How do I install Memory Server?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Memory Server need an API key?

No required environment variable is listed on this record.

What transport does Memory Server use?

Memory Server runs locally over stdio.

When was Memory Server last checked?

Last checked 2026-10-28. Persistent semantic memory service and MCP adapter for AI agents, powered by PostgreSQL and local Ollama models.

Memory Server MCP 伺服器

memory-server

Semantic memory service and MCP adapter backed by PostgreSQL, pgvector, and local Ollama models.

Current layout:

agent client <--stdio--> memory-mcp <--HTTP--> memoryd
                                          |
                                          +--> memory-common
                                          |
                                          +--> PostgreSQL + pgvector
                                          |
                                          +--> Ollama

Workspace crates:

memory-common: shared config, models, transcript parsing, error types, and HTTP/MCP payload types
memoryd: the HTTP service plus dream and ingest maintenance binaries
memory-mcp: the stdio MCP adapter that talks to memoryd over HTTP

Current features

Persistent semantic memories with embeddings stored in PostgreSQL
Hybrid retrieval: vector similarity plus PostgreSQL full-text search
Optional query expansion and LLM reranking for memory_search
Core-memory recall at session start with memory_recall
CRUD tools: store, search, list, get, update, delete
Memory graph: weighted edges between memories for cross-reference traversal and search expansion
Session transcript archival via session_log_store
Session-log fallback search when no durable memories match
Maintenance binaries for transcript ingest and dream/prune passes

Memory categories

Category	Purpose
`context`	Project conventions and stable background
`decision`	Architectural or workflow decisions
`error_fix`	Symptoms, root cause, and resolution
`plan`	Reusable implementation plans
`rule`	Durable instructions or constraints

memory_recall returns the categories considered core by the current implementation: decision, error_fix, plan, and rule.

Semantic search and standalone recall exclude task-scoped workflow artifacts by default. These retained audit records include plans tagged with a valid task:<uuid> or legacy superseded-for-task:<uuid> marker, earlier Plan revisions reached recursively through supersedes-plan:<uuid>, Decisions that target a marked Plan through reviewed-item:<uuid>, and session transcripts containing a valid task:<uuid> token. Pass include_workflow_artifacts=true to memory_search or memory_recall to use the previous inclusive behavior.

The default applies before vector, full-text, graph-neighbor, and session-log candidate limits, so protected records cannot consume an ordinary result window. Exact listing and direct retrieval remain inclusive, as do public neighbor, review, normalized-session, and session-log browsing surfaces. memory_bootstrap retains its stronger policy: its recall payload omits every Plan and review Decision even though standalone recall has an opt-in.

See the workflow artifact verification notes for relationship indexing, review contracts, query-plan comparisons, and tests.

Prerequisites

Rust 1.88+
PostgreSQL 17 with pgvector
Ollama with:
- embedding model: bge-m3
- generation models: defaults use llama3.1

Setup

1. Start PostgreSQL with pgvector

Example with Podman:

podman run -d --name memory-pg \
  -e POSTGRES_DB=memory \
  -e POSTGRES_USER=memory \
  -e POSTGRES_PASSWORD=memory \
  -p 5432:5432 \
  -v memory-pg-data:/var/lib/postgresql/data \
  pgvector/pgvector:pg17

2. Pull the Ollama models

ollama pull bge-m3
ollama pull llama3.1

3. Configure the server

Copy and edit config.toml.example if needed:

http_bind = "127.0.0.1:8080"
memoryd_url = "http://127.0.0.1:8080"
database_url = "postgres://memory:memory@localhost/memory"
ollama_url = "http://localhost:11434"
embedding_model = "bge-m3"
expand_model = "llama3.1"
rerank_model = "llama3.1"
dream_model = "llama3.1"
expand_num_ctx = 8192
rerank_num_ctx = 8192
dream_num_ctx = 8192

Configuration fields:

