Postgram

A self-hosted productivity and knowledge backend for humans and AI agents.

Website · Quick start · MCP guide · REST API · Demo

Postgram keeps the data you and your agents work from in one inspectable place: notes, documents, tasks, people, projects, interactions, decisions, and agent memory. Humans use the browser UI and CLI; agents use the same corpus over MCP, REST, or the CLI.

It is more than an agent-memory layer. Postgram preserves typed source objects, supports GTD-style task management and Markdown folder sync, combines full-text and vector retrieval with a knowledge graph, and separates short-lived agent working context from durable memory.

Watch the demo

Search across memories, documents, people, projects, and tasks

Why Postgram

• One private corpus across tools. Give different agents and devices access to the same data without tying it to one editor or hosted memory provider.
• Inspectable source data. Store typed entities instead of opaque chat summaries, then search, edit, link, archive, or delete them yourself.
• Search before graph expansion. Hybrid retrieval finds relevant entities; edge summaries tell an agent when related graph context is worth following.
• Working context is not durable memory. Session context has its own scope and lifecycle; grooming can archive it or distill selected context into durable memory.
• Operator control. Choose where PostgreSQL runs, which embedding and extraction providers are allowed, who receives API keys, and what is kept.

Postgram is built for one person or a small trusted team running a local or single-VM deployment. It is not a hosted service or a multi-tenant SaaS platform. Knowledge extraction is optional, and the provided Docker Compose setup binds the raw API and UI ports to loopback by default.

Quick Start (Docker Compose)

You need Git, Docker, and Docker Compose. Node.js 22+ is needed only for local development or for installing the pgm CLI; gpg is needed only for encrypted CLI backups.

1. Clone Postgram:

   bash

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   git clone https://github.com/ivo-toby/postgram.git
   cd postgram
   

2. Choose an embedding path before the first start. For the local default, install and start Ollama on the Docker host, then pull Postgram's default embedding model:

   bash

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   ollama pull bge-m3
   

   For hosted OpenAI embeddings instead, create a .env file containing a real key before starting Compose:

   dotenv

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   OPENAI_API_KEY=<your-openai-key>
   

3. Start the stack:

   Terminal

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   docker compose up -d --build
   

   The first run creates persistent Docker volumes for PostgreSQL and installation secrets. No .env file is required for the default Compose path.

4. Read the one-time bootstrap token:

   Terminal

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   docker compose logs mcp-server \
     | grep 'Bootstrap token:' \
     | tail -n 1
   

   The plaintext appears only in the original first-start logs. Capture it before recreating the API container or discarding those logs.

5. Open http://127.0.0.1:3000/admin, paste the token, create the first admin, enroll MFA, and follow the onboarding flow.

6. Confirm the selected provider in the Admin Config tab. If you add or change staged settings, save, validate, and apply them, then restart mcp-server when Admin marks a restart as required:

   Terminal

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   docker compose restart mcp-server
   

   When Ollama runs on the Docker host, its base URL is http://host.docker.internal:11434. Optional LLM relationship extraction is disabled by default and can use OpenAI, Anthropic, Ollama, or an OpenAI-compatible endpoint. Changing the embedding provider, model, or dimensions after the first start is migration work and is blocked from a simple config apply.

7. Check health, then create an API key in the Admin Overview tab. For the smoke test below, allow read and write, the memory entity type, and personal visibility:

   Terminal

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   curl -fsS http://127.0.0.1:3100/health
   

   The response should include "status":"ok" and "postgres":"connected".

8. Install the CLI and verify an authenticated write and search. Enrichment is asynchronous, so wait for pgm queue to report no pending work before the search:

   Terminal

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   npm install -g @ivotoby/postgram-cli
   export PGM_API_URL=http://127.0.0.1:3100
   export PGM_API_KEY='<plaintext-api-key>'
   
   pgm store "Postgram quick start is working" \
     --type memory \
     --visibility personal \
     --tags quickstart
   pgm queue
   pgm search "quick start"
   

If embeddings are unreachable, Postgram still starts and accepts writes, but enrichment and search will fail until the provider is available. See the full quick start and troubleshooting guide for the longer path.

For access from ChatGPT, Claude, or another remote MCP client, put Postgram behind HTTPS, enable OAuth, and follow the MCP integration guide. Do not publish the loopback development ports directly to the internet.

What It Does

Postgram provides:

• durable storage for typed entities: memory, person, project, task, interaction, document
• hybrid BM25 + vector search with asynchronous enrichment
• knowledge graph with typed directional edges between entities
• LLM-powered relationship extraction (OpenAI, Anthropic, or Ollama)
• document sync from local markdown repos via manifest comparison
• browser interfaces for knowledge work and guarded administration
• UMAP and PCA projections of embedded entities
• GTD-style capture, task organization, and Kanban views
• scoped API-key authentication and visibility restrictions
• a REST API for application and automation access
• a Streamable HTTP MCP endpoint for agent-native tool access
• a CLI (pgm) for humans and agents
• a container-local admin CLI (pgm-admin)
• Talon SQLite migration tooling
• encrypted backup support
• audit logging for mutating and privileged operations

How It Works

Postgram is a TypeScript Node.js application built around a service layer.

