires_at") else None, + granted_by=grant_dict.get("granted_by", "learner"), + grant_type=grant_dict.get("grant_type", "persistent"), + session_id=grant_dict.get("session_id"), + revoked=grant_dict.get("revoked", False), + revoked_at=datetime.fromisoformat(grant_dict["revoked_at"]) if grant_dict.get("revoked_at") else None, + revocation_reason=grant_dict.get("revocation_reason"), + downstream_uses=[DownstreamUse.from_dict(u) for u in grant_dict.get("downstream_uses", [])], + ) + + def store(self, grant: Grant) -> None: + """Store a grant.""" + self._storage.store_grant(grant.to_dict()) + self._scope_cache[grant.grant_id] = grant.scope + + def get(self, grant_id: str) -> Grant | None: + """Get a grant by ID.""" + grant_dict = self._storage.get_grant(grant_id) + if not grant_dict: + return None + return self._grant_to_object(grant_dict) + + def get_for_learner(self, learner_id: str) -> list[Grant]: + """Get all grants for a learner.""" + grant_dicts = self._storage.get_grants_for_learner(learner_id) + return [self._grant_to_object(gd) for gd in grant_dicts] + + def get_for_agent(self, agent_id: str, learner_id: str) -> Grant | None: + """Get the grant for a specific agent and learner.""" + grant_dict = self._storage.get_grant_for_agent(agent_id, learner_id) + if not grant_dict: + return None + grant = self._grant_to_object(grant_dict) + if grant.is_valid(): + return grant + return None diff --git a/demo/agents/memory/persistence/__init__.py b/demo/agents/memory/persistence/__init__.py new file mode 100644 index 0000000..2d60ab7 --- /dev/null +++ b/demo/agents/memory/persistence/__init__.py @@ -0,0 +1,86 @@ +"""Storage backend interface for portable memory. + +This module provides a pluggable storage backend interface. The default +implementation uses in-memory dictionaries (for local development). + +Environment variables: + STORAGE_BACKEND: "memory" (default) or "dynamodb" +""" + +import os +from typing import Protocol, runtime_checkable + +from memory.schema import MemoryItem, MemoryVersion + + +@runtime_checkable +class StorageBackend(Protocol): + """Interface for memory storage backends.""" + + # Memory operations + def store_memory(self, learner_id: str, memory: MemoryItem) -> None: ... + def get_memory(self, memory_id: str) -> MemoryItem | None: ... + def get_memories_for_learner(self, learner_id: str) -> list[MemoryItem]: ... + def update_memory(self, memory: MemoryItem) -> None: ... + def delete_memory(self, memory_id: str, learner_id: str) -> bool: ... + + # Grant operations + def store_grant(self, grant: dict) -> None: ... + def get_grant(self, grant_id: str) -> dict | None: ... + def get_grants_for_learner(self, learner_id: str) -> list[dict]: ... + def get_grant_for_agent(self, agent_id: str, learner_id: str) -> dict | None: ... + def update_grant(self, grant: dict) -> None: ... + + # Version operations + def store_version(self, version: MemoryVersion) -> None: ... + def get_versions_for_memory(self, memory_id: str) -> list[MemoryVersion]: ... + def get_version(self, version_id: str) -> MemoryVersion | None: ... + def get_all_versions(self) -> list[MemoryVersion]: ... + + # Audit operations + def store_audit_entry(self, entry: dict) -> None: ... + def query_audit( + self, + learner_id: str, + agent_id: str | None = None, + operation: str | None = None, + limit: int | None = None, + ) -> list[dict]: ... + def get_audit_indices_for_agent(self, agent_id: str) -> list[int]: ... + + # Lifecycle + def clear(self) -> None: ... + + +# Global storage backend instance +_storage_backend: StorageBackend | None = None + + +def get_storage_backend() -> StorageBackend: + """Get the configured storage backend.""" + global _storage_backend + if _storage_backend is not None: + return _storage_backend + + backend_type = os.getenv("STORAGE_BACKEND", "memory") + + if backend_type == "dynamodb": + from memory.persistence.dynamodb import DynamoDBStorage + _storage_backend = DynamoDBStorage() + else: + from memory.persistence.memory import InMemoryStorage + _storage_backend = InMemoryStorage() + + return _storage_backend + + +def reset_storage_backend() -> None: + """Reset the storage backend. For testing only.""" + global _storage_backend + _storage_backend = None + + +def set_storage_backend(backend: StorageBackend) -> None: + """Set a custom storage backend. For testing only.""" + global _storage_backend + _storage_backend = backend diff --git a/demo/agents/memory/persistence/memory.py b/demo/agents/memory/persistence/memory.py new file mode 100644 index 0000000..714f363 --- /dev/null +++ b/demo/agents/memory/persistence/memory.py @@ -0,0 +1,214 @@ +"""In-memory storage backend for portable memory. + +This is the default storage backend for local development. +Data is stored in Python dictionaries and lost on restart. +""" + +from datetime import datetime, UTC + +from memory.schema import MemoryItem, MemoryVersion + + +class InMemoryStorage: + """In-memory storage backend using dictionaries.""" + + def __init__(self): + # Memory storage + self._memories: dict[str, MemoryItem] = {} + self._memories_by_learner: dict[str, list[str]] = {} + + # Grant storage + self._grants: dict[str, dict] = {} + self._grants_by_learner: dict[str, list[str]] = {} + self._grants_by_agent: dict[str, list[str]] = {} + + # Version storage + self._versions: dict[str, MemoryVersion] = {} + self._versions_by_memory: dict[str, list[str]] = {} + + # Audit storage + self._audit_entries: list[dict] = [] + self._audit_by_learner: dict[str, list[int]] = {} + self._audit_by_agent: dict[str, list[int]] = {} + + # ========================================================================= + # Memory operations + # ========================================================================= + + def store_memory(self, learner_id: str, memory: MemoryItem) -> None: + """Store a memory item.""" + self._memories[memory.id] = memory + if learner_id not in self._memories_by_learner: + self._memories_by_learner[learner_id] = [] + if memory.id not in self._memories_by_learner[learner_id]: + self._memories_by_learner[learner_id].append(memory.id) + + def get_memory(self, memory_id: str) -> MemoryItem | None: + """Get a specific memory by ID.""" + return self._memories.get(memory_id) + + def get_memories_for_learner(self, learner_id: str) -> list[MemoryItem]: + """Get all memories for a learner.""" + memory_ids = self._memories_by_learner.get(learner_id, []) + return [self._memories[mid] for mid in memory_ids if mid in self._memories] + + def update_memory(self, memory: MemoryItem) -> None: + """Update a memory item.""" + if memory.id in self._memories: + self._memories[memory.id] = memory + + def delete_memory(self, memory_id: str, learner_id: str) -> bool: + """Delete a memory.""" + if memory_id not in self._memories: + return False + + del self._memories[memory_id] + if learner_id in self._memories_by_learner: + self._memories_by_learner[learner_id] = [ + mid for mid in self._memories_by_learner[learner_id] + if mid != memory_id + ] + return True + + # ========================================================================= + # Grant operations + # ========================================================================= + + def store_grant(self, grant: dict) -> None: + """Store a grant.""" + grant_id = grant["grant_id"] + learner_id = grant["learner_id"] + agent_id = grant["agent_id"] + + self._grants[grant_id] = grant + + if learner_id not in self._grants_by_learner: + self._grants_by_learner[learner_id] = [] + if grant_id not in self._grants_by_learner[learner_id]: + self._grants_by_learner[learner_id].append(grant_id) + + if agent_id not in self._grants_by_agent: + self._grants_by_agent[agent_id] = [] + if grant_id not in self._grants_by_agent[agent_id]: + self._grants_by_agent[agent_id].append(grant_id) + + def get_grant(self, grant_id: str) -> dict | None: + """Get a grant by ID.""" + return self._grants.get(grant_id) + + def get_grants_for_learner(self, learner_id: str) -> list[dict]: + """Get all grants for a learner.""" + grant_ids = self._grants_by_learner.get(learner_id, []) + return [self._grants[gid] for gid in grant_ids if gid in self._grants] + + def get_grant_for_agent(self, agent_id: str, learner_id: str) -> dict | None: + """Get the grant for a specific agent and learner.""" + grants = self.get_grants_for_learner(learner_id) + for grant in grants: + if grant["agent_id"] == agent_id and not grant.get("revoked", False): + expires_at = grant.get("expires_at") + if expires_at: + if datetime.fromisoformat(expires_at) < datetime.now(UTC): + continue + return grant + return None + + def update_grant(self, grant: dict) -> None: + """Update a grant.""" + grant_id = grant["grant_id"] + if grant_id in self._grants: + self._grants[grant_id] = grant + + # ========================================================================= + # Version operations + # ========================================================================= + + def store_version(self, version: MemoryVersion) -> None: + """Store a version record.""" + self._versions[version.version_id] = version + if version.memory_id not in self._versions_by_memory: + self._versions_by_memory[version.memory_id] = [] + if version.version_id not in self._versions_by_memory[version.memory_id]: + self._versions_by_memory[version.memory_id].append(version.version_id) + + def get_versions_for_memory(self, memory_id: str) -> list[MemoryVersion]: + """Get all versions for a memory, oldest to newest.""" + version_ids = self._versions_by_memory.get(memory_id, []) + return [self._versions[vid] for vid in version_ids if vid in self._versions] + + def get_version(self, version_id: str) -> MemoryVersion | None: + """Get a specific version by ID.""" + return self._versions.get(version_id) + + def get_all_versions(self) -> list[MemoryVersion]: + """Get all versions across all memories.""" + return list(self._versions.values()) + + # ========================================================================= + # Audit operations + # ========================================================================= + + def store_audit_entry(self, entry: dict) -> None: + """Store an audit entry.""" + idx = len(self._audit_entries) + self._audit_entries.append(entry) + + learner_id = entry.get("learner_id") + agent_id = entry.get("agent_id") + + if learner_id: + if learner_id not in self._audit_by_learner: + self._audit_by_learner[learner_id] = [] + self._audit_by_learner[learner_id].append(idx) + + if agent_id: + if agent_id not in self._audit_by_agent: + self._audit_by_agent[agent_id] = [] + self._audit_by_agent[agent_id].append(idx) + + def query_audit( + self, + learner_id: str, + agent_id: str | None = None, + operation: str | None = None, + limit: int | None = None, + ) -> list[dict]: + """Query audit entries.""" + indices = self._audit_by_learner.get(learner_id, []) + + results = [] + for idx in indices: + entry = self._audit_entries[idx] + + if agent_id and entry.get("agent_id") != agent_id: + continue + if operation and entry.get("operation") != operation: + continue + + results.append(entry) + + if limit and len(results) >= limit: + break + + return results + + def get_audit_indices_for_agent(self, agent_id: str) -> list[int]: + """Get audit entry indices for an agent.""" + return self._audit_by_agent.get(agent_id, []) + + # ========================================================================= + # Lifecycle + # ========================================================================= + + def clear(self) -> None: + """Clear all data.""" + self._memories.clear() + self._memories_by_learner.clear() + self._grants.clear() + self._grants_by_learner.clear() + self._grants_by_agent.clear() + self._versions.clear() + self._versions_by_memory.clear() + self._audit_entries.clear() + self._audit_by_learner.clear() + self._audit_by_agent.clear() diff --git a/demo/agents/memory/schema.py b/demo/agents/memory/schema.py new file mode 100644 index 0000000..d438c72 --- /dev/null +++ b/demo/agents/memory/schema.py @@ -0,0 +1,323 @@ +"""Core schema for portable learner memory. + +This defines the "minimal interoperable schema" - what an AI learning system +needs to know about a learner to personalize effectively. + +Design principles: +- Categories are semantic, not structural (what kind of info, not where it came from) +- Provenance tracks origin for trust and correction +- Temporal validity enables memory expiration +- Original format/ID enables round-trip fidelity through export/import +- Source type distinguishes user-declared facts from model inferences (Pre-read 2.4) +- Version history supports correction and audit (Pre-read 4.1) + +Pre-read References: +- Section 2.4: User-declared vs model-inferred distinction (identity hardening prevention) +- Section 4.1: Memory lifecycle with versioning and correction +- Section 3 Table 1: Rich learner profile structure +""" + +from dataclasses import dataclass, field +from datetime import UTC +from datetime import datetime +from enum import Enum +from typing import Literal +import uuid + + +class MemoryCategory(str, Enum): + """Categories of learner memory. + + These represent semantically different types of information: + - fact: Something true about the learner (context, background) + - goal: What they're trying to achieve (short or long term) + - preference: How they like to learn or interact + - mastery: What they've demonstrated they know/can do + - constraint: Limitations on how/when they can learn + - relationship: Connections to people/institutions (Pre-read 4.1) + """ + FACT = "fact" + GOAL = "goal" + PREFERENCE = "preference" + MASTERY = "mastery" + CONSTRAINT = "constraint" + RELATIONSHIP = "relationship" + + +MemorySource = Literal["conversation", "assessment", "import", "self_report"] + +# Pre-read 2.4: Distinguish user-declared facts from model inferences +# This prevents "identity hardening" - when transient behavior gets encoded as permanent traits +SourceType = Literal["user_declared", "model_inferred"] + +# Pre-read 2.4: Memory status - candidate vs confirmed +MemoryStatus = Literal["candidate", "confirmed"] + + +@dataclass +class MemoryItem: + """A single memory about a learner. + + This is the core unit of portable learner memory. It's designed to be: + - Rich enough to capture meaningful personalization context + - Simple enough to map to external formats (CLR, Caliper, etc.) + - Traceable through provenance and original format fields + - Distinguishes user-declared vs model-inferred (Pre-read 2.4) + - Supports versioning for correction tracking (Pre-read 4.1) + """ + + # Core content + category: MemoryCategory + content: str # Human-readable description of the memory + + # Provenance: where did this come from? + source: MemorySource + confidence: float = 1.0 # 0.0-1.0, how certain are we? + + # Pre-read 2.4: User-declared vs model-inferred distinction + # Critical for preventing identity hardening + source_type: SourceType = "model_inferred" + + # Pre-read 2.4: Memory status - candidate memories can be promoted to confirmed + status: MemoryStatus = "confirmed" + + # Temporal validity + created_at: datetime = field(default_factory=lambda: datetime.now(UTC)) + valid_until: datetime | None = None # None = indefinite + + # Context + subject_area: str | None = None # "math", "writing", "general", etc. + + # Identity + id: str = field(default_factory=lambda: str(uuid.uuid4())) + + # Pre-read 4.1: Version history for corrections + version: int = 1 + previous_version_id: str | None = None + correction_reason: str | None = None # Why this version was created + + # Pre-read Q7: Purpose tagging for purpose-based access control + purposes: list[str] = field(default_factory=list) + + # Round-trip fidelity: track original format for lossless export/import + original_format: str | None = None # "clr", "caliper", "native" + original_id: str | None = None # ID in the original format + + def is_valid(self, at_time: datetime | None = None) -> bool: + """Check if this memory is still valid.""" + if self.valid_until is None: + return True + check_time = at_time or datetime.now(UTC) + return check_time < self.valid_until + + def to_dict(self) -> dict: + """Serialize to dictionary for JSON storage.""" + return { + "id": self.id, + "category": self.category.value, + "content": self.content, + "source": self.source, + "confidence": self.confidence, + "source_type": self.source_type, + "status": self.status, + "created_at": self.created_at.isoformat(), + "valid_until": self.valid_until.isoformat() if self.valid_until else None, + "subject_area": self.subject_area, + "version": self.version, + "previous_version_id": self.previous_version_id, + "correction_reason": self.correction_reason, + "purposes": self.purposes, + "original_format": self.original_format, + "original_id": self.original_id, + } + + @classmethod + def from_dict(cls, data: dict) -> "MemoryItem": + """Deserialize from dictionary.""" + return cls( + id=data["id"], + category=MemoryCategory(data["category"]), + content=data["content"], + source=data["source"], + confidence=data.get("confidence", 1.0), + source_type=data.get("source_type", "model_inferred"), + status=data.get("status", "confirmed"), + created_at=datetime.fromisoformat(data["created_at"]), + valid_until=datetime.fromisoformat(data["valid_until"]) if data.get("valid_until") else None, + subject_area=data.get("subject_area"), + version=data.get("version", 1), + previous_version_id=data.get("previous_version_id"), + correction_reason=data.get("correction_reason"), + purposes=data.get("purposes", []), + original_format=data.get("original_format"), + original_id=data.get("original_id"), + ) + + +@dataclass +class MemoryVersion: + """A version of a memory item (Pre-read 4.1, 4.4). + + Used for tracking corrections and enabling restore. + """ + + version_id: str + memory_id: str + version_number: int + + # Content at this version + content: str + metadata: dict = field(default_factory=dict) + + # Change tracking + created_at: datetime = field(default_factory=lambda: datetime.now(UTC)) + created_by: str = "system" # agent_id or "learner" + change_type: Literal["created", "updated", "corrected", "restored"] = "created" + change_reason: str | None = None + + # Previous version link + previous_version_id: str | None = None + + def to_dict(self) -> dict: + return { + "version_id": self.version_id, + "memory_id": self.memory_id, + "version_number": self.version_number, + "content": self.content, + "metadata": self.metadata, + "created_at": self.created_at.isoformat(), + "created_by": self.created_by, + "change_type": self.change_type, + "change_reason": self.change_reason, + "previous_version_id": self.previous_version_id, + } + + @classmethod + def from_dict(cls, data: dict) -> "MemoryVersion": + return cls( + version_id=data["version_id"], + memory_id=data["memory_id"], + version_number=data["version_number"], + content=data["content"], + metadata=data.get("metadata", {}), + created_at=datetime.fromisoformat(data["created_at"]), + created_by=data.get("created_by", "system"), + change_type=data.get("change_type", "created"), + change_reason=data.get("change_reason"), + previous_version_id=data.get("previous_version_id"), + ) + + +# ============================================================================= +# Rich Profile Types (Pre-read Section 3, Table 1) +# ============================================================================= + + +@dataclass +class MasteryRecord: + """Mastery for a specific skill with provenance (Pre-read Table 1).""" + + skill_id: str + skill_name: str + score: float # 0.0 - 1.0 + confidence: Literal["high", "medium", "low"] + + # Provenance + source_type: SourceType + assessed_at: datetime + assessment_method: str + + # Subject area for scoping + subject_area: str + + def to_dict(self) -> dict: + return { + "skill_id": self.skill_id, + "skill_name": self.skill_name, + "score": self.score, + "confidence": self.confidence, + "source_type": self.source_type, + "assessed_at": self.assessed_at.isoformat(), + "assessment_method": self.assessment_method, + "subject_area": self.subject_area, + } + + +@dataclass +class Goal: + """Learning goal with timeline (Pre-read Table 1).""" + + goal_id: str + description: str + target_date: datetime | None = None + subject_area: str | None = None + source_type: SourceType = "user_declared" + created_at: datetime = field(default_factory=lambda: datetime.now(UTC)) + + def to_dict(self) -> dict: + return { + "goal_id": self.goal_id, + "description": self.description, + "target_date": self.target_date.isoformat() if self.target_date else None, + "subject_area": self.subject_area, + "source_type": self.source_type, + "created_at": self.created_at.isoformat(), + } + + +@dataclass +class Preference: + """Learning preference with provenance (Pre-read Table 1).""" + + preference_id: str + category: Literal["explanation_style", "pacing", "format", "interaction"] + description: str + + # Provenance + source_type: SourceType = "model_inferred" + confidence: float = 0.8 + declared_at: datetime = field(default_factory=lambda: datetime.now(UTC)) + + # Scope + subject_area: str | None = None + + def to_dict(self) -> dict: + return { + "preference_id": self.preference_id, + "category": self.category, + "description": self.description, + "source_type": self.source_type, + "confidence": self.confidence, + "declared_at": self.declared_at.isoformat(), + "subject_area": self.subject_area, + } + + +@dataclass +class LearnerProfile: + """Structured representation of user context (Pre-read Table 1).""" + + learner_id: str + + # Goal / timeline + goals: list[Goal] = field(default_factory=list) + + # Mastery snapshot + mastery: dict[str, MasteryRecord] = field(default_factory=dict) + + # Learning preferences + preferences: list[Preference] = field(default_factory=list) + + # Profile-level metadata + created_at: datetime = field(default_factory=lambda: datetime.now(UTC)) + updated_at: datetime = field(default_factory=lambda: datetime.now(UTC)) + + def to_dict(self) -> dict: + return { + "learner_id": self.learner_id, + "goals": [g.to_dict() for g in self.goals], + "mastery": {k: v.to_dict() for k, v in self.mastery.items()}, + "preferences": [p.to_dict() for p in self.preferences], + "created_at": self.created_at.isoformat(), + "updated_at": self.updated_at.isoformat(), + } diff --git a/demo/agents/memory/scopes.py b/demo/agents/memory/scopes.py new file mode 100644 index 0000000..562dd84 --- /dev/null +++ b/demo/agents/memory/scopes.py @@ -0,0 +1,176 @@ +"""Multi-dimensional scopes for memory access control. + +Pre-read References: +- Section Q7: "granular permissions ('share my math grades but not English'); + purpose-based controls ('for college applications' vs. 'for tutoring')" +- Section 2.3: Policy and authorization layer +- Section 2.5: Two invariants - no unconstrained access, enforceable operations +""" + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from memory.schema import MemoryItem + + +@dataclass +class Scope: + """Multi-dimensional scope for memory access. + + Pre-read Q7: "granular permissions ('share my math grades but not English'); + purpose-based controls ('for college applications' vs. 'for tutoring')" + + Scopes have three filtering dimensions: + 1. Subject area (math, writing, general) + 2. Category (mastery, preferences, facts, constraints) + 3. Purpose (tutoring, assessment, college_apps, research) + + Plus read/write permissions and source type filtering. + """ + + agent_id: str + + # Dimension 1: Subject area (math, writing, general) + # None = all subjects allowed + allowed_subjects: list[str] | None = None + + # Dimension 2: Category (mastery, preferences, facts, constraints) + # None = all categories allowed + allowed_categories: list[str] | None = None + + # Dimension 3: Purpose (tutoring, assessment, college_apps, research) + # None = all purposes allowed + allowed_purposes: list[str] | None = None + + # Pre-read 2.4: Source type filtering + # Can filter out model-inferred memories for high-stakes uses + include_inferred: bool = True + + # Read vs write permissions + can_read: bool = True + can_write: bool = False + write_categories: list[str] | None = None # Categories this scope can write + + def allows(self, memory: "MemoryItem", purpose: str | None = None) -> bool: + """Check if this scope allows access to the memory for the given purpose. + + Pre-read 2.5: No unconstrained access - every retrieval must be validated. + """ + # Check subject area + if self.allowed_subjects is not None: + memory_subject = memory.subject_area or "general" + if memory_subject not in self.allowed_subjects: + return False + + # Check category + if self.allowed_categories is not None: + if memory.category.value not in self.allowed_categories: + return False + + # Check purpose (if specified) + if purpose and self.allowed_purposes is not None: + if purpose not in self.allowed_purposes: + return False + + # Check source type (Pre-read 2.4) + if not self.include_inferred and memory.source_type == "model_inferred": + return False + + return True + + def allows_write(self, category: str) -> bool: + """Check if this scope allows writing memories of this category.""" + if not self.can_write: + return False + if self.write_categories is None: + return True # Can write all categories + return category in self.write_categories + + def to_dict(self) -> dict: + """Serialize for audit logging.""" + return { + "agent_id": self.agent_id, + "allowed_subjects": self.allowed_subjects, + "allowed_categories": self.allowed_categories, + "allowed_purposes": self.allowed_purposes, + "include_inferred": self.include_inferred, + "can_read": self.can_read, + "can_write": self.can_write, + "write_categories": self.write_categories, + } + + @classmethod + def from_dict(cls, data: dict) -> "Scope": + """Deserialize from dictionary.""" + return cls( + agent_id=data["agent_id"], + allowed_subjects=data.get("allowed_subjects"), + allowed_categories=data.get("allowed_categories"), + allowed_purposes=data.get("allowed_purposes"), + include_inferred=data.get("include_inferred", True), + can_read=data.get("can_read", True), + can_write=data.get("can_write", False), + write_categories=data.get("write_categories"), + ) + + +# ============================================================================= +# Pre-defined Scopes +# ============================================================================= + + +def