Egregore
egregore.xyz- Category
- AI Agents
- Rank
- No. 1049Tools index
- Pricing
- Open Source
- Type
- APP
- Use case
- Data, Retrieval & Knowledge · Productivity & Collaboration
- Interfaces
- CLI
- Builder
- egregore-labs
- GitHub
- 290 stars
- Date
About
A terminal-native platform that enables humans and AI agents to share persistent context and work together as a unified organizational mind. Built with Claude integration, Git-based memory, and Neo4j knowledge graphs for collaborative intelligence.
What it does
Egregore turns a team's Claude Code setup into a shared workspace that remembers. An installer creates two GitHub repositories, one for the instance and one for markdown notes, and wires shell hooks into each session: startup pulls the latest notes and shows a greeting, a pre-compaction hook saves context before the model loses it, and a stop hook flags unsaved work. Slash commands write handoffs, decisions and questions as files, then commit and push them. Codex and Pi agents join through a plain shell bridge that writes the same files. A hosted API adds a graph database over those notes; without it, graph lookups come back empty.
Why it's ranked here
The design choice that matters: team memory lives in an ordinary Git repository of markdown under an MIT license, so nothing is locked in a vendor database and every change has history. The mechanism is real, with a documented hook for each session event and a handoff pipeline that writes, indexes, commits and publishes in one step. The ceiling is the split product. Relationship queries, notifications and the graph layer route through Egregore's own hosted API, and managed hosting is announced as coming soon, so the open-source install delivers mostly the file half of the pitch.
What's good
Failure handling is deliberate. When a graph address is configured but the key is missing, lookups fail loudly instead of pretending the graph is empty, and a key that looks mismatched is only replaced after the server actually rejects it. Search over the notes runs locally on a pinned tool version and never downloads a model in the middle of a session: keyword search works from install, a 334 MB semantic model arrives in the background, and the 2.2 GB hybrid pack is opt-in per organisation. There are three install paths, including one for organisations that block third-party GitHub Apps.
Tradeoffs
The README promises no background processes and no network calls you don't see, yet the startup script fires a detached git push, the development guide lists a background health check that phones home to the API, and search starts a detached model download on first use. Usage telemetry is on unless you opt out, and the environment template says it records durations, which the README leaves out. Everything is shell scripts needing git, jq and curl. Graph-backed commands depend on a hosted service rather than a database you run, and the test suite's sync checks need a live Neo4j connection.
How to use it well
Best fit: a small team already working in Claude Code that wants handoffs and decisions to survive across sessions and people, and is comfortable with Git as the store. Start in local mode, use the handoff, reflect and save commands, and judge whether the markdown memory alone pays off before connecting the hosted API. Set the telemetry opt-out if that matters, and read the hook scripts before installing, as the README itself suggests. Codex, Pi and other shell-capable agents get a command-line bridge rather than the slash commands. It is not an issue tracker or a documentation site.
Technical notes+
bin/graph.sh never talks to Neo4j directly: it POSTs Cypher to the configured api_url at /api/graph/query with EGREGORE_API_KEY read from .env, returns an empty results object when the instance is in local mode or has no api_url, and exits 1 when api_url is set but the key is missing. Its opt-in read cache (EGREGORE_GRAPH_CACHE_TTL) classifies reads with a keyword regex over MERGE, CREATE, SET, DELETE, DETACH and REMOVE, and scopes cache directories by a cksum of URL and key so tenants never share entries. bin/session-start.sh probes /api/graph/test before replacing a key whose slug differs from config, and refetches a key from /api/org/<slug>/key using GITHUB_TOKEN in a background job. bin/handoff-run.sh normalises frontmatter to egregore-capture/v1, prepends the handoff index, then runs graph indexing, memory commit and push, and HTML artifact publish in parallel; notifications are proposals, never sent. bin/search.sh wraps qmd pinned at 2.5.3 via npx, scoped to a single collection. DEVELOPMENT.md documents startup-check as a fire-and-forget health checkin to the API. .env.example lists durations among telemetry fields. tests/README.md describes a pytest suite whose sync, retrieval and quality tests need NEO4J_URI. AGENTS.md carries runtime-routing instructions addressed to Claude Code, Pi, Prime Agent and Codex.
Observed
- License
- MIT (LICENSE, Copyright Egregore Labs)
- Language
- Bash scripts for the runtime; Python pytest suite for tests
- Install
- npx create-egregore, or a gh CLI installer that installs no third-party GitHub App (INSTALL-GH.md)
- Interfaces
- Claude Code slash commands and hooks; shell bridge for Codex, Pi and other shell-capable agents (AGENTS.md)
- Session hooks
- SessionStart, PreToolUse, PostToolUse, WorktreeCreate, PreCompact, Stop
- Memory store
- Separate Git repository of markdown, symlinked into the instance
- Knowledge graph
- Queried through the Egregore hosted API; returns empty results in local mode (bin/graph.sh)
- Search
- Local qmd index with keyword, semantic and opt-in hybrid tiers (bin/search.sh)
- Telemetry
- On by default; opt out with EGREGORE_NO_TELEMETRY or DO_NOT_TRACK
- Prerequisites
- git, gh, jq, curl; NixOS setup documented
- Tests
- pytest suite; sync, retrieval and quality tests require a Neo4j connection (tests/README.md)
Read from README.md, LICENSE, AGENTS.md, DEVELOPMENT.md, INSTALL-GH.md, bin/README.md, bin/session-start.sh, bin/graph.sh, bin/handoff-run.sh, bin/search.sh, tests/README.md, .env.example.
What it can do
Share persistent context between humans and AI agents
Conversations, documents, and project data → Shared memory accessible to all participants
Generate responses using Claude AI integration
User prompts and queries → AI-generated text responses and analysis
Version control organizational knowledge using Git
Knowledge updates and changes → Git commits with tracked memory evolution
Build knowledge graphs from shared information
Structured and unstructured data → Neo4j graph database with relationships
Execute terminal commands for platform management
Command-line instructions → System responses and configuration changes
Query knowledge graphs for insights
Search queries and relationship patterns → Connected information and data relationships
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