feat(backend/llm-registry): wire refresh_runtime_caches to Redis invalidation and pub/sub

After any admin DB mutation, clear the shared Redis cache, refresh this
process's in-memory state, then publish a notification so all other workers
reload from Redis without hitting the database.

.claude/skills/pr-test/SKILL.md

@@ -0,0 +1,534 @@
+---
+name: pr-test
+description: "E2E manual testing of PRs/branches using docker compose, agent-browser, and API calls. TRIGGER when user asks to manually test a PR, test a feature end-to-end, or run integration tests against a running system."
+user-invocable: true
+argument-hint: "[worktree path or PR number] — tests the PR in the given worktree. Optional flags: --fix (auto-fix issues found)"
+metadata:
+  author: autogpt-team
+  version: "1.0.0"
+---
+
+# Manual E2E Test
+
+Test a PR/branch end-to-end by building the full platform, interacting via browser and API, capturing screenshots, and reporting results.
+
+## Arguments
+
+- `$ARGUMENTS` — worktree path (e.g. `$REPO_ROOT`) or PR number
+- If `--fix` flag is present, auto-fix bugs found and push fixes (like pr-address loop)
+
+## Step 0: Resolve the target
+
+```bash
+# If argument is a PR number, find its worktree
+gh pr view {N} --json headRefName --jq '.headRefName'
+# If argument is a path, use it directly
+```
+
+Determine:
+- `REPO_ROOT` — the root repo directory: `git -C "$WORKTREE_PATH" worktree list | head -1 | awk '{print $1}'` (or `git rev-parse --show-toplevel` if not a worktree)
+- `WORKTREE_PATH` — the worktree directory
+- `PLATFORM_DIR` — `$WORKTREE_PATH/autogpt_platform`
+- `BACKEND_DIR` — `$PLATFORM_DIR/backend`
+- `FRONTEND_DIR` — `$PLATFORM_DIR/frontend`
+- `PR_NUMBER` — the PR number (from `gh pr list --head $(git branch --show-current)`)
+- `PR_TITLE` — the PR title, slugified (e.g. "Add copilot permissions" → "add-copilot-permissions")
+- `RESULTS_DIR` — `$REPO_ROOT/test-results/PR-{PR_NUMBER}-{slugified-title}`
+
+Create the results directory:
+```bash
+PR_NUMBER=$(cd $WORKTREE_PATH && gh pr list --head $(git branch --show-current) --repo Significant-Gravitas/AutoGPT --json number --jq '.[0].number')
+PR_TITLE=$(cd $WORKTREE_PATH && gh pr list --head $(git branch --show-current) --repo Significant-Gravitas/AutoGPT --json title --jq '.[0].title' | tr '[:upper:]' '[:lower:]' | sed 's/[^a-z0-9]/-/g' | sed 's/--*/-/g' | sed 's/^-//;s/-$//' | head -c 50)
+RESULTS_DIR="$REPO_ROOT/test-results/PR-${PR_NUMBER}-${PR_TITLE}"
+mkdir -p $RESULTS_DIR
+```
+
+**Test user credentials** (for logging into the UI or verifying results manually):
+- Email: `test@test.com`
+- Password: `testtest123`
+
+## Step 1: Understand the PR
+
+Before testing, understand what changed:
+
+```bash
+cd $WORKTREE_PATH
+git log --oneline dev..HEAD | head -20
+git diff dev --stat
+```
+
+Read the changed files to understand:
+1. What feature/fix does this PR implement?
+2. What components are affected? (backend, frontend, copilot, executor, etc.)
+3. What are the key user-facing behaviors to test?
+
+## Step 2: Write test scenarios
+
+Based on the PR analysis, write a test plan to `$RESULTS_DIR/test-plan.md`:
+
+```markdown
+# Test Plan: PR #{N} — {title}
+
+## Scenarios
+1. [Scenario name] — [what to verify]
+2. ...
+
+## API Tests (if applicable)
+1. [Endpoint] — [expected behavior]
+
+## UI Tests (if applicable)
+1. [Page/component] — [interaction to test]
+
+## Negative Tests
+1. [What should NOT happen]
+```
+
+**Be critical** — include edge cases, error paths, and security checks.
+
+## Step 3: Environment setup
+
+### 3a. Copy .env files from the root worktree
+
+The root worktree (`$REPO_ROOT`) has the canonical `.env` files with all API keys. Copy them to the target worktree:
+
+```bash
+# CRITICAL: .env files are NOT checked into git. They must be copied manually.
+cp $REPO_ROOT/autogpt_platform/.env $PLATFORM_DIR/.env
+cp $REPO_ROOT/autogpt_platform/backend/.env $BACKEND_DIR/.env
+cp $REPO_ROOT/autogpt_platform/frontend/.env $FRONTEND_DIR/.env
+```
+
+### 3b. Configure copilot authentication
+
+The copilot needs an LLM API to function. Two approaches (try subscription first):
+
+#### Option 1: Subscription mode (preferred — uses your Claude Max/Pro subscription)
+
+The `claude_agent_sdk` Python package **bundles its own Claude CLI binary** — no need to install `@anthropic-ai/claude-code` via npm. The backend auto-provisions credentials from environment variables on startup.
+
+Run the helper script to extract tokens from your host and auto-update `backend/.env` (works on macOS, Linux, and Windows/WSL):
+
+```bash
+# Extracts OAuth tokens and writes CLAUDE_CODE_OAUTH_TOKEN + CLAUDE_CODE_REFRESH_TOKEN into .env
+bash $BACKEND_DIR/scripts/refresh_claude_token.sh --env-file $BACKEND_DIR/.env
+```
+
+**How it works:** The script reads the OAuth token from:
+- **macOS**: system keychain (`"Claude Code-credentials"`)
+- **Linux/WSL**: `~/.claude/.credentials.json`
+- **Windows**: `%APPDATA%/claude/.credentials.json`
+
+It sets `CLAUDE_CODE_OAUTH_TOKEN`, `CLAUDE_CODE_REFRESH_TOKEN`, and `CHAT_USE_CLAUDE_CODE_SUBSCRIPTION=true` in the `.env` file. On container startup, the backend auto-provisions `~/.claude/.credentials.json` inside the container from these env vars. The SDK's bundled CLI then authenticates using that file. No `claude login`, no npm install needed.
+
+**Note:** The OAuth token expires (~24h). If copilot returns auth errors, re-run the script and restart: `$BACKEND_DIR/scripts/refresh_claude_token.sh --env-file $BACKEND_DIR/.env && docker compose up -d copilot_executor`
+
+#### Option 2: OpenRouter API key mode (fallback)
+
+If subscription mode doesn't work, switch to API key mode using OpenRouter:
+
+```bash
+# In $BACKEND_DIR/.env, ensure these are set:
+CHAT_USE_CLAUDE_CODE_SUBSCRIPTION=false
+CHAT_API_KEY=<value of OPEN_ROUTER_API_KEY from the same .env>
+CHAT_BASE_URL=https://openrouter.ai/api/v1
+CHAT_USE_CLAUDE_AGENT_SDK=true
+```
+
+Use `sed` to update these values:
+```bash
+ORKEY=$(grep "^OPEN_ROUTER_API_KEY=" $BACKEND_DIR/.env | cut -d= -f2)
+[ -n "$ORKEY" ] || { echo "ERROR: OPEN_ROUTER_API_KEY is missing in $BACKEND_DIR/.env"; exit 1; }
+perl -i -pe 's/CHAT_USE_CLAUDE_CODE_SUBSCRIPTION=true/CHAT_USE_CLAUDE_CODE_SUBSCRIPTION=false/' $BACKEND_DIR/.env
+# Add or update CHAT_API_KEY and CHAT_BASE_URL
+grep -q "^CHAT_API_KEY=" $BACKEND_DIR/.env && perl -i -pe "s|^CHAT_API_KEY=.*|CHAT_API_KEY=$ORKEY|" $BACKEND_DIR/.env || echo "CHAT_API_KEY=$ORKEY" >> $BACKEND_DIR/.env
+grep -q "^CHAT_BASE_URL=" $BACKEND_DIR/.env && perl -i -pe 's|^CHAT_BASE_URL=.*|CHAT_BASE_URL=https://openrouter.ai/api/v1|' $BACKEND_DIR/.env || echo "CHAT_BASE_URL=https://openrouter.ai/api/v1" >> $BACKEND_DIR/.env
+```
+
+### 3c. Stop conflicting containers
+
+```bash
+# Stop any running app containers (keep infra: supabase, redis, rabbitmq, clamav)
+docker ps --format "{{.Names}}" | grep -E "rest_server|executor|copilot|websocket|database_manager|scheduler|notification|frontend|migrate" | while read name; do
+  docker stop "$name" 2>/dev/null
+done
+```
+
+### 3e. Build and start
+
+```bash
+cd $PLATFORM_DIR && docker compose build --no-cache 2>&1 | tail -20
+if [ ${PIPESTATUS[0]} -ne 0 ]; then echo "ERROR: Docker build failed"; exit 1; fi
+
+cd $PLATFORM_DIR && docker compose up -d 2>&1 | tail -20
+if [ ${PIPESTATUS[0]} -ne 0 ]; then echo "ERROR: Docker compose up failed"; exit 1; fi
+```
+
+**Note:** If the container appears to be running old code (e.g. missing PR changes), use `docker compose build --no-cache` to force a full rebuild. Docker BuildKit may sometimes reuse cached `COPY` layers from a previous build on a different branch.
+
+**Expected time: 3-8 minutes** for build, 5-10 minutes with `--no-cache`.
+
+### 3f. Wait for services to be ready
+
+```bash
+# Poll until backend and frontend respond
+for i in $(seq 1 60); do
+  BACKEND=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:8006/docs 2>/dev/null)
+  FRONTEND=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:3000 2>/dev/null)
+  if [ "$BACKEND" = "200" ] && [ "$FRONTEND" = "200" ]; then
+    echo "Services ready"
+    break
+  fi
+  sleep 5
+done
+```
+
+
+### 3h. Create test user and get auth token
+
+```bash
+ANON_KEY=$(grep "NEXT_PUBLIC_SUPABASE_ANON_KEY=" $FRONTEND_DIR/.env | sed 's/.*NEXT_PUBLIC_SUPABASE_ANON_KEY=//' | tr -d '[:space:]')
+
+# Signup (idempotent — returns "User already registered" if exists)
+RESULT=$(curl -s -X POST 'http://localhost:8000/auth/v1/signup' \
+  -H "apikey: $ANON_KEY" \
+  -H 'Content-Type: application/json' \
+  -d '{"email":"test@test.com","password":"testtest123"}')
+
+# If "Database error finding user", restart supabase-auth and retry
+if echo "$RESULT" | grep -q "Database error"; then
+  docker restart supabase-auth && sleep 5
+  curl -s -X POST 'http://localhost:8000/auth/v1/signup' \
+    -H "apikey: $ANON_KEY" \
+    -H 'Content-Type: application/json' \
+    -d '{"email":"test@test.com","password":"testtest123"}'
+fi
+
+# Get auth token
+TOKEN=$(curl -s -X POST 'http://localhost:8000/auth/v1/token?grant_type=password' \
+  -H "apikey: $ANON_KEY" \
+  -H 'Content-Type: application/json' \
+  -d '{"email":"test@test.com","password":"testtest123"}' | jq -r '.access_token // ""')
+```
+
+**Use this token for ALL API calls:**
+```bash
+curl -H "Authorization: Bearer $TOKEN" http://localhost:8006/api/...
+```
+
+## Step 4: Run tests
+
+### Service ports reference
+
+| Service | Port | URL |
+|---------|------|-----|
+| Frontend | 3000 | http://localhost:3000 |
+| Backend REST | 8006 | http://localhost:8006 |
+| Supabase Auth (via Kong) | 8000 | http://localhost:8000 |
+| Executor | 8002 | http://localhost:8002 |
+| Copilot Executor | 8008 | http://localhost:8008 |
+| WebSocket | 8001 | http://localhost:8001 |
+| Database Manager | 8005 | http://localhost:8005 |
+| Redis | 6379 | localhost:6379 |
+| RabbitMQ | 5672 | localhost:5672 |
+
+### API testing
+
+Use `curl` with the auth token for backend API tests:
+
+```bash
+# Example: List agents
+curl -s -H "Authorization: Bearer $TOKEN" http://localhost:8006/api/graphs | jq . | head -20
+
+# Example: Create an agent
+curl -s -X POST http://localhost:8006/api/graphs \
+  -H "Authorization: Bearer $TOKEN" \
+  -H 'Content-Type: application/json' \
+  -d '{...}' | jq .
+
+# Example: Run an agent
+curl -s -X POST "http://localhost:8006/api/graphs/{graph_id}/execute" \
+  -H "Authorization: Bearer $TOKEN" \
+  -H 'Content-Type: application/json' \
+  -d '{"data": {...}}'
+
+# Example: Get execution results
+curl -s -H "Authorization: Bearer $TOKEN" \
+  "http://localhost:8006/api/graphs/{graph_id}/executions/{exec_id}" | jq .
+```
+
+### Browser testing with agent-browser
+
+```bash
+# Close any existing session
+agent-browser close 2>/dev/null || true
+
+# Use --session-name to persist cookies across navigations
+# This means login only needs to happen once per test session
+agent-browser --session-name pr-test open 'http://localhost:3000/login' --timeout 15000
+
+# Get interactive elements
+agent-browser --session-name pr-test snapshot | grep "textbox\|button"
+
+# Login
+agent-browser --session-name pr-test fill {email_ref} "test@test.com"
+agent-browser --session-name pr-test fill {password_ref} "testtest123"
+agent-browser --session-name pr-test click {login_button_ref}
+sleep 5
+
+# Dismiss cookie banner if present
+agent-browser --session-name pr-test click 'text=Accept All' 2>/dev/null || true
+
+# Navigate — cookies are preserved so login persists
+agent-browser --session-name pr-test open 'http://localhost:3000/copilot' --timeout 10000
+
+# Take screenshot
+agent-browser --session-name pr-test screenshot $RESULTS_DIR/01-page.png
+
+# Interact with elements
+agent-browser --session-name pr-test fill {ref} "text"
+agent-browser --session-name pr-test press "Enter"
+agent-browser --session-name pr-test click {ref}
+agent-browser --session-name pr-test click 'text=Button Text'
+
+# Read page content
+agent-browser --session-name pr-test snapshot | grep "text:"
+```
+
+**Key pages:**
+- `/copilot` — CoPilot chat (for testing copilot features)
+- `/build` — Agent builder (for testing block/node features)
+- `/build?flowID={id}` — Specific agent in builder
+- `/library` — Agent library (for testing listing/import features)
+- `/library/agents/{id}` — Agent detail with run history
+- `/marketplace` — Marketplace
+
+### Checking logs
+
+```bash
+# Backend REST server
+docker logs autogpt_platform-rest_server-1 2>&1 | tail -30
+
+# Executor (runs agent graphs)
+docker logs autogpt_platform-executor-1 2>&1 | tail -30
+
+# Copilot executor (runs copilot chat sessions)
+docker logs autogpt_platform-copilot_executor-1 2>&1 | tail -30
+
+# Frontend
+docker logs autogpt_platform-frontend-1 2>&1 | tail -30
+
+# Filter for errors
+docker logs autogpt_platform-executor-1 2>&1 | grep -i "error\|exception\|traceback" | tail -20
+```
+
+### Copilot chat testing
+
+The copilot uses SSE streaming. To test via API:
+
+```bash
+# Create a session
+SESSION_ID=$(curl -s -X POST 'http://localhost:8006/api/chat/sessions' \
+  -H "Authorization: Bearer $TOKEN" \
+  -H 'Content-Type: application/json' \
+  -d '{}' | jq -r '.id // .session_id // ""')
+
+# Stream a message (SSE - will stream chunks)
+curl -N -X POST "http://localhost:8006/api/chat/sessions/$SESSION_ID/stream" \
+  -H "Authorization: Bearer $TOKEN" \
+  -H 'Content-Type: application/json' \
+  -d '{"message": "Hello, what can you help me with?"}' \
+  --max-time 60 2>/dev/null | head -50
+```
+
+Or test via browser (preferred for UI verification):
+```bash
+agent-browser --session-name pr-test open 'http://localhost:3000/copilot' --timeout 10000
+# ... fill chat input and press Enter, wait 20-30s for response
+```
+
+## Step 5: Record results
+
+For each test scenario, record in `$RESULTS_DIR/test-report.md`:
+
+```markdown
+# E2E Test Report: PR #{N} — {title}
+Date: {date}
+Branch: {branch}
+Worktree: {path}
+
+## Environment
+- Docker services: [list running containers]
+- API keys: OpenRouter={present/missing}, E2B={present/missing}
+
+## Test Results
+
+### Scenario 1: {name}
+**Steps:**
+1. ...
+2. ...
+**Expected:** ...
+**Actual:** ...
+**Result:** PASS / FAIL
+**Screenshot:** {filename}.png
+**Logs:** (if relevant)
+
+### Scenario 2: {name}
+...
+
+## Summary
+- Total: X scenarios
+- Passed: Y
+- Failed: Z
+- Bugs found: [list]
+```
+
+Take screenshots at each significant step:
+```bash
+agent-browser --session-name pr-test screenshot $RESULTS_DIR/{NN}-{description}.png
+```
+
+## Step 6: Report results
+
+After all tests complete, output a summary to the user:
+
+1. Table of all scenarios with PASS/FAIL
+2. Screenshots of failures (read the PNG files to show them)
+3. Any bugs found with details
+4. Recommendations
+
+### Post test results as PR comment with screenshots
+
+Upload screenshots to the PR using the GitHub Git API (no local git operations — safe for worktrees).
+
+```bash
+# Upload screenshots via GitHub Git API (creates blobs, tree, commit, and ref remotely)
+REPO="Significant-Gravitas/AutoGPT"
+SCREENSHOTS_BRANCH="test-screenshots/pr-${PR_NUMBER}"
+SCREENSHOTS_DIR="test-screenshots/PR-${PR_NUMBER}"
+
+# Step 1: Create blobs for each screenshot
+declare -a TREE_ENTRIES
+for img in $RESULTS_DIR/*.png; do
+  BASENAME=$(basename "$img")
+  B64=$(base64 < "$img")
+  BLOB_SHA=$(gh api "repos/${REPO}/git/blobs" -f content="$B64" -f encoding="base64" --jq '.sha')
+  TREE_ENTRIES+=("-f" "tree[][path]=${SCREENSHOTS_DIR}/${BASENAME}" "-f" "tree[][mode]=100644" "-f" "tree[][type]=blob" "-f" "tree[][sha]=${BLOB_SHA}")
+done
+
+# Step 2: Create a tree with all screenshot blobs
+# Build the tree JSON manually since gh api doesn't handle arrays well
+TREE_JSON='['
+FIRST=true
+for img in $RESULTS_DIR/*.png; do
+  BASENAME=$(basename "$img")
+  B64=$(base64 < "$img")
+  BLOB_SHA=$(gh api "repos/${REPO}/git/blobs" -f content="$B64" -f encoding="base64" --jq '.sha')
+  if [ "$FIRST" = true ]; then FIRST=false; else TREE_JSON+=','; fi
+  TREE_JSON+="{\"path\":\"${SCREENSHOTS_DIR}/${BASENAME}\",\"mode\":\"100644\",\"type\":\"blob\",\"sha\":\"${BLOB_SHA}\"}"
+done
+TREE_JSON+=']'
+
+TREE_SHA=$(echo "$TREE_JSON" | gh api "repos/${REPO}/git/trees" --input - -f base_tree="" --jq '.sha' 2>/dev/null \
+  || echo "$TREE_JSON" | jq -c '{tree: .}' | gh api "repos/${REPO}/git/trees" --input - --jq '.sha')
+
+# Step 3: Create a commit pointing to that tree
+COMMIT_SHA=$(gh api "repos/${REPO}/git/commits" \
+  -f message="test: add E2E test screenshots for PR #${PR_NUMBER}" \
+  -f tree="$TREE_SHA" \
+  --jq '.sha')
+
+# Step 4: Create or update the ref (branch) — no local checkout needed
+gh api "repos/${REPO}/git/refs" \
+  -f ref="refs/heads/${SCREENSHOTS_BRANCH}" \
+  -f sha="$COMMIT_SHA" 2>/dev/null \
+  || gh api "repos/${REPO}/git/refs/heads/${SCREENSHOTS_BRANCH}" \
+    -X PATCH -f sha="$COMMIT_SHA" -f force=true
+
+# Step 5: Build image markdown and post the comment
+REPO_URL="https://raw.githubusercontent.com/${REPO}/${SCREENSHOTS_BRANCH}"
+IMAGE_MARKDOWN=""
+for img in $RESULTS_DIR/*.png; do
+  BASENAME=$(basename "$img")
+  IMAGE_MARKDOWN="$IMAGE_MARKDOWN
+![${BASENAME}](${REPO_URL}/${SCREENSHOTS_DIR}/${BASENAME})"
+done
+
+gh api "repos/${REPO}/issues/$PR_NUMBER/comments" -f body="$(cat <<EOF
+## 🧪 E2E Test Report
+
+$(cat $RESULTS_DIR/test-report.md)
+
+### Screenshots
+${IMAGE_MARKDOWN}
+EOF
+)"
+```
+
+This approach uses the GitHub Git API to create blobs, trees, commits, and refs entirely server-side. No local `git checkout` or `git push` — safe for worktrees and won't interfere with the PR branch.
+
+## Fix mode (--fix flag)
+
+When `--fix` is present, after finding a bug:
+
+1. Identify the root cause in the code
+2. Fix it in the worktree
+3. Rebuild the affected service: `cd $PLATFORM_DIR && docker compose up --build -d {service_name}`
+4. Re-test the scenario
+5. If fix works, commit and push:
+   ```bash
+   cd $WORKTREE_PATH
+   git add -A
+   git commit -m "fix: {description of fix}"
+   git push
+   ```
+6. Continue testing remaining scenarios
+7. After all fixes, run the full test suite again to ensure no regressions
+
+### Fix loop (like pr-address)
+
+```text
+test scenario → find bug → fix code → rebuild service → re-test
+→ repeat until all scenarios pass
+→ commit + push all fixes
+→ run full re-test to verify
+```
+
+## Known issues and workarounds
+
+### Problem: "Database error finding user" on signup
+**Cause:** Supabase auth service schema cache is stale after migration.
+**Fix:** `docker restart supabase-auth && sleep 5` then retry signup.
+
+### Problem: Copilot returns auth errors in subscription mode
+**Cause:** `CHAT_USE_CLAUDE_CODE_SUBSCRIPTION=true` but `CLAUDE_CODE_OAUTH_TOKEN` is not set or expired.
+**Fix:** Re-extract the OAuth token from macOS keychain (see step 3b, Option 1) and recreate the container (`docker compose up -d copilot_executor`). The backend auto-provisions `~/.claude/.credentials.json` from the env var on startup. No `npm install` or `claude login` needed — the SDK bundles its own CLI binary.
+
+### Problem: agent-browser can't find chromium
+**Cause:** The Dockerfile auto-provisions system chromium on all architectures (including ARM64). If your branch is behind `dev`, this may not be present yet.
+**Fix:** Check if chromium exists: `which chromium || which chromium-browser`. If missing, install it: `apt-get install -y chromium` and set `AGENT_BROWSER_EXECUTABLE_PATH=/usr/bin/chromium` in the container environment.
+
+### Problem: agent-browser selector matches multiple elements
+**Cause:** `text=X` matches all elements containing that text.
+**Fix:** Use `agent-browser snapshot` to get specific `ref=eNN` references, then use those: `agent-browser click eNN`.
+
+### Problem: Frontend shows cookie banner blocking interaction
+**Fix:** `agent-browser click 'text=Accept All'` before other interactions.
+
+### Problem: Container loses npm packages after rebuild
+**Cause:** `docker compose up --build` rebuilds the image, losing runtime installs.
+**Fix:** Add packages to the Dockerfile instead of installing at runtime.
+
+### Problem: Services not starting after `docker compose up`
+**Fix:** Wait and check health: `docker compose ps`. Common cause: migration hasn't finished. Check: `docker logs autogpt_platform-migrate-1 2>&1 | tail -5`. If supabase-db isn't healthy: `docker restart supabase-db && sleep 10`.
+
+### Problem: Docker uses cached layers with old code (PR changes not visible)
+**Cause:** `docker compose up --build` reuses cached `COPY` layers from previous builds. If the PR branch changes Python files but the previous build already cached that layer from `dev`, the container runs `dev` code.
+**Fix:** Always use `docker compose build --no-cache` for the first build of a PR branch. Subsequent rebuilds within the same branch can use `--build`.
+
+### Problem: `agent-browser open` loses login session
+**Cause:** Without session persistence, `agent-browser open` starts fresh.
+**Fix:** Use `--session-name pr-test` on ALL agent-browser commands. This auto-saves/restores cookies and localStorage across navigations. Alternatively, use `agent-browser eval "window.location.href = '...'"` to navigate within the same context.
+
+### Problem: Supabase auth returns "Database error querying schema"
+**Cause:** The database schema changed (migration ran) but supabase-auth has a stale schema cache.
+**Fix:** `docker restart supabase-db && sleep 10 && docker restart supabase-auth && sleep 8`. If user data was lost, re-signup.