Main components:

• PostgreSQL + pgvector for persistence and vector search
• Hono for the HTTP server
• MCP over Streamable HTTP for agent-facing tool access
• CLI/admin CLIs built with Commander
• background enrichment worker for chunking, embeddings, and LLM extraction

High-level flow:

1. a client stores or updates an entity
2. the entity is written immediately
3. enrichment runs asynchronously: chunking, embedding, and optionally LLM extraction
4. chunks and embeddings are produced in the background
5. edges are created from extracted relationships (if extraction is enabled)
6. search queries use hybrid BM25 + vector scoring, with optional graph expansion

Main Features

1. Typed Knowledge Storage

Store structured knowledge objects with:

• type (memory, person, project, task, interaction, document)
• content
• tags
• visibility (personal, work, shared)
• status
• arbitrary JSON metadata

Memory Roles

Postgram supports two roles for memory entities:

• durable_memory: long-term memory future agents should trust, such as decisions, preferences, constraints, root causes, and completed-work summaries.
• session_context: working context for resuming recent conversations. Session context is scoped to the calling client, embedded for semantic recall, and skipped by graph extraction.

Use session context for "where were we in this thread?" Use durable memory for "what should future agents remember as true?"

CLI users can write session context with pgm memory session-context and search it with pgm search --memory-role session_context.

Operators can groom stale session context with pgm-admin memory groom. Use --client-id <client-id> for one client or --all-clients to batch over every session-context scope. --all-clients keeps each client scope separate; it is operational batching, not cross-client consolidation. --older-than <duration> defaults to 7d and accepts values like 30m, 4h, 7d, or 0d. --dry-run previews eligible memories without calling the LLM. Grooming has no default candidate cap; pass --limit <n> when you want to process a bounded batch. --mode archive --yes archives eligible working context directly. --mode promote --yes uses the configured extraction LLM to decide whether each session-context memory should be promoted; promoted memories are distilled into new durable_memory entities, the source context is archived, and provenance is recorded with metadata.promoted_to plus a promoted_to edge.

Authenticated users and agents can self-groom only their own client-scoped session context:

pgm memory groom --dry-run --older-than 7d
pgm memory groom --older-than 14d --topic postgram --tag session-context --yes

The normal CLI derives scope from PGM_API_KEY; it does not accept --client-id, --all-clients, or promotion mode. Archive requires --yes. Optional filters are --topic, --session-id, and repeatable --tag.

MCP clients can use the groom_session_context tool with the same self scope:

{
  "mode": "dry_run",
  "older_than": "7d",
  "topic": "postgram",
  "session_id": "optional-session-id",
  "tags": ["session-context"]
}

MCP mode is dry_run or archive; promotion remains admin-only.

For scheduled maintenance, run grooming from the host that has access to the Postgram container. This cron example assesses eligible session context for all client scopes every three days at 03:17 and appends JSON output to a log. The wrapper detects that cron does not provide a TTY and runs non-interactively:

17 3 */3 * * cd /path/to/postgram && ./bin/pgm-admin --json memory groom --all-clients --older-than 7d --mode promote --yes >> /var/log/postgram-memory-groom.log 2>&1

Use --mode archive --yes instead if you want to archive eligible working context without LLM-assisted promotion. Run the same command with --dry-run first to verify the eligible set.

Operators can also review durable memory quality without mutating the durable claim itself:

./bin/pgm-admin memory groom-durable --dry-run --older-than 30d
./bin/pgm-admin memory groom-durable --mode mark --yes --older-than 30d

Durable grooming selects active durable_memory rows, including legacy memory rows with no metadata.memory_role, and classifies them as keep, needs_grooming, archive, or superseded. Mark mode writes metadata.durable_grooming with the outcome, reason, review timestamp, and any LLM suggestions. It does not rewrite content, change status, archive rows, or merge duplicates.

To actually clean the marked rows, apply the grooming labels:

./bin/pgm-admin memory apply-durable-grooming --dry-run
./bin/pgm-admin memory apply-durable-grooming --yes

Apply mode defaults to auto: needs_grooming memories are rewritten from the stored suggestion or the configured extraction LLM, while archive and superseded memories are archived. Rewrites clear stale chunks and re-queue embedding enrichment. Use --mode rewrite or --mode archive, plus --status, --topic, --tag, --visibility, or --limit, to narrow the batch.

2. Async Enrichment

Entities with content are persisted first and enriched later. Each entity tracks enrichment_status: pending, completed, or failed. Failed entities are retried up to 3 times with a 5-minute backoff.

3. Hybrid Search

Search blends vector cosine similarity (60%) with BM25 keyword ranking (40%) transparently. Broad searches select candidates through the HNSW index; small filtered sets use exact distance ranking, and HNSW falls back to exact ranking when its candidate scan cannot fill its target. Search requires a reachable embedding provider; if that provider is unavailable, writes still succeed but enrichment and search fail until it recovers. Results include:

• ranked results with blended scores
• similarity scores
• recency-adjusted scores
• matching chunk text
• optional 1-hop graph neighbors (expand_graph parameter)

4. Knowledge Graph

Entities can be connected by typed directional edges:

• relation types: involves, assigned_to, part_of, blocked_by, mentioned_in, related_to, or any custom type
• edges have a confidence score (1.0 for manual, LLM-assigned for extracted)
• graph traversal via expand with configurable depth (1-3 hops)
• duplicate edge prevention via UNIQUE(source_id, target_id, relation)
• edges are created manually via link/unlink or automatically by the LLM extraction pipeline

5. LLM Extraction

When enabled, the enrichment worker extracts relationships from entity content using an LLM. Extracted entity names are matched against existing entities and edges are created automatically.

