Agentic Memory MCP Server

Memory Fabric

File-first, local-first memory layer for MCP-compatible AI coding assistants.

Memory Fabric gives AI tools like Claude Code, Cursor, and GitHub Copilot a consistent, project-aware context layer without locking you into one model, editor, cloud provider, or operating system.

Memory is stored as human-readable Markdown with YAML frontmatter. No vector database. No cloud account. No embeddings required.

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Features

• MCP-native: exposes memory tools through the standard Model Context Protocol
• File-first: Markdown files are the source of truth, inspectable and commit-ready
• Local-first: core reads and writes work offline
• Secret-safe: API keys and credentials are redacted before writing
• Token-budget aware: assembles context within limits; never slices files mid-document
• Quality eval: scores memory usefulness and Dreaming before/after results locally
• Unicode-safe: works with any human language
• Graceful degradation: works without rg, git hooks, or Dreaming configured

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Status

v0.5.0 — live on PyPI. Core CLI and MCP tools work end-to-end.

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Installation

From PyPI (recommended)

Requires Python ≥ 3.11.

pipx install memory-fabric          # CLI only
pipx install "memory-fabric[mcp]"   # CLI + MCP server

Or with plain pip (inside a virtual environment):

pip install memory-fabric          # CLI only
pip install "memory-fabric[mcp]"   # CLI + MCP server

Zero-install one-off run (requires uv):

uvx --from "memory-fabric[mcp]" memory-fabric-mcp   # starts the MCP server on stdio

From GitHub (latest, pre-release)

# CLI only
pipx install "git+https://github.com/elViRafa/agentic-memory.git"

# CLI + MCP server
pipx install "memory-fabric[mcp] @ git+https://github.com/elViRafa/agentic-memory.git"

Or with plain pip (inside a virtual environment):

pip install "git+https://github.com/elViRafa/agentic-memory.git"          # CLI only
pip install "memory-fabric[mcp] @ git+https://github.com/elViRafa/agentic-memory.git"  # + MCP

Upgrading

Because Memory Fabric is in active development, we recommend upgrading regularly:

1. Upgrade the Package:

   # If installed via pipx:
   pipx upgrade memory-fabric
   
   # If installed in a virtual environment via pip:
   pip install --upgrade "memory-fabric[mcp]"
   
   # If installed from GitHub (pre-release):
   pipx install --force "memory-fabric[mcp] @ git+https://github.com/elViRafa/agentic-memory.git"
   

2. Restart MCP Clients: After upgrading, restart your IDE (Cursor, VS Code) or assistant process (Claude Code) to ensure the client reloads the updated memory-fabric-mcp server.

3. Refresh Local Projects (Optional): If you have projects initialized with older versions of Memory Fabric, navigate to the project directory and run:

   ai-memory init --install-hooks
   

   This will safely refresh starter templates and local git hook integration to the latest format without overwriting your existing memory markdown files.

Or clone and install in editable mode for local development:

git clone https://github.com/elViRafa/agentic-memory.git
cd agentic-memory
pip install -e .          # CLI only
pip install -e ".[mcp]"   # CLI + MCP server

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Quick Start

1. Initialize a project

ai-memory init

Creates .ai-memory/ in the current directory with starter sections and a .gitignore.

2. Check health

ai-memory doctor

3. Evaluate memory quality

ai-memory eval
ai-memory eval --json

eval scores whether memories are useful for coding assistants. It checks section coverage, starter-template content, summary quality, metadata, retrieval readiness, and likely secrets.

If .ai-memory/ exists, reports are saved under ignored local files:

.ai-memory/evals/latest.json
.ai-memory/evals/latest.md
.ai-memory/evals/<timestamp>-memory.json
.ai-memory/evals/<timestamp>-memory.md

If .ai-memory/ does not exist yet, eval prints a pre-init report only and creates no files.

4. Query memory

ai-memory query "authentication"

5. Run maintenance (Dreaming)

ai-memory dream --mode light
ai-memory dream --mode deep

Dreaming creates a snapshot before maintenance. You can evaluate whether a Dreaming run improved memory quality:

ai-memory eval --dream latest
ai-memory dream --mode light --eval

Dream eval compares the pre-dream snapshot to current memory and reports score delta, changed files, improvements, and regressions.