Field	Default	Purpose
`database_url`	`postgres://memory:memory@localhost/memory`	PostgreSQL connection string
`http_bind`	`127.0.0.1:8080`	Bind address for `memoryd`
`memoryd_url`	`http://127.0.0.1:8080`	Base URL used by `memory-mcp`
`guardrails_project`	required for MCP	Explicit work project for mandatory guardrails; use `general` for a general-only session
`ollama_url`	`http://localhost:11434`	Ollama base URL
`embedding_model`	`bge-m3`	Embedding model
`embedding_tokenizer_repo`	`None`	HF hub repo (e.g. `BAAI/bge-m3`) to download tokenizer from for guided truncation
`embedding_tokenizer_revision`	`main`	HF hub repo revision (branch/tag/SHA)
`expand_model`	`llama3.1`	Query expansion model
`rerank_model`	`llama3.1`	Search reranking model
`dream_model`	`llama3.1`	Dream/prune maintenance model
`expand_num_ctx`	`8192`	Context window for query expansion
`rerank_num_ctx`	`8192`	Context window for search reranking
`dream_num_ctx`	`8192`	Context window for dream maintenance
`generate_num_ctx`	`8192`	(Legacy) Context window for all generation calls; used as fallback

4. Build

cargo build -p memory-mcp --release
cargo build -p memoryd --release

When building a specific workspace package from the repository root, always pass -p <package>. For example, use cargo build -p memory-mcp --release instead of cargo build --bin memory-mcp --release, because the latter can still pull in other workspace members and unify their features.

5. Run

RUST_LOG=info ./target/release/memory-mcp ./config.toml
RUST_LOG=info ./target/release/memoryd ./config.toml

memoryd runs the HTTP service on http_bind. memory-mcp is the stdio adapter that calls memoryd_url.

MCP client setup

Minimal memory-mcp config:

memoryd_url = "http://127.0.0.1:8080"
guardrails_project = "memory-server"
resolution_context = { profile = "workstation-host", phase = "implementing", language = ["rust"] }
# api_token = "replace-me"

memoryd uses the full server config shown earlier. memory-mcp also requires an explicit guardrails_project and a trusted context sufficient to resolve the policies in that project. Startup fails if the daemon cannot deliver a nonempty mandatory pack. Configure api_token if enabled on the daemon. See guardrail delivery for rollout and reconnection behavior.

Codex

Add an MCP server entry to ~/.codex/config.toml:

[mcp_servers.memory]
command = "/absolute/path/to/target/release/memory-mcp"
args = ["/absolute/path/to/config.toml"]

[mcp_servers.memory.env]
RUST_LOG = "info"

Claude Code

Add a stdio MCP server entry to ~/.claude.json:

{
  "mcpServers": {
    "memory": {
      "type": "stdio",
      "command": "/absolute/path/to/target/release/memory-mcp",
      "args": ["/absolute/path/to/config.toml"]
    }
  }
}

Multi-agent / distributed setup

For use cases where multiple agents across different machines or locations need to share a common memory store:

1. Server-side configuration

By default, memoryd binds to 127.0.0.1, which only allows local connections. To make it reachable over a network, set http_bind to 0.0.0.0 (all interfaces) or a specific IP address in your config.toml:

http_bind = "0.0.0.0:8080"

2. Authentication

When the service is reachable over a network, you must enable api_token in the server config.toml. The default (no token) is only appropriate for private localhost use.

api_token = "your-secure-shared-secret"

3. Client-side configuration

Each agent's memory-mcp needs to know where the central memoryd is running. In each agent's local config.toml, set memoryd_url to the server's actual address and include the api_token:

memoryd_url = "http://memory.example.com:8080"
api_token = "your-secure-shared-secret"

4. Firewall and Reverse Proxy

memoryd speaks plain HTTP. For internet-facing setups or when traversing untrusted networks, it is recommended to run memoryd behind a reverse proxy (such as Nginx or Caddy) that provides TLS (HTTPS) termination.

5. Sharing memory across agents

Multiple agents (e.g. Claude Code on a laptop and Codex on a remote server) can all point to the same memoryd instance. They will share all memories within the same project namespace, enabling cross-agent collaboration and persistent context.

HTTP API

memoryd currently exposes:

GET /api/v1/health
POST /api/v1/memories
POST /api/v1/memories/search
GET /api/v1/memories/{id}
PATCH /api/v1/memories/{id}
DELETE /api/v1/memories/{id}
GET /api/v1/projects/{project}/recall
POST /api/v1/sessions

If api_token is set in config, all /api/v1/* routes except health require Authorization: Bearer <token>.