Supported providers:

These are configuration defaults, not model-quality recommendations. Graph extraction is a constrained structured-output task; validate the resulting edges on your own corpus before running a large backfill, especially with small local models.

6. Document Sync

Sync local directories of markdown files into postgram:

pgm sync ~/Documents/personal-notes
pgm sync ~/Documents/cf-notes --repo cf-notes --quiet

The CLI walks the directory for .md files, computes SHA-256 hashes, and sends a full manifest to the server. The server diffs against stored state and creates, updates, or archives document entities. Supports --dry-run and cron scheduling.

7. Access Control

API keys can be restricted by:

• scopes: read, write, delete, sync
• allowed entity types
• allowed visibility levels

8. Task Management

Tasks are first-class entities with convenience operations for:

• create (with GTD context and due dates)
• list (filtered by status and context)
• update
• complete (with completion timestamp)

9. Multiple Interfaces

The same service layer is exposed through:

• REST API
• Streamable HTTP MCP endpoint
• pgm CLI
• pgm-admin CLI (./bin/pgm-admin)
• Browser extensions for Chrome and Firefox — one-click web clipper that captures the current page or text selection via the REST API. Build with npm run -w @ivotoby/postgram-browser-extension-chrome package (or the Firefox equivalent); install unpacked from the per-package README.

Repository Layout

src/
  auth/            API key validation and auth middleware
  cli/             CLI for humans/agents and admin CLI
  db/              Pool and migrations
  migrate-talon/   Talon import path
  services/        Business logic (entities, search, edges, sync, extraction)
  transport/       REST and MCP adapters
  types/           Shared types
  util/            Errors, audit, logging

ui/                User-facing web UI and Admin UI
cli/               Published @ivotoby/postgram-cli package
docker/            Container entrypoint and secret bootstrap scripts
bin/               Local operator wrappers

packages/
  browser-extension-chrome/   Chromium web clipper (MV3)
  browser-extension-firefox/  Firefox web clipper (MV3)

tests/
  contract/        REST and MCP contract tests
  integration/     Service and CLI integration tests
  unit/            Pure logic tests

Requirements

• Docker and Docker Compose for the recommended deployment
• Node.js 22+ for the pgm CLI or local development
• a reachable OpenAI or Ollama embedding provider for enrichment and search

Optional:

• OpenAI API key (for OpenAI embeddings or extraction)
• Anthropic API key (for LLM extraction)
• Ollama (for local embeddings or LLM extraction)
• gpg (for encrypted CLI backups)

Docker Setup Details

1. Start Docker Compose

docker compose up -d --build

The default Compose path does not require manual .env edits. On first run it creates a persistent postgram_secrets Docker volume containing:

• the Postgres password used by the app container
• ADMIN_MFA_SECRET_KEY for encrypted admin TOTP seeds
• ADMIN_SETTINGS_ENCRYPTION_KEY for DB-backed provider secrets

If an existing Docker install already has POSTGRES_PASSWORD in .env, the first start after this change copies that legacy password into postgram_secrets/postgres-password instead of generating a different database password. Keep the old .env value in place for that first upgraded start.

The API binds to 127.0.0.1:3100 and the UI binds to 127.0.0.1:3000 by default. Use POSTGRAM_API_PORT=<port> or UI_PORT=<port> as shell overrides when running more than one local stack.

To use an existing Postgres cluster with Compose, set POSTGRES_HOST, POSTGRES_PORT, POSTGRES_DB, and POSTGRES_USER on mcp-server in a Compose override and remove the postgres dependency, as in the operator examples. If that external cluster requires password auth, set POSTGRES_PASSWORD in .env; if it uses passwordless local auth, leave POSTGRES_PASSWORD= blank. You can also bypass the split settings entirely by setting DATABASE_URL.

For embeddings, Compose preserves the OpenAI default when OPENAI_API_KEY is present. If no OpenAI key and no explicit EMBEDDING_PROVIDER are supplied, the container entrypoint chooses local Ollama embeddings so a clean stack can boot before provider secrets are configured.

2. Complete first admin setup

On first start, the API container prints a clear one-time bootstrap banner with the token:

Postgram first admin setup
Bootstrap token: ...
Open http://127.0.0.1:3000/admin and paste this token.

If the console has scrolled, read the same one-time bootstrap token from the trusted local operator channel:

docker compose logs mcp-server | grep 'Bootstrap token:' | tail -n 1

Then open http://127.0.0.1:3000/admin, create the first admin user, and complete MFA enrollment. The bootstrap token is stored hash-only in Postgres, expires after 24 hours, and is invalidated after the first admin is created. If you changed the Postgres target, copy the latest bootstrap-token log line; older lines may belong to a previous database and will be rejected.

After active MFA login, the Admin dashboard opens a guided onboarding flow until it is completed or deliberately skipped. The guide explains the setup path in plain operator language:

• what bootstrap, admin login, and MFA confirmation protect
• how provider settings, embedding dimensions, extraction models, and write-only provider secrets fit together
• when to validate and apply saved provider configuration
• why backup/restore is staged before switch-over
• how maintenance dry-runs, re-extraction, re-embedding, and edge pruning work

Onboarding progress is stored server-side in Postgres. Refreshing the browser, closing the tab, logging out and back in, or restarting the Docker containers resumes at the latest saved step as long as the existing pgdata volume is preserved. The Onboarding tab remains available from the dashboard after skip or completion.