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MCP Server — Install

One command per client. Safe to re-run: merges with your existing config, never overwrites it, and backs up the original file if it can't be parsed.

One-click install

These call uvx --from "memory-fabric[mcp]" memory-fabric-mcp under the hood — same canonical invocation as ai-memory install, just without the CLI step. Requires uv.

Add --project to write project-scoped config instead of the global/user config (where the client supports it). Add --dry-run to preview the change as a unified diff without writing anything. Add --uninstall to remove only the memory-fabric entry, leaving everything else in the file untouched.

Available MCP Tools

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Agentic Architecture (making agents use Memory Fabric automatically)

Registering the MCP server is necessary but not sufficient — AI agents will not use its tools unless explicitly instructed to do so.

Memory Fabric provides an automated Agentic Architecture to handle this for you.

When you run ai-memory init in your project, the CLI automatically deploys a complete suite of agent instruction files tailored for every major AI tool:

Single Source of Truth (Syncing)

All these files are generated from two canonical content blocks inside the Memory Fabric Python package. Running ai-memory sync-agents regenerates every platform file from these templates, guaranteeing perfect consistency. If you used ai-memory init --install-hooks, a pre-commit git hook runs this sync automatically on every commit.

For full details on the architecture, see AGENTIC_ARCHITECTURE.md.

Why this matters

Without these instruction files, agents will often explain they "don't use MCP tools automatically," or worse, they will write memory markdown files directly using native file-system tools—bypassing secret scanning, token budgeting, and background Dreaming management.

By simply running ai-memory init, you get zero-configuration, plug-and-play capability across the entire agent ecosystem.

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Using Memory Fabric in Other Projects

You can use a single installed instance of Memory Fabric across all your coding projects:

1. Scaffold the target project: Navigate to your target project's root folder and initialize it:

   cd /path/to/other-project
   ai-memory init --install-hooks
   

   This automatically creates .ai-memory/, deploys the Agentic Architecture rule files, sets up .gitignore, and installs the git post-commit hooks.

2. Global MCP Configuration: Verify your AI assistant's configuration points to your globally installed memory-fabric-mcp executable. The MCP tools automatically parse and use the active project's path passed by the assistant as the cwd argument, enabling a single global MCP server registration to support all your local workspaces.

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Project Memory Layout

.ai-memory/
|-- index.md
|-- architecture.md
|-- schemas.md
|-- decisions.md
|-- debt.md
|-- ubiquitous-language.md
|-- framework-rules.md
|-- evals/       # ignored local quality reports
|-- snapshots/   # ignored rollback baselines
|-- private/     # ignored personal notes
`-- .gitignore

All shared memory files use YAML frontmatter:

---
section: architecture
summary: "One-line fallback used when the file exceeds the token budget."
priority: high
tags: [api, auth]
schema_version: "1.3"
last_updated: 2026-06-01T12:00:00-04:00
---

Your memory content here.

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Global Memory

Developer-level preferences that apply across all projects are stored at:

global/directives.md is Tier 0: it is always loaded fully, bypassing token budgeting.

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CLI Reference

ai-memory [--cwd <path>] [--json] [--debug-llm] <command>

Commands:
  init            Create .ai-memory/ scaffolding
  status          Show memory status
  doctor          Validate memory files and environment
  install         Configure an MCP client to use memory-fabric (--client <name|all>)
  eval            Score memory quality or Dreaming quality
  dream           Run memory maintenance (--mode light|deep)
  query           Search memory
  store           CRUD operations on semantic memory store
  sync-global     Preview local-to-global promotions
  rollback        Restore from a snapshot

Eval examples:

ai-memory eval
ai-memory eval --json
ai-memory eval --llm-review
ai-memory eval --dream latest
ai-memory eval --dream memory-20260601T140000_0400

Optional LLM review is never enabled by default. When requested, deterministic local scores remain the source of truth; LLM notes are secondary and inputs are sanitized before review.

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LLM Configuration & MCP Sampling

Memory Fabric uses Large Language Models (LLMs) to perform semantic memory consolidation (Dreaming) and qualitative evaluation. You can configure this in two ways: via direct environment variables or via zero-config MCP Sampling.

