ledgermind 2.8.2

Monolithic Autonomous Agent Memory Management System

LedgerMind

v2.8.2 · Autonomous Memory Management System for AI Agents

LedgerMind is not a memory store — it is a living knowledge core that thinks, heals itself, and evolves without human intervention.

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✨ Why LedgerMind?

Feature	Mem0 / LangGraph	LedgerMind
Autonomous healing	❌	✅ (every 5 min)
Git-audit + versioning	❌	✅
4-bit on-device	❌	✅
Multi-agent namespacing	Partial	✅ Full

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What is LedgerMind?

Most AI memory systems are passive stores: you write, you read, and if the information becomes stale or contradictory — that is your problem. LedgerMind takes a fundamentally different approach.

LedgerMind is an autonomous knowledge lifecycle manager. It combines a hybrid storage engine (SQLite + Git) with a built-in reasoning layer that continuously monitors knowledge health, detects conflicts, distills raw experience into structured rules, and repairs itself — all in the background, without any intervention from the developer or the agent.

Core Capabilities

Capability	Description
Zero-Touch Automation	ledgermind install <client> automatically injects hooks into Claude, Cursor, or Gemini CLI for 100% transparent memory operations without MCP tool calls.
Autonomous Heartbeat	A background worker runs every 5 minutes: Git sync, reflection, decay, self-healing.
Probabilistic Reflection	v2.8.2: Discover patterns using float success weights (0.0-1.0) and Target Inheritance for better clustering.
Procedural Distillation	v2.8.2: Automatically converts successful trajectories into step-by-step instructions (procedural.steps).
Intelligent Conflict Resolution	Vector similarity analysis automatically supersedes outdated decisions (threshold: 70%).
Multi-agent Namespacing	Logical partitioning of memory for multiple agents within a single project.
4-bit GGUF Integration	Optimized for Termux/Android with embedding caching for maximum stability.
Autonomy Stress Testing	Built-in test suite for validating Falsifiability, Noise Immunity, and Deep Truth Resolution.
Canonical Target Registry	Auto-normalizes target names and resolves aliases to prevent memory fragmentation.
Autonomous Reflection	Proposals with confidence ≥ 0.9 are automatically promoted to active decisions.
Hybrid Storage	SQLite for fast queries + Git for cryptographic audit and version history.
MCP Server	15 tools with namespacing and pagination support for any compatible client.
Git Evolution	Automatically generates "Evolving Pattern" proposals based on code changes (minimum 2 commits).

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Architecture at a Glance

graph TD
    subgraph Core ["LedgerMind Core"]
        Bridge["Integration Bridge"]
        Memory["Memory (Main API)"]
        Server["MCP / REST Server"]
        
        Bridge --> Memory
        Server <--> Memory
        
        subgraph Stores ["Storage Layer"]
            Semantic["Semantic Store (Git + MD)"]
            Episodic["Episodic Store (SQLite)"]
            Vector["Vector Index (NumPy/Jina v5 GGUF)"]
        end
        
        Memory --> Semantic
        Memory --> Episodic
        Memory --> Vector
        
        subgraph Reasoning ["Reasoning Layer"]
            Conflict["Conflict Engine"]
            Reflection["Reflection Engine"]
            Decay["Decay Engine"]
            Merge["Merge Engine"]
            Distillation["Distillation Engine"]
        end
        
        Memory -.-> Reasoning
    end

    subgraph Background ["Background Process"]
        Worker["Background Worker (Heartbeat)"]
        Worker --- WorkerAction["Health Check | Git Sync | Reflection | Decay"]
        Worker -.-> Webhooks["HTTP Webhooks"]
    end
    
    Worker -.-> Memory

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Installation

# Basic install
pip install ledgermind

# With 4-bit vector search (recommended for CPU/Mobile)
pkg install clang cmake ninja
pip install llama-cpp-python
pip install ledgermind[vector]

Requirements: Python 3.10+, Git installed and configured in PATH.

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Quick Start

Option A: Zero-Touch Automation (Recommended)

The easiest way to use LedgerMind is to install the LedgerMind Hooks Pack. This automatically configures your LLM client to retrieve context before every prompt and record every interaction without the agent needing to manually call MCP tools.

Client Compatibility Matrix

Client	Event Hooks	Status	Zero-Touch Level
Claude Code	UserPromptSubmit, PostToolUse	✅	Full (Auto-record + RAG)
Cursor	beforeSubmitPrompt, afterAgentResponse	✅	Full (Auto-record + RAG)
Gemini CLI	BeforeAgent, AfterAgent	✅	Full (Auto-record + RAG)
Claude Desktop	Coming Soon	⏳	Manual MCP tools only
VS Code (Coprocess)	Under Development	🛠️	Manual MCP tools only

# Install hooks for your preferred client (claude, cursor, or gemini)
ledgermind-mcp install gemini --path ./memory

Now, simply use your client as usual. LedgerMind operates entirely in the background.

Option B: Library (Direct Integration)

from ledgermind.core.api.bridge import IntegrationBridge

# Using Jina v5 Small 4-bit GGUF for best accuracy on CPU
bridge = IntegrationBridge(
    memory_path="./memory", 
    vector_model=".ledgermind/models/v5-small-text-matching-Q4_K_M.gguf"
)

# Inject relevant context into your agent's prompt
context = bridge.memory.search_decisions("database migrations", namespace="prod_agent")

# Record a structured decision with namespacing
bridge.memory.record_decision(
    title="Use Alembic for all database migrations",
    target="database_migrations",
    rationale="Alembic provides version-controlled, reversible migrations.",
    namespace="prod_agent"
)

Option B: MCP Server (Secure)

# Set your API key for security
export LEDGERMIND_API_KEY="your-secure-key"

# Start the MCP server
ledgermind-mcp run --path ./memory

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Key Workflows

Workflow 1: Multi-agent Namespacing — Isolation Within One Core

# Agent A decision
memory.record_decision(title="Use PostgreSQL", target="db", namespace="agent_a")

# Agent B decision (same target, different namespace)
memory.record_decision(title="Use MongoDB", target="db", namespace="agent_b")

# Search only returns what belongs to the agent
memory.search_decisions("db", namespace="agent_a") # -> Returns PostgreSQL

Workflow 2: Hybrid Search & Evidence Boost

LedgerMind uses Reciprocal Rank Fusion (RRF) to combine Keyword and Vector search. Decisions with more "Evidence Links" (episodic events) receive a +20% boost per link to their final relevance score.

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Documentation

Document	Description
API Reference	Complete reference for all public methods
Integration Guide	Library and MCP integration patterns
MCP Tools Reference	All 15 MCP tools with namespacing and offset
Architecture	Deep dive into internals and design decisions
Configuration	API keys, Webhooks, and tuning

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Benchmarks (February 25, 2Y, v2.8.2)

LedgerMind (v2.8.2) is optimized for high-speed operation on Android/Termux as well as containerized environments. It includes built-in security for MCP and REST endpoints.

Retrieval Performance (Jina v5 Small Q4_K_M)

Metric	Mean (v2.8.2)	Note
Search p95 (ms)	29.2 ms	Hybrid RRF (Vector + Keyword)
Write p95 (ms)	251.4 ms	Optimized Metadata Indexing
Memory OPS	15.1 ops/s	Parallelized write throughput

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License

LedgerMind is distributed under the Non-Commercial Source Available License (NCSA).

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LedgerMind — the foundation of AI autonomy.