For local Docker testing, preserve the database volume:

docker compose up -d --build
docker compose restart mcp-server postgram-ui

Do not use docker compose down -v when testing onboarding resume behavior. That command removes named volumes, including the pgdata Postgres volume, and will reset the server-side onboarding state along with the database.

3. Check health

curl http://127.0.0.1:3100/health

Expected:

• status: "ok"
• postgres: "connected"

4. Configure providers and create API keys

Use the Admin dashboard in the browser for the supported happy path:

• Onboarding tab: resume, skip, or complete the Docker-first setup guide.
• Config tab: save provider settings and write-only provider secrets.
• Overview tab: create Postgram API keys, inspect health, queue, stats, config/model/job status, and audit rows.
• Maintenance tab: run safe dry-run previews and poll job status before any destructive apply.
• Backup tab: download a gzipped v2 archive containing a data-only PostgreSQL custom dump plus redacted runtime configuration. Restore is intentionally staged: the server rejects legacy v1/full-schema archives, accepts only approved Postgram table-data entries from pg_restore --list, creates the trusted schema from bundled migrations, and restores the accepted data into a new database name. Health checks run before operator-approved switch-over. If the restored database misbehaves, roll back by restoring the previous POSTGRES_DB or DATABASE_URL setting and restarting mcp-server/postgram-ui; the old database is left untouched for this emergency path.

Normal Docker setup and maintenance should not require pgm-admin after startup/bootstrap. The pgm-admin CLI remains documented below for emergency recovery, embedding migrations, raw SQL inspection, and advanced operator jobs.

Docker Secret Backup And Failure Behavior

Back up the postgram_secrets Docker volume separately from database backups. Database backups contain encrypted provider secrets and encrypted TOTP factors; they do not contain the installation keys needed to decrypt them.

Losing or replacing ADMIN_MFA_SECRET_KEY prevents existing TOTP factors from being verified. Losing or replacing ADMIN_SETTINGS_ENCRYPTION_KEY prevents stored provider secrets from being decrypted. With the wrong settings key, provider config reads remain redacted, provider apply/runtime secret use fails closed, and operators must restore the original key or re-save provider secrets after a deliberate rotation/recovery procedure.

For Docker Compose, missing secret files are generated only on an empty postgram_secrets volume. Invalid persisted secret files fail container startup before the server binds. Optional env overrides still work, but keep those values outside database backups and browser storage.

Environment Variables

Server

Embeddings

When Postgram runs in Docker and Ollama runs directly on the Docker host, use http://host.docker.internal:11434 for EMBEDDING_BASE_URL; localhost inside the container points at the Postgram container, not the host machine.

See specs/002-local-embeddings/quickstart.md for a walkthrough of fresh-install-on-Ollama and migrating from OpenAI.

LLM Extraction

Semantic neighbor linking: the LLM extraction pass only finds entities that are explicitly named in the source content. It misses entities that are thematically related but not cited by name — a weekly kickoff meeting about the same initiative, a wiki page covering the same strategy, a decision memo about the same project. When EXTRACTION_SEMANTIC_NEIGHBORS_ENABLED=true, a second pass runs after LLM extraction that queries the knowledge store for entities whose stored chunk embeddings are cosine-similar to the source entity's own embeddings, and links them with related_to. No extra LLM or embedding API calls are needed — the source entity's chunks are already stored by the enrichment step that runs before extraction. Edges created by this pass carry source = 'semantic-neighbor' so they are distinguishable from LLM-extracted edges. Entities already linked by the LLM pass are excluded to avoid a weaker related_to edge shadowing a stronger-typed edge for the same pair.

Backfilling and maintaining neighbor edges: the ./bin/pgm-admin link-neighbors command runs the semantic neighbor pass directly — no LLM calls, no extraction queue, just cosine similarity over stored chunks. Use it to backfill an existing graph or as a recurring maintenance job after new entities are added.

# Backfill all enriched entities (safe to re-run — edges are upserted).
./bin/pgm-admin link-neighbors --all

# Only documents:
./bin/pgm-admin link-neighbors --type document

# Single entity:
./bin/pgm-admin link-neighbors --id <uuid>

# Preview what would be linked and at what similarity — no edges created:
./bin/pgm-admin link-neighbors --id <uuid> --dry-run
./bin/pgm-admin link-neighbors --all --dry-run

# Tune the similarity threshold or edge cap:
./bin/pgm-admin link-neighbors --all --min-similarity 0.75 --max-neighbors 5

# Process in bounded batches (oldest-first):
./bin/pgm-admin link-neighbors --all --limit 500

Use --dry-run to inspect actual cosine similarity scores before committing edges — especially useful when tuning --min-similarity for a new embedding model. The output shows each entity and its candidate neighbors with their raw similarity scores.

If you also want to re-run LLM extraction at the same time (e.g. after enabling EXTRACTION_SEMANTIC_NEIGHBORS_ENABLED=true), use reextract instead — the worker runs both the LLM pass and the neighbor pass together:

./bin/pgm-admin reextract --all

Note: --clean-edges on reextract only removes edges with source='llm-extraction' — it does not touch semantic-neighbor edges. For a full clean slate:

DELETE FROM edges WHERE source = 'semantic-neighbor';

Scheduling as a recurring maintenance job: because link-neighbors is cheap (no LLM calls) and idempotent (edges are upserted, not duplicated), it works well as a weekly cron job that keeps the neighbor graph fresh as new entities are added. Example cron entry running every Sunday at 02:00:

0 2 * * 0 cd /path/to/postgram && ./bin/pgm-admin link-neighbors --all

Or with Docker Compose:

./bin/pgm-admin link-neighbors --all

Auto-created entities: when EXTRACTION_AUTO_CREATE_ENTITIES=true, entities that didn't exist before a document mentioned them are inserted with content = the extracted name, tags including auto-created, and metadata.auto_created_by = 'llm-extraction' plus metadata.source_entity_id pointing at the document that caused the creation. They enter the normal embedding queue so they become searchable, but they are deliberately excluded from the extraction queue — their only content is a bare name, so asking the LLM "what does Alice relate to?" with no context would just free-associate new stubs in a loop. To review or clean them up:

pgm list --tags auto-created --type person
# or wholesale prune:
docker compose exec postgres psql -U postgram -d postgram -c \
  "DELETE FROM entities WHERE 'auto-created' = ANY(tags);"

CLI

Admin CLI

Backup

Running The Server

Pre-built Docker image

Pull from GitHub Container Registry:

docker pull ghcr.io/ivo-toby/postgram:latest

Images are multi-arch (linux/amd64, linux/arm64). Tags available:

• latest — most recent build of main
• main — same as latest, explicit branch name
• sha-<short> — pinned to a specific commit

The docker-compose.yml in this repo builds locally by default; to use the pre-built image instead, replace build: . with image: ghcr.io/ivo-toby/postgram:latest for the mcp-server service.

Local development

npm run dev

Production-style local run:

npm run build
npm start

The server exposes:

• REST API at http://127.0.0.1:3100/api
• MCP endpoint at http://127.0.0.1:3100/mcp
• Health endpoint at http://127.0.0.1:3100/health

Authentication

Create an API key from the Admin dashboard at http://127.0.0.1:3000/admin. The plaintext key is displayed once in the browser and cannot be recovered after dismissal or reload.

Export it for CLI use:

export PGM_API_URL=http://127.0.0.1:3100
export PGM_API_KEY='<plaintext-key>'

REST API Overview

Entity endpoints

• POST /api/entities — store entity
• GET /api/entities/:id — recall entity
• PATCH /api/entities/:id — update entity
• DELETE /api/entities/:id — soft-delete entity
• GET /api/entities — list entities

Search

• POST /api/search — hybrid BM25+vector search (supports expand_graph and include_content)

REST search keeps full entity content by default for backwards compatibility. Pass include_content: false to return matched chunks without hydrating or serializing full result and graph-neighbor content. Other REST routes continue to return their existing full JSON responses. Compact JSON and TOON remain transport-layer conveniences for MCP and the CLI.

Tasks

• POST /api/tasks — create task
• GET /api/tasks — list tasks
• PATCH /api/tasks/:id — update task
• POST /api/tasks/:id/complete — complete task

Document sync

• POST /api/sync/diff — diff local manifest against server; returns paths to upload and delete
• POST /api/sync/upload — upload a batch of file contents
• POST /api/sync/finalize — archive orphans and restore stale matches
• POST /api/sync — single-shot push (retained for MCP and small syncs)
• GET /api/sync/status/:repo — get sync status

pgm sync uses the three-phase protocol (diff → batched upload → finalize) so large repos don't send a single oversized payload. Each upload batch is capped at ~50 files or ~4 MB, whichever comes first.

Knowledge graph

• POST /api/edges — create edge
• DELETE /api/edges/:id — delete edge
• GET /api/entities/:id/edges — list edges for entity
• GET /api/entities/:id/graph — expand graph neighborhood

Queue / enrichment

• GET /api/queue — enrichment + extraction queue status. Pass ?include_failures=true (optionally &failure_limit=N, default 20, max 100) to also receive the most recent failed entities with their error messages, e.g.:

  config.json

  Copy

  {
    "embedding": {
      "pending": 0,
      "completed": 120,
      "failed": 0,
      "retry_eligible": 0,
      "oldest_pending_secs": null
    },
    "extraction": { "pending": 2, "completed": 98, "failed": 3 },
    "failures": [
      {
        "id": "…",
        "type": "document",
        "kind": "extraction",
        "error": "llm context exceeded",
        "path": "notes/long.md",
        "updatedAt": "2026-04-22T10:12:33Z"
      }
    ]
  }
  

All /api/* routes require Authorization: Bearer <api-key>.

MCP Overview

MCP is served over Streamable HTTP at:

http://127.0.0.1:3100/mcp

Exposed tools:

• store, recall, search, update, delete, queue
• store_session_context, groom_session_context
• task_create, task_list, task_update, task_complete
• sync_push, sync_status
• link, unlink, expand

The MCP tool behavior is intentionally aligned with the REST surface, but token-heavy outputs default to compact agent-friendly responses:

• write acknowledgements (store, store_session_context, update, task writes, link) return compact ids/status/version instead of echoing full metadata and timestamps
• search, task_list, and expand return compact rows/graph payloads by default; compact search contains the matched chunk rather than full result or neighbor content and may include edges.count and edges.relations as cheap traversal affordances
• pass full_response: true to get the full REST-shaped payload, including complete entity content
• pass toon: true on list-like tools (search, task_list, expand) to receive compact TOON text from the MCP layer

Search is the discovery step: inspect compact IDs, scores, and matched chunks, then call recall only for the selected entities whose complete content is needed. Compact edges summaries contain counts and relation labels only. They do not include neighbor content. Use expand_graph or expand when the user needs causes, provenance, decisions, dependencies, blockers, ownership, involvement, discussion participants, connected context, or graph-based disambiguation. Avoid expansion for direct facts already present in the matched chunk.

