Remembra Relay

What does this agent do, and when should you use it?

Remembra Relay is an MCP server plus CLI (remembra-relay) that produces session handoffs built from git facts when a coding agent's session ends. On close, it reads the branch, commits, changed files and unpushed commits from git, and for Claude Code and Codex also test runs from the local transcript; on the next session, the brief command or the session_brief MCP tool delivers what is done, failing and next in about 1,500 tokens, checking the agent's own summary against the recorded facts. Projects are identified by git remote, so handoffs work across machines, worktrees and vendors, and every handoff is kept in order in a trail. Underneath is a standalone memory API (Python/TypeScript SDKs, REST API, 21 MCP tools) with fact extraction, entity resolution, hybrid retrieval and PII protection. It runs on Remembra Cloud (free tier available) or self-hosted under the MIT license with Docker, Qdrant and Ollama.

At session end, remembra-relay close reads the branch, commits, changed and uncommitted files and unpushed commits from git; for Claude Code and Codex it also parses test runs and open items from the local transcript (which never leaves the machine, with secrets redacted before anything is sent). At the next session start, remembra-relay brief or the session_brief MCP tool renders a ~1,500-token brief: who worked last, what is done, what is failing and the next step — the done/not done/failing sections come from git and test runs, never from an LLM, and the agent's own summary is shown as unverified or contradicted against those facts. remembra-relay trail lists every handoff in order. The MCP server (remembra-mcp) exposes 21 tools: store_memory/recall_memories for memory, search_entities/timeline for entities and time, send_to_inbox/get_inbox for messaging between agents, and more. The underlying memory layer is directly usable via the Python SDK (from remembra import Memory; memory.store(...); memory.recall(...)) or the REST API.

1. A solo developer switching the same repo between a laptop, a server and worktrees who wants each new session to start with real context
2. A developer who uses Claude Code by day and Codex CLI by night and needs vendor-agnostic handoffs
3. An engineer picking up someone else's work who wants ground truth from git rather than the previous agent's self-report
4. Users of Cursor, Gemini CLI or any MCP client, who read briefs and write handoffs through MCP tools
5. Developers adding a persistent memory layer with entity resolution and hybrid retrieval to an app via the Python or TypeScript SDK

How do you install or deploy this agent?

Cloud route (get a free key at app.remembra.dev first):

bash

pipx install --force 'remembra[mcp]>=0.16'
remembra-install --all
remembra-relay connect --apply

Self-hosted (starts Remembra + Qdrant + Ollama locally, no API keys needed):

bash

curl -sSL https://raw.githubusercontent.com/remembra-ai/remembra/main/quickstart.sh | bash

Or with Docker Compose:

bash

git clone https://github.com/remembra-ai/remembra && cd remembra
docker compose -f docker-compose.quickstart.yml up -d

How do you use this agent?

After installing, run remembra-relay connect alone first to preview every hook change without writing, then connect --apply to write them (each file is backed up); in Codex, run /hooks once to trust the hooks. Use your coding agent as usual: the close hook fires at session end, and the brief hook or the session_brief MCP tool delivers the handoff at session start. Review handoffs manually:

bash

remembra-relay trail

Verify setup:

bash

remembra-doctor all

Use the memory API directly:

python

from remembra import Memory

memory = Memory(user_id="user_123")
memory.store("Had a meeting with Sarah from Acme Corp. She prefers email over Slack.")
result = memory.recall("How should I contact Sarah?")
print(result.context)

What are this agent's strengths and limitations?

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

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