Zero-LLM agent memory for Claude Code and AI agents: local-first BM25, dense-vector, and reciprocal-rank-fusion retrieval. Returns original passages verbatim by default. Available on PyPI as fidelis-memory. MIT.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
- !README contains suspicious pattern: eval\s*\(
claude mcp add fidelis -- uvx --from{
"mcpServers": {
"fidelis": {
"command": "uvx",
"args": ["--from"]
}
}
}Resumen de MCP Servers
# Fidelis Memory
<!-- mcp-name: io.github.hermes-labs-ai/fidelis-memory -->
## Local-first, zero-LLM memory for Codex, Claude Code, and AI agents.
**73.0% end-to-end QA on LongMemEval-S (LLM-answered over Fidelis retrieval; the retrieval path is zero-LLM by default, with opt-in LLM tiers used only for pointer selection). 83.2% R@1 retrieval. $0/query.**
Stop re-explaining context to your agent. fidelis returns your original notes verbatim, local-first, fast, about 60 seconds to install. Your agent already calls an LLM to think; it should not need another one just to remember. Designed for developers. The default zero-LLM retrieval path does not send memory content to an LLM. The documented `fidelis init` service configuration also disables mem0 and Chroma telemetry. That can reduce third-party data exposure, but deployments still own their security and compliance assessment.
[](LICENSE)
[](#known-limitations)
[](tests/)
[](https://registry.modelcontextprotocol.io/v0.1/servers/io.github.hermes-labs-ai%2Ffidelis-memory/versions/0.0.97)
[](https://hermes-labs.ai)
```
your notes / sessions
↓
local memory store (~/.cogito/, fully local)
↓
fidelis retrieval (BM25 + dense + RRF, no LLM)
↓
original passages (verbatim, never rephrased)
↓
Codex / Claude Code / your agent
```
What fidelis is:
- **fast** - ~216 ms local retrieval (full benchmark mean; vector-only path is faster)
- **cheap** - $0/query retrieval cost
- **private** - local memory store by default
- **faithful** - original stored passages returned, not paraphrases
- **proven** - benchmarked on LongMemEval-S (470 questions, public benchmark), with raw evidence in [`experiments/zeroLLM-FLAGSHIP-evidence/`](experiments/zeroLLM-FLAGSHIP-evidence/)
- **installable** - Codex or Claude Code via MCP in about 60 seconds
---
## Quickstart
```bash
# 0. one-time: Ollama + the local embedder (~280 MB)
brew install ollama && ollama serve &
ollama pull nomic-embed-text
# 1. install Fidelis Memory from PyPI
python3 -m pip install "fidelis-memory==0.0.97"
fidelis init # background service (launchd / systemd)
fidelis watch ~/notes # auto-ingests markdown
fidelis mcp install --client codex # or omit for Claude Code
fidelis mcp serve # runs the MCP server over stdio
# Restart your agent client. Memory is on.
```
Using Gemini CLI? After the local prerequisites and `fidelis init`, install
the native v0.0.97 extension directly:
```bash
gemini extensions install https://github.com/hermes-labs-ai/fidelis
```
The extension launches the released MCP package through `uvx` and includes the
[`GEMINI.md`](GEMINI.md) context file. [See the Gemini CLI extension details](#gemini-cli-extension).
> **Package-name note:** install Hermes Labs' package as `fidelis-memory`.
> The import name and CLI remain `fidelis`. The separate PyPI project named
> `fidelis` belongs to [NGdust/fidelis](https://github.com/NGdust/fidelis).
Linux users swap `brew install ollama` for the equivalent install from [ollama.com](https://ollama.com). [See Requirements](#requirements).
Fidelis Memory 0.0.97 is also published in the
[official MCP Registry](https://registry.modelcontextprotocol.io/v0.1/servers/io.github.hermes-labs-ai%2Ffidelis-memory/versions/0.0.97)
as `io.github.hermes-labs-ai/fidelis-memory`. Registry-aware clients can launch
the same released server directly from PyPI:
```bash
uvx --from "fidelis-memory==0.0.97" fidelis mcp serve
```
This starts the MCP stdio process; run `fidelis init` first when the local
Fidelis service and store have not already been configured. Version 0.0.94
introduced supported Codex MCP installation and context-sensitive orientation;
0.0.96 added the independently discoverable registry release; 0.0.97 is the
first tagged release that carries the Gemini CLI extension manifest.
## What you notice immediately
After the four commands above, the next time you open Codex or Claude Code:
- It stops asking you to repeat context you already wrote down.
- You can ask "what did we decide last week about auth?" - and the answer cites your actual decision, not a generic OAuth lecture.
- Architecture rationale you wrote in a markdown file two months ago surfaces when relevant.
- Your project context carries across sessions instead of resetting at every new conversation.
- Failed migration notes, naming conventions, founder voice memos - all queryable in your agent's normal flow.
Most of fidelis's value is *not* the benchmark; it's not having to explain the same thing twice.
## Most AI memory systems rewrite your notes
Most memory systems rephrase content on the way out. The specific fact gets summarized into something general. fidelis solves this structurally - there is no LLM in the default retrieval path, so the store returns exactly what you put in.
You store:
```text
auth tokens expire after 3600 seconds.
The 3600s window is non-configurable in our current contract.
```
A lossy memory layer may return:
```text
authentication has a configurable timeout
```
fidelis returns:
```text
auth tokens expire after 3600 seconds.
The 3600s window is non-configurable in our current contract.