The underlying API remains JSON; compacting and TOON happen only in MCP/CLI handlers.

Native Remote Connectors (ChatGPT and Claude)

Local MCP clients can connect with a static bearer API key. ChatGPT accounts with custom-connector/developer-mode access and Claude's Connectors UI can connect to a public Postgram endpoint through OAuth, without storing a static API-key header in the client settings:

OAUTH_ENABLED=true
PUBLIC_BASE_URL=https://postgram.example.com

Add ${PUBLIC_BASE_URL}/mcp as the connector URL in ChatGPT or Claude. The client discovers /.well-known/oauth-protected-resource/mcp, registers through /oauth/register, opens /oauth/authorize, and receives OAuth tokens from /oauth/token. The endpoint must be reachable over public HTTPS.

The authorize page asks for an existing Postgram API key once. Tokens issued from that approval inherit the API key's scopes, client_id, allowed entity types, and allowed visibility. If the source API key is revoked, OAuth access and refresh tokens derived from it stop working. Existing Authorization: Bearer <api-key> clients and /mcp?apiKey=... keep working unchanged.

CLI (pgm)

Install from npm

npm install -g @ivotoby/postgram-cli

Then configure once:

export PGM_API_URL=http://<postgram-host>:3100
export PGM_API_KEY=<your-api-key>
# or persist them in ~/.pgmrc as JSON: { "api_url": "...", "api_key": "..." }

Run from source (for development)

From the repo root, invoke the TypeScript entrypoint directly — no build step needed, and it picks up local changes immediately:

npx tsx cli/src/pgm.ts <command>
# e.g.
npx tsx cli/src/pgm.ts sync ~/Documents/personal-notes --repo personal-notes

Entity commands

pgm store "decided to use pgvector" --type memory --tags decisions
pgm search "database decisions"
pgm search "database decisions" --type memory          # filter by entity type
pgm search "who worked on embeddings" --expand-graph   # include graph neighbours
pgm search "database decisions" --json                 # matched chunks for agents
pgm search "database decisions" --json --full-response # complete legacy search response
pgm search "database decisions" --limit 5 --toon       # compact discovery output
pgm recall <selected-entity-id>                         # complete selected content
pgm list --json                                        # compact JSON rows
pgm list --json --full-response                        # full API-shaped rows
pgm list --toon                                        # compact TOON rows
pgm expand <id> --json                                 # compact graph JSON
pgm expand <id> --toon                                 # compact TOON graph
pgm recall <id>
pgm list --type memory
pgm update <id> --content "updated text" --version 1
pgm delete <id>

Task commands

pgm task add "set up monitoring" --context @focus-work --status next
pgm task list --status next
pgm task update <id> --status waiting --version 1
pgm task complete <id> --version 2

Document sync

pgm sync ~/Documents/personal-notes
pgm sync ~/Documents/cf-notes --repo cf-notes --dry-run
pgm sync ~/Documents/personal-notes --quiet  # for cron

Knowledge graph

pgm link <source-id> <target-id> --relation involves
pgm expand <entity-id> --depth 2
pgm unlink <edge-id>

Backup

pgm backup --encrypt --output /tmp/postgram-backups/

Admin CLI (pgm-admin)

The supported Docker happy path uses the browser Admin dashboard for bootstrap, provider configuration, API-key creation, status inspection, and safe maintenance dry-runs. pgm-admin remains available for emergency recovery, embedding migrations, raw SQL inspection, and advanced operator jobs.

The easy CLI path uses the bin/pgm-admin wrapper shipped in the repo. It runs pgm-admin via docker exec when the container is up, and falls back to docker compose run --rm when it isn't (useful for first-boot migrations or when the startup dimension gate is refusing to boot):

./bin/pgm-admin <command> [args...]

For cron or other non-interactive automation, call Docker with -T so it does not try to allocate a TTY:

docker compose exec -T mcp-server /app/docker-entrypoint.sh pgm-admin <command>

Examples:

./bin/pgm-admin key create --name local --scopes read,write,delete --visibility personal,work,shared
./bin/pgm-admin stats
./bin/pgm-admin embeddings migrate --target-dimensions 1024 --dry-run
docker compose stop mcp-server
./bin/pgm-admin embeddings migrate --target-dimensions 1024 --yes
docker compose up -d mcp-server

For an embedding provider, model, or dimension change made in Admin, save, validate, and apply the target settings before running that migration sequence. The wrapper refuses --yes while mcp-server is running so the live enrichment worker cannot process the re-embedding queue with its previous in-memory provider. The dry-run is safe while the service is running.

Shell alias for daily use (add to ~/.bashrc or ~/.zshrc on your docker host):

alias pgm-admin='/path/to/postgram/bin/pgm-admin'
# then just: pgm-admin stats

Override with env if your service/container names differ:

PGM_SERVICE=mcp-server PGM_CONTAINER=my-postgram-mcp-server-1 ./bin/pgm-admin stats

Direct equivalent without the wrapper (for reference):

docker compose exec -T mcp-server /app/docker-entrypoint.sh pgm-admin <command>
# or, when the container is down:
docker compose run --rm mcp-server pgm-admin <command>

The entrypoint is required for commands executed in an already-running container. It reconstructs Docker-managed values such as DATABASE_URL, which are exported for the server process but are not present in a plain docker compose exec environment.