autogpt_platform/backend/backend/api/features/v1.py

@@ -592,6 +592,11 @@ async def fulfill_checkout(user_id: Annotated[str, Security(get_user_id)]):
 async def configure_user_auto_top_up(
     request: AutoTopUpConfig, user_id: Annotated[str, Security(get_user_id)]
 ) -> str:
+    """Configure auto top-up settings and perform an immediate top-up if needed.
+
+    Raises HTTPException(422) if the request parameters are invalid or if
+    the credit top-up fails.
+    """
     if request.threshold < 0:
         raise HTTPException(status_code=422, detail="Threshold must be greater than 0")
     if request.amount < 500 and request.amount != 0:
@@ -606,10 +611,20 @@ async def configure_user_auto_top_up(
     user_credit_model = await get_user_credit_model(user_id)
     current_balance = await user_credit_model.get_credits(user_id)
 
-    if current_balance < request.threshold:
-        await user_credit_model.top_up_credits(user_id, request.amount)
-    else:
-        await user_credit_model.top_up_credits(user_id, 0)
+    try:
+        if current_balance < request.threshold:
+            await user_credit_model.top_up_credits(user_id, request.amount)
+        else:
+            await user_credit_model.top_up_credits(user_id, 0)
+    except ValueError as e:
+        known_messages = (
+            "must not be negative",
+            "already exists for user",
+            "No payment method found",
+        )
+        if any(msg in str(e) for msg in known_messages):
+            raise HTTPException(status_code=422, detail=str(e))
+        raise
 
     await set_auto_top_up(
         user_id, AutoTopUpConfig(threshold=request.threshold, amount=request.amount)

autogpt_platform/backend/backend/api/features/workspace/routes.py

@@ -188,6 +188,7 @@ async def upload_file(
     user_id: Annotated[str, fastapi.Security(get_user_id)],
     file: UploadFile,
     session_id: str | None = Query(default=None),
+    overwrite: bool = Query(default=False),
 ) -> UploadFileResponse:
     """
     Upload a file to the user's workspace.
@@ -248,7 +249,9 @@ async def upload_file(
     # Write file via WorkspaceManager
     manager = WorkspaceManager(user_id, workspace.id, session_id)
     try:
-        workspace_file = await manager.write_file(content, filename)
+        workspace_file = await manager.write_file(
+            content, filename, overwrite=overwrite
+        )
     except ValueError as e:
         raise fastapi.HTTPException(status_code=409, detail=str(e)) from e
 

autogpt_platform/backend/backend/api/rest_api.py

@@ -1,3 +1,4 @@
+import asyncio
 import contextlib
 import logging
 import platform
@@ -37,8 +38,10 @@ import backend.api.features.workspace.routes as workspace_routes
 import backend.data.block
 import backend.data.db
 import backend.data.graph
+import backend.data.llm_registry
 import backend.data.user
 import backend.integrations.webhooks.utils
+import backend.server.v2.llm
 import backend.util.service
 import backend.util.settings
 from backend.api.features.library.exceptions import (
@@ -117,16 +120,56 @@ async def lifespan_context(app: fastapi.FastAPI):
 
     AutoRegistry.patch_integrations()
 
+    # Load LLM registry before initializing blocks so blocks can use registry data.
+    # Tries Redis first (fast path on warm restart), falls back to DB.
+    # Note: Graceful fallback for now since no blocks consume registry yet (comes in PR #5)
+    try:
+        await backend.data.llm_registry.refresh_llm_registry()
+        logger.info("LLM registry loaded successfully at startup")
+    except Exception as e:
+        logger.warning(
+            f"Failed to load LLM registry at startup: {e}. "
+            "Blocks will initialize with empty registry."
+        )
+
+    # Start background task so this worker reloads its in-process cache whenever
+    # another worker (e.g. the admin API) refreshes the registry.
+    _registry_subscription_task = asyncio.create_task(
+        backend.data.llm_registry.subscribe_to_registry_refresh(
+            backend.data.llm_registry.refresh_llm_registry
+        )
+    )
+
     await backend.data.block.initialize_blocks()
 
     await backend.data.user.migrate_and_encrypt_user_integrations()
     await backend.data.graph.fix_llm_provider_credentials()
-    await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
+    try:
+        await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
+    except Exception as e:
+        err_str = str(e)
+        if "AgentNode" in err_str or "does not exist" in err_str:
+            logger.warning(
+                f"migrate_llm_models skipped: AgentNode table not found ({e}). "
+                "This is expected in test environments."
+            )
+        else:
+            logger.error(
+                f"migrate_llm_models failed unexpectedly: {e}",
+                exc_info=True,
+            )
+
     await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
 
     with launch_darkly_context():
         yield
 
+    _registry_subscription_task.cancel()
+    try:
+        await _registry_subscription_task
+    except asyncio.CancelledError:
+        pass
+
     try:
         await shutdown_cloud_storage_handler()
     except Exception as e:
@@ -210,13 +253,22 @@ instrument_fastapi(
 def handle_internal_http_error(status_code: int = 500, log_error: bool = True):
     def handler(request: fastapi.Request, exc: Exception):
         if log_error:
-            logger.exception(
-                "%s %s failed. Investigate and resolve the underlying issue: %s",
-                request.method,
-                request.url.path,
-                exc,
-                exc_info=exc,
-            )
+            if status_code >= 500:
+                logger.exception(
+                    "%s %s failed. Investigate and resolve the underlying issue: %s",
+                    request.method,
+                    request.url.path,
+                    exc,
+                    exc_info=exc,
+                )
+            else:
+                logger.warning(
+                    "%s %s failed with %d: %s",
+                    request.method,
+                    request.url.path,
+                    status_code,
+                    exc,
+                )
 
         hint = (
             "Adjust the request and retry."
@@ -266,12 +318,10 @@ async def validation_error_handler(
 
 
 app.add_exception_handler(PrismaError, handle_internal_http_error(500))
-app.add_exception_handler(
-    FolderAlreadyExistsError, handle_internal_http_error(409, False)
-)
-app.add_exception_handler(FolderValidationError, handle_internal_http_error(400, False))
-app.add_exception_handler(NotFoundError, handle_internal_http_error(404, False))
-app.add_exception_handler(NotAuthorizedError, handle_internal_http_error(403, False))
+app.add_exception_handler(FolderAlreadyExistsError, handle_internal_http_error(409))
+app.add_exception_handler(FolderValidationError, handle_internal_http_error(400))
+app.add_exception_handler(NotFoundError, handle_internal_http_error(404))
+app.add_exception_handler(NotAuthorizedError, handle_internal_http_error(403))
 app.add_exception_handler(RequestValidationError, validation_error_handler)
 app.add_exception_handler(pydantic.ValidationError, validation_error_handler)
 app.add_exception_handler(MissingConfigError, handle_internal_http_error(503))
@@ -348,6 +398,16 @@ app.include_router(
     tags=["oauth"],
     prefix="/api/oauth",
 )
+app.include_router(
+    backend.server.v2.llm.router,
+    tags=["v2", "llm"],
+    prefix="/api",
+)
+app.include_router(
+    backend.server.v2.llm.admin_router,
+    tags=["v2", "llm", "admin"],
+    prefix="/api",
+)
 
 app.mount("/external-api", external_api)
 

autogpt_platform/backend/backend/blocks/llm.py

@@ -796,6 +796,19 @@ async def llm_call(
             )
         prompt = result.messages
 
+    # Sanitize unpaired surrogates in message content to prevent
+    # UnicodeEncodeError when httpx encodes the JSON request body.
+    for msg in prompt:
+        content = msg.get("content")
+        if isinstance(content, str):
+            try:
+                content.encode("utf-8")
+            except UnicodeEncodeError:
+                logger.warning("Sanitized unpaired surrogates in LLM prompt content")
+                msg["content"] = content.encode("utf-8", errors="surrogatepass").decode(
+                    "utf-8", errors="replace"
+                )
+
     # Calculate available tokens based on context window and input length
     estimated_input_tokens = estimate_token_count(prompt)
     model_max_output = llm_model.max_output_tokens or int(2**15)

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -934,7 +934,7 @@ class SmartDecisionMakerBlock(Block):
             )
 
         except Exception as e:
-            logger.error(f"Tool execution with manager failed: {e}")
+            logger.warning(f"Tool execution with manager failed: {e}")
             # Return error response
             return _create_tool_response(
                 tool_call.id,

autogpt_platform/backend/backend/copilot/prompting.py

@@ -12,34 +12,18 @@ from backend.copilot.tools import TOOL_REGISTRY
 # Shared technical notes that apply to both SDK and baseline modes
 _SHARED_TOOL_NOTES = f"""\
 
-### Sharing files with the user
-After saving a file to the persistent workspace with `write_workspace_file`,
-share it with the user by embedding the `download_url` from the response in
-your message as a Markdown link or image:
+### Sharing files
+After `write_workspace_file`, embed the `download_url` in Markdown:
+- File: `[report.csv](workspace://file_id#text/csv)`
+- Image: `![chart](workspace://file_id#image/png)`
+- Video: `![recording](workspace://file_id#video/mp4)`
 
-- **Any file** — shows as a clickable download link:
-  `[report.csv](workspace://file_id#text/csv)`
-- **Image** — renders inline in chat:
-  `![chart](workspace://file_id#image/png)`
-- **Video** — renders inline in chat with player controls:
-  `![recording](workspace://file_id#video/mp4)`
-
-The `download_url` field in the `write_workspace_file` response is already
-in the correct format — paste it directly after the `(` in the Markdown.
-
-### Passing file content to tools — @@agptfile: references
-Instead of copying large file contents into a tool argument, pass a file
-reference and the platform will load the content for you.
-
-Syntax: `@@agptfile:<uri>[<start>-<end>]`
-
-- `<uri>` **must** start with `workspace://` or `/` (absolute path):
-  - `workspace://<file_id>` — workspace file by ID
-  - `workspace:///<path>` — workspace file by virtual path
-  - `/absolute/local/path` — ephemeral or sdk_cwd file
-  - E2B sandbox absolute path (e.g. `/home/user/script.py`)
-- `[<start>-<end>]` is an optional 1-indexed inclusive line range.
-- URIs that do not start with `workspace://` or `/` are **not** expanded.
+### File references — @@agptfile:
+Pass large file content to tools by reference: `@@agptfile:<uri>[<start>-<end>]`
+- `workspace://<file_id>` or `workspace:///<path>` — workspace files
+- `/absolute/path` — local/sandbox files
+- `[start-end]` — optional 1-indexed line range
+- Multiple refs per argument supported. Only `workspace://` and absolute paths are expanded.
 
