agent-memory-sdk 0.1.2

Persistent semantic memory for AI agents with replay, restore, verify, and ignore decisions

Agent Memory

Persistent semantic memory for AI agents with intelligent decision-making.

🚀 Created by: TheProdSDE

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🎯 The Problem

Most AI memory systems simply retrieve and inject past context into every prompt. This leads to:

• 💰 Higher token costs - Unnecessary context in every query
• 🎭 Inconsistent responses - No validation of stale or incorrect memories
• ⏱️ Poor performance - Always processing memory, even when irrelevant
• 🤖 No intelligence - Memory is treated as a dumb cache

✨ The Solution

Agent Memory is a decision layer that intelligently chooses when and how to use memory:

flowchart TD
    A[User Query] --> B[Resolve Memory]
    B --> C[Decision Engine]
    C -->|High confidence match| D[🔄 Replay: Return stored answer]
    C -->|Moderate match| E[📋 Restore: Inject as context]
    C -->|Needs validation| F[✅ Verify: Validate before reuse]
    C -->|No match| G[❌ None: Answer from scratch]
    
    style D fill:#0d47a1,color:#ffffff
    style E fill:#e65100,color:#ffffff
    style F fill:#1b5e20,color:#ffffff
    style G fill:#b71c1c,color:#ffffff

Benefits:

• ✅ Response consistency - Reuse proven answers
• ✅ Lower token usage - Only inject when beneficial
• ✅ Faster responses - Instant replay for repeated queries
• ✅ Better long-term behavior - Agents learn when to trust memory

---

🏗️ Architecture

flowchart LR
    subgraph Input["Input Layer"]
        Q[User Query]
    end
    
    subgraph Retrieval["Retrieval Layer"]
        BM25[BM25 Keyword Search]
        Vector[Vector Semantic Search]
        Fusion[Reciprocal Rank Fusion]
    end
    
    subgraph Decision["Decision Layer"]
        Policy[Scoring Policy]
        Engine[Decision Engine]
    end
    
    subgraph Storage["Storage Layer"]
        Chroma[ChromaDB]
        DB[(Local Persistence)]
    end
    
    subgraph Output["Output Layer"]
        Replay[Replay Action]
        Restore[Restore Action]
        Verify[Verify Action]
        None[None Action]
    end
    
    Q --> BM25
    Q --> Vector
    BM25 --> Fusion
    Vector --> Fusion
    Fusion --> Policy
    Policy --> Engine
    Engine --> Storage
    Storage --> Engine
    Engine --> Replay
    Engine --> Restore
    Engine --> Verify
    Engine --> None
    
    style Input fill:#4a148c,color:#ffffff
    style Retrieval fill:#0d47a1,color:#ffffff
    style Decision fill:#e65100,color:#ffffff
    style Storage fill:#1b5e20,color:#ffffff
    style Output fill:#b71c1c,color:#ffffff

How It Works

1. Query Input: User query enters the system
2. Hybrid Retrieval: BM25 (keyword) + Vector (semantic) search with RRF fusion
3. Policy Scoring: Multi-factor scoring (semantic + recency + confidence + usage)
4. Decision Engine: Intelligently selects the best action
5. Action Execution: Returns appropriate response based on decision

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📦 Features

🎯 Decision-Based Memory

Memory is not automatically injected. Each query results in one of four actions:

Action	Behavior	Use Case
Replay	Return previous answer	Exact or near-identical queries
Restore	Inject memory as context	Similar queries needing adaptation
Verify	Validate before reuse	Facts, workflows, tool outputs
None	Ignore memory	Unrelated queries

🔍 Hybrid Retrieval Pipeline

flowchart TD
    A[Query] --> B[BM25 Search]
    A --> C[Vector Search]
    B --> D[Reciprocal Rank Fusion]
    C --> D
    D --> E[Policy Reranking]
    E --> F[Top K Results]
    
    style B fill:#f57f17,color:#ffffff
    style C fill:#2e7d32,color:#ffffff
    style D fill:#1565c0,color:#ffffff
    style E fill:#6a1b9a,color:#ffffff