Main commands:

• key create, key list, key revoke

• audit — query audit logs

• model list, model set-active

• reembed --all — mark entities for re-embedding (optionally --type <type>; pair with --model <id> to switch the active embedding model in the same transaction)

• reextract --all — reset extraction_status = 'pending' and clear any stored extraction_error so the worker retries extraction (e.g. after switching to a better LLM). Key flags:

  • --type <type> — scope to a specific entity type
  • --only-failed — only re-queue entities whose extraction previously failed
  • --no-edges-only — only re-queue entities that have no LLM-extracted edges; useful for targeted maintenance without re-processing entities that already linked correctly (combine with --type document to catch large documents that silently produced no edges)
  • --clean-edges — delete existing source='llm-extraction' edges for the in-scope entities before re-queuing, giving a clean-slate redo rather than appending alongside old edges
  • --limit <n> — cap how many entities are queued (oldest-first)

  User-created edges (source != 'llm-extraction') are never touched.

• improve-graph — queue entities for re-extraction with an optional per-run model/provider override stored on the row. The worker uses the override instead of the env-configured default, then clears it on success. Existing edges are kept by default (no wipe) — overlapping edges have their confidence overwritten by the new run. Key flags:

  • --all, --type <type>, --id <uuid> — scope what to queue
  • --model <name> — e.g. claude-sonnet-4-6; stored per-row
  • --provider <name> — openai | anthropic | ollama | openai-compatible; stored per-row
  • --no-edges-only — only queue entities with no LLM-extracted edges
  • --clean-edges — wipe existing LLM edges before queueing
  • --limit <n> — cap the queue size

  Typical maintenance run targeting gaps without paying for the full graph:

  bash

  Copy

  ./bin/pgm-admin improve-graph --type document --no-edges-only --provider ollama --model <model>
  

• prune-edges --below <threshold> — delete edges with confidence below the threshold. Scoped to source='llm-extraction' by default; pass --source any to include all, or --source <name> for a specific one. Supports --relation <name> and --dry-run for a safe preview.

• validate-edges — run an LLM-as-judge quality pass. For each source='llm-extraction' edge (configurable via --source), asks the configured extraction LLM whether the relationship is supported by the source content; removes edges it judges invalid or below --min-confidence (default 0.4). Tracks last_validated_at in edge metadata and skips edges validated within --skip-validated-days (default 7) — run as a maintenance cron without redoing work. Flags: --limit <n> (default 100), --force, --dry-run. Requires EXTRACTION_ENABLED=true and the usual EXTRACTION_PROVIDER / EXTRACTION_MODEL env vars; costs ≈ one LLM call per edge.

• sql "<statement>" — execute a raw SQL statement against the database. Accepts a positional argument or reads from stdin for multi-line queries. SELECT results are printed tab-separated (or as JSON with --json); DML commands print the affected row count.

  Dockerfile

  Copy

  ./bin/pgm-admin sql "SELECT id, type, extraction_status FROM entities LIMIT 5"
  ./bin/pgm-admin sql --json "SELECT COUNT(*) FROM edges WHERE source = 'llm-extraction'"
  
  # pipe multi-line SQL from a file
  cat fix.sql | ./bin/pgm-admin sql
  

• stats — entity counts, chunk count, DB size

• embeddings migrate — switch embedding dimensions (see specs/002-local-embeddings/quickstart.md)

Graph Maintenance

The knowledge graph builds up over time as LLM extraction links entities together. Occasionally edges go missing (e.g. after a provider change, a max_tokens limit being hit, or a model outage) or need refreshing. The admin CLI has tools to handle this without re-processing the entire graph.

Finding gaps

Entities that completed extraction but produced no edges are the primary signal of a silent failure:

./bin/pgm-admin sql "
  SELECT id, char_length(content) AS chars, created_at
  FROM entities
  WHERE type = 'document'
    AND extraction_status = 'completed'
    AND NOT EXISTS (
      SELECT 1 FROM edges WHERE source_id = id AND source = 'llm-extraction'
    )
  ORDER BY chars DESC
  LIMIT 20
"

Targeted re-extraction (no wipe)

Re-queue only the entities with no edges. Existing edges on other entities are untouched:

# Using the default extraction model
./bin/pgm-admin reextract --type document --no-edges-only

# Using a local Ollama model (zero API cost)
./bin/pgm-admin improve-graph --type document --no-edges-only --provider ollama --model <model>

Full re-extraction pass

When you want to redo everything (e.g. after switching to a better model):

# Wipe and redo — gives a clean slate
./bin/pgm-admin reextract --all --clean-edges

# Or scope to documents only
./bin/pgm-admin reextract --type document --clean-edges

Confidence pruning

Remove low-confidence edges left behind by older or weaker models:

./bin/pgm-admin prune-edges --below 0.5 --dry-run   # preview
./bin/pgm-admin prune-edges --below 0.5             # apply

Edge validation

Run an LLM-as-judge pass to remove edges not supported by the source content:

./bin/pgm-admin validate-edges --dry-run --limit 200
./bin/pgm-admin validate-edges --limit 200

Monitoring queue progress

pgm queue                              # via pgm CLI
./bin/pgm-admin sql "SELECT extraction_status, COUNT(*) FROM entities GROUP BY 1"

Talon Migration

docker compose cp /path/to/talon.sqlite mcp-server:/tmp/talon.sqlite

docker compose exec -T mcp-server \
  node dist/migrate-talon/index.js /tmp/talon.sqlite \
  --api-base-url http://127.0.0.1:3100 \
  --api-key "$PGM_API_KEY"

Useful flags: --dry-run, --thread <id>, --batch-size <n>, --skip-embeddings

Testing

npm test            # all tests
npm run lint        # eslint
npm run build       # typecheck
npm run test:coverage
npm run benchmark:search -- --assert  # 5k-entity/6k-chunk latency gate

The search benchmark reports p50/p95 latency for three profiles — cold_unique_queries (every query pays a provider round trip), memory_cache_hit, and database_cache_hit (in-process cache empty, so only the persisted cache can serve it) — along with the number of embedding provider calls each profile made and an EXPLAIN (ANALYZE, BUFFERS) summary of the hybrid SQL. It stubs the embedding provider with a fixed delay, so it measures SQL time and cache hit rate; it says nothing about real provider latency.