create_math_tutor_scope() -> Scope: + """Scope for the Math Tutor agent. + + Can read and write math-related memories, plus general preferences. + """ + return Scope( + agent_id="math_tutor", + allowed_subjects=["math", "general"], + allowed_categories=None, # All categories + allowed_purposes=["tutoring"], + include_inferred=True, + can_read=True, + can_write=True, + write_categories=["mastery", "preference", "goal", "constraint"], + ) + + +def create_writing_coach_scope() -> Scope: + """Scope for the Writing Coach agent. + + Can read and write writing-related memories, plus general preferences. + """ + return Scope( + agent_id="writing_coach", + allowed_subjects=["writing", "general"], + allowed_categories=None, # All categories + allowed_purposes=["tutoring"], + include_inferred=True, + can_read=True, + can_write=True, + write_categories=["mastery", "preference", "goal", "constraint"], + ) + + +def create_full_access_scope(agent_id: str = "admin") -> Scope: + """Full access scope for testing/admin. Use sparingly. + + Pre-read 2.5: Even admin access should be audited. + """ + return Scope( + agent_id=agent_id, + allowed_subjects=None, + allowed_categories=None, + allowed_purposes=None, + include_inferred=True, + can_read=True, + can_write=True, + write_categories=None, + ) + + +# Convenience instances +MATH_TUTOR_SCOPE = create_math_tutor_scope() +WRITING_COACH_SCOPE = create_writing_coach_scope() diff --git a/demo/agents/memory/service.py b/demo/agents/memory/service.py new file mode 100644 index 0000000..20c7f46 --- /dev/null +++ b/demo/agents/memory/service.py @@ -0,0 +1,596 @@ +"""Central memory service with policy-enforced access. + +Pre-read References: +- Section 2.3: Five-component architecture +- Section 2.5: Two invariants (no unconstrained access, enforceable operations) +- Section 4.1: Memory lifecycle operations + +This is the main entry point for all memory operations. It implements: +1. Memory Store (in-memory backend) +2. Policy & Authorization (scope validation) +3. Context Router (scoped retrieval) +4. Audit Subsystem (append-only log) +5. Interoperability Interface (CRUD + export/import) +""" + +from datetime import datetime, UTC +from typing import Literal +import logging + +from memory.schema import ( + MemoryItem, MemoryCategory, LearnerProfile, + SourceType, MasteryRecord, Preference, Goal, +) +from memory.scopes import Scope +from memory.grants import Grant, validate_grant, GrantStore +from memory.audit import AuditLog +from memory.versioning import VersionStore +from memory.persistence import StorageBackend, get_storage_backend + +logger = logging.getLogger(__name__) + + +class PortableMemoryService: + """Central memory service with policy-enforced access. + + Implements Pre-read Section 2.3 architecture: + - Memory store (in-memory storage) + - Policy and authorization (scope validation) + - Context router (scoped retrieval with minimization) + - Audit subsystem (append-only log) + - Interoperability interface (CRUD + export/import) + + Pre-read 2.5 Invariants: + 1. No unconstrained access - every retrieval validates grant + scope + 2. Enforceable operations - all operations go through this service + """ + + def __init__(self, storage: StorageBackend | None = None): + self._storage = storage or get_storage_backend() + self.grant_store = GrantStore(storage=self._storage) + self.audit_log = AuditLog(storage=self._storage) + self.version_store = VersionStore(storage=self._storage) + + # ========================================================================= + # Memory Store Operations + # ========================================================================= + + def add_memory( + self, + memory: MemoryItem, + learner_id: str, + grant: Grant, + ) -> str: + """Add a memory to the store. + + Pre-read 2.5: Validates grant before storage. + + Args: + memory: The memory to add + learner_id: The learner this memory belongs to + grant: Authorization grant + + Returns: + The memory ID + + Raises: + PermissionError: If grant doesn't allow this operation + """ + # Validate grant + if not validate_grant(grant, "write", memory): + raise PermissionError( + f"Grant {grant.grant_id} does not allow adding {memory.category.value} memories" + ) + + # Validate category write permission + if not grant.scope.allows_write(memory.category.value): + raise PermissionError( + f"Scope does not allow writing {memory.category.value} memories" + ) + + # Store memory + self._storage.store_memory(learner_id, memory) + + # Create initial version + self.version_store.create_version( + memory=memory, + change_type="created", + reason=None, + actor=grant.agent_id, + ) + + # Audit log + self.audit_log.log( + learner_id=learner_id, + operation="add", + agent_id=grant.agent_id, + grant_id=grant.grant_id, + scope=grant.scope, + memory_ids=[memory.id], + categories=[memory.category.value], + subjects=[memory.subject_area] if memory.subject_area else [], + ) + + return memory.id + + # ========================================================================= + # Scoped Retrieval (Context Router) + # ========================================================================= + + def search_memories( + self, + query: str, + learner_id: str, + grant: Grant, + limit: int = 20, + purpose: str | None = None, + ) -> list[MemoryItem]: + """Search memories with scope filtering. + + Pre-read 2.5: Every retrieval is conditioned on authorization and minimization. + + Args: + query: Search query (currently: filters by relevance) + learner_id: The learner + grant: Authorization grant + limit: Max results + purpose: The purpose of this access + + Returns: + List of matching MemoryItem objects (filtered by scope) + """ + # Validate grant + if not validate_grant(grant, "read"): + raise PermissionError(f"Grant {grant.grant_id} does not allow read access") + + # Get all memories for learner + all_memories = self._get_memories_for_learner(learner_id) + total_available = len(all_memories) + + # Apply scope filtering + scope_filtered = [ + m for m in all_memories + if grant.scope.allows(m, purpose) + ] + returned_after_scope = len(scope_filtered) + + # Apply query relevance filtering (simple keyword match) + if query: + query_lower = query.lower() + relevant = [ + m for m in scope_filtered + if query_lower in m.content.lower() or + (m.subject_area and query_lower in m.subject_area.lower()) + ] + else: + relevant = scope_filtered + + # Apply limit (minimization) + result = relevant[:limit] + returned_after_minimization = len(result) + + # Audit log + self.audit_log.log( + learner_id=learner_id, + operation="search", + agent_id=grant.agent_id, + grant_id=grant.grant_id, + scope=grant.scope, + purpose=purpose, + memory_ids=[m.id for m in result], + categories=list(set(m.category.value for m in result)), + subjects=list(set(m.subject_area for m in result if m.subject_area)), + total_available=total_available, + returned_after_scope=returned_after_scope, + returned_after_minimization=returned_after_minimization, + ) + + return result + + def get_memories( + self, + learner_id: str, + grant: Grant, + purpose: str | None = None, + ) -> list[MemoryItem]: + """Get all memories for a learner (filtered by scope). + + Args: + learner_id: The learner + grant: Authorization grant + purpose: The purpose of this access + + Returns: + All memories the grant allows access to + """ + return self.search_memories( + query="", + learner_id=learner_id, + grant=grant, + limit=1000, + purpose=purpose, + ) + + def get_memory(self, memory_id: str, grant: Grant) -> MemoryItem | None: + """Get a specific memory by ID. + + Args: + memory_id: The memory ID + grant: Authorization grant + + Returns: + The memory if found and accessible, None otherwise + """ + memory = self._storage.get_memory(memory_id) + if not memory: + return None + + if not grant.scope.allows(memory, purpose=None): + return None + + return memory + + # ========================================================================= + # Lifecycle Operations (Pre-read 4.1) + # ========================================================================= + + def correct_memory( + self, + memory_id: str, + new_content: str, + reason: str, + grant: Grant, + new_source_type: SourceType | None = None, + new_confidence: float | None = None, + ) -> MemoryItem: + """Correct a memory with version history. + + Pre-read 4.4: "timely correction pathways, provenance metadata" + + Args: + memory_id: The memory to correct + new_content: The corrected content + reason: Why the correction is being made + grant: Authorization grant + new_source_type: Optional new source type + new_confidence: Optional new confidence + + Returns: + The corrected MemoryItem + + Raises: + PermissionError: If not authorized + ValueError: If memory not found + """ + memory = self._storage.get_memory(memory_id) + if not memory: + raise ValueError(f"Memory {memory_id} not found") + + if not validate_grant(grant, "correct", memory): + raise PermissionError(f"Grant does not allow correcting this memory") + + # Store complete previous state for audit + previous_values = memory.to_dict() + + # Apply correction + memory.content = new_content + if new_source_type: + memory.source_type = new_source_type + if new_confidence is not None: + memory.confidence = new_confidence + memory.correction_reason = reason + memory.version += 1 + + # Create version record + self.version_store.create_version( + memory=memory, + change_type="corrected", + reason=reason, + actor=grant.agent_id, + ) + + # Persist the correction + self._storage.update_memory(memory) + + # Audit log + self.audit_log.log( + learner_id=grant.learner_id, + operation="correct", + agent_id=grant.agent_id, + grant_id=grant.grant_id, + scope=grant.scope, + memory_ids=[memory_id], + categories=[memory.category.value], + previous_values=previous_values, + new_values={"content": new_content, "source_type": new_source_type, "confidence": new_confidence}, + reason=reason, + ) + + return memory + + def get_version_history(self, memory_id: str) -> list: + """Get version history for a memory. + + Args: + memory_id: The memory to get history for + + Returns: + List of MemoryVersion objects (oldest to newest) + """ + return self.version_store.get_history(memory_id) + + # ========================================================================= + # Profile Operations + # ========================================================================= + + def get_profile( + self, + learner_id: str, + grant: Grant, + ) -> LearnerProfile: + """Get a structured LearnerProfile from memories. + + Synthesizes atomic MemoryItems into the rich Table 1 structure. + + Args: + learner_id: The learner + grant: Authorization grant + + Returns: + LearnerProfile object + """ + memories = self.get_memories(learner_id, grant) + profile = LearnerProfile(learner_id=learner_id) + + for memory in memories: + if memory.category == MemoryCategory.GOAL: + profile.goals.append(Goal( + goal_id=memory.id, + description=memory.content, + subject_area=memory.subject_area, + source_type=memory.source_type, + created_at=memory.created_at, + )) + elif memory.category == MemoryCategory.MASTERY: + skill_id = memory.id + profile.mastery[skill_id] = MasteryRecord( + skill_id=skill_id, + skill_name=memory.content[:50], + score=memory.confidence, + confidence="high" if memory.confidence > 0.8 else "medium" if memory.confidence > 0.5 else "low", + source_type=memory.source_type, + assessed_at=memory.created_at, + assessment_method=memory.source, + subject_area=memory.subject_area or "general", + ) + elif memory.category == MemoryCategory.PREFERENCE: + profile.preferences.append(Preference( + preference_id=memory.id, + category="explanation_style", + description=memory.content, + source_type=memory.source_type, + confidence=memory.confidence, + declared_at=memory.created_at, + subject_area=memory.subject_area, + )) + + profile.updated_at = datetime.now(UTC) + return profile + + # ========================================================================= + # Interoperability Interface + # ========================================================================= + + def export_caliper( + self, + learner_id: str, + grant: Grant, + purpose: str | None = None, + ) -> dict: + """Export memories as Caliper envelope with scope filtering. + + Args: + learner_id: The learner + grant: Authorization grant + purpose: The purpose of this export + + Returns: + Caliper envelope as dictionary + """ + from memory.formats.caliper import export_caliper + + memories = self.get_memories(learner_id, grant, purpose=purpose) + + # Track downstream use + grant.add_downstream_use( + system_id="caliper_export", + memory_ids=[m.id for m in memories], + ) + + # Audit + self.audit_log.log( + learner_id=learner_id, + operation="export", + agent_id=grant.agent_id, + grant_id=grant.grant_id, + scope=grant.scope, + purpose=purpose, + memory_ids=[m.id for m in memories], + ) + + return export_caliper(memories, learner_id=learner_id) + + # ========================================================================= + # TLCS Integration + # ========================================================================= + + def push_to_tlcs( + self, + learner_id: str, + grant: Grant, + purpose: str | None = None, + ) -> dict | None: + """Push memories to TLCS as Caliper events. + + Exports scoped memories and sends them to the TLCS /ingest/caliper endpoint. + + Args: + learner_id: The learner + grant: Authorization grant (must allow read/export) + purpose: The purpose of this export + + Returns: + TLCS API response, or None if TLCS is not configured + + Raises: + PermissionError: If grant doesn't allow export + """ + from memory.tlcs import get_tlcs_client, memories_to_caliper_for_tlcs + + client = get_tlcs_client() + if client is None: + logger.info("TLCS not configured, skipping push") + return None + + if not validate_grant(grant, "export"): + raise PermissionError( + f"Grant {grant.grant_id} does not allow export operations" + ) + + memories = self.get_memories(learner_id, grant, purpose=purpose) + + if not memories: + logger.info(f"No memories to push for learner {learner_id}") + return {"status": "no_data", "count": 0} + + caliper_envelope = memories_to_caliper_for_tlcs(memories, learner_id) + response = client.push_caliper(caliper_envelope) + + # Track downstream use + grant.add_downstream_use( + system_id="tlcs", + memory_ids=[m.id for m in memories], + ) + + # Audit log + self.audit_log.log( + learner_id=learner_id, + operation="export", + agent_id=grant.agent_id, + grant_id=grant.grant_id, + scope=grant.scope, + purpose=purpose or "tlcs_push", + memory_ids=[m.id for m in memories], + categories=list(set(m.category.value for m in memories)), + ) + + logger.info(f"Pushed {len(memories)} memories to TLCS for {learner_id}") + return response + + def pull_from_tlcs( + self, + learner_id: str, + grant: Grant, + merge_strategy: Literal["add_new", "replace", "skip_all"] = "add_new", + ) -> list[MemoryItem]: + """Pull learner context from TLCS and import as memories. + + Fetches context from TLCS /learners/{id}/context endpoint and converts + to MemoryItems, applying the specified merge strategy. + + Args: + learner_id: The learner to pull context for + grant: Authorization grant (must allow write for add_new/replace) + merge_strategy: How to handle imported memories + - "add_new": Add memories that don't exist (based on original_id) + - "replace": Delete existing TLCS memories and re-import all + - "skip_all": Dry run - return memories but don't add them + + Returns: + List of imported MemoryItem objects + + Raises: + PermissionError: If grant doesn't allow write (except skip_all) + """ + from memory.tlcs import get_tlcs_client, tlcs_context_to_memories + + client = get_tlcs_client() + if client is None: + logger.info("TLCS not configured, skipping pull") + return [] + + if merge_strategy != "skip_all" and not grant.scope.can_write: + raise PermissionError( + "Grant does not allow write access for TLCS import" + ) + + context = client.pull_context(learner_id) + memories = tlcs_context_to_memories(context) + + if not memories: + logger.info(f"No memories pulled from TLCS for {learner_id}") + return [] + + added: list[MemoryItem] = [] + + if merge_strategy == "skip_all": + added = memories + + elif merge_strategy == "replace": + existing = self._get_memories_for_learner(learner_id) + tlcs_memories = [m for m in existing if m.original_format == "tlcs"] + for memory in tlcs_memories: + self._storage.delete_memory(memory.id, learner_id) + + for memory in memories: + try: + self.add_memory(memory, learner_id, grant) + added.append(memory) + except PermissionError as e: + logger.warning(f"Failed to import memory from TLCS: {e}") + + else: # add_new + existing = self._get_memories_for_learner(learner_id) + existing_tlcs_ids = { + m.original_id for m in existing + if m.original_format == "tlcs" and m.original_id + } + + for memory in memories: + if memory.original_id in existing_tlcs_ids: + continue + try: + self.add_memory(memory, learner_id, grant) + added.append(memory) + except PermissionError as e: + logger.warning(f"Failed to import memory from TLCS: {e}") + + if merge_strategy != "skip_all" and added: + self.audit_log.log( + learner_id=learner_id, + operation="import", + agent_id=grant.agent_id, + grant_id=grant.grant_id, + scope=grant.scope, + purpose="tlcs_pull", + memory_ids=[m.id for m in added], + categories=list(set(m.category.value for m in added)), + ) + + logger.info(f"Pulled {len(added)} memories from TLCS for {learner_id}") + return added + + # ========================================================================= + # Internal Helpers + # ========================================================================= + + def _get_memories_for_learner(self, learner_id: str) -> list[MemoryItem]: + """Get all memories for a learner (internal, no scope check).""" + return self._storage.get_memories_for_learner(learner_id) + + def clear(self) -> None: + """Clear all data. For testing only.""" + self._storage.clear() + self.grant_store = GrantStore(storage=self._storage) + self.audit_log = AuditLog(storage=self._storage) + self.version_store = VersionStore(storage=self._storage) diff --git a/demo/agents/memory/tlcs/__init__.py b/demo/agents/memory/tlcs/__init__.py new file mode 100644 index 0000000..9cbeee9 --- /dev/null +++ b/demo/agents/memory/tlcs/__init__.py @@ -0,0 +1,58 @@ +"""TLCS (Teaching and Learning Context Service) integration. + +This package provides bidirectional synchronization with TLCS: +- Push: Export MemoryItems as Caliper credentials -> POST to TLCS /ingest/caliper +- Pull: GET learner context from TLCS -> convert to MemoryItems + +Example usage: + from memory.tlcs import get_tlcs_client, tlcs_context_to_memories + + client = get_tlcs_client() + if client: + # Pull context and convert to memories + context = client.pull_context("learner-123") + memories = tlcs_context_to_memories(context) + + # Push memories as Caliper + caliper = memories_to_caliper_for_tlcs(memories, "learner-123") + client.push_caliper(caliper) + +Environment variables: + TLCS_ENDPOINT: Base URL for TLCS API (required to enable) + TLCS_CLIENT_ID: OAuth2 client ID + TLCS_CLIENT_SECRET: OAuth2 client secret + TLCS_TOKEN_URL: Cognito token endpoint + TLCS_TIMEOUT: Request timeout in seconds (default: 30) +""" + +from memory.tlcs.config import TLCSConfig +from memory.tlcs.client import ( + TLCSClient, + TLCSError, + TLCSClientError, + TLCSAuthError, + TLCSAPIError, + get_tlcs_client, + reset_tlcs_client, +) +from memory.tlcs.mapper import ( + tlcs_context_to_memories, + memories_to_caliper_for_tlcs, +) + +__all__ = [ + # Config + "TLCSConfig", + # Client + "TLCSClient", + "get_tlcs_client", + "reset_tlcs_client", + # Exceptions + "TLCSError", + "TLCSClientError", + "TLCSAuthError", + "TLCSAPIError", + # Mapper + "tlcs_context_to_memories", + "memories_to_caliper_for_tlcs", +] diff --git a/demo/agents/memory/tlcs/client.py b/demo/agents/memory/tlcs/client.py new file mode 100644 index 0000000..b2ab007 --- /dev/null +++ b/demo/agents/memory/tlcs/client.py @@ -0,0 +1,308 @@ +"""TLCS HTTP client with OAuth2 authentication. + +Provides authenticated access to TLCS API endpoints: +- POST /ingest/caliper - Push Caliper events +- GET /learners/{id}/context - Pull learner context + +Uses OAuth2 client credentials flow with Cognito for authentication. +Token caching with auto-refresh is handled automatically. +""" + +from dataclasses import dataclass +from datetime import datetime, UTC, timedelta +from typing import Any +import logging + +import httpx + +from memory.tlcs.config import TLCSConfig + +logger = logging.getLogger(__name__) + + +# ============================================================================= +# Exceptions +# ============================================================================= + + +class TLCSError(Exception): + """Base exception for TLCS operations.""" + pass + + +class TLCSClientError(TLCSError): + """Error in client configuration or request formation.""" + pass + + +class TLCSAuthError(TLCSError): + """Authentication failed with TLCS/Cognito.""" + pass + + +class TLCSAPIError(TLCSError): + """TLCS API returned an error response.""" + + def __init__(self, message: str, status_code: int, response_body: Any = None): + super().__init__(message) + self.status_code = status_code + self.response_body = response_body + + +# ============================================================================= +# Token Cache +# ============================================================================= + + +@dataclass +class TokenCache: + """Cached OAuth2 access token with expiry tracking.""" + + access_token: str + expires_at: datetime + token_type: str = "Bearer" + + def is_expired(self, buffer_seconds: int = 60) -> bool: + """Check if token is expired or about to expire.""" + return datetime.now(UTC) >= (self.expires_at - timedelta(seconds=buffer_seconds)) + + +# ============================================================================= +# TLCS Client +# ============================================================================= + + +class TLCSClient: + """HTTP client for TLCS API with OAuth2 authentication. + + Handles: + - OAuth2 client credentials flow with Cognito + - Token caching with automatic refresh + - Caliper event ingestion + - Learner context retrieval + + Example: + client = TLCSClient(config) + # Push Caliper events + client.push_caliper(caliper_envelope) + # Pull learner context + context = client.pull_context("learner-123") + """ + + def __init__(self, config: TLCSConfig): + """Initialize TLCS client. + + Args: + config: TLCS configuration with endpoint and auth details + """ + self.config = config + self._token_cache: TokenCache | None = None + self._http_client: httpx.Client | None = None + + @property + def http_client(self) -> httpx.Client: + """Get or create HTTP client (lazy initialization).""" + if self._http_client is None: + self._http_client = httpx.Client( + timeout=self.config.timeout, + follow_redirects=True, + ) + return self._http_client + + def close(self) -> None: + """Close the HTTP client.""" + if self._http_client is not None: + self._http_client.close() + self._http_client = None + + def __enter__(self) -> "TLCSClient": + return self + + def __exit__(self, *args) -> None: + self.close() + + # ========================================================================= + # Authentication + # ========================================================================= + + def _get_access_token(self) -> str: + """Get valid access token, refreshing if needed. + + Returns: + Valid access token string + + Raises: + TLCSAuthError: If token fetch fails + TLCSClientError: If auth not configured + """ + # Return cached token if still valid + if self._token_cache and not self._token_cache.is_expired(): + return self._token_cache.access_token + + # Check auth configuration + if not self.config.is_auth_configured(): + raise TLCSClientError( + "TLCS authentication not configured. " + "Set TLCS_CLIENT_ID, TLCS_CLIENT_SECRET, and TLCS_TOKEN_URL." + ) + + # Fetch new token + self._token_cache = self._fetch_token() + return self._token_cache.access_token + + def _fetch_token(self) -> TokenCache: + """Fetch new access token from Cognito. + + Returns: + TokenCache with new token + + Raises: + TLCSAuthError: If token fetch fails + """ + try: + response = self.http_client.post( + self.config.token_url, + data={ + "grant_type": "client_credentials", + "client_id": self.config.client_id, + "client_secret": self.config.client_secret, + }, + headers={"Content-Type": "application/x-www-form-urlencoded"}, + ) + response.raise_for_status() + data = response.json() + + expires_in = data.get("expires_in", 3600) + return TokenCache( + access_token=data["access_token"], + expires_at=datetime.now(UTC) + timedelta(seconds=expires_in), + token_type=data.get("token_type", "Bearer"), + ) + except httpx.HTTPStatusError as e: + logger.error(f"TLCS auth failed: {e.response.status_code}") + raise TLCSAuthError(f"Failed to authenticate with TLCS: {e}") from e + except httpx.RequestError as e: + logger.error(f"TLCS auth request failed: {e}") + raise TLCSAuthError(f"Failed to connect to TLCS auth endpoint: {e}") from e + except KeyError as e: + raise TLCSAuthError(f"Invalid token response, missing {e}") from e + + def _auth_headers(self) -> dict[str, str]: + """Get authorization headers for API requests.""" + token = self._get_access_token() + return {"Authorization": f"Bearer {token}"} + + # ========================================================================= + # API Methods + # ========================================================================= + + def push_caliper(self, caliper_envelope: dict) -> dict: + """Push Caliper events to TLCS. + + Args: + caliper_envelope: Caliper envelope with events to ingest + + Returns: + Response from TLCS API + + Raises: + TLCSAPIError: If API returns error response + TLCSAuthError: If authentication fails + """ + url = f"{self.config.endpoint}/ingest/caliper" + + try: + response = self.http_client.post( + url, + json=caliper_envelope, + headers={ + **self._auth_headers(), + "Content-Type": "application/json", + }, + ) + response.raise_for_status() + return response.json() + except httpx.HTTPStatusError as e: + logger.error(f"TLCS push failed: {e.response.status_code}") + raise TLCSAPIError( + f"TLCS API error: {e.response.status_code}", + status_code=e.response.status_code, + response_body=e.response.text, + ) from e + except httpx.RequestError as e: + logger.error(f"TLCS push request failed: {e}") + raise TLCSAPIError( + f"Failed to connect to TLCS: {e}", + status_code=0, + ) from e + + def pull_context(self, learner_id: str) -> dict: + """Pull learner context from TLCS. + + Args: + learner_id: Identifier for the learner + + Returns: + Learner context from TLCS + + Raises: + TLCSAPIError: If API returns error response + TLCSAuthError: If authentication fails + """ + url = f"{self.config.endpoint}/learners/{learner_id}/context" + + try: + response = self.http_client.get( + url, + headers=self._auth_headers(), + ) + response.raise_for_status() + return response.json() + except httpx.HTTPStatusError as e: + logger.error(f"TLCS pull failed: {e.response.status_code}") + raise TLCSAPIError( + f"TLCS API error: {e.response.status_code}", + status_code=e.response.status_code, + response_body=e.response.text, + ) from e + except httpx.RequestError as e: + logger.error(f"TLCS pull request failed: {e}") + raise TLCSAPIError( + f"Failed to connect to TLCS: {e}", + status_code=0, + ) from e + + +# ============================================================================= +# Singleton Access +# ============================================================================= + +_client_instance: TLCSClient | None = None + + +def get_tlcs_client() -> TLCSClient | None: + """Get singleton TLCS client instance. + + Returns: + TLCSClient if TLCS is configured, None otherwise. + This enables graceful degradation when TLCS is not available. + """ + global _client_instance + + if _client_instance is not None: + return _client_instance + + config = TLCSConfig.from_env() + if config is None: + return None + + _client_instance = TLCSClient(config) + return _client_instance + + +def reset_tlcs_client() -> None: + """Reset singleton client (for testing).""" + global _client_instance + if _client_instance is not None: + _client_instance.close() + _client_instance = None diff --git a/demo/agents/memory/tlcs/config.py b/demo/agents/memory/tlcs/config.py new file mode 100644 index 0000000..7990429 --- /dev/null +++ b/demo/agents/memory/tlcs/config.py @@ -0,0 +1,58 @@ +"""TLCS integration configuration. + +Configuration for connecting to the TLCS API. Loads from environment variables +and supports graceful degradation when TLCS is not configured. + +Environment variables: + TLCS_ENDPOINT: Base URL for TLCS API (required to enable) + TLCS_CLIENT_ID: OAuth2 client ID for Cognito + TLCS_CLIENT_SECRET: OAuth2 client secret + TLCS_TOKEN_URL: Cognito token endpoint + TLCS_TIMEOUT: Request timeout in seconds (default: 30) +""" + +from dataclasses import dataclass +import os + + +@dataclass +class TLCSConfig: + """Configuration for TLCS integration. + + Attributes: + endpoint: Base URL for TLCS API (e.g., https://api.tlcs.example.com) + client_id: OAuth2 client ID for Cognito authentication + client_secret: OAuth2 client secret + token_url: Cognito token endpoint URL + timeout: Request timeout in seconds + """ + + endpoint: str + client_id: str + client_secret: str + token_url: str + timeout: int = 30 + + @classmethod + def from_env(cls) -> "TLCSConfig | None": + """Load configuration from environment variables. + + Returns: + TLCSConfig if TLCS_ENDPOINT is set, None otherwise. + This enables graceful degradation when TLCS is not configured. + """ + endpoint = os.getenv("TLCS_ENDPOINT") + if not endpoint: + return None + + return cls( + endpoint=endpoint.rstrip("/"), + client_id=os.getenv("TLCS_CLIENT_ID", ""), + client_secret=os.getenv("TLCS_CLIENT_SECRET", ""), + token_url=os.getenv("TLCS_TOKEN_URL", ""), + timeout=int(os.getenv("TLCS_TIMEOUT", "30")), + ) + + def is_auth_configured(self) -> bool: + """Check if OAuth2 authentication is fully configured.""" + return bool(self.client_id and self.client_secret and self.token_url) diff --git a/demo/agents/memory/tlcs/mapper.py b/demo/agents/memory/tlcs/mapper.py new file mode 100644 index 0000000..856a4a9 --- /dev/null +++ b/demo/agents/memory/tlcs/mapper.py @@ -0,0 +1,208 @@ +"""Bidirectional mapping between TLCS context and MemoryItems. + +This module provides conversion functions: +- tlcs_context_to_memories(): TLCS response -> MemoryItems +- memories_to_caliper_for_tlcs(): MemoryItems -> Caliper envelope for TLCS +""" + +from datetime import datetime, UTC +from typing import Any +import uuid + +from memory.schema import MemoryItem, MemoryCategory +from memory.formats.caliper import export_caliper + + +# ============================================================================= +# TLCS to MemoryItem Mapping +# ============================================================================= + +# Map TLCS claim categories to MemoryCategory +TLCS_CATEGORY_MAP = { + # Mastery/competency claims + "mastery": MemoryCategory.MASTERY, + "competency": MemoryCategory.MASTERY, + "skill": MemoryCategory.MASTERY, + "proficiency": MemoryCategory.MASTERY, + # Goals + "goal": MemoryCategory.GOAL, + "objective": MemoryCategory.GOAL, + "target": MemoryCategory.GOAL, + # Preferences + "preference": MemoryCategory.PREFERENCE, + "learning_style": MemoryCategory.PREFERENCE, + "style": MemoryCategory.PREFERENCE, + # Facts + "fact": MemoryCategory.FACT, + "context": MemoryCategory.FACT, + "background": MemoryCategory.FACT, + # Constraints + "constraint": MemoryCategory.CONSTRAINT, + "limitation": MemoryCategory.CONSTRAINT, + "pacing": MemoryCategory.CONSTRAINT, + # Relationships + "relationship": MemoryCategory.RELATIONSHIP, +} + + +def _parse_confidence(value: Any) -> float: + """Extract confidence from TLCS claim value. + + TLCS may represent confidence/mastery as: + - A float directly (0.0-1.0) + - A percentage (0-100) + - A dict with a "value" or "confidence" key + - A string like "0.85" or "85%" + + Returns: + Normalized confidence value between 0.0 and 1.0 + """ + if value is None: + return 0.8 # Default confidence + + if isinstance(value, dict): + for key in ("confidence", "value", "score", "level"): + if key in value: + return _parse_confidence(value[key]) + return 0.8 + + if isinstance(value, (int, float)): + if value > 1: + return min(1.0, value / 100.0) + return float(value) + + if isinstance(value, str): + cleaned = value.rstrip("%").strip() + try: + num = float(cleaned) + if num > 1: + return min(1.0, num / 100.0) + return num + except ValueError: + return 0.8 + + return 0.8 + + +def _map_category(tlcs_category: str) -> MemoryCategory: + """Map TLCS claim category to MemoryCategory. + + Args: + tlcs_category: Category string from TLCS + + Returns: + Corresponding MemoryCategory + """ + normalized = tlcs_category.lower().strip() + + if normalized in TLCS_CATEGORY_MAP: + return TLCS_CATEGORY_MAP[normalized] + + for key, category in TLCS_CATEGORY_MAP.items(): + if key in normalized or normalized in key: + return category + + return MemoryCategory.FACT + + +def tlcs_context_to_memories(context: dict) -> list[MemoryItem]: + """Convert TLCS learner context to MemoryItems. + + Expected TLCS context structure: + { + "learner_id": "...", + "claims": [ + { + "id": "claim-123", + "category": "mastery", + "description": "Linear equations", + "value": 0.85, + "subject_area": "math", + "created_at": "2024-01-15T10:00:00Z", + ... + }, + ... + ] + } + + Args: + context: TLCS context response + + Returns: + List of MemoryItem objects + """ + memories = [] + claims = context.get("claims", []) + + for claim in claims: + claim_id = claim.get("id", str(uuid.uuid4())) + category_str = claim.get("category", "fact") + description = claim.get("description", "") + + if not description: + description = claim.get("content", claim.get("name", claim.get("text", ""))) + + if not description: + continue + + category = _map_category(category_str) + confidence = _parse_confidence(claim.get("value", claim.get("confidence"))) + subject_area = claim.get("subject_area", claim.get("domain", claim.get("subject"))) + + created_at_str = claim.get("created_at", claim.get("timestamp")) + if created_at_str: + try: + created_at = datetime.fromisoformat(created_at_str.rstrip("Z")) + except (ValueError, AttributeError): + created_at = datetime.now(UTC) + else: + created_at = datetime.now(UTC) + + source_type = claim.get("source_type", "model_inferred") + if source_type not in ("user_declared", "model_inferred"): + source_type = "model_inferred" + + memory = MemoryItem( + id=str(uuid.uuid4()), + category=category, + content=description, + source="import", + confidence=confidence, + source_type=source_type, + status="confirmed", + created_at=created_at, + subject_area=subject_area, + original_format="tlcs", + original_id=claim_id, + ) + memories.append(memory) + + return memories + + +# ============================================================================= +# MemoryItem to TLCS/Caliper Mapping +# ============================================================================= + + +def memories_to_caliper_for_tlcs( + memories: list[MemoryItem], + learner_id: str, +) -> dict: + """Convert MemoryItems to Caliper envelope for TLCS ingestion. + + This reuses the existing caliper.py export logic to ensure consistency. + + Args: + memories: List of MemoryItems to export + learner_id: Learner identifier for Caliper actor + + Returns: + Caliper envelope ready for TLCS /ingest/caliper endpoint + """ + return export_caliper( + memories=memories, + learner_id=learner_id, + sensor_id="urn:uuid:portable-memory-sensor", + app_id="urn:uuid:portable-memory-tutor", + ) diff --git a/demo/agents/memory/versioning.py b/demo/agents/memory/versioning.py new file mode 100644 index 0000000..2a56de5 --- /dev/null +++ b/demo/agents/memory/versioning.py @@ -0,0 +1,80 @@ +"""Version history for memory items. + +Pre-read References: +- Section 4.1: "APIs for read/search/write/update/delete with versioning" +- Section 4.4: "timely correction pathways, provenance metadata" +""" + +from datetime import datetime, UTC +from typing import Literal, TYPE_CHECKING +import uuid + +if TYPE_CHECKING: + from memory.schema import MemoryItem, MemoryVersion + + +class VersionStore: + """Version history storage for memory items.""" + + def __init__(self, storage=None): + from memory.persistence import get_storage_backend + self._storage = storage or get_storage_backend() + + def create_version( + self, + memory: "MemoryItem", + change_type: Literal["created", "updated", "corrected", "restored"], + reason: str | None, + actor: str, + ) -> "MemoryVersion": + """Create a new version record for a memory.""" + from memory.schema import MemoryVersion + + version_id = str(uuid.uuid4()) + + existing_versions = self._storage.get_versions_for_memory(memory.id) + previous_version_id = existing_versions[-1].version_id if existing_versions else None + version_number = len(existing_versions) + 1 + + version = MemoryVersion( + version_id=version_id, + memory_id=memory.id, + version_number=version_number, + content=memory.content, + metadata={ + "category": memory.category.value, + "source": memory.source, + "source_type": memory.source_type, + "confidence": memory.confidence, + "subject_area": memory.subject_area, + "status": memory.status, + "purposes": memory.purposes, + }, + created_at=datetime.now(UTC), + created_by=actor, + change_type=change_type, + change_reason=reason, + previous_version_id=previous_version_id, + ) + + self._storage.store_version(version) + return version + + def get_history(self, memory_id: str) -> list["MemoryVersion"]: + """Get full version history for a memory (oldest to newest).""" + return self._storage.get_versions_for_memory(memory_id) + + def get_version(self, version_id: str) -> "MemoryVersion | None": + """Get a specific version by ID.""" + return self._storage.get_version(version_id) + + def get_latest_version(self, memory_id: str) -> "MemoryVersion | None": + """Get the most recent version of a memory.""" + versions = self._storage.get_versions_for_memory(memory_id) + if not versions: + return None + return versions[-1] + + def count_versions(self, memory_id: str) -> int: + """Count total versions for a memory.""" + return len(self._storage.get_versions_for_memory(memory_id)) diff --git a/demo/agents/pyproject.toml b/demo/agents/pyproject.toml new file mode 100644 index 0000000..98dd78b --- /dev/null +++ b/demo/agents/pyproject.toml @@ -0,0 +1,48 @@ +[project] +name = "tlcs-demo-agents" +version = "0.1.0" +description = "AI tutoring agents demonstrating TLCS learner memory capabilities" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "langgraph>=0.2.0", + "langchain-openai>=0.3.0", + "langchain-core>=0.3.0", + "pydantic>=2.0.0", + "python-dotenv>=1.0.0", + "httpx>=0.27.0", +] + +[project.optional-dependencies] +aws = [ + "langchain-aws>=0.2.0", + "boto3>=1.34.0", +] +api = [ + "fastapi>=0.115.0", + "uvicorn>=0.32.0", +] +dev = [ + "langgraph-cli[inmem]>=0.1.60", + "pytest>=8.0.0", + "pytest-asyncio>=0.24.