Policy scoring considers:

• 📊 Semantic similarity (70% weight)
• 📅 Recency (15% weight)
• ✅ Confidence score (20% weight)
• 🔄 Usage frequency (10% weight)

🗃️ Structured Memory

Store memories with type and scope for better organization:

Memory Types:

• conversation - Chat history
• fact - Verifiable information
• workflow - Step-by-step processes
• document - Long-form content
• tool_output - API/tool responses
• code - Code snippets
• summary - Consolidated memories
• preference - User preferences

Scopes:

• session - Current conversation
• user - User-specific
• project - Project-specific
• workspace - Workspace-wide
• team - Team-shared
• global - Application-wide

⏰ Time-to-Live (TTL)

Automatic expiration with flexible TTL:

# Absolute time
memory.remember(query, response, ttl="30d")  # 30 days
memory.remember(query, response, ttl="2h")   # 2 hours

# Relative time
memory.remember(query, response, ttl=3600)   # 1 hour in seconds

📊 Observability

Full transparency into decision-making:

decision = memory.resolve(query)
print(decision)  # Decision object
print(decision.explain())  # Detailed score breakdown

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🚀 Quick Start

Installation

# From PyPI
pip install agent-memory

# From source (development)
git clone https://github.com/TheProdSDE/agent-memory.git
cd agent-memory
pip install -e ".[dev]"

Basic Usage

from agent_memory import Memory, MemoryAction, MemoryType

# Initialize memory
memory = Memory(persist_dir=".agent_memory")

# Store a memory
memory.remember(
    query="How do I reset my password?",
    response="Go to Settings → Security → Reset Password and follow the email link.",
    type=MemoryType.CONVERSATION,
    tags=["auth", "faq"],
    confidence=0.95
)

# Store a fact that requires verification
memory.remember(
    query="Current API rate limit",
    response="1000 requests/minute per API key.",
    type=MemoryType.FACT,
    requires_verification=True
)

# Resolve a query
decision = memory.resolve("How do I reset my password?")

# Handle the decision
match decision.action:
    case MemoryAction.REPLAY:
        print(f"Replaying: {decision.response}")
    case MemoryAction.RESTORE:
        context = memory.format_restore_context(decision)
        print(f"Context: {context}")
        # Use with your LLM: llm(query, context=context)
    case MemoryAction.VERIFY:
        print(f"Verify: {decision.memory.response}")
        # Validate with tools before reuse
    case MemoryAction.NONE:
        print("No relevant memory - answer from scratch")

---

🛠️ API Reference

Core Methods

# Memory management
memory.remember(query, response, *, type, scope, tags, confidence, ttl, metadata)
memory.get(memory_id)
memory.list(limit=100, offset=0, *, scope, include_archived, type)
memory.forget(memory_id)
memory.archive(memory_id)

# Query and resolve
decision = memory.resolve(query, *, mode, top_k, scope, enable_verify)

# Maintenance
memory.cleanup(delete=False)  # Mark expired as expired
memory.cleanup(delete=True)   # Delete expired
memory.consolidate(similarity_threshold=0.95)  # Merge duplicates
memory.stats()  # Get usage statistics

Decision Object

class MemoryDecision:
    action: MemoryAction  # REPLAY, RESTORE, VERIFY, NONE
    confidence: float     # 0.0 - 1.0
    query: str            # Original query
    reason: str           # Human-readable reason
    reasons: list[str]    # Detailed reasons
    response: str | None  # For REPLAY action
    memory: MemoryEntry | None  # For REPLAY/VERIFY
    context: list[RetrievalResult]  # For RESTORE/VERIFY
    
    def explain(self) -> str:  # Detailed score breakdown
        return "..."

---

🔌 MCP Server Integration

Expose Agent Memory as MCP tools for Cursor, VS Code, and other MCP-compatible agents.

