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* fix(mothership): stop inlining full execution traces for the logs context Tagging a run via "Troubleshoot in Chat" (or any @-mention of a logs context) resolved through processExecutionLogFromDb, which materialized the ENTIRE execution trace (every block's input/output, nested tool-call spans) and inlined it directly into the prompt. For any non-trivial run this repeatedly blew the context window, forcing multiple compactions and eventually auto-stopping the agent before it could investigate anything. Every other context resolver in this file already avoids this by sending a lightweight pointer instead of a full inline dump (workflow/blocks/ workflow_block contexts point into the VFS). Logs contexts have no VFS materialization to point at, but the equivalent lightweight mechanism already exists as a tool: query_logs supports incremental disclosure (overview for timing/cost, full for a scoped block's input/output, or pattern to grep the trace) and is already registered for the mothership agent. Now processExecutionLogFromDb sends a compact summary (id, workflow, level, trigger, timing, cost) plus a note pointing the model at query_logs with the executionId, instead of materializing and embedding the trace. Also drops the now-unused executionData column from the select projection, so resolving a logs context no longer fetches a potentially large JSONB blob it never reads. * improvement(mothership): send a bounded block overview instead of a bare tool pointer Follow-up to the previous commit's fix (stop inlining full execution traces). A pure text pointer telling the model to call query_logs made the agent's very first useful action against a tagged run contingent on it noticing and correctly acting on prose in a JSON blob it may only skim — every sibling resolver in this file instead returns a deterministic mechanism (a VFS path) the model reads on demand. There's no VFS materialization for individual execution logs, but the same deterministic signal is available cheaply: toOverview() (the exact projection query_logs's own "overview" view already returns) walks the raw trace spans and produces a compact tree — block name/type/status/ timing/cost, no input or output — without touching large-value refs at all. The summary now includes that tree, so the model can see which block failed on the first turn, and the note narrows to what still requires a tool call: a block's actual input/output/error, or a grep. materializeExecutionData is still called, but it's a no-op for the common inline case (it only unwraps a top-level object-storage pointer for runs whose whole trace was offloaded as one blob) and was needed to reach traceSpans at all for those heavier runs — exactly the runs most worth an overview. A serialized-size cap (mirroring query-logs.ts's own truncation fallback, scaled down since this lands in the prompt unconditionally) drops the overview if a pathological span count pushes it over budget, falling back to the note alone. Extends the tests: the happy path now asserts the overview tree is present and that no raw input/output payload leaks into the serialized summary, plus a new test for the size-cap fallback.
A workspace to build, deploy and manage AI agents and workflows.
Quickstart
Cloud-hosted: sim.ai
Self-hosted
npx simstudio
Docker must be installed and running. Use -p, --port <port> to run Sim on a different port, or --no-pull to skip pulling the latest Docker images.
Capabilities
- Connect 1,000+ integrations and every major LLM
- Add Slack, Notion, HubSpot, Salesforce, databases, and more
- Build agents visually, conversationally, or with code
- Ingest files, knowledge bases, and structured table data
- Monitor runs, logs, schedules, and workflow activity
One workspace, every surface
Chat and workflows are just the start — tables, files, knowledge, and scheduled tasks all live in the same workspace.
Tables — a database, built in |
Files — one store for your team and every agent |
Knowledge — your agents' memory |
Scheduled tasks — runs on your schedule |
Self-hosting
Docker Compose
git clone https://github.com/simstudioai/sim.git && cd sim
docker compose -f docker-compose.prod.yml up -d
Sim also supports local models via Ollama and vLLM. See the Docker self-hosting docs for setup details.
Manual Setup
Requirements: Bun, Node.js v20+, PostgreSQL 12+ with pgvector
- Clone and install:
git clone https://github.com/simstudioai/sim.git
cd sim
bun install
bun run prepare # Set up pre-commit hooks
- Set up PostgreSQL with pgvector:
docker run --name simstudio-db -e POSTGRES_PASSWORD=your_password -e POSTGRES_DB=simstudio -p 5432:5432 -d pgvector/pgvector:pg17
Or install manually via the pgvector guide.
- Configure environment:
cp apps/sim/.env.example apps/sim/.env
# Create your secrets
perl -i -pe "s/your_encryption_key/$(openssl rand -hex 32)/" apps/sim/.env
perl -i -pe "s/your_internal_api_secret/$(openssl rand -hex 32)/" apps/sim/.env
perl -i -pe "s/your_api_encryption_key/$(openssl rand -hex 32)/" apps/sim/.env
# DB configs for migration
cp packages/db/.env.example packages/db/.env
# Edit both .env files to set DATABASE_URL="postgresql://postgres:your_password@localhost:5432/simstudio"
- Run migrations:
cd packages/db && bun run db:migrate
- Start development servers:
bun run dev:full # Starts Next.js app and realtime socket server
Or run separately: bun run dev (Next.js) and cd apps/sim && bun run dev:sockets (realtime).
Chat API Keys
Chat is a Sim-managed service. To use Chat on a self-hosted instance:
- Go to https://sim.ai → Settings → Chat keys and generate a Chat API key
- Set
COPILOT_API_KEYenvironment variable in your self-hosted apps/sim/.env file to that value
Environment Variables
See the environment variables reference for the full list, or apps/sim/.env.example for defaults.
Tech Stack
Next.js · Bun · PostgreSQL · Drizzle · Better Auth · Tailwind — and the rest of the stack
- Framework: Next.js (App Router)
- Runtime: Bun
- Database: PostgreSQL with Drizzle ORM
- Authentication: Better Auth
- Schema Validation: Zod
- UI: Shadcn, Tailwind CSS
- Streaming Markdown: Streamdown
- State Management: Zustand, TanStack Query
- Flow Editor: ReactFlow
- Docs: Fumadocs
- Monorepo: Turborepo
- Realtime: Socket.io
- Background Jobs: Trigger.dev
- Remote Code Execution: E2B
- Isolated Code Execution: isolated-vm
Contributing
We welcome contributions! Please see our Contributing Guide for details.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