```
The non-configurable qualifier survives. So does every other detail you wrote down.
## What this enables in Codex, Claude Code, GitHub Copilot CLI, Gemini CLI, and OpenClaw
Once `fidelis mcp install --client codex`, `--client copilot`, `--client gemini`, `--client openclaw`, or the default Claude install is run, ask your agent:
- *"What did we decide about auth?"*
- *"What failed last time we tried this migration?"*
- *"Which billing constraint was non-configurable?"*
- *"What did I say about Sarah's onboarding flow?"*
The MCP `fidelis_recall` tool gives the agent the original passages before it composes an answer, not paraphrased summaries. The answer can stay grounded in what you wrote, with the qualifiers intact.
> **fidelis retrieves memory without an LLM. Your agent still uses its normal LLM to answer using the retrieved context.** "Zero-LLM" applies to the memory hot path, not to your agent.
### GitHub Copilot CLI
Copilot CLI loads MCP servers from `mcp-config.json` in its configuration
directory (`~/.copilot` by default, or `$COPILOT_HOME`). Fidelis writes the
documented stdio entry there atomically, backing up any existing file and
leaving other servers untouched:
```bash
fidelis mcp install --client copilot # writes ~/.copilot/mcp-config.json
copilot # restart, then /mcp list shows "fidelis"
# /mcp show fidelis lists its tools
fidelis mcp uninstall --client copilot # removes only the fidelis entry
```
Use `--settings /path/to/mcp-config.json` to target a different file. The
`copilot` binary is not required at install time; if you prefer the host CLI,
the equivalent registration is
`copilot mcp add fidelis -- "$(python3 -c 'import sys;print(sys.executable)')" "$(python3 -c 'import fidelis.mcp_cmd as m;print(m.MCP_SERVER_FILE)')"`.
Copilot does not currently expose a hook or automatic-recall mechanism to
third-party servers, so recall happens when the agent calls the
`fidelis_recall`, `fidelis_orient`, or `fidelis_health` tools.
### Gemini CLI
Gemini CLI has native MCP management — `gemini mcp add|remove|list`, shipped
in v0.1.19 — and Fidelis registers itself through it rather than editing
`settings.json`. That matters: Gemini reads `settings.json` as
JSON-with-comments and its own writer round-trips your `//` and `/* */`
comments. A rewrite by Fidelis would silently delete them.
```bash
fidelis mcp install --client gemini # gemini mcp add → ~/.gemini/settings.json
gemini # restart, or run /mcp reload in a live session
gemini mcp list # shows "fidelis" and whether it connects
fidelis mcp uninstall --client gemini # gemini mcp remove, verified
```
`--scope project` targets `./.gemini/settings.json` instead of the default
`--scope user` (`~/.gemini/settings.json`); Fidelis refuses `--scope project`
in your home directory, where Gemini collapses the two to the same file.
Requires Gemini CLI v0.1.19 or newer on `PATH`, and an auth method already
configured — Gemini refuses every `gemini mcp` subcommand until one is.
Because `gemini mcp add` overwrites a same-named entry without asking and
`gemini mcp remove` exits 0 even when the name is absent, Fidelis reads the
targeted `settings.json` back after every run. It refuses to touch a `fidelis`
entry it does not recognize (`--force` overrides), and reports a silent no-op
or an unexpected entry as a failure rather than as success. Unrelated servers,
their `env` secrets, other settings keys, and the file's permission bits are
left as they were.
Recall happens when the agent calls the `fidelis_recall`, `fidelis_orient`, or
`fidelis_health` tools.
### OpenClaw
OpenClaw keeps outbound MCP servers under `mcp.servers` in its JSON5 config
(`~/.openclaw/openclaw.json`, or `$OPENCLAW_CONFIG_PATH`). Because JSON5 allows
comments and trailing commas, Fidelis neither writes that file nor parses it:
it delegates every write to the documented `openclaw mcp add` CLI, and asks
OpenClaw's own read-only surface — `openclaw mcp show fidelis --json`, falling
back to `openclaw mcp list --json` — both before writing and afterwards to
confirm what landed.
```bash
fidelis mcp install --client openclaw # openclaw mcp add fidelis --command … --arg …
openclaw mcp reload # pick up the new server
Lo que la gente pregunta sobre fidelis
¿Qué es hermes-labs-ai/fidelis?
+
hermes-labs-ai/fidelis es mcp servers para el ecosistema de Claude AI. Zero-LLM agent memory for Claude Code and AI agents: local-first BM25, dense-vector, and reciprocal-rank-fusion retrieval. Returns original passages verbatim by default. Available on PyPI as fidelis-memory. MIT. Tiene 23 estrellas en GitHub y su última actualización registrada es del 2026-09-13.
¿Cómo se instala fidelis?
+
Puedes instalar fidelis clonando el repositorio (https://github.com/hermes-labs-ai/fidelis) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar hermes-labs-ai/fidelis?
+
Nuestro agente de seguridad ha analizado hermes-labs-ai/fidelis y le ha asignado un Trust Score de 85/100 (tier: Trusted). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene hermes-labs-ai/fidelis?
+
hermes-labs-ai/fidelis es mantenido por hermes-labs-ai. La última actividad registrada en GitHub es del 2026-09-13, con 0 issues abiertos.
¿Hay alternativas a fidelis?
+
Sí. En ClaudeWave puedes explorar mcp servers similares en /categories/mcp, ordenados por popularidad o actividad reciente.
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