 Examples:
 ```
@@ -50,21 +34,9 @@ Examples:
 @@agptfile:/home/user/script.py
 ```
 
-You can embed a reference inside any string argument, or use it as the entire
-value.  Multiple references in one argument are all expanded.
+**Structured data**: When the entire argument is a single file reference, the platform auto-parses by extension/MIME. Supported: JSON, JSONL, CSV, TSV, YAML, TOML, Parquet, Excel (.xlsx only; legacy `.xls` is NOT supported). Unrecognised formats return plain string.
 
-**Structured data**: When the **entire** argument value is a single file
-reference (no surrounding text), the platform automatically parses the file
-content based on its extension or MIME type.  Supported formats: JSON, JSONL,
-CSV, TSV, YAML, TOML, Parquet, and Excel (.xlsx — first sheet only).
-For example, pass `@@agptfile:workspace://<id>` where the file is a `.csv` and
-the rows will be parsed into `list[list[str]]` automatically.  If the format is
-unrecognised or parsing fails, the content is returned as a plain string.
-Legacy `.xls` files are **not** supported — only the modern `.xlsx` format.
-
-**Type coercion**: The platform also coerces expanded values to match the
-block's expected input types.  For example, if a block expects `list[list[str]]`
-and the expanded value is a JSON string, it will be parsed into the correct type.
+**Type coercion**: The platform auto-coerces expanded string values to match block input types (e.g. JSON string → `list[list[str]]`).
 
 ### Media file inputs (format: "file")
 Some block inputs accept media files — their schema shows `"format": "file"`.
@@ -166,17 +138,12 @@ def _build_storage_supplement(
 
 ## Tool notes
 
-### Shell commands
-- The SDK built-in Bash tool is NOT available.  Use the `bash_exec` MCP tool
-  for shell commands — it runs {sandbox_type}.
-
-### Working directory
-- Your working directory is: `{working_dir}`
-- All SDK file tools AND `bash_exec` operate on the same filesystem
-- Use relative paths or absolute paths under `{working_dir}` for all file operations
+### Shell & filesystem
+- The SDK built-in Bash tool is NOT available. Use `bash_exec` for shell commands ({sandbox_type}). Working dir: `{working_dir}`
+- SDK file tools (Read/Write/Edit/Glob/Grep) and `bash_exec` share one filesystem — use relative or absolute paths under this dir.
+- `read_workspace_file`/`write_workspace_file` operate on **persistent cloud workspace storage** (separate from the working dir).
 
 ### Two storage systems — CRITICAL to understand
-
 1. **{storage_system_1_name}** (`{working_dir}`):
 {characteristics}
 {persistence}

autogpt_platform/backend/backend/copilot/sdk/service.py

@@ -2,13 +2,11 @@
 
 import asyncio
 import base64
-import functools
 import json
 import logging
 import os
 import re
 import shutil
-import subprocess
 import sys
 import time
 import uuid
@@ -77,6 +75,7 @@ from ..tracking import track_user_message
 from .compaction import CompactionTracker, filter_compaction_messages
 from .response_adapter import SDKResponseAdapter
 from .security_hooks import create_security_hooks
+from .subscription import validate_subscription as _validate_claude_code_subscription
 from .tool_adapter import (
     create_copilot_mcp_server,
     get_copilot_tool_names,
@@ -458,37 +457,6 @@ def _resolve_sdk_model() -> str | None:
     return model
 
 
-@functools.cache
-def _validate_claude_code_subscription() -> None:
-    """Validate Claude CLI is installed and responds to `--version`.
-
-    Cached so the blocking subprocess check runs at most once per process
-    lifetime.  A failure (CLI not installed) is a config error that requires
-    a process restart anyway.
-    """
-    claude_path = shutil.which("claude")
-    if not claude_path:
-        raise RuntimeError(
-            "Claude Code CLI not found. Install it with: "
-            "npm install -g @anthropic-ai/claude-code"
-        )
-    result = subprocess.run(
-        [claude_path, "--version"],
-        capture_output=True,
-        text=True,
-        timeout=10,
-    )
-    if result.returncode != 0:
-        raise RuntimeError(
-            f"Claude CLI check failed (exit {result.returncode}): "
-            f"{result.stderr.strip()}"
-        )
-    logger.info(
-        "Claude Code subscription mode: CLI version %s",
-        result.stdout.strip(),
-    )
-
-
 def _build_sdk_env(
     session_id: str | None = None,
     user_id: str | None = None,

autogpt_platform/backend/backend/copilot/sdk/subscription.py

@@ -0,0 +1,144 @@
+"""Claude Code subscription auth helpers.
+
+Handles locating the SDK-bundled CLI binary, provisioning credentials from
+environment variables, and validating that subscription auth is functional.
+"""
+
+import functools
+import json
+import logging
+import os
+import shutil
+import subprocess
+
+logger = logging.getLogger(__name__)
+
+
+def find_bundled_cli() -> str:
+    """Locate the Claude CLI binary bundled inside ``claude_agent_sdk``.
+
+    Falls back to ``shutil.which("claude")`` if the SDK bundle is absent.
+    """
+    try:
+        from claude_agent_sdk._internal.transport.subprocess_cli import (
+            SubprocessCLITransport,
+        )
+
+        path = SubprocessCLITransport._find_bundled_cli(None)  # type: ignore[arg-type]
+        if path:
+            return str(path)
+    except Exception:
+        pass
+    system_path = shutil.which("claude")
+    if system_path:
+        return system_path
+    raise RuntimeError(
+        "Claude CLI not found — neither the SDK-bundled binary nor a "
+        "system-installed `claude` could be located."
+    )
+
+
+def provision_credentials_file() -> None:
+    """Write ``~/.claude/.credentials.json`` from env when running headless.
+
+    If ``CLAUDE_CODE_OAUTH_TOKEN`` is set (an OAuth *access* token obtained
+    from ``claude auth status`` or extracted from the macOS keychain), this
+    helper writes a minimal credentials file so the bundled CLI can
+    authenticate without an interactive ``claude login``.
+
+    A ``CLAUDE_CODE_REFRESH_TOKEN`` env var is optional but recommended —
+    it lets the CLI silently refresh an expired access token.
+    """
+    access_token = os.environ.get("CLAUDE_CODE_OAUTH_TOKEN", "").strip()
+    if not access_token:
+        return
+
+    creds_dir = os.path.expanduser("~/.claude")
+    creds_path = os.path.join(creds_dir, ".credentials.json")
+
+    # Don't overwrite an existing credentials file (e.g. from a volume mount).
+    if os.path.exists(creds_path):
+        logger.debug("Credentials file already exists at %s — skipping", creds_path)
+        return
+
+    os.makedirs(creds_dir, exist_ok=True)
+
+    creds = {
+        "claudeAiOauth": {
+            "accessToken": access_token,
+            "refreshToken": os.environ.get("CLAUDE_CODE_REFRESH_TOKEN", "").strip(),
+            "expiresAt": 0,
+            "scopes": [
+                "user:inference",
+                "user:profile",
+                "user:sessions:claude_code",
+            ],
+        }
+    }
+    with open(creds_path, "w") as f:
+        json.dump(creds, f)
+    logger.info("Provisioned Claude credentials file at %s", creds_path)
+
+
+@functools.cache
+def validate_subscription() -> None:
+    """Validate the bundled Claude CLI is reachable and authenticated.
+
+    Cached so the blocking subprocess check runs at most once per process
+    lifetime.  On first call, also provisions ``~/.claude/.credentials.json``
+    from the ``CLAUDE_CODE_OAUTH_TOKEN`` env var when available.
+    """
+    provision_credentials_file()
+
+    cli = find_bundled_cli()
+    result = subprocess.run(
+        [cli, "--version"],
+        capture_output=True,
+        text=True,
+        timeout=10,
+    )
+    if result.returncode != 0:
+        raise RuntimeError(
+            f"Claude CLI check failed (exit {result.returncode}): "
+            f"{result.stderr.strip()}"
+        )
+    logger.info(
+        "Claude Code subscription mode: CLI version %s",
+        result.stdout.strip(),
+    )
+
+    # Verify the CLI is actually authenticated.
+    auth_result = subprocess.run(
+        [cli, "auth", "status"],
+        capture_output=True,
+        text=True,
+        timeout=10,
+        env={
+            **os.environ,
+            "ANTHROPIC_API_KEY": "",
+            "ANTHROPIC_AUTH_TOKEN": "",
+            "ANTHROPIC_BASE_URL": "",
+        },
+    )
+    if auth_result.returncode != 0:
+        raise RuntimeError(
+            "Claude CLI is not authenticated. Either:\n"
+            "  • Set CLAUDE_CODE_OAUTH_TOKEN env var (from `claude auth status` "
+            "or macOS keychain), or\n"
+            "  • Mount ~/.claude/.credentials.json into the container, or\n"
+            "  • Run `claude login` inside the container."
+        )
+    try:
+        status = json.loads(auth_result.stdout)
+        if not status.get("loggedIn"):
+            raise RuntimeError(
+                "Claude CLI reports loggedIn=false. Set CLAUDE_CODE_OAUTH_TOKEN "
+                "or run `claude login`."
+            )
+        logger.info(
+            "Claude subscription auth: method=%s, email=%s",
+            status.get("authMethod"),
+            status.get("email"),
+        )
+    except json.JSONDecodeError:
+        logger.warning("Could not parse `claude auth status` output")

autogpt_platform/backend/backend/copilot/tools/add_understanding.py

@@ -22,13 +22,12 @@ class AddUnderstandingTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return """Capture and store information about the user's business context,
-workflows, pain points, and automation goals. Call this tool whenever the user
-shares information about their business. Each call incrementally adds to the
-existing understanding - you don't need to provide all fields at once.
-
-Use this to build a comprehensive profile that helps recommend better agents
-and automations for the user's specific needs."""
+        return (
+            "Store user's business context, workflows, pain points, and automation goals. "
+            "Call whenever the user shares business info. Each call incrementally merges "
+            "with existing data — provide only the fields you have. "
+            "Builds a profile that helps recommend better agents for the user's needs."
+        )
 
     @property
     def parameters(self) -> dict[str, Any]:

autogpt_platform/backend/backend/copilot/tools/agent_browser.py

@@ -410,18 +410,11 @@ class BrowserNavigateTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Navigate to a URL using a real browser. Returns an accessibility "
-            "tree snapshot listing the page's interactive elements with @ref IDs "
-            "(e.g. @e3) that can be used with browser_act. "
-            "Session persists — cookies and login state carry over between calls. "
-            "Use this (with browser_act) for multi-step interaction: login flows, "
-            "form filling, button clicks, or anything requiring page interaction. "
-            "For plain static pages, prefer web_fetch — no browser overhead. "
-            "For authenticated pages: navigate to the login page first, use browser_act "
-            "to fill credentials and submit, then navigate to the target page. "
-            "Note: for slow SPAs, the returned snapshot may reflect a partially-loaded "
-            "state. If elements seem missing, use browser_act with action='wait' and a "
-            "CSS selector or millisecond delay, then take a browser_screenshot to verify."
+            "Navigate to a URL in a real browser. Returns accessibility tree with @ref IDs "
+            "for browser_act. Session persists (cookies/auth carry over). "
+            "For static pages, prefer web_fetch. "
+            "For SPAs, elements may load late — use browser_act with wait + browser_screenshot to verify. "
+            "For auth: navigate to login, fill creds and submit with browser_act, then navigate to target."
         )
 
     @property
@@ -431,13 +424,13 @@ class BrowserNavigateTool(BaseTool):
             "properties": {
                 "url": {
                     "type": "string",
-                    "description": "The HTTP/HTTPS URL to navigate to.",
+                    "description": "HTTP/HTTPS URL to navigate to.",
                 },
                 "wait_for": {
                     "type": "string",
                     "enum": ["networkidle", "load", "domcontentloaded"],
                     "default": "networkidle",
-                    "description": "When to consider navigation complete. Use 'networkidle' for SPAs (default).",
+                    "description": "Navigation completion strategy (default: networkidle).",
                 },
             },
             "required": ["url"],
@@ -556,14 +549,12 @@ class BrowserActTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Interact with the current browser page. Use @ref IDs from the "
-            "snapshot (e.g. '@e3') to target elements. Returns an updated snapshot. "
-            "Supported actions: click, dblclick, fill, type, scroll, hover, press, "
+            "Interact with the current browser page using @ref IDs from the snapshot. "
+            "Actions: click, dblclick, fill, type, scroll, hover, press, "
             "check, uncheck, select, wait, back, forward, reload. "
-            "fill clears the field before typing; type appends without clearing. "
-            "wait accepts a CSS selector (waits for element) or milliseconds string (e.g. '1000'). "
-            "Example login flow: fill @e1 with email → fill @e2 with password → "
-            "click @e3 (submit) → browser_navigate to the target page."
+            "fill clears field first; type appends. "
+            "wait accepts CSS selector or milliseconds (e.g. '1000'). "
+            "Returns updated snapshot."
         )
 
     @property
@@ -589,30 +580,21 @@ class BrowserActTool(BaseTool):
                         "forward",
                         "reload",
                     ],
-                    "description": "The action to perform.",
+                    "description": "Action to perform.",
                 },
                 "target": {
                     "type": "string",
-                    "description": (
-                        "Element to target. Use @ref from snapshot (e.g. '@e3'), "
-                        "a CSS selector, or a text description. "
-                        "Required for: click, dblclick, fill, type, hover, check, uncheck, select. "
-                        "For wait: a CSS selector to wait for, or milliseconds as a string (e.g. '1000')."
-                    ),
+                    "description": "@ref ID (e.g. '@e3'), CSS selector, or text. Required for: click, dblclick, fill, type, hover, check, uncheck, select. For wait: CSS selector or milliseconds string (e.g. '1000').",
                 },
                 "value": {
                     "type": "string",
-                    "description": (
-                        "For fill/type: the text to enter. "
-                        "For press: key name (e.g. 'Enter', 'Tab', 'Control+a'). "
-                        "For select: the option value to select."
-                    ),
+                    "description": "Text for fill/type, key for press (e.g. 'Enter'), option for select.",
                 },
                 "direction": {
                     "type": "string",
                     "enum": ["up", "down", "left", "right"],
                     "default": "down",
-                    "description": "For scroll: direction to scroll.",
+                    "description": "Scroll direction (default: down).",
                 },
             },
             "required": ["action"],
@@ -759,12 +741,10 @@ class BrowserScreenshotTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Take a screenshot of the current browser page and save it to the workspace. "
-            "IMPORTANT: After calling this tool, immediately call read_workspace_file "
-            "with the returned file_id to display the image inline to the user — "
-            "the screenshot is not visible until you do this. "
-            "With annotate=true (default), @ref labels are overlaid on interactive "
-            "elements, making it easy to see which @ref ID maps to which element on screen."
+            "Screenshot the current browser page and save to workspace. "
+            "annotate=true overlays @ref labels on elements. "
+            "IMPORTANT: After calling, you MUST immediately call read_workspace_file with the "
+            "returned file_id to display the image inline."
         )
 
     @property
@@ -775,12 +755,12 @@ class BrowserScreenshotTool(BaseTool):
                 "annotate": {
                     "type": "boolean",
                     "default": True,
-                    "description": "Overlay @ref labels on interactive elements (default: true).",
+                    "description": "Overlay @ref labels (default: true).",
                 },
                 "filename": {
                     "type": "string",
                     "default": "screenshot.png",
-                    "description": "Filename to save in the workspace.",
+                    "description": "Workspace filename (default: screenshot.png).",
                 },
             },
         }

autogpt_platform/backend/backend/copilot/tools/agent_output.py

@@ -108,22 +108,12 @@ class AgentOutputTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return """Retrieve execution outputs from agents in the user's library.
-
-        Identify the agent using one of:
-        - agent_name: Fuzzy search in user's library
-        - library_agent_id: Exact library agent ID
-        - store_slug: Marketplace format 'username/agent-name'
-
-        Select which run to retrieve using:
-        - execution_id: Specific execution ID
-        - run_time: 'latest' (default), 'yesterday', 'last week', or ISO date 'YYYY-MM-DD'
-
-        Wait for completion (optional):
-        - wait_if_running: Max seconds to wait if execution is still running (0-300).
-          If the execution is running/queued, waits up to this many seconds for completion.
-          Returns current status on timeout. If already finished, returns immediately.
-        """
+        return (
+            "Retrieve execution outputs from a library agent. "
+            "Identify by agent_name, library_agent_id, or store_slug. "
+            "Filter by execution_id or run_time. "
+            "Optionally wait for running executions."
+        )
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -132,32 +122,29 @@ class AgentOutputTool(BaseTool):
             "properties": {
                 "agent_name": {
                     "type": "string",
-                    "description": "Agent name to search for in user's library (fuzzy match)",
+                    "description": "Agent name (fuzzy match).",
                 },
                 "library_agent_id": {
                     "type": "string",
-                    "description": "Exact library agent ID",
+                    "description": "Library agent ID.",
                 },
                 "store_slug": {
                     "type": "string",
-                    "description": "Marketplace identifier: 'username/agent-slug'",
+                    "description": "Marketplace 'username/agent-name'.",
                 },
                 "execution_id": {
                     "type": "string",
-                    "description": "Specific execution ID to retrieve",
+                    "description": "Specific execution ID.",
                 },
                 "run_time": {
                     "type": "string",
-                    "description": (
-                        "Time filter: 'latest', 'yesterday', 'last week', or 'YYYY-MM-DD'"
-                    ),
+                    "description": "Time filter: 'latest', 'today', 'yesterday', 'last week', 'last 7 days', 'last month', 'last 30 days', 'YYYY-MM-DD', or ISO datetime.",
                 },
                 "wait_if_running": {
                     "type": "integer",
-                    "description": (
-                        "Max seconds to wait if execution is still running (0-300). "
-                        "If running, waits for completion. Returns current state on timeout."
-                    ),
+                    "description": "Max seconds to wait if still running (0-300). Returns current state on timeout.",
+                    "minimum": 0,
+                    "maximum": 300,
                 },
             },
             "required": [],

autogpt_platform/backend/backend/copilot/tools/bash_exec.py

@@ -42,15 +42,9 @@ class BashExecTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Execute a Bash command or script. "
-            "Full Bash scripting is supported (loops, conditionals, pipes, "
-            "functions, etc.). "
-            "The working directory is shared with the SDK Read/Write/Edit/Glob/Grep "
-            "tools — files created by either are immediately visible to both. "
-            "Execution is killed after the timeout (default 30s, max 120s). "
-            "Returns stdout and stderr. "
-            "Useful for file manipulation, data processing, running scripts, "
-            "and installing packages."
+            "Execute a Bash command or script. Shares filesystem with SDK file tools. "
+            "Useful for scripts, data processing, and package installation. "
+            "Killed after timeout (default 30s, max 120s)."
         )
 