Configuration for Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "agent-memory": {
      "command": "agent-memory-mcp",
      "env": {
        "AGENT_MEMORY_DIR": "~/.agent_memory"
      }
    }
  }
}

Available MCP Tools

Tool	Description
remember_memory	Store a query/response pair
resolve_memory	Retrieve and decide action
list_memories	List with pagination
get_memory	Fetch single memory
forget_memory	Delete memory
archive_memory	Archive memory
consolidate_memories	Merge duplicates

Docker-based MCP (Recommended)

{
  "mcpServers": {
    "agent-memory": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-v", "agent_memory_data:/home/appuser/.agent_memory",
        "ghcr.io/theprodsde/agent-memory:latest",
        "agent-memory-mcp"
      ]
    }
  }
}

---

📊 CLI Reference

# Show help
agent-memory --help

# Store a memory
agent-memory remember "query" "response" \
  --type conversation \
  --scope user \
  --ttl 30d

# Resolve a query
agent-memory resolve "query" --explain

# Show statistics
agent-memory stats

# Cleanup expired memories
agent-memory cleanup --delete

# Run benchmark
agent-memory benchmark --seed --repeat 3

# Run evaluation
agent-memory eval --datasets ./benchmarks/datasets

---

🏃 Benchmark & Evaluation

Benchmark

# Quick benchmark with default queries
agent-memory benchmark

# With seeded data from eval datasets
agent-memory benchmark --seed --repeat 3

# Custom baseline comparison
agent-memory benchmark --baseline-ms 500

Evaluation

# Run all datasets
agent-memory eval

# Specific dataset directory
agent-memory eval --datasets ./benchmarks/datasets

Included Datasets:

• coding_agent.json - Code-related queries
• customer_support.json - Support scenarios
• research_agent.json - Research workflows

---

📈 Current Status (v0.1.0-alpha)

✅ Implemented

• Hybrid retrieval (BM25 + Vector + RRF fusion)
• Decision engine (replay / restore / verify / none)
• decision.explain() observability
• Benchmark & evaluation datasets
• TTL + memory states + cleanup
• CLI (remember, resolve, stats, benchmark, eval)
• MCP support for Cursor and other clients
• Comprehensive documentation
• All tests passing
• CI/CD pipeline
• Docker support

🚧 Roadmap

Feature	Status	ETA
Async API	✅ Completed	v0.1.0-alpha
SQLite backend	✅ Completed	v0.1.0-alpha
Redis backend	📋 Planned	v0.2.0
Postgres backend	📋 Planned	v0.3.0
FastAPI server + dashboard	📋 Planned	v0.3.0
Memory graph	📋 Planned	v0.4.0
Confidence learning	📋 Planned	v0.4.0
Multi-agent support	📋 Planned	v0.5.0

---

🛡️ Tech Stack

Component	Technology
Language	Python 3.10+
Storage	ChromaDB (local embeddings)
Retrieval	BM25 + Vector Search + RRF
Interface	MCP (Model Context Protocol)
CLI	argparse
Testing	pytest + pytest-asyncio
Linting	ruff
Type Checking	mypy
CI/CD	GitHub Actions
Container	Docker + docker-compose

No API keys required - Everything runs locally!

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📚 Documentation

• Getting Started - Installation and basic usage
• Architecture - Deep dive into the system design
• Memory Model - Understanding memory types and states
• Policies - Customizing scoring and decision logic
• Benchmarks - Performance metrics and evaluation
• FAQ - Common questions and troubleshooting

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🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests (python -m pytest tests/)
5. Run linting (ruff check agent_memory/ tests/)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Development Setup

# Clone the repository
git clone https://github.com/TheProdSDE/agent-memory.git
cd agent-memory

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks
pip install pre-commit
pre-commit install

# Run tests
make test

# Run all checks
make check

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• ChromaDB - Vector database
• Rank-BM25 - BM25 implementation
• MCP - Model Context Protocol
• FastMCP - MCP server framework

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

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Agent Memory helps agents decide: Replay → Restore → Verify → Ignore

Built with ❤️ by TheProdSDE