Resolution Precedence

When an operation requires an LLM, Memory Fabric resolves the provider in the following order:

1. Direct LLM Provider: Uses direct API calls if MEMORY_FABRIC_LLM_PROVIDER is set in the environment along with the corresponding API keys.
2. Native MCP Sampling: If no direct provider is set (or configured keys are missing) AND the command is executed within an active MCP session where the parent agent supports sampling, Memory Fabric delegates the LLM call to the agent itself.
3. Graceful Local Fallback: If neither is available, it degrades gracefully to local, deterministic regex-based deduplication without semantic synthesis.

Using MCP Sampling in Dreaming

MCP Sampling allows Memory Fabric to run semantic Dreaming and evaluations without configuring any local API keys or provider environment variables. Instead, it uses the host client's (e.g. Claude Code) native LLM connection.

How It Works

1. When your AI assistant calls the dream_tool or evaluate_memory_fabric_tool via MCP, the Memory Fabric server checks if the client session supports sampling capabilities.
2. If supported and no direct provider is configured, the server sends a create_message request back to the client.
3. The client assistant executes the LLM reasoning under the hood and returns the consolidated memory JSON or evaluation notes.

Key Benefits

• Zero-Config: No need to configure or expose GEMINI_API_KEY, OPENAI_API_KEY, or ANTHROPIC_API_KEY to the background MCP server process.
• Unified Context: The consolidation uses the same model and settings configured in your main coding assistant.

[!IMPORTANT] CLI Limitation: MCP Sampling relies on an active client-server session. Since the standalone terminal CLI (ai-memory) runs outside of an MCP client, running ai-memory dream from the command line cannot use MCP Sampling. To use LLM-based consolidation via the CLI, you must configure a direct LLM provider.

Client-Driven / Split-Tool Dreaming (Avoiding Deadlocks & Timeouts)

In some sequential/blocking MCP client environments (like IDE extensions or agentic loops), executing a nested MCP Sampling request (create_message) while the client is waiting for a tool response can lead to a JSON-RPC deadlock or execution timeouts.

To completely avoid this re-entrancy issue, Memory Fabric provides a client-driven split-tool dreaming protocol:

1. Prepare Payload: The agent/client first calls prepare_dream_payload_tool. This returns a consolidation_prompt and a candidate_store ID, but performs no blocking LLM reasoning.
2. Execute Locally: The client uses its own local LLM connection/context to execute the returned consolidation_prompt and captures the raw JSON output.
3. Apply Results: The client sends the raw LLM JSON response back by calling apply_dream_results_tool(candidate_store=..., llm_response=...). Memory Fabric parses the response, applies the consolidated changes to the candidate folder, and saves the new memory states.

Direct LLM Providers Configuration

To use the CLI with LLMs, or to bypass MCP Sampling with a specific provider, set the following environment variables:

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LLM Debugging

If you want to view the exact prompts sent and responses received by the LLM providers (Gemini, OpenAI, Anthropic, Ollama), you can enable LLM debug logging.

• Via the CLI: Pass the --debug-llm global flag before the subcommand:

  ai-memory --debug-llm dream --mode deep
  

  This automatically prints logs to sys.stderr and appends them to a file named llm_debug.log (in .ai-memory/llm_debug.log if the directory exists, otherwise in the current working directory).

• Via Environment Variables: Set the MEMORY_FABRIC_LLM_DEBUG variable in your environment:

  • stderr: Output prompts and raw JSON responses only to sys.stderr.
  • 1 or true: Output to sys.stderr and write to the default llm_debug.log file.
  • /path/to/log.txt (or any other custom value): Append logs to a custom file path.

To protect your credentials and API keys, headers like Authorization and x-api-key are automatically redacted as [REDACTED] in the debug logs.

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Write Safety

Every write path runs secret detection before saving. Detected secrets are replaced with [REDACTED_SECRET] and returned in the redactions field of the response. File locking prevents concurrent write corruption.

Eval scans existing memories for likely secrets but does not rewrite existing files. It reports what needs manual review.

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Requirements

• Python >= 3.11
• mcp >= 1.0.0 (optional, required for MCP server only)
• rg (optional; ripgrep speeds up keyword search, Python fallback used when absent)

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License

MIT