     @property
@@ -60,13 +54,11 @@ class BashExecTool(BaseTool):
             "properties": {
                 "command": {
                     "type": "string",
-                    "description": "Bash command or script to execute.",
+                    "description": "Bash command or script.",
                 },
                 "timeout": {
                     "type": "integer",
-                    "description": (
-                        "Max execution time in seconds (default 30, max 120)."
-                    ),
+                    "description": "Max seconds (default 30, max 120).",
                     "default": 30,
                 },
             },

autogpt_platform/backend/backend/copilot/tools/continue_run_block.py

@@ -30,12 +30,7 @@ class ContinueRunBlockTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Continue executing a block after human review approval. "
-            "Use this after a run_block call returned review_required. "
-            "Pass the review_id from the review_required response. "
-            "The block will execute with the original pre-approved input data."
-        )
+        return "Resume block execution after a run_block call returned review_required. Pass the review_id."
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -44,10 +39,7 @@ class ContinueRunBlockTool(BaseTool):
             "properties": {
                 "review_id": {
                     "type": "string",
-                    "description": (
-                        "The review_id from a previous review_required response. "
-                        "This resumes execution with the pre-approved input data."
-                    ),
+                    "description": "review_id from the review_required response.",
                 },
             },
             "required": ["review_id"],

autogpt_platform/backend/backend/copilot/tools/create_agent.py

@@ -23,12 +23,8 @@ class CreateAgentTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Create a new agent workflow. Pass `agent_json` with the complete "
-            "agent graph JSON you generated using block schemas from find_block. "
-            "The tool validates, auto-fixes, and saves.\n\n"
-            "IMPORTANT: Before calling this tool, search for relevant existing agents "
-            "using find_library_agent that could be used as building blocks. "
-            "Pass their IDs in the library_agent_ids parameter."
+            "Create a new agent from JSON (nodes + links). Validates, auto-fixes, and saves. "
+            "Before calling, search for existing agents with find_library_agent."
         )
 
     @property
@@ -42,34 +38,21 @@ class CreateAgentTool(BaseTool):
             "properties": {
                 "agent_json": {
                     "type": "object",
-                    "description": (
-                        "The agent JSON to validate and save. "
-                        "Must contain 'nodes' and 'links' arrays, and optionally "
-                        "'name' and 'description'."
-                    ),
+                    "description": "Agent graph with 'nodes' and 'links' arrays.",
                 },
                 "library_agent_ids": {
                     "type": "array",
                     "items": {"type": "string"},
-                    "description": (
-                        "List of library agent IDs to use as building blocks."
-                    ),
+                    "description": "Library agent IDs as building blocks.",
                 },
                 "save": {
                     "type": "boolean",
-                    "description": (
-                        "Whether to save the agent. Default is true. "
-                        "Set to false for preview only."
-                    ),
+                    "description": "Save the agent (default: true). False for preview.",
                     "default": True,
                 },
                 "folder_id": {
                     "type": "string",
-                    "description": (
-                        "Optional folder ID to save the agent into. "
-                        "If not provided, the agent is saved at root level. "
-                        "Use list_folders to find available folders."
-                    ),
+                    "description": "Folder ID to save into (default: root).",
                 },
             },
             "required": ["agent_json"],

autogpt_platform/backend/backend/copilot/tools/customize_agent.py

@@ -23,9 +23,7 @@ class CustomizeAgentTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Customize a marketplace or template agent. Pass `agent_json` "
-            "with the complete customized agent JSON. The tool validates, "
-            "auto-fixes, and saves."
+            "Customize a marketplace/template agent. Validates, auto-fixes, and saves."
         )
 
     @property
@@ -39,32 +37,21 @@ class CustomizeAgentTool(BaseTool):
             "properties": {
                 "agent_json": {
                     "type": "object",
-                    "description": (
-                        "Complete customized agent JSON to validate and save. "
-                        "Optionally include 'name' and 'description'."
-                    ),
+                    "description": "Customized agent JSON with nodes and links.",
                 },
                 "library_agent_ids": {
                     "type": "array",
                     "items": {"type": "string"},
-                    "description": (
-                        "List of library agent IDs to use as building blocks."
-                    ),
+                    "description": "Library agent IDs as building blocks.",
                 },
                 "save": {
                     "type": "boolean",
-                    "description": (
-                        "Whether to save the customized agent. Default is true."
-                    ),
+                    "description": "Save the agent (default: true). False for preview.",
                     "default": True,
                 },
                 "folder_id": {
                     "type": "string",
-                    "description": (
-                        "Optional folder ID to save the agent into. "
-                        "If not provided, the agent is saved at root level. "
-                        "Use list_folders to find available folders."
-                    ),
+                    "description": "Folder ID to save into (default: root).",
                 },
             },
             "required": ["agent_json"],

autogpt_platform/backend/backend/copilot/tools/edit_agent.py

@@ -23,12 +23,8 @@ class EditAgentTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Edit an existing agent. Pass `agent_json` with the complete "
-            "updated agent JSON you generated. The tool validates, auto-fixes, "
-            "and saves.\n\n"
-            "IMPORTANT: Before calling this tool, if the changes involve adding new "
-            "functionality, search for relevant existing agents using find_library_agent "
-            "that could be used as building blocks."
+            "Edit an existing agent. Validates, auto-fixes, and saves. "
+            "Before calling, search for existing agents with find_library_agent."
         )
 
     @property
@@ -42,33 +38,20 @@ class EditAgentTool(BaseTool):
             "properties": {
                 "agent_id": {
                     "type": "string",
-                    "description": (
-                        "The ID of the agent to edit. "
-                        "Can be a graph ID or library agent ID."
-                    ),
+                    "description": "Graph ID or library agent ID to edit.",
                 },
                 "agent_json": {
                     "type": "object",
-                    "description": (
-                        "Complete updated agent JSON to validate and save. "
-                        "Must contain 'nodes' and 'links'. "
-                        "Include 'name' and/or 'description' if they need "
-                        "to be updated."
-                    ),
+                    "description": "Updated agent JSON with nodes and links.",
                 },
                 "library_agent_ids": {
                     "type": "array",
                     "items": {"type": "string"},
-                    "description": (
-                        "List of library agent IDs to use as building blocks for the changes."
-                    ),
+                    "description": "Library agent IDs as building blocks.",
                 },
                 "save": {
                     "type": "boolean",
-                    "description": (
-                        "Whether to save the changes. "
-                        "Default is true. Set to false for preview only."
-                    ),
+                    "description": "Save changes (default: true). False for preview.",
                     "default": True,
                 },
             },

autogpt_platform/backend/backend/copilot/tools/feature_requests.py

@@ -134,11 +134,7 @@ class SearchFeatureRequestsTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Search existing feature requests to check if a similar request "
-            "already exists before creating a new one. Returns matching feature "
-            "requests with their ID, title, and description."
-        )
+        return "Search existing feature requests. Check before creating a new one."
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -234,14 +230,9 @@ class CreateFeatureRequestTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Create a new feature request or add a customer need to an existing one. "
-            "Always search first with search_feature_requests to avoid duplicates. "
-            "If a matching request exists, pass its ID as existing_issue_id to add "
-            "the user's need to it instead of creating a duplicate. "
-            "IMPORTANT: Never include personally identifiable information (PII) in "
-            "the title or description — no names, emails, phone numbers, company "
-            "names, or other identifying details. Write titles and descriptions in "
-            "generic, feature-focused language."
+            "Create a feature request or add need to existing one. "
+            "Search first to avoid duplicates. Pass existing_issue_id to add to existing. "
+            "Never include PII (names, emails, phone numbers, company names) in title/description."
         )
 
     @property
@@ -251,28 +242,15 @@ class CreateFeatureRequestTool(BaseTool):
             "properties": {
                 "title": {
                     "type": "string",
-                    "description": (
-                        "Title for the feature request. Must be generic and "
-                        "feature-focused — do not include any user names, emails, "
-                        "company names, or other PII."
-                    ),
+                    "description": "Feature request title. No names, emails, or company info.",
                 },
                 "description": {
                     "type": "string",
-                    "description": (
-                        "Detailed description of what the user wants and why. "
-                        "Must not contain any personally identifiable information "
-                        "(PII) — describe the feature need generically without "
-                        "referencing specific users, companies, or contact details."
-                    ),
+                    "description": "What the user wants and why. No names, emails, or company info.",
                 },
                 "existing_issue_id": {
                     "type": "string",
-                    "description": (
-                        "If adding a need to an existing feature request, "
-                        "provide its Linear issue ID (from search results). "
-                        "Omit to create a new feature request."
-                    ),
+                    "description": "Linear issue ID to add need to (from search results).",
                 },
             },
             "required": ["title", "description"],

autogpt_platform/backend/backend/copilot/tools/find_agent.py

@@ -18,10 +18,7 @@ class FindAgentTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Discover agents from the marketplace based on capabilities and "
-            "user needs, or look up a specific agent by its creator/slug ID."
-        )
+        return "Search marketplace agents by capability, or look up by slug ('username/agent-name')."
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -30,7 +27,7 @@ class FindAgentTool(BaseTool):
             "properties": {
                 "query": {
                     "type": "string",
-                    "description": "Search query describing what the user wants to accomplish, or a creator/slug ID (e.g. 'username/agent-name') for direct lookup. Use single keywords for best results.",
+                    "description": "Search keywords, or 'username/agent-name' for direct slug lookup.",
                 },
             },
             "required": ["query"],

autogpt_platform/backend/backend/copilot/tools/find_block.py

@@ -54,13 +54,9 @@ class FindBlockTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Search for available blocks by name or description, or look up a "
-            "specific block by its ID. "
-            "Blocks are reusable components that perform specific tasks like "
-            "sending emails, making API calls, processing text, etc. "
-            "IMPORTANT: Use this tool FIRST to get the block's 'id' before calling run_block. "
-            "The response includes each block's id, name, and description. "
-            "Call run_block with the block's id **with no inputs** to see detailed inputs/outputs and execute it."
+            "Search blocks by name or description. Returns block IDs for run_block. "
+            "Always call this FIRST to get block IDs before using run_block. "
+            "Then call run_block with the block's id and empty input_data to see its detailed schema."
         )
 
     @property
@@ -70,19 +66,11 @@ class FindBlockTool(BaseTool):
             "properties": {
                 "query": {
                     "type": "string",
-                    "description": (
-                        "Search query to find blocks by name or description, "
-                        "or a block ID (UUID) for direct lookup. "
-                        "Use keywords like 'email', 'http', 'text', 'ai', etc."
-                    ),
+                    "description": "Search keywords (e.g. 'email', 'http', 'ai').",
                 },
                 "include_schemas": {
                     "type": "boolean",
-                    "description": (
-                        "If true, include full input_schema and output_schema "
-                        "for each block. Use when generating agent JSON that "
-                        "needs block schemas. Default is false."
-                    ),
+                    "description": "Include full input/output schemas (for agent JSON generation).",
                     "default": False,
                 },
             },

autogpt_platform/backend/backend/copilot/tools/find_library_agent.py

@@ -19,13 +19,8 @@ class FindLibraryAgentTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Search for or list agents in the user's library. Use this to find "
-            "agents the user has already added to their library, including agents "
-            "they created or added from the marketplace. "
-            "When creating agents with sub-agent composition, use this to get "
-            "the agent's graph_id, graph_version, input_schema, and output_schema "
-            "needed for AgentExecutorBlock nodes. "
-            "Omit the query to list all agents."
+            "Search user's library agents. Returns graph_id, schemas for sub-agent composition. "
+            "Omit query to list all."
         )
 
     @property
@@ -35,10 +30,7 @@ class FindLibraryAgentTool(BaseTool):
             "properties": {
                 "query": {
                     "type": "string",
-                    "description": (
-                        "Search query to find agents by name or description. "
-                        "Omit to list all agents in the library."
-                    ),
+                    "description": "Search by name/description. Omit to list all.",
                 },
             },
             "required": [],

autogpt_platform/backend/backend/copilot/tools/fix_agent.py

@@ -22,20 +22,10 @@ class FixAgentGraphTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Auto-fix common issues in an agent JSON graph. Applies fixes for:\n"
-            "- Missing or invalid UUIDs on nodes and links\n"
-            "- StoreValueBlock prerequisites for ConditionBlock\n"
-            "- Double curly brace escaping in prompt templates\n"
-            "- AddToList/AddToDictionary prerequisite blocks\n"
-            "- CodeExecutionBlock output field naming\n"
-            "- Missing credentials configuration\n"
-            "- Node X coordinate spacing (800+ units apart)\n"
-            "- AI model default parameters\n"
-            "- Link static properties based on input schema\n"
-            "- Type mismatches (inserts conversion blocks)\n\n"
-            "Returns the fixed agent JSON plus a list of fixes applied. "
-            "After fixing, the agent is re-validated. If still invalid, "
-            "the remaining errors are included in the response."
+            "Auto-fix common agent JSON issues: missing/invalid UUIDs, StoreValueBlock prerequisites, "
+            "double curly brace escaping, AddToList/AddToDictionary prerequisites, credentials, "
+            "node spacing, AI model defaults, link static properties, and type mismatches. "
+            "Returns fixed JSON and list of fixes applied."
         )
 
     @property

autogpt_platform/backend/backend/copilot/tools/get_agent_building_guide.py

@@ -42,12 +42,7 @@ class GetAgentBuildingGuideTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Returns the complete guide for building agent JSON graphs, including "
-            "block IDs, link structure, AgentInputBlock, AgentOutputBlock, "
-            "AgentExecutorBlock (for sub-agent composition), and MCPToolBlock usage. "
-            "Call this before generating agent JSON to ensure correct structure."
-        )
+        return "Get the agent JSON building guide (nodes, links, AgentExecutorBlock, MCPToolBlock usage). Call before generating agent JSON."
 
     @property
     def parameters(self) -> dict[str, Any]:

autogpt_platform/backend/backend/copilot/tools/get_doc_page.py

@@ -25,8 +25,7 @@ class GetDocPageTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Get the full content of a documentation page by its path. "
-            "Use this after search_docs to read the complete content of a relevant page."
+            "Read full documentation page content by path (from search_docs results)."
         )
 
     @property
@@ -36,10 +35,7 @@ class GetDocPageTool(BaseTool):
             "properties": {
                 "path": {
                     "type": "string",
-                    "description": (
-                        "The path to the documentation file, as returned by search_docs. "
-                        "Example: 'platform/block-sdk-guide.md'"
-                    ),
+                    "description": "Doc file path (e.g. 'platform/block-sdk-guide.md').",
                 },
             },
             "required": ["path"],

autogpt_platform/backend/backend/copilot/tools/get_mcp_guide.py

@@ -38,11 +38,7 @@ class GetMCPGuideTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Returns the MCP tool guide: known hosted server URLs (Notion, Linear, "
-            "Stripe, Intercom, Cloudflare, Atlassian) and authentication workflow. "
-            "Call before using run_mcp_tool if you need a server URL or auth info."
-        )
+        return "Get MCP server URLs and auth guide. Call before run_mcp_tool if you need a server URL or auth info."
 
     @property
     def parameters(self) -> dict[str, Any]:

autogpt_platform/backend/backend/copilot/tools/manage_folders.py

@@ -88,10 +88,7 @@ class CreateFolderTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Create a new folder in the user's library to organize agents. "
-            "Optionally nest it inside an existing folder using parent_id."
-        )
+        return "Create a library folder. Use parent_id to nest inside another folder."
 
