Fidelis Memory

io.github.hermes-labs-aiPublished by GitHub account hermes-labs-aiv0.2.0Updated Oct 24, 2026

Recall locally stored agent memories through an MCP-compatible host.

The publisher's own server: Memory.fit

VerifiedSTDIODesktop onlyFiles & StorageKnowledge & Memory

Overview

AI-generated overview

Local memory server that lets an agent store notes verbatim and recall the original passages across sessions.

What you can do
  • Recall stored notes and passages from a local memory store
  • Store new text and notes verbatim for later sessions
  • Correct or supersede earlier records while keeping their history
  • Fetch and inspect individual memory records and their validity status
  • Run explicit hybrid keyword-plus-vector search over the shared store
When to use it
Useful when an agent needs to carry exact wording, conditions or caveats from earlier sessions into a new one, rather than relying on a summary. Suited to single-machine, local-first setups where notes should stay on the user's own computer.
Requirements
Runs locally as a Python package (Python 3.10+, macOS or Ubuntu) launched via uvx or pip. Needs a local Ollama service with the nomic-embed-text model for embeddings; no model API key is required. The FIDELIS_PORT environment variable sets the local server port (default 19420). Installation registers a per-user background service that starts at login.
Before you install
Setup installs a background service (launchd or systemd --user) that runs at login and keeps a shared store under the user's home directory; recall searches that whole store, not just the notes just added. Deleting the store erases all saved memories. Optional extraction and model-assisted routes can transform or filter input, and retrieved text may be sent to the agent's model provider. The release is a single-machine pre-release.

Installation

In SourceWeft

  1. Open Fidelis Memory in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Fidelis Memory

[Fidelis Memory mascot]

Your agent can forget a summary. Fidelis brings back the note.

Local memory for Codex, Claude Code, and other agents: save text across sessions and retrieve the original passage when it matters.

by Hermes Labs

PyPI · Connect an agent · Technical reference

A project summary might say “billing migration rolled back.” The original note says why it failed and do not retry until the idempotency fix is verified. When your next session depends on that condition, the missing sentence matters.

Fidelis stores your Markdown and text notes and retrieves their stored wording. Its default retrieval does not ask a generative model to rewrite your memory.

Try it with one note

This path supports Python 3.10+, macOS or Ubuntu, and a local Ollama service. Start Ollama if needed (ollama serve in a separate terminal). No model API key is required. The current release is 0.3.0rc1.

bash
ollama pull nomic-embed-textpython3 -m venv ~/.venvs/fidelissource ~/.venvs/fidelis/bin/activatepython3 -m pip install "fidelis-memory[hybrid]==0.3.0rc1"fidelis init
demo_dir=$(mktemp -d)cat > "$demo_dir/atlas.md" <<'NOTE'Atlas billing migration:Duplicate charges appeared in staging. Rolled back.Do not retry until the idempotency fix is verified.NOTE
fidelis watch "$demo_dir" --oncefidelis recall-hybrid "Atlas billing migration retry condition" --tier zero_llm

fidelis watch and fidelis recall-hybrid use the background service started by fidelis init.

Look for both the rollback and the retry condition in the retrieved text. This is retrieval of a saved note, not a generated answer. For your own files, replace "$demo_dir" with a notes directory. If retrieval fails, run fidelis health and confirm Ollama has nomic-embed-text available. Keep the virtual environment after fidelis init; the background service uses it.

What this changes on your machine: fidelis init installs a per-user background service (launchd on macOS, systemd --user on Linux) that starts fidelis-server automatically at login. fidelis watch sends the note to that server, which saves it in the local store at ~/.cogito/store by default, not in the temporary demo directory. fidelis recall-hybrid also queries that same running server and searches the whole shared store, so its results can include notes you saved earlier, not only the demo note. The watcher also records ingested files in ~/.fidelis/watched.json. To remove the default service on macOS, run fidelis init --uninstall; it stops the service and deletes the launchd plist only. On Linux, find the installed unit with systemctl --user list-unit-files '*fidelis-server*', then run systemctl --user disable --now <unit>, delete that unit file from ~/.config/systemd/user/, and run systemctl --user daemon-reload. Neither path deletes your data. The store is shared by everything you save with Fidelis, so deleting ~/.cogito/store erases all of it, not just the demo note; do that only on a throwaway install. Also delete the temporary demo directory ($demo_dir), and ~/.fidelis/watched.json if you want its record of the file path gone.

Connect an agent

Once the note is retrievable locally, install the MCP connection for your client:

bash
fidelis mcp install --client codex# Or, for Claude Code:fidelis mcp install

Restart the client and try: “Use Fidelis to find my Atlas billing migration note. What must happen before we retry? Quote the relevant text.”

The client gets tools to recall, store, correct, fetch, and inspect memory. It decides when to call them; installing the connection does not make every turn recall automatically. Other clients and uninstall commands include Cursor, Gemini CLI, Copilot CLI, and OpenClaw. A separate Pi extension provides prompt-time recall.

What Fidelis preserves

  • Original wording. watch, store, and other verbatim write paths keep the text you supplied, so retrieval can return the condition that a summary might omit.
  • Local retrieval. Fast vector search is the default MCP recall path. Explicit hybrid search combines keyword and vector results without a generative LLM at its zero_llm tier; local embeddings still require Ollama.
  • Corrections over time. A correction can supersede a prior record without erasing it. Recall can show validity and historical status instead of silently replacing the old note.

Fidelis also has optional extraction and model-assisted routes. Those can transform or filter inputs; use the verbatim path when the original wording is the point. Retrieved text can still be wrong or out of date, and your agent's model provider may see it when answering.

Current scope

Fidelis 0.3.0rc1 is a single-machine pre-release, not a hosted team memory service. The LongMemEval-S retrieval run measured whether the redesigned default path retrieved material across 470 questions; it did not measure answer accuracy. See the results, user-fit guide, and security policy for deeper detail.

Full reference · Upgrade notes · Contribute · Changelog

Apache-2.0. Hermes Labs builds agentic infrastructure for autonomous systems.

Source: README.md at commit 9504c4a

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

2
  1. v0.2.0LatestSep 20, 2026
  2. v0.1.0Sep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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