Available MCP tools

Tool	Purpose
`memory_server_version`	Return version plus git hash
`memory_store`	Store a new memory
`memory_search`	Hybrid semantic search within a project
`memory_recall`	Load core memories for a project
`memory_rules`	Load general + project durable rules
`memory_bootstrap`	Load effective rules plus non-rule core recall
`memory_list`	Browse memories by project/category
`memory_get`	Fetch a single memory by UUID
`memory_update`	Update summary/content/tags and re-embed if needed
`memory_neighbors`	List neighbor memories reachable via graph edges
`memory_delete`	Delete a memory by UUID
`session_start`	Create or upsert a normalized shared session
`session_message_append`	Append a prompt/response/tool event to a shared session
`session_finalize`	Finalize a shared session into searchable chunks
`session_log_store`	Store a full session transcript for archival/search
`review_queue`	List memories tagged `review-needed`, with optional category filter
`review_submit`	Store a review decision and mark the original reviewed

Policy identity for Rules

Rules may carry structured policy metadata. A legacy Rule has policy: null and remains contextual; existing rows are never classified from their text or tags. A classified Rule has a project-local policy_key, a positive revision, a delivery_class of contextual or mandatory, a server-controlled state, and an optional predecessor UUID in supersedes. The active revision is resolved by memory_rules and memory_bootstrap; superseded revisions remain available through memory_get, memory_list, and search. Applicable mandatory Rules are delivered regardless of tags or include_general.

Classified Rules may also declare selectors for profile, phase, language, and tool, plus exact values for shared settings. Omitted selector dimensions match every execution. Selector members within one dimension are alternatives; dimensions combine. For example, {"selectors":{"profile":["workstation-host"],"language":["rust"]},"values":{"rust.build.target_storage":"persistent-disk"}} applies only to Rust work on the workstation host. These fields are immutable within a revision. One active revision remains per (project, policy_key); environment variants use distinct keys.

MCP execution context comes from the operator's resolution_context in config.toml, not from tool arguments, tags, the daemon's host, or model text. The default is unknown. A tool context argument can only assert values already bound at startup. Direct authenticated HTTP callers may pass a typed URL-encoded JSON context query value. The hook uses the operator-supplied MEMORY_RESOLUTION_CONTEXT JSON environment value. Missing context for a participating scoped policy fails visibly with policy_context_required. See policy resolution for precedence, diagnostics, canonical output, and rollout details. The pinned host/container conversion manifest and operator steps are in storage policy conversion.

To publish a new root, call memory_store with category rule and a complete write payload such as "policy":{"policy_key":"build.storage","revision":1,"delivery_class":"contextual"}. To revise it, store a new Rule with a higher revision and supersedes set to the current active revision's UUID. Classified content, summary, tags, and delivery class are immutable; editing them requires a new revision. A key is 1–128 ASCII bytes, starting with a lowercase letter or digit, followed only by lowercase letters, digits, ., _, :, or -.

To classify an existing legacy Rule while preserving its UUID and contents:

Call memory_get(id) and inspect its project, category, content, summary, tags, and policy: null status. Copy the updated_at: field from the metadata header before the blank line that introduces content. It is the persisted UTC instant with nine fractional digits and Z, for example 2026-09-26T12:34:27.123456000Z. The separate Updated: minute display is never a concurrency token.
Call memory_update with only id, that exact expected_updated_at value, and the complete write payload. Example: {"id":"<legacy-rule-uuid>","expected_updated_at":"2026-09-26T12:34:27.123456000Z","policy":{"policy_key":"build.storage","revision":1,"delivery_class":"contextual"}}. Replace the example timestamp with the actual value just read.
On success, call memory_get on the same UUID to verify the committed identity. If the server returns policy_stale_assignment, read and inspect the changed Rule again before deciding whether to retry. Never refresh the token and classify text you have not inspected.

Stop older write-capable memoryd instances before enabling classification; they do not have the new immutability guards. The policy migration rolls back only while every Rule remains unclassified. Once classification occurs, its down migration refuses to discard identity and history. Use a separately authorized export or restore procedure for that case.

memory_search behavior:

expands the user query with the configured LLM
runs hybrid vector + FTS retrieval against durable memories
expands seed results via graph edges (same-project by default)
optionally reranks the combined set with the configured rerank model (disabled by default)
falls back to session-log search if no durable memories match
excludes task-scoped workflow artifacts at every retrieval stage unless include_workflow_artifacts=true

Additional binaries

`ingest`

Parses a JSONL transcript file and stores it into session_logs and session_log_chunks.

cargo run -p memoryd --release --bin ingest -- ./config.toml /path/to/transcript.jsonl

Dry run:

cargo run -p memoryd --release --bin ingest -- --dry-run ./config.toml /path/to/transcript.jsonl

`dream`

Runs maintenance passes that merge near-duplicate memories and prune stale low-importance memories. plan and rule memories are protected from these mutations.

cargo run -p memoryd --release --bin dream -- ./config.toml

Dry run:

cargo run -p memoryd --release --bin dream -- --dry-run ./config.toml

Database notes

Migrations currently create and evolve:

memories
session_logs
session_log_chunks
full-text search support on memories and session logs
HNSW vector indexes for semantic retrieval

The current schema is migration-driven. For the next planned shape, see docs/http-api-v1.md.