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["graph", "memory"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] + +[tool.ruff] +line-length = 100 +target-version = "py311" + +[tool.ruff.lint] +select = ["E", "F", "I", "W"] +ignore = ["E501"] diff --git a/demo/agents/scripts/__init__.py b/demo/agents/scripts/__init__.py new file mode 100644 index 0000000..54f4184 --- /dev/null +++ b/demo/agents/scripts/__init__.py @@ -0,0 +1 @@ +"""Demo scripts for TLCS agents.""" diff --git a/demo/agents/scripts/demo.py b/demo/agents/scripts/demo.py new file mode 100644 index 0000000..3550564 --- /dev/null +++ b/demo/agents/scripts/demo.py @@ -0,0 +1,262 @@ +#!/usr/bin/env python3 +"""Demo script showing AI tutoring with memory extraction. + +This demo shows a REAL AI conversation: +1. Learner sends a message +2. AI tutor responds (using LLM) +3. Memories are extracted from conversation (using LLM) +4. You can see both the conversation AND the memories extracted + +Run this to see the full flow: + uv run python scripts/demo.py + +Or run specific demos: + uv run python scripts/demo.py --conversation # Single conversation + uv run python scripts/demo.py --multi # Multi-turn conversation + uv run python scripts/demo.py --all # Everything (default) + +Environment variables: + OPENAI_API_KEY: Required for OpenAI models + MODEL_PROVIDER: 'openai' (default) or 'bedrock' +""" + +import argparse +import os +import sys + +# Ensure we can import from the package +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from dotenv import load_dotenv + +load_dotenv() + +from langchain_core.messages import HumanMessage +from graph.main import build_graph +from memory.schema import MemoryItem +from memory.formats.caliper import export_caliper + + +def print_header(title: str): + """Print a formatted section header.""" + print("\n" + "=" * 60) + print(f" {title}") + print("=" * 60) + + +def demo_conversation(): + """Demo: Single conversation with memory extraction. + + This shows: + - Learner introduces themselves + - AI tutor responds with a personalized greeting + - Memories are extracted from what the learner said + """ + print_header("DEMO: AI Conversation with Memory Extraction") + print(""" +This demo shows a REAL AI conversation: +1. Learner introduces themselves +2. AI tutor responds (using LLM) +3. Memories are extracted from conversation (using LLM) + """) + + print("[Building LangGraph agent...]") + graph = build_graph() + + # Simulate a learner introducing themselves + learner_message = ( + "Hi! I'm Maria. I work as a nurse with night shifts, " + "so I only have about 30 minutes a day to study. I'm trying to " + "pass my algebra final next month. I've always struggled with " + "word problems but I'm pretty good with basic equations." + ) + + state = { + "messages": [HumanMessage(content=learner_message)], + "memories": [], + "learner_id": "urn:uuid:maria-demo", + } + + print("\n" + "-" * 60) + print("[LEARNER]: " + learner_message) + print("-" * 60) + + print("\n[AI Tutor is thinking...]") + result = graph.invoke(state) + + print("\n" + "-" * 60) + print("[AI TUTOR]: " + result["messages"][-1].content) + print("-" * 60) + + print("\n" + "=" * 60) + print(" MEMORIES EXTRACTED FROM CONVERSATION") + print("=" * 60) + + memories = result.get("memories", []) + if memories: + for i, mem in enumerate(memories, 1): + category = mem.get("category", "unknown") + content = mem.get("content", "") + confidence = mem.get("confidence", 0) + subject = mem.get("subject_area", "general") or "general" + print(f"\n {i}. [{category.upper()}] {content}") + print(f" Confidence: {confidence:.0%} | Subject: {subject}") + else: + print("\n (No memories extracted)") + + print(f"\n Total memories extracted: {len(memories)}") + + return result + + +def demo_multi_turn(): + """Demo: Multi-turn conversation showing memory accumulation.""" + print_header("DEMO: Multi-Turn Conversation (Memory Accumulation)") + print(""" +This demo shows how memories accumulate across conversation turns. +Each turn may add new memories while avoiding duplicates. + """) + + print("[Building LangGraph agent...]") + graph = build_graph() + + turns = [ + "Hi, I'm trying to learn algebra. I'm a bit nervous because I haven't done math in years.", + "I work at a coffee shop, so I'm actually pretty good with money calculations.", + "Can you explain what a variable is? I remember hearing about X and Y but I don't really get it.", + ] + + state = { + "messages": [], + "memories": [], + "learner_id": "urn:uuid:multi-turn-demo", + } + + for i, turn in enumerate(turns, 1): + print(f"\n{'='*60}") + print(f" TURN {i}") + print("=" * 60) + + print(f"\n[LEARNER]: {turn}") + + state["messages"].append(HumanMessage(content=turn)) + + print("\n[AI Tutor is thinking...]") + result = graph.invoke(state) + + # Update state with result + state = result + + response = result["messages"][-1].content + # Truncate long responses for readability + if len(response) > 300: + response = response[:300] + "..." + + print(f"\n[AI TUTOR]: {response}") + print(f"\n[Total memories so far]: {len(result['memories'])}") + + print("\n" + "=" * 60) + print(" FINAL MEMORY SUMMARY") + print("=" * 60) + + for i, mem in enumerate(state["memories"], 1): + category = mem.get("category", "unknown") + content = mem.get("content", "") + print(f" {i}. [{category}] {content}") + + return state + + +def demo_with_export(): + """Demo: Conversation with Caliper export.""" + print_header("DEMO: Conversation + Caliper Export") + print(""" +This demo shows the full flow: +1. AI conversation with memory extraction +2. Export memories to Caliper format (for TLCS) + """) + + # Run conversation first + result = demo_conversation() + memories = result.get("memories", []) + + if not memories: + print("\n[No memories to export]") + return result + + # Convert to MemoryItem objects + memory_items = [MemoryItem.from_dict(m) for m in memories] + + # Export to Caliper + print("\n" + "=" * 60) + print(" CALIPER EXPORT (for TLCS)") + print("=" * 60) + + caliper = export_caliper(memory_items) + events = caliper.get("data", []) + + print(f"\n Generated {len(events)} Caliper events") + print("\n Event types:") + for event in events[:3]: + event_type = event.get("type", "unknown") + obj = event.get("object", {}) + name = obj.get("name", "unknown")[:40] + print(f" - {event_type}: {name}...") + + if len(events) > 3: + print(f" ... and {len(events) - 3} more") + + print("\n These events can be POSTed to TLCS /ingest/caliper") + + return result + + +def main(): + parser = argparse.ArgumentParser(description="Demo AI Tutoring with Memory") + parser.add_argument("--conversation", action="store_true", help="Single conversation demo") + parser.add_argument("--multi", action="store_true", help="Multi-turn conversation demo") + parser.add_argument("--export", action="store_true", help="Conversation with Caliper export") + parser.add_argument("--all", action="store_true", help="Run all demos (default)") + + args = parser.parse_args() + + # Check for API key + if not os.getenv("OPENAI_API_KEY") and os.getenv("MODEL_PROVIDER", "openai") == "openai": + print("Error: OPENAI_API_KEY environment variable is required.") + print("Set it with: export OPENAI_API_KEY=your-key") + print("Or use Bedrock with: export MODEL_PROVIDER=bedrock") + sys.exit(1) + + # Default to conversation if no specific demo selected + run_all = args.all or not any([args.conversation, args.multi, args.export]) + + print("\n" + "=" * 60) + print(" TLCS DEMO AGENTS - AI Tutoring with Memory") + print("=" * 60) + print("\nThis demo shows REAL AI conversations with memory extraction.") + print("The AI tutor responds using an LLM, and memories are extracted") + print("from the conversation automatically.") + + if args.conversation or run_all: + demo_conversation() + + if args.multi or run_all: + demo_multi_turn() + + if args.export: + demo_with_export() + + print("\n" + "=" * 60) + print(" DEMO COMPLETE") + print("=" * 60) + print(""" +Key points demonstrated: +1. Real AI conversation (not scripted) +2. Automatic memory extraction from learner messages +3. Memory accumulation across conversation turns +4. Caliper export for TLCS integration + """) + + +if __name__ == "__main__": + main() diff --git a/demo/agents/scripts/demo_alice.py b/demo/agents/scripts/demo_alice.py new file mode 100644 index 0000000..81b74b5 --- /dev/null +++ b/demo/agents/scripts/demo_alice.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python3 +"""Alice Story Demo - Cross-Tool Memory Sharing. + +Pre-read Section 3 (verbatim): +> Alice has been learning mathematics on 'Math Kingdom' for several months. +> Over time, the system has built a learner profile capturing her current mastery +> (strong on algebraic manipulation, weaker on word problems), her pacing preferences +> (short sessions on weekdays, longer on weekends), and the explanation styles that +> work best for her (worked examples before formal proofs). When Alice decides to +> switch to 'Fantasy Academy,' instead of starting from scratch she authorizes +> scoped access to her portable learner profile - limited to math-related mastery +> signals and learning preferences. Within minutes, Fantasy Academy imports this +> context, maps it to its own curriculum, and begins personalized recommendations +> on day one. + +This demo shows: +1. Math Tutor extracts memories from Alice's conversation +2. Writing Coach sees only general preferences (not math mastery) +3. Scoped exports show different content +4. Audit log tracks all operations +""" + +import os +import sys + +# Ensure we can import from the package +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from dotenv import load_dotenv + +load_dotenv() + +from memory.schema import MemoryItem, MemoryCategory +from memory.service import PortableMemoryService +from memory.scopes import ( + create_math_tutor_scope, + create_writing_coach_scope, + create_full_access_scope, +) +from memory.grants import create_grant + + +def print_header(title: str): + """Print a formatted section header.""" + print("\n" + "=" * 60) + print(f" {title}") + print("=" * 60) + + +def print_memories(memories: list[MemoryItem], title: str): + """Print memories in a readable format.""" + print(f"\n{title} ({len(memories)} memories):") + if not memories: + print(" (none)") + return + for m in memories: + source_marker = "[U]" if m.source_type == "user_declared" else "[I]" + content_preview = m.content[:60] if len(m.content) > 60 else m.content + print(f" {source_marker} [{m.category.value}] {content_preview}...") + if m.subject_area: + print(f" subject: {m.subject_area}, confidence: {m.confidence:.2f}") + + +def run_alice_demo(): + """Run the Alice story demo.""" + print_header("ALICE STORY DEMO - Cross-Tool Memory Sharing") + print(""" +This demo shows the Pre-read Section 3 scenario: +- Alice learns with Math Tutor, building a learner profile +- She switches to Writing Coach with scoped access +- Scoped exports show different content for different purposes + """) + + # Create service + service = PortableMemoryService() + learner_id = "alice" + + # ========================================================================== + # PHASE 1: Math Tutoring Session + # ========================================================================== + print_header("PHASE 1: Math Tutoring Session") + print("Alice talks with Math Tutor, who extracts memories...") + + # Create grant for math tutor + math_scope = create_math_tutor_scope() + math_grant = create_grant( + learner_id=learner_id, + agent_id="math_tutor", + scope=math_scope, + ) + service.grant_store.store(math_grant) + + # Add memories as if extracted from conversation + alice_memories = [ + # From Alice's explicit statements (user_declared) + MemoryItem( + category=MemoryCategory.GOAL, + content="I'm preparing for a placement exam in 8 weeks", + source="conversation", + confidence=0.95, + source_type="user_declared", + subject_area="math", + ), + MemoryItem( + category=MemoryCategory.PREFERENCE, + content="I prefer worked examples before formal proofs", + source="conversation", + confidence=0.95, + source_type="user_declared", + subject_area=None, # General preference + ), + MemoryItem( + category=MemoryCategory.PREFERENCE, + content="Graphs help me understand better", + source="conversation", + confidence=0.9, + source_type="user_declared", + subject_area=None, # General preference + ), + # From tutor's observations (model_inferred) + MemoryItem( + category=MemoryCategory.MASTERY, + content="Strong on linear equations", + source="conversation", + confidence=0.92, + source_type="model_inferred", + subject_area="math", + ), + MemoryItem( + category=MemoryCategory.MASTERY, + content="Shows good factoring skills", + source="conversation", + confidence=0.78, + source_type="model_inferred", + subject_area="math", + ), + MemoryItem( + category=MemoryCategory.MASTERY, + content="Struggles with word problems", + source="conversation", + confidence=0.48, + source_type="model_inferred", + subject_area="math", + ), + MemoryItem( + category=MemoryCategory.CONSTRAINT, + content="25-30 minutes available on weekdays, 60 minutes on weekends", + source="conversation", + confidence=1.0, + source_type="user_declared", + subject_area=None, + ), + ] + + # Add each memory + for memory in alice_memories: + service.add_memory(memory, learner_id, math_grant) + + # Show what was extracted + all_math_memories = service.get_memories(learner_id, math_grant) + print_memories(all_math_memories, "Math Tutor's view of Alice") + + # ========================================================================== + # PHASE 2: Switch to Writing Coach + # ========================================================================== + print_header("PHASE 2: Switch to Writing Coach") + print("Alice switches to Writing Coach with SCOPED access...") + print("Writing Coach can only see writing + general memories.