     @property
     def requires_auth(self) -> bool:
@@ -104,22 +101,19 @@ class CreateFolderTool(BaseTool):
             "properties": {
                 "name": {
                     "type": "string",
-                    "description": "Name for the new folder (max 100 chars).",
+                    "description": "Folder name (max 100 chars).",
                 },
                 "parent_id": {
                     "type": "string",
-                    "description": (
-                        "ID of the parent folder to nest inside. "
-                        "Omit to create at root level."
-                    ),
+                    "description": "Parent folder ID (omit for root).",
                 },
                 "icon": {
                     "type": "string",
-                    "description": "Optional icon identifier for the folder.",
+                    "description": "Icon identifier.",
                 },
                 "color": {
                     "type": "string",
-                    "description": "Optional hex color code (#RRGGBB).",
+                    "description": "Hex color (#RRGGBB).",
                 },
             },
             "required": ["name"],
@@ -175,13 +169,9 @@ class ListFoldersTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "List the user's library folders. "
-            "Omit parent_id to get the full folder tree. "
-            "Provide parent_id to list only direct children of that folder. "
-            "Set include_agents=true to also return the agents inside each folder "
-            "and root-level agents not in any folder. Always set include_agents=true "
-            "when the user asks about agents, wants to see what's in their folders, "
-            "or mentions agents alongside folders."
+            "List library folders. Omit parent_id for full tree. "
+            "Set include_agents=true when user asks about agents, wants to see "
+            "what's in their folders, or mentions agents alongside folders."
         )
 
     @property
@@ -195,17 +185,11 @@ class ListFoldersTool(BaseTool):
             "properties": {
                 "parent_id": {
                     "type": "string",
-                    "description": (
-                        "List children of this folder. "
-                        "Omit to get the full folder tree."
-                    ),
+                    "description": "List children of this folder (omit for full tree).",
                 },
                 "include_agents": {
                     "type": "boolean",
-                    "description": (
-                        "Whether to include the list of agents inside each folder. "
-                        "Defaults to false."
-                    ),
+                    "description": "Include agents in each folder (default: false).",
                 },
             },
             "required": [],
@@ -357,10 +341,7 @@ class MoveFolderTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Move a folder to a different parent folder. "
-            "Set target_parent_id to null to move to root level."
-        )
+        return "Move a folder. Set target_parent_id to null for root."
 
     @property
     def requires_auth(self) -> bool:
@@ -373,14 +354,11 @@ class MoveFolderTool(BaseTool):
             "properties": {
                 "folder_id": {
                     "type": "string",
-                    "description": "ID of the folder to move.",
+                    "description": "Folder ID.",
                 },
                 "target_parent_id": {
                     "type": ["string", "null"],
-                    "description": (
-                        "ID of the new parent folder. "
-                        "Use null to move to root level."
-                    ),
+                    "description": "New parent folder ID (null for root).",
                 },
             },
             "required": ["folder_id"],
@@ -433,10 +411,7 @@ class DeleteFolderTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Delete a folder from the user's library. "
-            "Agents inside the folder are moved to root level (not deleted)."
-        )
+        return "Delete a folder. Agents inside move to root (not deleted)."
 
     @property
     def requires_auth(self) -> bool:
@@ -499,10 +474,7 @@ class MoveAgentsToFolderTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Move one or more agents to a folder. "
-            "Set folder_id to null to move agents to root level."
-        )
+        return "Move agents to a folder. Set folder_id to null for root."
 
     @property
     def requires_auth(self) -> bool:
@@ -516,13 +488,11 @@ class MoveAgentsToFolderTool(BaseTool):
                 "agent_ids": {
                     "type": "array",
                     "items": {"type": "string"},
-                    "description": "List of library agent IDs to move.",
+                    "description": "Library agent IDs to move.",
                 },
                 "folder_id": {
                     "type": ["string", "null"],
-                    "description": (
-                        "Target folder ID. Use null to move to root level."
-                    ),
+                    "description": "Target folder ID (null for root).",
                 },
             },
             "required": ["agent_ids"],

autogpt_platform/backend/backend/copilot/tools/run_agent.py

@@ -104,19 +104,11 @@ class RunAgentTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return """Run or schedule an agent from the marketplace or user's library.
-
-        The tool automatically handles the setup flow:
-        - Returns missing inputs if required fields are not provided
-        - Returns missing credentials if user needs to configure them
-        - Executes immediately if all requirements are met
-        - Schedules execution if cron expression is provided
-
-        Identify the agent using either:
-        - username_agent_slug: Marketplace format 'username/agent-name'
-        - library_agent_id: ID of an agent in the user's library
-
-        For scheduled execution, provide: schedule_name, cron, and optionally timezone."""
+        return (
+            "Run or schedule an agent. Automatically checks inputs and credentials. "
+            "Identify by username_agent_slug ('user/agent') or library_agent_id. "
+            "For scheduling, provide schedule_name + cron."
+        )
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -125,40 +117,38 @@ class RunAgentTool(BaseTool):
             "properties": {
                 "username_agent_slug": {
                     "type": "string",
-                    "description": "Agent identifier in format 'username/agent-name'",
+                    "description": "Marketplace format 'username/agent-name'.",
                 },
                 "library_agent_id": {
                     "type": "string",
-                    "description": "Library agent ID from user's library",
+                    "description": "Library agent ID.",
                 },
                 "inputs": {
                     "type": "object",
-                    "description": "Input values for the agent",
+                    "description": "Input values for the agent.",
                     "additionalProperties": True,
                 },
                 "use_defaults": {
                     "type": "boolean",
-                    "description": "Set to true to run with default values (user must confirm)",
+                    "description": "Run with default values (confirm with user first).",
                 },
                 "schedule_name": {
                     "type": "string",
-                    "description": "Name for scheduled execution (triggers scheduling mode)",
+                    "description": "Name for scheduled execution. Providing this triggers scheduling mode (also requires cron).",
                 },
                 "cron": {
                     "type": "string",
-                    "description": "Cron expression (5 fields: min hour day month weekday)",
+                    "description": "Cron expression (min hour day month weekday).",
                 },
                 "timezone": {
                     "type": "string",
-                    "description": "IANA timezone for schedule (default: UTC)",
+                    "description": "IANA timezone (default: UTC).",
                 },
                 "wait_for_result": {
                     "type": "integer",
-                    "description": (
-                        "Max seconds to wait for execution to complete (0-300). "
-                        "If >0, blocks until the execution finishes or times out. "
-                        "Returns execution outputs when complete."
-                    ),
+                    "description": "Max seconds to wait for completion (0-300).",
+                    "minimum": 0,
+                    "maximum": 300,
                 },
             },
             "required": [],

autogpt_platform/backend/backend/copilot/tools/run_block.py

@@ -45,13 +45,10 @@ class RunBlockTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Execute a specific block with the provided input data. "
-            "IMPORTANT: You MUST call find_block first to get the block's 'id' - "
-            "do NOT guess or make up block IDs. "
-            "On first attempt (without input_data), returns detailed schema showing "
-            "required inputs and outputs. Then call again with proper input_data to execute. "
-            "If a block requires human review, use continue_run_block with the "
-            "review_id after the user approves."
+            "Execute a block. IMPORTANT: Always get block_id from find_block first "
+            "— do NOT guess or fabricate IDs. "
+            "Call with empty input_data to see schema, then with data to execute. "
+            "If review_required, use continue_run_block."
         )
 
     @property
@@ -61,28 +58,14 @@ class RunBlockTool(BaseTool):
             "properties": {
                 "block_id": {
                     "type": "string",
-                    "description": (
-                        "The block's 'id' field from find_block results. "
-                        "NEVER guess this - always get it from find_block first."
-                    ),
-                },
-                "block_name": {
-                    "type": "string",
-                    "description": (
-                        "The block's human-readable name from find_block results. "
-                        "Used for display purposes in the UI."
-                    ),
+                    "description": "Block ID from find_block results.",
                 },
                 "input_data": {
                     "type": "object",
-                    "description": (
-                        "Input values for the block. "
-                        "First call with empty {} to see the block's schema, "
-                        "then call again with proper values to execute."
-                    ),
+                    "description": "Input values. Use {} first to see schema.",
                 },
             },
-            "required": ["block_id", "block_name", "input_data"],
+            "required": ["block_id", "input_data"],
         }
 
     @property

autogpt_platform/backend/backend/copilot/tools/run_mcp_tool.py

@@ -57,10 +57,9 @@ class RunMCPToolTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Connect to an MCP (Model Context Protocol) server to discover and execute its tools. "
-            "Two-step: (1) call with server_url to list available tools, "
-            "(2) call again with server_url + tool_name + tool_arguments to execute. "
-            "Call get_mcp_guide for known server URLs and auth details."
+            "Discover and execute MCP server tools. "
+            "Call with server_url only to list tools, then with tool_name + tool_arguments to execute. "
+            "Call get_mcp_guide first for server URLs and auth."
         )
 
     @property
@@ -70,24 +69,15 @@ class RunMCPToolTool(BaseTool):
             "properties": {
                 "server_url": {
                     "type": "string",
-                    "description": (
-                        "URL of the MCP server (Streamable HTTP endpoint), "
-                        "e.g. https://mcp.example.com/mcp"
-                    ),
+                    "description": "MCP server URL (Streamable HTTP endpoint).",
                 },
                 "tool_name": {
                     "type": "string",
-                    "description": (
-                        "Name of the MCP tool to execute. "
-                        "Omit on first call to discover available tools."
-                    ),
+                    "description": "Tool to execute. Omit to discover available tools.",
                 },
                 "tool_arguments": {
                     "type": "object",
-                    "description": (
-                        "Arguments to pass to the selected tool. "
-                        "Must match the tool's input schema returned during discovery."
-                    ),
+                    "description": "Arguments matching the tool's input schema.",
                 },
             },
             "required": ["server_url"],

autogpt_platform/backend/backend/copilot/tools/search_docs.py

@@ -38,11 +38,7 @@ class SearchDocsTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Search the AutoGPT platform documentation for information about "
-            "how to use the platform, build agents, configure blocks, and more. "
-            "Returns relevant documentation sections. Use get_doc_page to read full content."
-        )
+        return "Search platform documentation by keyword. Use get_doc_page to read full results."
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -51,10 +47,7 @@ class SearchDocsTool(BaseTool):
             "properties": {
                 "query": {
                     "type": "string",
-                    "description": (
-                        "Search query to find relevant documentation. "
-                        "Use natural language to describe what you're looking for."
-                    ),
+                    "description": "Documentation search query.",
                 },
             },
             "required": ["query"],

autogpt_platform/backend/backend/copilot/tools/tool_schema_test.py

@@ -0,0 +1,119 @@
+"""Schema regression tests for all registered CoPilot tools.
+
+Validates that every tool in TOOL_REGISTRY produces a well-formed schema:
+- description is non-empty
+- all `required` fields exist in `properties`
+- every property has a `type` and `description`
+- total schema character budget does not regress past threshold
+"""
+
+import json
+from typing import Any, cast
+
+import pytest
+
+from backend.copilot.tools import TOOL_REGISTRY
+
+# Character budget (~4 chars/token heuristic, targeting ~8000 tokens)
+_CHAR_BUDGET = 32_000
+
+
+@pytest.fixture(scope="module")
+def all_tool_schemas() -> list[tuple[str, Any]]:
+    """Return (tool_name, openai_schema) pairs for every registered tool."""
+    return [(name, tool.as_openai_tool()) for name, tool in TOOL_REGISTRY.items()]
+
+
+def _get_parametrize_data() -> list[tuple[str, object]]:
+    """Build parametrize data at collection time."""
+    return [(name, tool.as_openai_tool()) for name, tool in TOOL_REGISTRY.items()]
+
+
+@pytest.mark.parametrize(
+    "tool_name,schema",
+    _get_parametrize_data(),
+    ids=[name for name, _ in _get_parametrize_data()],
+)
+class TestToolSchema:
+    """Validate schema invariants for every registered tool."""
+
+    def test_description_non_empty(self, tool_name: str, schema: dict) -> None:
+        desc = schema["function"].get("description", "")
+        assert desc, f"Tool '{tool_name}' has an empty description"
+
+    def test_required_fields_exist_in_properties(
+        self, tool_name: str, schema: dict
+    ) -> None:
+        params = schema["function"].get("parameters", {})
+        properties = params.get("properties", {})
+        required = params.get("required", [])
+        for field in required:
+            assert field in properties, (
+                f"Tool '{tool_name}': required field '{field}' "
+                f"not found in properties {list(properties.keys())}"
+            )
+
+    def test_every_property_has_type_and_description(
+        self, tool_name: str, schema: dict
+    ) -> None:
+        params = schema["function"].get("parameters", {})
+        properties = params.get("properties", {})
+        for prop_name, prop_def in properties.items():
+            assert (
+                "type" in prop_def
+            ), f"Tool '{tool_name}', property '{prop_name}' is missing 'type'"
+            assert (
+                "description" in prop_def
+            ), f"Tool '{tool_name}', property '{prop_name}' is missing 'description'"
+
+
+def test_browser_act_action_enum_complete() -> None:
+    """Assert browser_act action enum still contains all 14 supported actions.
+
+    This prevents future PRs from accidentally dropping actions during description
+    trimming. The enum is the authoritative list — this locks it at 14 values.
+    """
+    tool = TOOL_REGISTRY["browser_act"]
+    schema = tool.as_openai_tool()
+    fn_def = schema["function"]
+    params = cast(dict[str, Any], fn_def.get("parameters", {}))
+    actions = params["properties"]["action"]["enum"]
+    expected = {
+        "click",
+        "dblclick",
+        "fill",
+        "type",
+        "scroll",
+        "hover",
+        "press",
+        "check",
+        "uncheck",
+        "select",
+        "wait",
+        "back",
+        "forward",
+        "reload",
+    }
+    assert set(actions) == expected, (
+        f"browser_act action enum changed. Got {set(actions)}, expected {expected}. "
+        "If you added/removed an action, update this test intentionally."
+    )
+
+
+def test_total_schema_char_budget() -> None:
+    """Assert total tool schema size stays under the character budget.
+
+    This locks in the 34% token reduction from #12398 and prevents future
+    description bloat from eroding the gains. Uses character count with a
+    ~4 chars/token heuristic (budget of 32000 chars ≈ 8000 tokens).
+    Character count is tokenizer-agnostic — no dependency on GPT or Claude
+    tokenizers — while still providing a stable regression gate.
+    """
+    schemas = [tool.as_openai_tool() for tool in TOOL_REGISTRY.values()]
+    serialized = json.dumps(schemas)
+    total_chars = len(serialized)
+    assert total_chars < _CHAR_BUDGET, (
+        f"Tool schemas use {total_chars} chars (~{total_chars // 4} tokens), "
+        f"exceeding budget of {_CHAR_BUDGET} chars (~{_CHAR_BUDGET // 4} tokens). "
+        f"Description bloat detected — trim descriptions or raise the budget intentionally."
+    )

autogpt_platform/backend/backend/copilot/tools/validate_agent.py

@@ -22,17 +22,9 @@ class ValidateAgentGraphTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Validate an agent JSON graph for correctness. Checks:\n"
-            "- All block_ids reference real blocks\n"
-            "- All links reference valid source/sink nodes and fields\n"
-            "- Required input fields are wired or have defaults\n"
-            "- Data types are compatible across links\n"
-            "- Nested sink links use correct notation\n"
-            "- Prompt templates use proper curly brace escaping\n"
-            "- AgentExecutorBlock configurations are valid\n\n"
-            "Call this after generating agent JSON to verify correctness. "
-            "If validation fails, either fix issues manually based on the error "
-            "descriptions, or call fix_agent_graph to auto-fix common problems."
+            "Validate agent JSON for correctness: block_ids, links, required fields, "
+            "type compatibility, nested sink notation, prompt brace escaping, "
+            "and AgentExecutorBlock configs. On failure, use fix_agent_graph to auto-fix."
         )
 
     @property
@@ -46,11 +38,7 @@ class ValidateAgentGraphTool(BaseTool):
             "properties": {
                 "agent_json": {
                     "type": "object",
-                    "description": (
-                        "The agent JSON to validate. Must contain 'nodes' and 'links' arrays. "
-                        "Each node needs: id (UUID), block_id, input_default, metadata. "
-                        "Each link needs: id (UUID), source_id, source_name, sink_id, sink_name."
-                    ),
+                    "description": "Agent JSON with 'nodes' and 'links' arrays.",
                 },
             },
             "required": ["agent_json"],

autogpt_platform/backend/backend/copilot/tools/web_fetch.py

@@ -59,13 +59,7 @@ class WebFetchTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Fetch the content of a public web page by URL. "
-            "Returns readable text extracted from HTML by default. "
-            "Useful for reading documentation, articles, and API responses. "
-            "Only supports HTTP/HTTPS GET requests to public URLs "
-            "(private/internal network addresses are blocked)."
-        )
+        return "Fetch a public web page. Public URLs only — internal addresses blocked. Returns readable text from HTML by default."
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -74,14 +68,11 @@ class WebFetchTool(BaseTool):
             "properties": {
                 "url": {
                     "type": "string",
-                    "description": "The public HTTP/HTTPS URL to fetch.",
+                    "description": "Public HTTP/HTTPS URL.",
                 },
                 "extract_text": {
                     "type": "boolean",
-                    "description": (
-                        "If true (default), extract readable text from HTML. "
-                        "If false, return raw content."
-                    ),
+                    "description": "Extract text from HTML (default: true).",
                     "default": True,
                 },
             },

autogpt_platform/backend/backend/copilot/tools/workspace_files.py

@@ -27,6 +27,8 @@ from .models import ErrorResponse, ResponseType, ToolResponseBase
 
 logger = logging.getLogger(__name__)
 