Hooks

For mandatory startup guardrails, use the managed memory-hooks session-start helper with a protected version-two or version-three configuration and a fixed installation anchor. It fetches a fresh classified mandatory pack for every supported SessionStart, publishes its exact text to Codex or Claude Code, and records the local output result for later validation. Provisioning, client registration, root migration, and recovery are described in docs/hooks-delivery.md. Startup delivery is not action gating. The managed memory-hooks pre-tool command checks a fresh pack for every observed PreToolUse event, records a required private audit entry, and returns an empty neutral result only after exact snapshot equality. See managed gate operations for registration, denial, recovery, coverage and maintenance. See managed delegated hooks for the direct-child Claude contract, pinned client evidence, recovery, and coverage limits.

The shell scripts below are legacy optional recall and transcript-capture features. They do not establish mandatory delivery and should be configured separately from the managed helper. Their /tmp state and remote session writes are not imported by memory-hooks.

A Claude Code PreCompact hook script is included at hooks/pre-compact.sh. It runs the ingest binary against the session transcript before compaction.

Additional hook scripts are available for durable rule bootstrap and per-message session capture:

hooks/bootstrap.sh: fetches and caches memory_bootstrap output for the current session and creates the normalized remote session row
hooks/capture-message.sh: append a user or assistant message to the normalized remote session stream
hooks/pre-command.sh: retired migration denial; remove old PreToolUse registrations and use the managed gate
hooks/session-stop.sh: final flush of the normalized session into searchable chunks

This lets you save each prompt and each response as the session unfolds, with agent identity, instead of only storing a transcript at compaction time.

Example wiring with explicit agent identities:

{
  "hooks": {
    "SessionStart": [{
      "hooks": [{
        "type": "command",
        "command": "/absolute/path/to/memory-server/hooks/bootstrap.sh claude"
      }]
    }],
    "UserPromptSubmit": [{
      "hooks": [{
        "type": "command",
        "command": "/absolute/path/to/memory-server/hooks/capture-message.sh claude user"
      }]
    }],
    "AssistantResponse": [{
      "hooks": [{
        "type": "command",
        "command": "/absolute/path/to/memory-server/hooks/capture-message.sh claude assistant"
      }]
    }],
    "PreCompact": [{
      "hooks": [{
        "type": "command",
        "command": "/absolute/path/to/memory-server/hooks/pre-compact.sh"
      }]
    }],
    "Stop": [{
      "hooks": [{
        "type": "command",
        "command": "/absolute/path/to/memory-server/hooks/session-stop.sh"
      }]
    }]
  }
}

Notes:

Use claude and codex as explicit first arguments if both clients write into the same memory service. That makes the stored session events attributable during search/finalization.
The exact event names vary by client. Map these scripts to the closest available events in each client.
The scripts expect jq and curl.
They read memoryd_url and optional api_token from config.toml, or from MEMORY_SERVER_CONFIG if you want to point at another config file.
Hook state is cached under /tmp/memory-server-hooks/<external-session-id>/.

The managed helper's trust and private-state boundary is documented in docs/hooks-trust.md. Its configuration and state are separate from the legacy hook files. Existing historical raw command logs are not automatically scrubbed, and separately configured transcript systems may still capture raw tool input.

For durable instruction enforcement, prefer the following flow over duplicating guidance in AGENTS.md or CLAUDE.md:

Store durable instructions as rule memories. Put cross-project rules under project general; put repo-specific rules under that repo's project name.
Use managed memory-hooks session-start for the mandatory pack. Optional memory_bootstrap(project) can still add supporting non-rule recall.
Call memory_rules(project) from pre-action hooks when only the enforceable rule set is needed.
Keep hooks focused on deterministic enforcement and verification that does not preempt the client's own permission flow: ensuring bootstrap has happened where needed and recording compliance failures.
Keep memory rules focused on durable intent and policy that the model must follow but that a shell hook cannot reliably derive on its own.