Targeted suites:

npx vitest run tests/unit/
npx vitest run tests/integration/
npx vitest run tests/contract/

Current Status

Postgram is actively developed by one maintainer and used daily in a personal deployment. Entity storage, task management, Markdown sync, hybrid search, knowledge-graph traversal, memory lifecycles, OAuth, the user-facing web UI, and the guarded Admin UI are implemented. The project is deliberately optimized for personal and small-team self-hosting rather than multi-tenant scale.

Notes And Limitations

• Postgram is optimized for personal/small-team scale
• Non-Compose deployments default to OpenAI embeddings; Compose selects OpenAI when a key is present and otherwise selects local Ollama. Search requires the selected provider to be reachable.
• LLM extraction is optional and disabled by default
• Backup encryption requires gpg

Claude Code skill

A portable Claude Code skill for using pgm from your own agent lives in skill/postgram/SKILL.md. Copy the skill/postgram/ directory into your own project's .claude/skills/ (or your user-level ~/.claude/skills/) and the agent will know when to invoke pgm store, pgm search, pgm link, etc. It assumes the CLI is on PATH and PGM_API_URL + PGM_API_KEY are set. The skill file is deliberately not under .claude/ in this repo so you can decide where to put it.

Optimising your global CLAUDE.md

To get the most out of Postgram across sessions, add Postgram-aware guidance to your global ~/.claude/CLAUDE.md. A ready-to-use template is provided at templates/CLAUDE.md — it covers when to search (with type filters), how to inspect compact edges.count/edges.relations, when to use expand_graph, when to store, when to link, and general principles. Copy the relevant sections into your own CLAUDE.md and Claude will proactively use the MCP tools to persist and recall knowledge without being asked. Its default retrieval flow is search for compact matched chunks, then recall only the selected entities that require complete content.

For coding agents that should avoid broad knowledge-work behavior, use templates/AGENTS.coding.md or templates/CLAUDE.coding.md. It narrows Postgram usage to session-context memory and durable development memory only.

Releases & CI

The CLI package publishes to npm as @ivotoby/postgram-cli on every merge to main, driven by semantic-release v25 and conventional commits scoped to cli (e.g. feat(cli): ...). Non-CLI-scoped commits don't bump the CLI version. Workflow: .github/workflows/release-cli.yml.

Publishing uses an npm Automation token stored as the NPM_TOKEN repository secret. The --provenance flag is passed at publish time so every release gets a Sigstore-signed provenance attestation regardless.

First-time setup:

1. On npmjs.com: Avatar → Access Tokens → Generate New Token → Automation
2. GitHub repo: Settings → Secrets and variables → Actions → New repository secret → name NPM_TOKEN, value: the token from step 1
3. Subsequent publishes happen automatically from the workflow.

The server's Docker image publishes to ghcr.io/ivo-toby/postgram on every merge to main and on semver tag pushes (multi-arch amd64 + arm64). Workflow: .github/workflows/docker.yml. Uses the built-in GITHUB_TOKEN; no extra secret required, but repo packages:write permission must be enabled.

Official MCP Registry

Postgram is published as io.github.ivo-toby/postgram in the official MCP Registry. The registry metadata in server.json describes the public GHCR image and its Streamable HTTP endpoint.

Registry releases are intentionally manual. After changing server.json, wait for the docker-publish workflow on main to finish, then run the publish-mcp-registry workflow. It verifies the official publisher download, pins the current multi-architecture main image by digest, validates the metadata, authenticates with GitHub OIDC, and publishes it. Increase the top-level version in server.json before publishing a metadata update; the registry treats each published version as immutable.

Licensing

Postgram uses a deliberate multi-license structure:

• The server, Admin CLI, and browser UI are licensed under the GNU Affero General Public License v3.0 only.
• The published pgm CLI, portable agent integrations under skill/ and templates/, and browser extensions under packages/browser-extension-chrome/ and packages/browser-extension-firefox/ are licensed under the MIT License.
• Documentation prose in docs/ and this README is licensed under Creative Commons Attribution 4.0 International.
• The code and documentation licenses do not grant rights to use Postgram names, logos, or other brand identifiers as trademarks. See the trademark policy.

The AGPL permits commercial use. Its network copyleft requires operators of a modified Postgram service to offer the corresponding source to users who interact with that modified service over a network. See LICENSING.md for the exact path boundaries and practical examples.

Contributions are welcome under the process in CONTRIBUTING.md. Contributors must accept the Postgram Contributor License Agreement before a contribution can be merged.

Related Docs

• Phase 1 MVP spec
• Phase 1 enhancements design
• Phase 2 document sync design
• Phase 3 knowledge graph design
• Phase 4 local embeddings spec
• Manual test plan