+_MAX_FILE_SIZE_MB = Config().max_file_size_mb
+
 # Sentinel file_id used when a tool-result file is read directly from the local
 # host filesystem (rather than from workspace storage).
 _LOCAL_TOOL_RESULT_FILE_ID = "local"
@@ -415,13 +417,7 @@ class ListWorkspaceFilesTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "List files in the user's persistent workspace (cloud storage). "
-            "These files survive across sessions. "
-            "For ephemeral session files, use the SDK Read/Glob tools instead. "
-            "Returns file names, paths, sizes, and metadata. "
-            "Optionally filter by path prefix."
-        )
+        return "List persistent workspace files. For ephemeral session files, use SDK Glob/Read instead. Optionally filter by path prefix."
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -430,24 +426,17 @@ class ListWorkspaceFilesTool(BaseTool):
             "properties": {
                 "path_prefix": {
                     "type": "string",
-                    "description": (
-                        "Optional path prefix to filter files "
-                        "(e.g., '/documents/' to list only files in documents folder). "
-                        "By default, only files from the current session are listed."
-                    ),
+                    "description": "Filter by path prefix (e.g. '/documents/').",
                 },
                 "limit": {
                     "type": "integer",
-                    "description": "Maximum number of files to return (default 50, max 100)",
+                    "description": "Max files to return (default 50, max 100).",
                     "minimum": 1,
                     "maximum": 100,
                 },
                 "include_all_sessions": {
                     "type": "boolean",
-                    "description": (
-                        "If true, list files from all sessions. "
-                        "Default is false (only current session's files)."
-                    ),
+                    "description": "Include files from all sessions (default: false).",
                 },
             },
             "required": [],
@@ -530,18 +519,11 @@ class ReadWorkspaceFileTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Read a file from the user's persistent workspace (cloud storage). "
-            "These files survive across sessions. "
-            "For ephemeral session files, use the SDK Read tool instead. "
-            "Specify either file_id or path to identify the file. "
-            "For small text files, returns content directly. "
-            "For large or binary files, returns metadata and a download URL. "
-            "Use 'save_to_path' to copy the file to the working directory "
-            "(sandbox or ephemeral) for processing with bash_exec or file tools. "
-            "Use 'offset' and 'length' for paginated reads of large files "
-            "(e.g., persisted tool outputs). "
-            "Paths are scoped to the current session by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
+            "Read a file from persistent workspace. Specify file_id or path. "
+            "Small text/image files return inline; large/binary return metadata+URL. "
+            "Use save_to_path to copy to working dir for processing. "
+            "Use offset/length for paginated reads. "
+            "Paths scoped to current session; use /sessions/<id>/... for cross-session access."
         )
 
     @property
@@ -551,48 +533,30 @@ class ReadWorkspaceFileTool(BaseTool):
             "properties": {
                 "file_id": {
                     "type": "string",
-                    "description": "The file's unique ID (from list_workspace_files)",
+                    "description": "File ID from list_workspace_files.",
                 },
                 "path": {
                     "type": "string",
-                    "description": (
-                        "The virtual file path (e.g., '/documents/report.pdf'). "
-                        "Scoped to current session by default."
-                    ),
+                    "description": "Virtual file path (e.g. '/documents/report.pdf').",
                 },
                 "save_to_path": {
                     "type": "string",
-                    "description": (
-                        "If provided, save the file to this path in the working "
-                        "directory (cloud sandbox when E2B is active, or "
-                        "ephemeral dir otherwise) so it can be processed with "
-                        "bash_exec or file tools. "
-                        "The file content is still returned in the response."
-                    ),
+                    "description": "Copy file to this working directory path for processing.",
                 },
                 "force_download_url": {
                     "type": "boolean",
-                    "description": (
-                        "If true, always return metadata+URL instead of inline content. "
-                        "Default is false (auto-selects based on file size/type)."
-                    ),
+                    "description": "Always return metadata+URL instead of inline content.",
                 },
                 "offset": {
                     "type": "integer",
-                    "description": (
-                        "Character offset to start reading from (0-based). "
-                        "Use with 'length' for paginated reads of large files."
-                    ),
+                    "description": "Character offset for paginated reads (0-based).",
                 },
                 "length": {
                     "type": "integer",
-                    "description": (
-                        "Maximum number of characters to return. "
-                        "Defaults to full file. Use with 'offset' for paginated reads."
-                    ),
+                    "description": "Max characters to return for paginated reads.",
                 },
             },
-            "required": [],  # At least one must be provided
+            "required": [],  # At least one of file_id or path must be provided
         }
 
     @property
@@ -755,15 +719,10 @@ class WriteWorkspaceFileTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Write or create a file in the user's persistent workspace (cloud storage). "
-            "These files survive across sessions. "
-            "For ephemeral session files, use the SDK Write tool instead. "
-            "Provide content as plain text via 'content', OR base64-encoded via "
-            "'content_base64', OR copy a file from the ephemeral working directory "
-            "via 'source_path'. Exactly one of these three is required. "
-            f"Maximum file size is {Config().max_file_size_mb}MB. "
-            "Files are saved to the current session's folder by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
+            "Write a file to persistent workspace (survives across sessions). "
+            "Provide exactly one of: content (text), content_base64 (binary), "
+            f"or source_path (copy from working dir). Max {_MAX_FILE_SIZE_MB}MB. "
+            "Paths scoped to current session; use /sessions/<id>/... for cross-session access."
         )
 
     @property
@@ -773,51 +732,31 @@ class WriteWorkspaceFileTool(BaseTool):
             "properties": {
                 "filename": {
                     "type": "string",
-                    "description": "Name for the file (e.g., 'report.pdf')",
+                    "description": "Filename (e.g. 'report.pdf').",
                 },
                 "content": {
                     "type": "string",
-                    "description": (
-                        "Plain text content to write. Use this for text files "
-                        "(code, configs, documents, etc.). "
-                        "Mutually exclusive with content_base64 and source_path."
-                    ),
+                    "description": "Plain text content. Mutually exclusive with content_base64/source_path.",
                 },
                 "content_base64": {
                     "type": "string",
-                    "description": (
-                        "Base64-encoded file content. Use this for binary files "
-                        "(images, PDFs, etc.). "
-                        "Mutually exclusive with content and source_path."
-                    ),
+                    "description": "Base64-encoded binary content. Mutually exclusive with content/source_path.",
                 },
                 "source_path": {
                     "type": "string",
-                    "description": (
-                        "Path to a file in the ephemeral working directory to "
-                        "copy to workspace (e.g., '/tmp/copilot-.../output.csv'). "
-                        "Use this to persist files created by bash_exec or SDK Write. "
-                        "Mutually exclusive with content and content_base64."
-                    ),
+                    "description": "Working directory path to copy to workspace. Mutually exclusive with content/content_base64.",
                 },
                 "path": {
                     "type": "string",
-                    "description": (
-                        "Optional virtual path where to save the file "
-                        "(e.g., '/documents/report.pdf'). "
-                        "Defaults to '/{filename}'. Scoped to current session."
-                    ),
+                    "description": "Virtual path (e.g. '/documents/report.pdf'). Defaults to '/{filename}'.",
                 },
                 "mime_type": {
                     "type": "string",
-                    "description": (
-                        "Optional MIME type of the file. "
-                        "Auto-detected from filename if not provided."
-                    ),
+                    "description": "MIME type. Auto-detected from filename if omitted.",
                 },
                 "overwrite": {
                     "type": "boolean",
-                    "description": "Whether to overwrite if file exists at path (default: false)",
+                    "description": "Overwrite if file exists (default: false).",
                 },
             },
             "required": ["filename"],
@@ -859,10 +798,10 @@ class WriteWorkspaceFileTool(BaseTool):
             return resolved
         content: bytes = resolved
 
-        max_size = Config().max_file_size_mb * 1024 * 1024
+        max_size = _MAX_FILE_SIZE_MB * 1024 * 1024
         if len(content) > max_size:
             return ErrorResponse(
-                message=f"File too large. Maximum size is {Config().max_file_size_mb}MB",
+                message=f"File too large. Maximum size is {_MAX_FILE_SIZE_MB}MB",
                 session_id=session_id,
             )
 
@@ -944,12 +883,7 @@ class DeleteWorkspaceFileTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return (
-            "Delete a file from the user's persistent workspace (cloud storage). "
-            "Specify either file_id or path to identify the file. "
-            "Paths are scoped to the current session by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
-        )
+        return "Delete a file from persistent workspace. Specify file_id or path. Paths scoped to current session; use /sessions/<id>/... for cross-session access."
 
     @property
     def parameters(self) -> dict[str, Any]:
@@ -958,17 +892,14 @@ class DeleteWorkspaceFileTool(BaseTool):
             "properties": {
                 "file_id": {
                     "type": "string",
-                    "description": "The file's unique ID (from list_workspace_files)",
+                    "description": "File ID from list_workspace_files.",
                 },
                 "path": {
                     "type": "string",
-                    "description": (
-                        "The virtual file path (e.g., '/documents/report.pdf'). "
-                        "Scoped to current session by default."
-                    ),
+                    "description": "Virtual file path.",
                 },
             },
-            "required": [],  # At least one must be provided
+            "required": [],  # At least one of file_id or path must be provided
         }
 
     @property

autogpt_platform/backend/backend/data/graph.py

@@ -38,7 +38,7 @@ from backend.util.request import parse_url
 from .block import BlockInput
 from .db import BaseDbModel
 from .db import prisma as db
-from .db import query_raw_with_schema, transaction
+from .db import execute_raw_with_schema, query_raw_with_schema, transaction
 from .dynamic_fields import is_tool_pin, sanitize_pin_name
 from .includes import AGENT_GRAPH_INCLUDE, AGENT_NODE_INCLUDE, MAX_GRAPH_VERSIONS_FETCH
 from .model import CredentialsFieldInfo, CredentialsMetaInput, is_credentials_field_name
@@ -1669,16 +1669,15 @@ async def migrate_llm_models(migrate_to: LlmModel):
 
     # Update each block
     for id, path in llm_model_fields.items():
-        query = f"""
-            UPDATE platform."AgentNode"
+        query = """
+            UPDATE {schema_prefix}"AgentNode"
             SET "constantInput" = jsonb_set("constantInput", $1, to_jsonb($2), true)
             WHERE "agentBlockId" = $3
             AND "constantInput" ? ($4)::text
-            AND "constantInput"->>($4)::text NOT IN {escaped_enum_values}
-            """
+            AND "constantInput"->>($4)::text NOT IN """ + escaped_enum_values
 
-        await db.execute_raw(
-            query,  # type: ignore - is supposed to be LiteralString
+        await execute_raw_with_schema(
+            query,
             [path],
             migrate_to.value,
             id,

autogpt_platform/backend/backend/data/llm_registry/__init__.py

@@ -0,0 +1,40 @@
+"""LLM Registry - Dynamic model management system."""
+
+from backend.blocks.llm import ModelMetadata
+from .notifications import (
+    publish_registry_refresh_notification,
+    subscribe_to_registry_refresh,
+)
+from .registry import (
+    RegistryModel,
+    RegistryModelCost,
+    RegistryModelCreator,
+    clear_registry_cache,
+    get_all_model_slugs_for_validation,
+    get_all_models,
+    get_default_model_slug,
+    get_enabled_models,
+    get_model,
+    get_schema_options,
+    refresh_llm_registry,
+)
+
+__all__ = [
+    # Models
+    "ModelMetadata",
+    "RegistryModel",
+    "RegistryModelCost",
+    "RegistryModelCreator",
+    # Cache management
+    "clear_registry_cache",
+    "publish_registry_refresh_notification",
+    "subscribe_to_registry_refresh",
+    # Read functions
+    "refresh_llm_registry",
+    "get_model",
+    "get_all_models",
+    "get_enabled_models",
+    "get_schema_options",
+    "get_default_model_slug",
+    "get_all_model_slugs_for_validation",
+]

autogpt_platform/backend/backend/data/llm_registry/notifications.py

@@ -0,0 +1,84 @@
+"""Pub/sub notifications for LLM registry cross-process synchronisation."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Awaitable, Callable
+
+logger = logging.getLogger(__name__)
+
+REGISTRY_REFRESH_CHANNEL = "llm_registry:refresh"
+
+
+async def publish_registry_refresh_notification() -> None:
+    """Publish a refresh signal so all other workers reload their in-process cache."""
+    from backend.data.redis_client import get_redis_async
+
+    try:
+        redis = await get_redis_async()
+        await redis.publish(REGISTRY_REFRESH_CHANNEL, "refresh")
+        logger.debug("Published LLM registry refresh notification")
+    except Exception as e:
+        logger.warning("Failed to publish registry refresh notification: %s", e)
+
+
+async def subscribe_to_registry_refresh(
+    on_refresh: Callable[[], Awaitable[None]],
+) -> None:
+    """Listen for registry refresh signals and call on_refresh each time one arrives.
+
+    Designed to run as a long-lived background asyncio.Task.  Automatically
+    reconnects if the Redis connection drops.
+
+    Args:
+        on_refresh: Async callable invoked on each refresh signal.
+                    Typically ``llm_registry.refresh_llm_registry``.
+    """
+    from backend.data.redis_client import HOST, PASSWORD, PORT
+    from redis.asyncio import Redis as AsyncRedis
+
+    while True:
+        try:
+            # Dedicated connection — pub/sub must not share a connection used
+            # for regular commands.
+            redis_sub = AsyncRedis(
+                host=HOST, port=PORT, password=PASSWORD, decode_responses=True
+            )
+            pubsub = redis_sub.pubsub()
+            await pubsub.subscribe(REGISTRY_REFRESH_CHANNEL)
+            logger.info("Subscribed to LLM registry refresh channel")
+
+            while True:
+                try:
+                    message = await pubsub.get_message(
+                        ignore_subscribe_messages=True, timeout=1.0
+                    )
+                    if (
+                        message
+                        and message["type"] == "message"
+                        and message["channel"] == REGISTRY_REFRESH_CHANNEL
+                    ):
+                        logger.debug("LLM registry refresh signal received")
+                        try:
+                            await on_refresh()
+                        except Exception as e:
+                            logger.error(
+                                "Error in registry on_refresh callback: %s", e
+                            )
+                except asyncio.CancelledError:
+                    raise
+                except Exception as e:
+                    logger.warning(
+                        "Error processing registry refresh message: %s", e
+                    )
+                    await asyncio.sleep(1)
+
+        except asyncio.CancelledError:
+            logger.info("LLM registry subscription task cancelled")
+            break
+        except Exception as e:
+            logger.warning(
+                "LLM registry subscription error: %s. Retrying in 5s...", e
+            )
+            await asyncio.sleep(5)