Memory Graph

Memories are connected by weighted edges stored in the memory_edges table. Edges enable graph-aware search expansion and cross-reference navigation.

Edge types

Relation	Direction	Description
`references`	directed	Explicit reference from one memory to another
`related_tag`	undirected	Shared non-structural tags between memories
`similar`	undirected	Embedding cosine similarity neighborhood

Edge origins

Origin	When created
`content_uuid_ref`	Write-time: UUID found in memory content
`structural_tag_ref`	Write-time: structural tag like `plan:<uuid>`
`shared_tag`	Dream maintenance: shared topical tags
`embedding_neighbor`	Dream maintenance: cosine similarity 0.75–0.92
`usage_reinforcement`	Future: successful retrieval signals
`manual`	Future: explicit user/admin edits

Search expansion

memory_search expands results via graph edges between outer RRF and LLM reranking. Expansion follows non-suppressed edges with weight ≥ 0.5.

Scope policy (all conservative by default):

Same-project edges: always followed
general project: only when include_general=true
Foreign projects: only when cross_project=true, optionally filtered by project_allowlist
Task-scoped workflow artifacts: only when include_workflow_artifacts=true

Score decay per hop: 0.7×, with additional discounts for general (0.9×) and foreign projects (0.5×).

Graph maintenance

The dream binary includes a graph refresh phase that runs before merge/prune. It builds similar and related_tag edges using idempotent upserts. ON DELETE CASCADE on both foreign keys ensures edges are cleaned up when memories are deleted.

Graph refresh remains inclusive, but merge and prune maintenance exclude workflow artifacts during candidate generation and recheck provenance inside the mutation transaction. Historical workflow plans, reviews, and transcripts are retained for audit and explicit retrieval.

Review Workflow

For cross-agent collaboration, use the review-needed tag on any memory to request review, and the review_queue/review_submit tools to manage the workflow.

Plan reviews

Claude stores a plan memory tagged review-needed.
Codex calls review_queue(project, category: "plan") to find pending plan reviews.
Codex reviews the plan and calls review_submit(...).
review_submit stores a decision memory linked to the plan and updates the original plan tags from review-needed to reviewed.

Code reviews

An agent stores a context memory tagged review-needed and code-review, with git range and description in the content.
Another agent calls review_queue(project) or review_queue(project, category: "context") to find pending reviews.
The reviewer inspects the code and calls review_submit(...).
review_submit stores a decision memory and retags the original.

Worker Workflows

When launching short-lived, targeted worker sessions, you must avoid context dilution. Unrestricted bootstraps (memory_bootstrap) in highly specialized workers (e.g. ones that solely write frontend CSS vs ones that manage SQL migrations) will pollute the AI's context with rules and guidelines meant for entirely different phases of the project.

For isolated workers, configure the execution context at MCP startup. Use include_recall=false when only policies are needed. Tags can reduce optional contextual guidance, but they never select an execution profile or suppress applicable mandatory policies. Rules and memories can still carry tags such as lang:rust or phase:planning for contextual retrieval.

At session start, specialized workers call memory_rules(project) with a trusted startup context; optional tags filter contextual guidance only.
For retrieval, workers must exclusively use memory_search(tags=...) targeted to their operational domain.
If creating rules or plans intended for specialized agents, always ensure they are tagged with the relevant lang:* or phase:* identifiers.

Skills

Repo-managed skills live under skills/.

Current shared skill:

skills/review/SKILL.md: unified workflow for requesting and performing reviews (plans, code, etc.) with review_queue and review_submit

Install symlink(s) for local clients with:

./scripts/install-skills.sh all

Or target one client:

./scripts/install-skills.sh codex
./scripts/install-skills.sh claude

This symlinks the repo-managed skill into:

~/.codex/skills/review
~/.claude/skills/review

Development

Run tests with:

cargo test

Useful files:

src/tools.rs: MCP tool surface
src/db.rs: SQL access layer
src/transcript.rs: JSONL transcript parsing and chunking
docs/http-api-v1.md: planned HTTP API split

The managed Rust gate resolves bounded static build paths and calls the Linux storage evaluator against structured mandatory policy. Protected v6 execution/locality proof and durable audit v3 are required before a neutral result reaches native client permissions.

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