\n") + + # Create grant for writing coach + writing_scope = create_writing_coach_scope() + writing_grant = create_grant( + learner_id=learner_id, + agent_id="writing_coach", + scope=writing_scope, + ) + service.grant_store.store(writing_grant) + + # What does writing coach see? + writing_memories = service.get_memories(learner_id, writing_grant) + print_memories(writing_memories, "Writing Coach's view of Alice") + + # Show what was filtered out + print(f"\n Scope filtering:") + print(f" Total memories: {len(all_math_memories)}") + print(f" After writing scope: {len(writing_memories)}") + print(f" Filtered out: {len(all_math_memories) - len(writing_memories)} (math-specific)") + + # Add a writing-specific memory + writing_memory = MemoryItem( + category=MemoryCategory.GOAL, + content="I want to improve my essay writing for college applications", + source="conversation", + confidence=0.95, + source_type="user_declared", + subject_area="writing", + ) + service.add_memory(writing_memory, learner_id, writing_grant) + + print("\n Writing Coach added a new goal about essay writing.") + + # ========================================================================== + # PHASE 3: Scoped Exports + # ========================================================================== + print_header("PHASE 3: Scoped Exports (Caliper)") + print("Different exports for different purposes...\n") + + # Math-scoped export + math_caliper = service.export_caliper(learner_id, math_grant, purpose="tutoring") + math_events = math_caliper.get("data", []) + print(f" Math Tutor export: {len(math_events)} Caliper events") + for event in math_events[:3]: + obj = event.get("object", {}) + name = obj.get("name", "unknown")[:50] + print(f" - {name}...") + + # Writing-scoped export + writing_caliper = service.export_caliper(learner_id, writing_grant, purpose="tutoring") + writing_events = writing_caliper.get("data", []) + print(f"\n Writing Coach export: {len(writing_events)} Caliper events") + for event in writing_events[:3]: + obj = event.get("object", {}) + name = obj.get("name", "unknown")[:50] + print(f" - {name}...") + + # Full access export (for comparison) + full_scope = create_full_access_scope() + full_grant = create_grant( + learner_id=learner_id, + agent_id="full_access", + scope=full_scope, + ) + service.grant_store.store(full_grant) + + full_caliper = service.export_caliper(learner_id, full_grant, purpose="export") + full_events = full_caliper.get("data", []) + print(f"\n Full access export: {len(full_events)} Caliper events") + + # ========================================================================== + # PHASE 4: Audit Summary + # ========================================================================== + print_header("PHASE 4: Audit Summary") + print("All operations were logged for user inspection.\n") + + entries = service.audit_log.query(learner_id) + print(f" Total audit entries: {len(entries)}") + + ops_by_type = {} + for entry in entries: + ops_by_type[entry.operation] = ops_by_type.get(entry.operation, 0) + 1 + + print(f" Operations by type:") + for op, count in sorted(ops_by_type.items()): + print(f" - {op}: {count}") + + # Show recent entries + print(f"\n Recent operations:") + for entry in entries[-5:]: + ts = entry.timestamp.strftime("%H:%M:%S") + print(f" {ts} | {entry.operation:8} | {entry.agent_id:15} | {entry.memory_count} memories") + + # ========================================================================== + # Summary + # ========================================================================== + print_header("DEMO COMPLETE") + print(""" +Key points demonstrated: +1. Multi-dimensional scoping (subject, category, purpose) +2. Cross-tool memory sharing with different views +3. Source type distinction (user_declared vs model_inferred) +4. Purpose-based filtering for different export contexts +5. Complete audit trail of all operations + """) + + +if __name__ == "__main__": + run_alice_demo() diff --git a/demo/agents/scripts/demo_alice_conversation.py b/demo/agents/scripts/demo_alice_conversation.py new file mode 100644 index 0000000..deb0834 --- /dev/null +++ b/demo/agents/scripts/demo_alice_conversation.py @@ -0,0 +1,384 @@ +#!/usr/bin/env python3 +"""Alice Story Demo - Full AI Conversation with TLCS Integration. + +This demo shows the complete flow from Pre-read Section 3: +1. Alice has a REAL conversation with Math Tutor (LLM) +2. Memories are extracted from conversation (LLM) +3. Memories are pushed to TLCS as Caliper events +4. Writing Coach pulls memories from TLCS +5. Writing Coach has a personalized conversation with Alice + +This demonstrates: +- Real AI tutoring conversations +- Automatic memory extraction +- TLCS integration via Caliper +- Cross-tool memory sharing with scoping + +Usage: + # With mock TLCS (no server needed) + uv run python scripts/demo_alice_conversation.py --mock + + # With real TLCS + uv run python scripts/demo_alice_conversation.py + +Environment variables: + OPENAI_API_KEY: Required for OpenAI models + MODEL_PROVIDER: 'openai' (default) or 'bedrock' + TLCS_ENDPOINT: TLCS API URL (for real mode) +""" + +import argparse +import json +import os +import sys +from unittest.mock import MagicMock, patch + +# Ensure we can import from the package +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from dotenv import load_dotenv + +load_dotenv() + +from langchain_core.messages import HumanMessage +from graph.main import build_graph +from graph.prompts.tutor import format_memory_context +from memory.schema import MemoryItem +from memory.service import PortableMemoryService +from memory.scopes import create_math_tutor_scope, create_writing_coach_scope +from memory.grants import create_grant +from memory.formats.caliper import export_caliper + + +def print_header(title: str): + """Print a formatted section header.""" + print("\n" + "=" * 70) + print(f" {title}") + print("=" * 70) + + +def print_subheader(title: str): + """Print a formatted subsection header.""" + print("\n" + "-" * 70) + print(f" {title}") + print("-" * 70) + + +def create_mock_tlcs_response(memories: list[dict]): + """Create mock TLCS response from memories.""" + return { + "learner_id": "alice", + "claims": [ + { + "id": f"tlcs-{m.get('id', i)}", + "category": m.get("category", "fact"), + "description": m.get("content", ""), + "value": m.get("confidence", 0.8), + "subject_area": m.get("subject_area"), + "created_at": m.get("created_at", "2024-03-01T10:00:00Z"), + } + for i, m in enumerate(memories) + ], + } + + +def run_demo(use_mock: bool = True, verbose: bool = False): + """Run the Alice conversation demo with TLCS integration.""" + + print_header("ALICE STORY - AI Tutoring with TLCS Memory") + print(""" +Pre-read Section 3: Alice learns with Math Kingdom, then switches to +Fantasy Academy with scoped access to her learner profile. + +This demo shows REAL AI conversations with TLCS integration: +1. Alice talks to Math Tutor → memories extracted +2. Push memories to TLCS as Caliper events +3. Writing Coach pulls from TLCS (scoped access) +4. Writing Coach talks to Alice (personalized) + """) + + if use_mock: + print("[MODE: Mock TLCS - no real server needed]\n") + else: + print("[MODE: Real TLCS Integration]") + print(f" Endpoint: {os.getenv('TLCS_ENDPOINT')}") + print(f" Token URL: {os.getenv('TLCS_TOKEN_URL')}") + print(f" Client ID: {os.getenv('TLCS_CLIENT_ID')[:10]}..." if os.getenv('TLCS_CLIENT_ID') else " Client ID: NOT SET") + print() + + # Check for API key + if not os.getenv("OPENAI_API_KEY") and os.getenv("MODEL_PROVIDER", "openai") == "openai": + print("Error: OPENAI_API_KEY required for AI conversations.") + print("Set it with: export OPENAI_API_KEY=your-key") + sys.exit(1) + + # Initialize + learner_id = "alice" + service = PortableMemoryService() + + # ========================================================================= + # PHASE 1: Alice talks to Math Tutor + # ========================================================================= + print_header("PHASE 1: Alice Talks to Math Tutor") + print("[Building Math Tutor agent...]") + + math_graph = build_graph() + + # Alice's introduction + alice_intro = ( + "Hi! I'm Alice and I'm preparing for a math placement exam in about 8 weeks. " + "I've been learning on my own but I'm struggling. I work part-time so I only " + "have about 30 minutes on weekdays and maybe an hour on weekends to study. " + "I think I'm pretty good at solving equations but word problems really confuse me. " + "I learn best when I see worked examples before trying things myself." + ) + + state = { + "messages": [HumanMessage(content=alice_intro)], + "memories": [], + "learner_id": f"urn:uuid:{learner_id}", + } + + print_subheader("Alice introduces herself") + print(f"\n[ALICE]: {alice_intro}") + + print("\n[Math Tutor is thinking...]") + result = math_graph.invoke(state) + + tutor_response = result["messages"][-1].content + print(f"\n[MATH TUTOR]: {tutor_response[:500]}...") + + # Show extracted memories + extracted_memories = result.get("memories", []) + + print_subheader("Memories Extracted from Conversation") + print(f"\nThe AI extracted {len(extracted_memories)} memories from Alice's message:\n") + + for i, mem in enumerate(extracted_memories, 1): + category = mem.get("category", "unknown").upper() + content = mem.get("content", "") + confidence = mem.get("confidence", 0) + subject = mem.get("subject_area", "general") or "general" + print(f" {i}. [{category}] {content}") + print(f" Confidence: {confidence:.0%} | Subject: {subject}") + + # Store memories in service with math tutor grant + math_scope = create_math_tutor_scope() + math_grant = create_grant(learner_id=learner_id, agent_id="math_tutor", scope=math_scope) + service.grant_store.store(math_grant) + + for mem_dict in extracted_memories: + memory = MemoryItem.from_dict(mem_dict) + # Keep subject_area as-is - None means "general" for scope filtering + # The scope.allows() method treats None as "general" + service.add_memory(memory, learner_id, math_grant) + + # ========================================================================= + # PHASE 2: Push to TLCS as Caliper + # ========================================================================= + print_header("PHASE 2: Push Memories to TLCS (Caliper)") + + # Get all memories for export + all_memories = service.get_memories(learner_id, math_grant) + memory_items = [MemoryItem.from_dict(m.to_dict()) for m in all_memories] + + # Export to Caliper + caliper_envelope = export_caliper(memory_items) + caliper_events = caliper_envelope.get("data", []) + + print(f"\nConverted {len(memory_items)} memories to {len(caliper_events)} Caliper events") + print("\nCaliper event types being sent to TLCS:") + for event in caliper_events[:3]: + event_type = event.get("type", "unknown") + obj = event.get("object", {}) + name = obj.get("name", "")[:50] + print(f" - {event_type}: {name}...") + if len(caliper_events) > 3: + print(f" ... and {len(caliper_events) - 3} more") + + # Verbose: show actual Caliper JSON + if verbose and caliper_events: + print_subheader("VERBOSE: Sample Caliper Event (JSON)") + sample_event = caliper_events[0] + print(json.dumps(sample_event, indent=2, default=str)) + + # Simulate TLCS push + if use_mock: + print("\n[MOCK] POST /ingest/caliper → 202 Accepted") + print("[MOCK] TLCS stored memories for learner 'alice'") + tlcs_memories = extracted_memories # Mock: same memories come back + else: + # Real TLCS push would happen here + response = service.push_to_tlcs(learner_id, math_grant, purpose="sync") + print(f"\n[TLCS] Response: {response}") + + # ========================================================================= + # PHASE 3: Writing Coach pulls from TLCS (scoped) + # ========================================================================= + print_header("PHASE 3: Writing Coach Pulls from TLCS (Scoped)") + + # Create writing coach grant with scoped access + writing_scope = create_writing_coach_scope() + writing_grant = create_grant(learner_id=learner_id, agent_id="writing_coach", scope=writing_scope) + service.grant_store.store(writing_grant) + + print("\nWriting Coach scope allows:") + print(f" - Subject areas: {writing_scope.allowed_subjects or 'all'}") + print(f" - Categories: {[c.value for c in writing_scope.allowed_categories] if writing_scope.allowed_categories else 'all'}") + + # Pull from TLCS (or use local in mock mode) + if use_mock: + print("\n[MOCK] GET /learners/alice/context") + # In mock mode, memories are already in local storage from Phase 1 + # Get scoped memories (what writing coach can see) + scoped_memories = service.get_memories(learner_id, writing_grant) + else: + # Real TLCS: pull memories from TLCS and merge into local storage + print("\n[TLCS] GET /learners/alice/context") + pulled_memories = service.pull_from_tlcs(learner_id, writing_grant, merge_strategy="add_new") + print(f"[TLCS] Pulled {len(pulled_memories)} memories from TLCS") + # Now get scoped view + scoped_memories = service.get_memories(learner_id, writing_grant) + + print(f"\nTLCS returned {len(extracted_memories)} total memories") + print(f"After writing scope filter: {len(scoped_memories)} memories visible") + + print("\nMemories Writing Coach can see:") + for mem in scoped_memories: + print(f" - [{mem.category.value}] {mem.content[:50]}...") + + print("\nMemories FILTERED OUT (math-specific):") + filtered_count = len(extracted_memories) - len(scoped_memories) + print(f" ({filtered_count} math mastery memories hidden from writing coach)") + + # Verbose: show exactly which memories were filtered out + if verbose: + print_subheader("VERBOSE: Filtered Memories (Writing Coach Cannot See)") + scoped_ids = {m.id for m in scoped_memories} + all_stored = service._get_memories_for_learner(learner_id) + for mem in all_stored: + if mem.id not in scoped_ids: + subject = mem.subject_area or "general" + print(f" [HIDDEN] [{mem.category.value}] {mem.content}") + print(f" Subject: {subject} (not in ['writing', 'general'] or is math-specific)") + + # ========================================================================= + # PHASE 4: Writing Coach talks to Alice (personalized) + # ========================================================================= + print_header("PHASE 4: Writing Coach Talks to Alice (Personalized)") + + print("[Building Writing Coach agent with Alice's context...]") + + # Build a new graph for writing coach + writing_graph = build_graph() + + # Alice asks for writing help + alice_writing = ( + "Hi! I'm working on my college application essay and I'm not sure how to start. " + "Can you help me brainstorm?" + ) + + # Pre-load the scoped memories into state + scoped_memory_dicts = [m.to_dict() for m in scoped_memories] + + # Verbose: show the context being injected into Writing Coach + if verbose: + print_subheader("VERBOSE: Context Injected into Writing Coach Prompt") + context_text = format_memory_context(scoped_memory_dicts) + print(f"\n{context_text}") + print("\n[This context is prepended to the Writing Coach's system prompt]") + + writing_state = { + "messages": [HumanMessage(content=alice_writing)], + "memories": scoped_memory_dicts, # Writing coach has Alice's context! + "learner_id": f"urn:uuid:{learner_id}", + } + + print_subheader("Alice asks for writing help") + print(f"\n[ALICE]: {alice_writing}") + + print("\n[Writing Coach is thinking... (has Alice's context from TLCS)]") + writing_result = writing_graph.invoke(writing_state) + + coach_response = writing_result["messages"][-1].content + print(f"\n[WRITING COACH]: {coach_response[:600]}...") + + # ========================================================================= + # PHASE 5: Summary + # ========================================================================= + print_header("DEMO COMPLETE - What We Demonstrated") + + print(""" +1. REAL AI CONVERSATION + - Math Tutor responded to Alice using an LLM + - Writing Coach responded with personalized advice + +2. AUTOMATIC MEMORY EXTRACTION + - LLM extracted structured memories from Alice's introduction + - Categories: facts, goals, preferences, mastery, constraints + +3. TLCS INTEGRATION (Caliper) + - Memories converted to Caliper AssessmentEvent format + - Pushed to TLCS /ingest/caliper endpoint + - Pulled from TLCS /learners/{id}/context endpoint + +4. CROSS-TOOL SCOPING + - Writing Coach only saw general preferences (not math mastery) + - Alice's math struggles stayed private to math tutor + - Demonstrates Pre-read Section 3 "Fantasy Academy" scenario + +5. PERSONALIZATION FROM TLCS + - Writing Coach used TLCS context to personalize response + - Knew about Alice's time constraints, learning preferences + """) + + # Audit summary + print_subheader("Audit Trail") + entries = service.audit_log.query(learner_id) + print(f"\nTotal operations logged: {len(entries)}") + for entry in entries[-5:]: + ts = entry.timestamp.strftime("%H:%M:%S") + print(f" {ts} | {entry.operation:8} | {entry.agent_id:15}") + + +def main(): + parser = argparse.ArgumentParser(description="Alice Story Demo with AI + TLCS") + parser.add_argument( + "--mock", + action="store_true", + default=True, + help="Use mock TLCS (default, no server needed)", + ) + parser.add_argument( + "--real", + action="store_true", + help="Use real TLCS (requires TLCS_ENDPOINT)", + ) + parser.add_argument( + "--quiet", "-q", + action="store_true", + help="Hide detailed output (Caliper JSON, injected context, filtered memories)", + ) + args = parser.parse_args() + + use_mock = not args.real + verbose = not args.quiet + + if args.real: + missing = [] + for var in ["TLCS_ENDPOINT", "TLCS_TOKEN_URL", "TLCS_CLIENT_ID", "TLCS_CLIENT_SECRET"]: + if not os.getenv(var): + missing.append(var) + if missing: + print("Error: --real requires the following environment variables:") + for var in missing: + print(f" - {var}") + print("\nGet these from CDK deploy output:") + print(" npx cdk deploy TrustedLearningContextServiceStack-dev --context stage=dev") + sys.exit(1) + + run_demo(use_mock=use_mock, verbose=verbose) + + +if __name__ == "__main__": + main() diff --git a/demo/agents/scripts/demo_tlcs_sync.py b/demo/agents/scripts/demo_tlcs_sync.py new file mode 100644 index 0000000..c38972b --- /dev/null +++ b/demo/agents/scripts/demo_tlcs_sync.py @@ -0,0 +1,265 @@ +#!/usr/bin/env python3 +"""TLCS Integration Demo - Push and Pull with TLCS. + +This demo shows bidirectional synchronization with TLCS: +1. Add memories to portable-memory +2. Push memories to TLCS as Caliper events +3. Pull learner context from TLCS and convert to memories + +Usage: + # With mock TLCS (no server needed) + uv run python scripts/demo_tlcs_sync.py --mock + + # With real TLCS (requires configuration) + uv run python scripts/demo_tlcs_sync.py + +Environment variables (for real TLCS): + TLCS_ENDPOINT: Base URL for TLCS API + TLCS_CLIENT_ID: OAuth2 client ID + TLCS_CLIENT_SECRET: OAuth2 client secret + TLCS_TOKEN_URL: Cognito token endpoint +""" + +import argparse +import os +import sys +from unittest.mock import MagicMock, patch + +# Ensure we can import from the package +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from dotenv import load_dotenv + +load_dotenv() + +from memory.schema import MemoryItem, MemoryCategory +from memory.service import PortableMemoryService +from memory.scopes import create_full_access_scope +from memory.grants import create_grant + + +def print_header(title: str): + """Print a formatted section header.""" + print("\n" + "=" * 60) + print(f" {title}") + print("=" * 60) + + +def print_memories(memories: list[MemoryItem], title: str): + """Print memories in a readable format.""" + print(f"\n{title} ({len(memories)} memories):") + if not memories: + print(" (none)") + return + for m in memories: + source_marker = "[U]" if m.source_type == "user_declared" else "[I]" + format_marker = f"({m.original_format})" if m.original_format else "" + content_preview = m.content[:50] if len(m.content) > 50 else m.content + print(f" {source_marker} [{m.category.value}] {content_preview}... {format_marker}") + + +def create_mock_tlcs_client(): + """Create a mock TLCS client for demo without real TLCS.""" + mock_client = MagicMock() + + # Mock push_caliper response + mock_client.push_caliper.return_value = { + "status": "accepted", + "events_processed": 3, + "message": "Caliper events ingested successfully", + } + + # Mock pull_context response with sample TLCS data + mock_client.pull_context.return_value = { + "learner_id": "alice", + "claims": [ + { + "id": "tlcs-claim-1", + "category": "mastery", + "description": "Proficient in quadratic equations", + "value": 0.88, + "subject_area": "math", + "created_at": "2024-03-01T10:00:00Z", + }, + { + "id": "tlcs-claim-2", + "category": "goal", + "description": "Complete calculus course by summer", + "value": 1.0, + "subject_area": "math", + "created_at": "2024-03-05T14:30:00Z", + }, + { + "id": "tlcs-claim-3", + "category": "preference", + "description": "Prefers video explanations over text", + "value": 0.9, + "subject_area": None, + "created_at": "2024-02-20T09:15:00Z", + }, + ], + } + + return mock_client + + +def run_demo(use_mock: bool = False): + """Run the TLCS integration demo.""" + print_header("TLCS Integration Demo") + + if use_mock: + print("\nRunning in MOCK mode - no real TLCS server needed.") + else: + if not os.getenv("TLCS_ENDPOINT"): + print("\nError: TLCS_ENDPOINT not set. Use --mock for demo without TLCS.") + sys.exit(1) + print(f"\nUsing TLCS at: {os.getenv('TLCS_ENDPOINT')}") + + # Create service + service = PortableMemoryService() + learner_id = "alice" + + # Create full access grant + scope = create_full_access_scope() + grant = create_grant( + learner_id=learner_id, + agent_id="tlcs_demo", + scope=scope, + ) + service.grant_store.store(grant) + + # ========================================================================== + # PHASE 1: Add memories locally + # ========================================================================== + print_header("PHASE 1: Add Local Memories") + print("Adding memories to portable-memory store...") + + memories = [ + MemoryItem( + category=MemoryCategory.MASTERY, + content="Strong understanding of linear algebra concepts", + source="assessment", + confidence=0.92, + source_type="model_inferred", + subject_area="math", + ), + MemoryItem( + category=MemoryCategory.GOAL, + content="Preparing for graduate school entrance exam", + source="conversation", + confidence=0.95, + source_type="user_declared", + subject_area="general", + ), + MemoryItem( + category=MemoryCategory.PREFERENCE, + content="Learns best with interactive exercises", + source="conversation", + confidence=0.85, + source_type="user_declared", + subject_area=None, + ), + ] + + for memory in memories: + service.add_memory(memory, learner_id, grant) + + local_memories = service.get_memories(learner_id, grant) + print_memories(local_memories, "Local memories") + + # ========================================================================== + # PHASE 2: Push to TLCS + # ========================================================================== + print_header("PHASE 2: Push to TLCS") + print("Exporting memories as Caliper events and sending to TLCS...") + + if use_mock: + # Use mock client + mock_client = create_mock_tlcs_client() + with patch("memory.tlcs.get_tlcs_client", return_value=mock_client): + response = service.push_to_tlcs(learner_id, grant, purpose="sync") + else: + response = service.push_to_tlcs(learner_id, grant, purpose="sync") + + if response: + print(f"\n TLCS Response: {response}") + else: + print("\n TLCS not configured or no data to push") + + # ========================================================================== + # PHASE 3: Pull from TLCS + # ========================================================================== + print_header("PHASE 3: Pull from TLCS") + print("Fetching learner context from TLCS...") + + if use_mock: + mock_client = create_mock_tlcs_client() + with patch("memory.tlcs.get_tlcs_client", return_value=mock_client): + pulled = service.pull_from_tlcs( + learner_id, grant, merge_strategy="add_new" + ) + else: + pulled = service.pull_from_tlcs(learner_id, grant, merge_strategy="add_new") + + print_memories(pulled, "Memories pulled from TLCS") + + # ========================================================================== + # PHASE 4: View Combined Memories + # ========================================================================== + print_header("PHASE 4: Combined Memory Store") + print("All memories after TLCS sync...") + + all_memories = service.get_memories(learner_id, grant) + print_memories(all_memories, "All memories") + + # Show breakdown + local_count = sum(1 for m in all_memories if m.original_format != "tlcs") + tlcs_count = sum(1 for m in all_memories if m.original_format == "tlcs") + print(f"\n Local memories: {local_count}") + print(f" TLCS memories: {tlcs_count}") + print(f" Total: {len(all_memories)}") + + # ========================================================================== + # PHASE 5: Audit Summary + # ========================================================================== + print_header("PHASE 5: Audit Summary") + + entries = service.audit_log.query(learner_id) + print(f"\n Total audit entries: {len(entries)}") + + ops_by_type = {} + for entry in entries: + ops_by_type[entry.operation] = ops_by_type.get(entry.operation, 0) + 1 + + print(" Operations by type:") + for op, count in sorted(ops_by_type.items()): + print(f" - {op}: {count}") + + # ========================================================================== + # Summary + # ========================================================================== + print_header("DEMO COMPLETE") + print(""" +Key points demonstrated: +1. Local memory storage with portable-memory +2. Push to TLCS via Caliper format +3. Pull from TLCS with automatic conversion +4. Merge strategies for imported data +5. 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