autogpt_platform/backend/backend/data/llm_registry/registry.py

@@ -0,0 +1,254 @@
+"""Core LLM registry implementation for managing models dynamically."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any
+
+import prisma.models
+from pydantic import BaseModel, ConfigDict
+
+from backend.blocks.llm import ModelMetadata
+from backend.util.cache import cached
+
+logger = logging.getLogger(__name__)
+
+
+class RegistryModelCost(BaseModel):
+    """Cost configuration for an LLM model."""
+
+    model_config = ConfigDict(frozen=True)
+
+    unit: str  # "RUN" or "TOKENS"
+    credit_cost: int
+    credential_provider: str
+    credential_id: str | None = None
+    credential_type: str | None = None
+    currency: str | None = None
+    metadata: dict[str, Any] = {}
+
+
+class RegistryModelCreator(BaseModel):
+    """Creator information for an LLM model."""
+
+    model_config = ConfigDict(frozen=True)
+
+    id: str
+    name: str
+    display_name: str
+    description: str | None = None
+    website_url: str | None = None
+    logo_url: str | None = None
+
+
+class RegistryModel(BaseModel):
+    """Represents a model in the LLM registry."""
+
+    model_config = ConfigDict(frozen=True)
+
+    slug: str
+    display_name: str
+    description: str | None = None
+    metadata: ModelMetadata
+    capabilities: dict[str, Any] = {}
+    extra_metadata: dict[str, Any] = {}
+    provider_display_name: str
+    is_enabled: bool
+    is_recommended: bool = False
+    costs: tuple[RegistryModelCost, ...] = ()
+    creator: RegistryModelCreator | None = None
+
+    # Typed capability fields from DB schema
+    supports_tools: bool = False
+    supports_json_output: bool = False
+    supports_reasoning: bool = False
+    supports_parallel_tool_calls: bool = False
+
+
+# L1 in-process cache — Redis is the shared L2 via @cached(shared_cache=True)
+_dynamic_models: dict[str, RegistryModel] = {}
+_schema_options: list[dict[str, str]] = []
+_lock = asyncio.Lock()
+
+
+def _record_to_registry_model(record: prisma.models.LlmModel) -> RegistryModel:  # type: ignore[name-defined]
+    """Transform a raw Prisma LlmModel record into a RegistryModel instance."""
+    costs = tuple(
+        RegistryModelCost(
+            unit=str(cost.unit),
+            credit_cost=cost.creditCost,
+            credential_provider=cost.credentialProvider,
+            credential_id=cost.credentialId,
+            credential_type=cost.credentialType,
+            currency=cost.currency,
+            metadata=dict(cost.metadata or {}),
+        )
+        for cost in (record.Costs or [])
+    )
+
+    creator = None
+    if record.Creator:
+        creator = RegistryModelCreator(
+            id=record.Creator.id,
+            name=record.Creator.name,
+            display_name=record.Creator.displayName,
+            description=record.Creator.description,
+            website_url=record.Creator.websiteUrl,
+            logo_url=record.Creator.logoUrl,
+        )
+
+    capabilities = dict(record.capabilities or {})
+
+    if not record.Provider:
+        logger.warning(
+            "LlmModel %s has no Provider despite NOT NULL FK - "
+            "falling back to providerId %s",
+            record.slug,
+            record.providerId,
+        )
+    provider_name = record.Provider.name if record.Provider else record.providerId
+    provider_display = (
+        record.Provider.displayName if record.Provider else record.providerId
+    )
+    creator_name = record.Creator.displayName if record.Creator else "Unknown"
+
+    if record.priceTier not in (1, 2, 3):
+        logger.warning(
+            "LlmModel %s has out-of-range priceTier=%s, defaulting to 1",
+            record.slug,
+            record.priceTier,
+        )
+    price_tier = record.priceTier if record.priceTier in (1, 2, 3) else 1
+
+    metadata = ModelMetadata(
+        provider=provider_name,
+        context_window=record.contextWindow,
+        max_output_tokens=(
+            record.maxOutputTokens
+            if record.maxOutputTokens is not None
+            else record.contextWindow
+        ),
+        display_name=record.displayName,
+        provider_name=provider_display,
+        creator_name=creator_name,
+        price_tier=price_tier,
+    )
+
+    return RegistryModel(
+        slug=record.slug,
+        display_name=record.displayName,
+        description=record.description,
+        metadata=metadata,
+        capabilities=capabilities,
+        extra_metadata=dict(record.metadata or {}),
+        provider_display_name=provider_display,
+        is_enabled=record.isEnabled,
+        is_recommended=record.isRecommended,
+        costs=costs,
+        creator=creator,
+        supports_tools=record.supportsTools,
+        supports_json_output=record.supportsJsonOutput,
+        supports_reasoning=record.supportsReasoning,
+        supports_parallel_tool_calls=record.supportsParallelToolCalls,
+    )
+
+
+@cached(maxsize=1, ttl_seconds=300, shared_cache=True, refresh_ttl_on_get=True)
+async def _fetch_registry_from_db() -> list[RegistryModel]:
+    """Fetch all LLM models from the database.
+
+    Results are cached in Redis (shared_cache=True) so subsequent calls within
+    the TTL window skip the DB entirely — both within this process and across
+    all other workers that share the same Redis instance.
+    """
+    records = await prisma.models.LlmModel.prisma().find_many(  # type: ignore[attr-defined]
+        include={"Provider": True, "Costs": True, "Creator": True}
+    )
+    logger.info("Fetched %d LLM models from database", len(records))
+    return [_record_to_registry_model(r) for r in records]
+
+
+def clear_registry_cache() -> None:
+    """Invalidate the shared Redis cache for the registry DB fetch.
+
+    Call this before refresh_llm_registry() after any admin DB mutation so the
+    next fetch hits the database rather than serving the now-stale cached data.
+    """
+    _fetch_registry_from_db.cache_clear()
+
+
+async def refresh_llm_registry() -> None:
+    """Refresh the in-process L1 cache from Redis/DB.
+
+    On the first call (or after clear_registry_cache()), fetches fresh data
+    from the database and stores it in Redis.  Subsequent calls by other
+    workers hit the Redis cache instead of the DB.
+    """
+    async with _lock:
+        try:
+            models = await _fetch_registry_from_db()
+            new_models = {m.slug: m for m in models}
+
+            global _dynamic_models, _schema_options
+            _dynamic_models = new_models
+            _schema_options = _build_schema_options()
+
+            logger.info(
+                "LLM registry refreshed: %d models, %d schema options",
+                len(_dynamic_models),
+                len(_schema_options),
+            )
+        except Exception as e:
+            logger.error("Failed to refresh LLM registry: %s", e, exc_info=True)
+            raise
+
+
+def _build_schema_options() -> list[dict[str, str]]:
+    """Build schema options for model selection dropdown. Only includes enabled models."""
+    return [
+        {
+            "label": model.display_name,
+            "value": model.slug,
+            "group": model.metadata.provider,
+            "description": model.description or "",
+        }
+        for model in sorted(
+            _dynamic_models.values(), key=lambda m: m.display_name.lower()
+        )
+        if model.is_enabled
+    ]
+
+
+def get_model(slug: str) -> RegistryModel | None:
+    """Get a model by slug from the registry."""
+    return _dynamic_models.get(slug)
+
+
+def get_all_models() -> list[RegistryModel]:
+    """Get all models from the registry (including disabled)."""
+    return list(_dynamic_models.values())
+
+
+def get_enabled_models() -> list[RegistryModel]:
+    """Get only enabled models from the registry."""
+    return [model for model in _dynamic_models.values() if model.is_enabled]
+
+
+def get_schema_options() -> list[dict[str, str]]:
+    """Get schema options for model selection dropdown (enabled models only)."""
+    return list(_schema_options)
+
+
+def get_default_model_slug() -> str | None:
+    """Get the default model slug (first recommended, or first enabled)."""
+    models = sorted(_dynamic_models.values(), key=lambda m: m.display_name)
+    recommended = next(
+        (m.slug for m in models if m.is_recommended and m.is_enabled), None
+    )
+    return recommended or next((m.slug for m in models if m.is_enabled), None)
+
+
+def get_all_model_slugs_for_validation() -> list[str]:
+    """Get all model slugs for validation (enabled models only)."""
+    return [model.slug for model in _dynamic_models.values() if model.is_enabled]

autogpt_platform/backend/backend/data/llm_registry/registry_test.py

@@ -0,0 +1,358 @@
+"""Unit tests for the LLM registry module."""
+
+from __future__ import annotations
+
+import asyncio
+from unittest.mock import AsyncMock, Mock, patch
+
+import pytest
+import pydantic
+
+from backend.data.llm_registry.registry import (
+    RegistryModel,
+    RegistryModelCost,
+    RegistryModelCreator,
+    _build_schema_options,
+    _record_to_registry_model,
+    get_default_model_slug,
+    get_schema_options,
+    refresh_llm_registry,
+)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_mock_record(**overrides):
+    """Build a realistic mock Prisma LlmModel record."""
+    provider = Mock()
+    provider.name = "openai"
+    provider.displayName = "OpenAI"
+
+    record = Mock()
+    record.slug = "openai/gpt-4o"
+    record.displayName = "GPT-4o"
+    record.description = "Latest GPT model"
+    record.providerId = "provider-uuid"
+    record.Provider = provider
+    record.creatorId = "creator-uuid"
+    record.Creator = None
+    record.contextWindow = 128000
+    record.maxOutputTokens = 16384
+    record.priceTier = 2
+    record.isEnabled = True
+    record.isRecommended = False
+    record.supportsTools = True
+    record.supportsJsonOutput = True
+    record.supportsReasoning = False
+    record.supportsParallelToolCalls = True
+    record.capabilities = {}
+    record.metadata = {}
+    record.Costs = []
+
+    for key, value in overrides.items():
+        setattr(record, key, value)
+    return record
+
+
+def _make_registry_model(**kwargs) -> RegistryModel:
+    """Build a minimal RegistryModel for testing registry-level functions."""
+    from backend.blocks.llm import ModelMetadata
+
+    defaults = dict(
+        slug="openai/gpt-4o",
+        display_name="GPT-4o",
+        description=None,
+        metadata=ModelMetadata(
+            provider="openai",
+            context_window=128000,
+            max_output_tokens=16384,
+            display_name="GPT-4o",
+            provider_name="OpenAI",
+            creator_name="Unknown",
+            price_tier=2,
+        ),
+        capabilities={},
+        extra_metadata={},
+        provider_display_name="OpenAI",
+        is_enabled=True,
+        is_recommended=False,
+    )
+    defaults.update(kwargs)
+    return RegistryModel(**defaults)
+
+
+# ---------------------------------------------------------------------------
+# _record_to_registry_model tests
+# ---------------------------------------------------------------------------
+
+
+def test_record_to_registry_model():
+    """Happy-path: well-formed record produces a correct RegistryModel."""
+    record = _make_mock_record()
+    model = _record_to_registry_model(record)
+
+    assert model.slug == "openai/gpt-4o"
+    assert model.display_name == "GPT-4o"
+    assert model.description == "Latest GPT model"
+    assert model.provider_display_name == "OpenAI"
+    assert model.is_enabled is True
+    assert model.is_recommended is False
+    assert model.supports_tools is True
+    assert model.supports_json_output is True
+    assert model.supports_reasoning is False
+    assert model.supports_parallel_tool_calls is True
+    assert model.metadata.provider == "openai"
+    assert model.metadata.context_window == 128000
+    assert model.metadata.max_output_tokens == 16384
+    assert model.metadata.price_tier == 2
+    assert model.creator is None
+    assert model.costs == ()
+
+
+def test_record_to_registry_model_missing_provider(caplog):
+    """Record with no Provider relation falls back to providerId and logs a warning."""
+    record = _make_mock_record(Provider=None, providerId="provider-uuid")
+    with caplog.at_level("WARNING"):
+        model = _record_to_registry_model(record)
+
+    assert "no Provider" in caplog.text
+    assert model.metadata.provider == "provider-uuid"
+    assert model.provider_display_name == "provider-uuid"
+
+
+def test_record_to_registry_model_missing_creator():
+    """When Creator is None, creator_name defaults to 'Unknown' and creator field is None."""
+    record = _make_mock_record(Creator=None)
+    model = _record_to_registry_model(record)
+
+    assert model.creator is None
+    assert model.metadata.creator_name == "Unknown"
+
+
+def test_record_to_registry_model_with_creator():
+    """When Creator is present, it is parsed into RegistryModelCreator."""
+    creator_mock = Mock()
+    creator_mock.id = "creator-uuid"
+    creator_mock.name = "openai"
+    creator_mock.displayName = "OpenAI"
+    creator_mock.description = "AI company"
+    creator_mock.websiteUrl = "https://openai.com"
+    creator_mock.logoUrl = "https://openai.com/logo.png"
+
+    record = _make_mock_record(Creator=creator_mock)
+    model = _record_to_registry_model(record)
+
+    assert model.creator is not None
+    assert isinstance(model.creator, RegistryModelCreator)
+    assert model.creator.id == "creator-uuid"
+    assert model.creator.display_name == "OpenAI"
+    assert model.metadata.creator_name == "OpenAI"
+
+
+def test_record_to_registry_model_null_max_output_tokens():
+    """maxOutputTokens=None falls back to contextWindow."""
+    record = _make_mock_record(maxOutputTokens=None, contextWindow=64000)
+    model = _record_to_registry_model(record)
+
+    assert model.metadata.max_output_tokens == 64000
+
+
+def test_record_to_registry_model_invalid_price_tier(caplog):
+    """Out-of-range priceTier is coerced to 1 and a warning is logged."""
+    record = _make_mock_record(priceTier=99)
+    with caplog.at_level("WARNING"):
+        model = _record_to_registry_model(record)
+
+    assert "out-of-range priceTier" in caplog.text
+    assert model.metadata.price_tier == 1
+
+
+def test_record_to_registry_model_with_costs():
+    """Costs are parsed into RegistryModelCost tuples."""
+    cost_mock = Mock()
+    cost_mock.unit = "TOKENS"
+    cost_mock.creditCost = 10
+    cost_mock.credentialProvider = "openai"
+    cost_mock.credentialId = None
+    cost_mock.credentialType = None
+    cost_mock.currency = "USD"
+    cost_mock.metadata = {}
+
+    record = _make_mock_record(Costs=[cost_mock])
+    model = _record_to_registry_model(record)
+
+    assert len(model.costs) == 1
+    cost = model.costs[0]
+    assert isinstance(cost, RegistryModelCost)
+    assert cost.unit == "TOKENS"
+    assert cost.credit_cost == 10
+    assert cost.credential_provider == "openai"
+
+
+# ---------------------------------------------------------------------------
+# get_default_model_slug tests
+# ---------------------------------------------------------------------------
+
+
+def test_get_default_model_slug_recommended():
+    """Recommended model is preferred over non-recommended enabled models."""
+    import backend.data.llm_registry.registry as reg
+
+    reg._dynamic_models = {
+        "openai/gpt-4o": _make_registry_model(
+            slug="openai/gpt-4o", display_name="GPT-4o", is_recommended=False
+        ),
+        "openai/gpt-4o-recommended": _make_registry_model(
+            slug="openai/gpt-4o-recommended",
+            display_name="GPT-4o Recommended",
+            is_recommended=True,
+        ),
+    }
+
+    result = get_default_model_slug()
+    assert result == "openai/gpt-4o-recommended"
+
+
+def test_get_default_model_slug_fallback():
+    """With no recommended model, falls back to first enabled (alphabetical)."""
+    import backend.data.llm_registry.registry as reg
+
+    reg._dynamic_models = {
+        "openai/gpt-4o": _make_registry_model(
+            slug="openai/gpt-4o", display_name="GPT-4o", is_recommended=False
+        ),
+        "openai/gpt-3.5": _make_registry_model(
+            slug="openai/gpt-3.5", display_name="GPT-3.5", is_recommended=False
+        ),
+    }
+
+    result = get_default_model_slug()
+    # Sorted alphabetically: GPT-3.5 < GPT-4o
+    assert result == "openai/gpt-3.5"
+
+
+def test_get_default_model_slug_empty():
+    """Empty registry returns None."""
+    import backend.data.llm_registry.registry as reg
+
+    reg._dynamic_models = {}
+
+    result = get_default_model_slug()
+    assert result is None
+
+
+# ---------------------------------------------------------------------------
+# _build_schema_options / get_schema_options tests
+# ---------------------------------------------------------------------------
+
+
+def test_build_schema_options():
+    """Only enabled models appear, sorted case-insensitively."""
+    import backend.data.llm_registry.registry as reg
+
+    reg._dynamic_models = {
+        "openai/gpt-4o": _make_registry_model(
+            slug="openai/gpt-4o", display_name="GPT-4o", is_enabled=True
+        ),
+        "openai/disabled": _make_registry_model(
+            slug="openai/disabled", display_name="Disabled Model", is_enabled=False
+        ),
+        "openai/gpt-3.5": _make_registry_model(
+            slug="openai/gpt-3.5", display_name="gpt-3.5", is_enabled=True
+        ),
+    }
+
+    options = _build_schema_options()
+    slugs = [o["value"] for o in options]
+
+    # disabled model should be excluded
+    assert "openai/disabled" not in slugs
+    # only enabled models
+    assert "openai/gpt-4o" in slugs
+    assert "openai/gpt-3.5" in slugs
+    # case-insensitive sort: "gpt-3.5" < "GPT-4o" (both lowercase: "gpt-3.5" < "gpt-4o")
+    assert slugs.index("openai/gpt-3.5") < slugs.index("openai/gpt-4o")
+
+    # Verify structure
+    for option in options:
+        assert "label" in option
+        assert "value" in option
+        assert "group" in option
+        assert "description" in option
+
+
+def test_get_schema_options_returns_copy():
+    """Mutating the returned list does not affect the internal cache."""
+    import backend.data.llm_registry.registry as reg
+
+    reg._dynamic_models = {
+        "openai/gpt-4o": _make_registry_model(slug="openai/gpt-4o", display_name="GPT-4o"),
+    }
+    reg._schema_options = _build_schema_options()
+
+    options = get_schema_options()
+    original_length = len(options)
+    options.append({"label": "Injected", "value": "evil/model", "group": "evil", "description": ""})
+
+    # Internal state should be unchanged
+    assert len(get_schema_options()) == original_length
+
+
+# ---------------------------------------------------------------------------
+# Pydantic frozen model tests
+# ---------------------------------------------------------------------------
+
+
+def test_registry_model_frozen():
+    """Pydantic frozen=True should reject attribute assignment."""
+    model = _make_registry_model()
+
+    with pytest.raises((pydantic.ValidationError, TypeError)):
+        model.slug = "changed/slug"  # type: ignore[misc]
+
+
+def test_registry_model_cost_frozen():
+    """RegistryModelCost is also frozen."""
+    cost = RegistryModelCost(
+        unit="TOKENS",
+        credit_cost=5,
+        credential_provider="openai",
+    )
+    with pytest.raises((pydantic.ValidationError, TypeError)):
+        cost.unit = "RUN"  # type: ignore[misc]
+
+
+# ---------------------------------------------------------------------------
+# refresh_llm_registry tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_refresh_llm_registry():
+    """Mock prisma find_many, verify cache is populated after refresh."""
+    import backend.data.llm_registry.registry as reg
+
+    record = _make_mock_record()
+    mock_find_many = AsyncMock(return_value=[record])
+
+    with patch("prisma.models.LlmModel.prisma") as mock_prisma_cls:
+        mock_prisma_instance = Mock()
+        mock_prisma_instance.find_many = mock_find_many
+        mock_prisma_cls.return_value = mock_prisma_instance
+
+        # Clear state first
+        reg._dynamic_models = {}
+        reg._schema_options = []
+
+        await refresh_llm_registry()
+
+    assert "openai/gpt-4o" in reg._dynamic_models
+    model = reg._dynamic_models["openai/gpt-4o"]
+    assert isinstance(model, RegistryModel)
+    assert model.slug == "openai/gpt-4o"
+    # Schema options should be populated too
+    assert len(reg._schema_options) == 1
+    assert reg._schema_options[0]["value"] == "openai/gpt-4o"

autogpt_platform/backend/backend/executor/manager.py

@@ -224,7 +224,7 @@ async def execute_node(
     # Sanity check: validate the execution input.
     input_data, error = validate_exec(node, data.inputs, resolve_input=False)
     if input_data is None:
-        log_metadata.error(f"Skip execution, input validation error: {error}")
+        log_metadata.warning(f"Skip execution, input validation error: {error}")
         yield "error", error
         return
 

autogpt_platform/backend/backend/server/v2/llm/__init__.py

@@ -0,0 +1,6 @@
+"""LLM registry API (public + admin)."""
+
+from .admin_routes import router as admin_router
+from .routes import router
+
+__all__ = ["router", "admin_router"]

autogpt_platform/backend/backend/server/v2/llm/admin_model.py

@@ -0,0 +1,115 @@
+"""Request/response models for LLM registry admin API."""
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class CreateLlmProviderRequest(BaseModel):
+    """Request model for creating an LLM provider."""
+
+    name: str = Field(
+        ..., description="Provider identifier (e.g., 'openai', 'anthropic')"
+    )
+    display_name: str = Field(..., description="Human-readable provider name")
+    description: str | None = Field(None, description="Provider description")
+    default_credential_provider: str | None = Field(
+        None, description="Default credential system identifier"
+    )
+    default_credential_id: str | None = Field(None, description="Default credential ID")
+    default_credential_type: str | None = Field(
+        None, description="Default credential type"
+    )
+    metadata: dict[str, Any] = Field(
+        default_factory=dict, description="Additional metadata"
+    )
+
+
+class UpdateLlmProviderRequest(BaseModel):
+    """Request model for updating an LLM provider."""
+
+    display_name: str | None = Field(None, description="Human-readable provider name")
+    description: str | None = Field(None, description="Provider description")
+    default_credential_provider: str | None = Field(
+        None, description="Default credential system identifier"
+    )
+    default_credential_id: str | None = Field(None, description="Default credential ID")
+    default_credential_type: str | None = Field(
+        None, description="Default credential type"
+    )
+    metadata: dict[str, Any] | None = Field(None, description="Additional metadata")
+
+
+class CreateLlmModelRequest(BaseModel):
+    """Request model for creating an LLM model."""
+
+    slug: str = Field(..., description="Model slug (e.g., 'gpt-4', 'claude-3-opus')")
+    display_name: str = Field(..., description="Human-readable model name")
+    description: str | None = Field(None, description="Model description")
+    provider_id: str = Field(..., description="Provider ID (UUID)")
+    creator_id: str | None = Field(None, description="Creator ID (UUID)")
+    context_window: int = Field(
+        ..., description="Maximum context window in tokens", gt=0
+    )
+    max_output_tokens: int | None = Field(
+        None, description="Maximum output tokens (None if unlimited)", gt=0
+    )
+    price_tier: int = Field(
+        ..., description="Price tier (1=cheapest, 2=medium, 3=expensive)", ge=1, le=3
+    )
+    is_enabled: bool = Field(default=True, description="Whether the model is enabled")
+    is_recommended: bool = Field(
+        default=False, description="Whether the model is recommended"
+    )
+    supports_tools: bool = Field(default=False, description="Supports function calling")
+    supports_json_output: bool = Field(
+        default=False, description="Supports JSON output mode"
+    )
+    supports_reasoning: bool = Field(
+        default=False, description="Supports reasoning mode"
+    )
+    supports_parallel_tool_calls: bool = Field(
+        default=False, description="Supports parallel tool calls"
+    )
+    capabilities: dict[str, Any] = Field(
+        default_factory=dict, description="Additional capabilities"
+    )
+    metadata: dict[str, Any] = Field(
+        default_factory=dict, description="Additional metadata"
+    )
+    costs: list[dict[str, Any]] = Field(
+        default_factory=list, description="Cost entries for the model"
+    )
+
+
+class UpdateLlmModelRequest(BaseModel):
+    """Request model for updating an LLM model."""
+
+    display_name: str | None = Field(None, description="Human-readable model name")
+    description: str | None = Field(None, description="Model description")
+    creator_id: str | None = Field(None, description="Creator ID (UUID)")
+    context_window: int | None = Field(
+        None, description="Maximum context window in tokens", gt=0
+    )
+    max_output_tokens: int | None = Field(
+        None, description="Maximum output tokens (None if unlimited)", gt=0
+    )
+    price_tier: int | None = Field(
+        None, description="Price tier (1=cheapest, 2=medium, 3=expensive)", ge=1, le=3
+    )
+    is_enabled: bool | None = Field(None, description="Whether the model is enabled")
+    is_recommended: bool | None = Field(
+        None, description="Whether the model is recommended"
+    )
+    supports_tools: bool | None = Field(None, description="Supports function calling")
+    supports_json_output: bool | None = Field(
+        None, description="Supports JSON output mode"
+    )
+    supports_reasoning: bool | None = Field(None, description="Supports reasoning mode")
+    supports_parallel_tool_calls: bool | None = Field(
+        None, description="Supports parallel tool calls"
+    )
+    capabilities: dict[str, Any] | None = Field(
+        None, description="Additional capabilities"
+    )
+    metadata: dict[str, Any] | None = Field(None, description="Additional metadata")

autogpt_platform/backend/backend/server/v2/llm/admin_routes.py

@@ -0,0 +1,689 @@
+"""Admin API for LLM registry management.
+
+Provides endpoints for:
+- Reading creators (GET)
+- Creating, updating, and deleting models
+- Creating, updating, and deleting providers
+
+All endpoints require admin authentication. Mutations refresh the registry cache.
+"""
+
+import logging
+from typing import Any
+
+import prisma
+import autogpt_libs.auth
+from fastapi import APIRouter, HTTPException, Security, status
+
+from backend.server.v2.llm import db_write
+from backend.server.v2.llm.admin_model import (
+    CreateLlmModelRequest,
+    CreateLlmProviderRequest,
+    UpdateLlmModelRequest,
+    UpdateLlmProviderRequest,
+)
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+
+def _map_provider_response(provider: Any) -> dict[str, Any]:
+    """Map Prisma provider model to response dict."""
+    return {
+        "id": provider.id,
+        "name": provider.name,
+        "display_name": provider.displayName,
+        "description": provider.description,
+        "default_credential_provider": provider.defaultCredentialProvider,
+        "default_credential_id": provider.defaultCredentialId,
+        "default_credential_type": provider.defaultCredentialType,
+        "metadata": dict(provider.metadata or {}),
+        "created_at": provider.createdAt.isoformat() if provider.createdAt else None,
+        "updated_at": provider.updatedAt.isoformat() if provider.updatedAt else None,
+    }
+
+
+def _map_model_response(model: Any) -> dict[str, Any]:
+    """Map Prisma model to response dict."""
+    return {
+        "id": model.id,
+        "slug": model.slug,
+        "display_name": model.displayName,
+        "description": model.description,
+        "provider_id": model.providerId,
+        "creator_id": model.creatorId,
+        "context_window": model.contextWindow,
+        "max_output_tokens": model.maxOutputTokens,
+        "price_tier": model.priceTier,
+        "is_enabled": model.isEnabled,
+        "is_recommended": model.isRecommended,
+        "supports_tools": model.supportsTools,
+        "supports_json_output": model.supportsJsonOutput,
+        "supports_reasoning": model.supportsReasoning,
+        "supports_parallel_tool_calls": model.supportsParallelToolCalls,
+        "capabilities": dict(model.capabilities or {}),
+        "metadata": dict(model.metadata or {}),
+        "created_at": model.createdAt.isoformat() if model.createdAt else None,
+        "updated_at": model.updatedAt.isoformat() if model.updatedAt else None,
+    }
+
+
+def _map_creator_response(creator: Any) -> dict[str, Any]:
+    """Map Prisma creator model to response dict."""
+    return {
+        "id": creator.id,
+        "name": creator.name,
+        "display_name": creator.displayName,
+        "description": creator.description,
+        "website_url": creator.websiteUrl,
+        "logo_url": creator.logoUrl,
+        "metadata": dict(creator.metadata or {}),
+        "created_at": creator.createdAt.isoformat() if creator.createdAt else None,
+        "updated_at": creator.updatedAt.isoformat() if creator.updatedAt else None,
+    }
+
+
+@router.post(
+    "/llm/models",
+    status_code=status.HTTP_201_CREATED,
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def create_model(
+    request: CreateLlmModelRequest,
+) -> dict[str, Any]:
+    """Create a new LLM model.
+
+    Requires admin authentication.
+    """
+    try:
+        import prisma.models as pm
+
+        # Resolve provider name to ID
+        provider = await pm.LlmProvider.prisma().find_unique(
+            where={"name": request.provider_id}
+        )
+        if not provider:
+            # Try as UUID fallback
+            provider = await pm.LlmProvider.prisma().find_unique(
+                where={"id": request.provider_id}
+            )
+        if not provider:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Provider '{request.provider_id}' not found",
+            )
+
+        model = await db_write.create_model(
+            slug=request.slug,
+            display_name=request.display_name,
+            provider_id=provider.id,
+            context_window=request.context_window,
+            price_tier=request.price_tier,
+            description=request.description,
+            creator_id=request.creator_id,
+            max_output_tokens=request.max_output_tokens,
+            is_enabled=request.is_enabled,
+            is_recommended=request.is_recommended,
+            supports_tools=request.supports_tools,
+            supports_json_output=request.supports_json_output,
+            supports_reasoning=request.supports_reasoning,
+            supports_parallel_tool_calls=request.supports_parallel_tool_calls,
+            capabilities=request.capabilities,
+            metadata=request.metadata,
+        )
+        # Create costs if provided in the raw request body
+        if hasattr(request, 'costs') and request.costs:
+            for cost_input in request.costs:
+                await pm.LlmModelCost.prisma().create(
+                    data={
+                        "unit": cost_input.get("unit", "RUN"),
+                        "creditCost": int(cost_input.get("credit_cost", 1)),
+                        "credentialProvider": provider.name,
+                        "metadata": prisma.Json(cost_input.get("metadata", {})),
+                        "Model": {"connect": {"id": model.id}},
+                    }
+                )
+
+        await db_write.refresh_runtime_caches()
+        logger.info(f"Created model '{request.slug}' (id: {model.id})")
+
+        # Re-fetch with costs included
+        model = await pm.LlmModel.prisma().find_unique(
+            where={"id": model.id},
+            include={"Costs": True, "Creator": True},
+        )
+        return _map_model_response(model)
+    except ValueError as e:
+        logger.warning(f"Model creation validation failed: {e}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.exception(f"Failed to create model: {e}")
+        raise HTTPException(status_code=500, detail="Failed to create model")
+
+
+@router.patch(
+    "/llm/models/{slug:path}",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def update_model(
+    slug: str,
+    request: UpdateLlmModelRequest,
+) -> dict[str, Any]:
+    """Update an existing LLM model.
+
+    Requires admin authentication.
+    """
+    try:
+        # Find model by slug first to get ID
+        import prisma.models
+
+        existing = await prisma.models.LlmModel.prisma().find_unique(
+            where={"slug": slug}
+        )
+        if not existing:
+            raise HTTPException(
+                status_code=404, detail=f"Model with slug '{slug}' not found"
+            )
+
+        model = await db_write.update_model(
+            model_id=existing.id,
+            display_name=request.display_name,
+            description=request.description,
+            creator_id=request.creator_id,
+            context_window=request.context_window,
+            max_output_tokens=request.max_output_tokens,
+            price_tier=request.price_tier,
+            is_enabled=request.is_enabled,
+            is_recommended=request.is_recommended,
+            supports_tools=request.supports_tools,
+            supports_json_output=request.supports_json_output,
+            supports_reasoning=request.supports_reasoning,
+            supports_parallel_tool_calls=request.supports_parallel_tool_calls,
+            capabilities=request.capabilities,
+            metadata=request.metadata,
+        )
+        await db_write.refresh_runtime_caches()
+        logger.info(f"Updated model '{slug}' (id: {model.id})")
+        return _map_model_response(model)
+    except ValueError as e:
+        logger.warning(f"Model update validation failed: {e}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.exception(f"Failed to update model: {e}")
+        raise HTTPException(status_code=500, detail="Failed to update model")
+
+
+@router.delete(
+    "/llm/models/{slug:path}",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def delete_model(
+    slug: str,
+    replacement_model_slug: str | None = None,
+) -> dict[str, Any]:
+    """Delete an LLM model with optional migration.
+
+    If workflows are using this model and no replacement_model_slug is given,
+    returns 400 with the node count. Provide replacement_model_slug to migrate
+    affected nodes before deletion.
+    """
+    try:
+        import prisma.models
+
+        existing = await prisma.models.LlmModel.prisma().find_unique(
+            where={"slug": slug}
+        )
+        if not existing:
+            raise HTTPException(
+                status_code=404, detail=f"Model with slug '{slug}' not found"
+            )
+
+        result = await db_write.delete_model(
+            model_id=existing.id,
+            replacement_model_slug=replacement_model_slug,
+        )
+        await db_write.refresh_runtime_caches()
+        logger.info(
+            f"Deleted model '{slug}' (migrated {result['nodes_migrated']} nodes)"
+        )
+        return result
+    except ValueError as e:
+        logger.warning(f"Model deletion validation failed: {e}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.exception(f"Failed to delete model: {e}")
+        raise HTTPException(status_code=500, detail="Failed to delete model")
+
+
+@router.get(
+    "/llm/models/{slug:path}/usage",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def get_model_usage(slug: str) -> dict[str, Any]:
+    """Get usage count for a model — how many workflow nodes reference it."""
+    try:
+        return await db_write.get_model_usage(slug)
+    except Exception as e:
+        logger.exception(f"Failed to get model usage: {e}")
+        raise HTTPException(status_code=500, detail="Failed to get model usage")
+
+
+@router.post(
+    "/llm/models/{slug:path}/toggle",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def toggle_model(
+    slug: str,
+    request: dict[str, Any],
+) -> dict[str, Any]:
+    """Toggle a model's enabled status with optional migration when disabling.
+
+    Body params:
+        is_enabled: bool
+        migrate_to_slug: optional str
+        migration_reason: optional str
+        custom_credit_cost: optional int
+    """
+    try:
+        import prisma.models
+
+        existing = await prisma.models.LlmModel.prisma().find_unique(
+            where={"slug": slug}
+        )
+        if not existing:
+            raise HTTPException(
+                status_code=404, detail=f"Model with slug '{slug}' not found"
+            )
+
+        result = await db_write.toggle_model_with_migration(
+            model_id=existing.id,
+            is_enabled=request.get("is_enabled", True),
+            migrate_to_slug=request.get("migrate_to_slug"),
+            migration_reason=request.get("migration_reason"),
+            custom_credit_cost=request.get("custom_credit_cost"),
+        )
+        await db_write.refresh_runtime_caches()
+        logger.info(
+            f"Toggled model '{slug}' enabled={request.get('is_enabled')} "
+            f"(migrated {result['nodes_migrated']} nodes)"
+        )
+        return result
+    except ValueError as e:
+        logger.warning(f"Model toggle failed: {e}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.exception(f"Failed to toggle model: {e}")
+        raise HTTPException(status_code=500, detail="Failed to toggle model")
+
+
+@router.get(
+    "/llm/migrations",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def list_migrations(
+    include_reverted: bool = False,
+) -> dict[str, Any]:
+    """List model migrations."""
+    try:
+        migrations = await db_write.list_migrations(
+            include_reverted=include_reverted
+        )
+        return {"migrations": migrations}
+    except Exception as e:
+        logger.exception(f"Failed to list migrations: {e}")
+        raise HTTPException(
+            status_code=500, detail="Failed to list migrations"
+        )
+
+
+@router.post(
+    "/llm/migrations/{migration_id}/revert",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def revert_migration(
+    migration_id: str,
+    re_enable_source_model: bool = True,
+) -> dict[str, Any]:
+    """Revert a model migration, restoring affected nodes."""
+    try:
+        result = await db_write.revert_migration(
+            migration_id=migration_id,
+            re_enable_source_model=re_enable_source_model,
+        )
+        await db_write.refresh_runtime_caches()
+        logger.info(
+            f"Reverted migration {migration_id}: "
+            f"{result['nodes_reverted']} nodes restored"
+        )
+        return result
+    except ValueError as e:
+        logger.warning(f"Migration revert failed: {e}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.exception(f"Failed to revert migration: {e}")
+        raise HTTPException(
+            status_code=500, detail="Failed to revert migration"
+        )
+
+
+@router.post(
+    "/llm/providers",
+    status_code=status.HTTP_201_CREATED,
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def create_provider(
+    request: CreateLlmProviderRequest,
+) -> dict[str, Any]:
+    """Create a new LLM provider.
+
+    Requires admin authentication.
+    """
+    try:
+        provider = await db_write.create_provider(
+            name=request.name,
+            display_name=request.display_name,
+            description=request.description,
+            default_credential_provider=request.default_credential_provider,
+            default_credential_id=request.default_credential_id,
+            default_credential_type=request.default_credential_type,
+            metadata=request.metadata,
+        )
+        await db_write.refresh_runtime_caches()
+        logger.info(f"Created provider '{request.name}' (id: {provider.id})")
+        return _map_provider_response(provider)
+    except ValueError as e:
+        logger.warning(f"Provider creation validation failed: {e}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.exception(f"Failed to create provider: {e}")
+        raise HTTPException(status_code=500, detail="Failed to create provider")
+
+
+@router.patch(
+    "/llm/providers/{name}",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def update_provider(
+    name: str,
+    request: UpdateLlmProviderRequest,
+) -> dict[str, Any]:
+    """Update an existing LLM provider.
+
+    Requires admin authentication.
+    """
+    try:
+        # Find provider by name first to get ID
+        import prisma.models
+
+        existing = await prisma.models.LlmProvider.prisma().find_unique(
+            where={"name": name}
+        )
+        if not existing:
+            raise HTTPException(
+                status_code=404, detail=f"Provider with name '{name}' not found"
+            )
+
+        provider = await db_write.update_provider(
+            provider_id=existing.id,
+            display_name=request.display_name,
+            description=request.description,
+            default_credential_provider=request.default_credential_provider,
+            default_credential_id=request.default_credential_id,
+            default_credential_type=request.default_credential_type,
+            metadata=request.metadata,
+        )
+        await db_write.refresh_runtime_caches()
+        logger.info(f"Updated provider '{name}' (id: {provider.id})")
+        return _map_provider_response(provider)
+    except ValueError as e:
+        logger.warning(f"Provider update validation failed: {e}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.exception(f"Failed to update provider: {e}")
+        raise HTTPException(status_code=500, detail="Failed to update provider")
+
+
+@router.delete(
+    "/llm/providers/{name}",
+    status_code=status.HTTP_204_NO_CONTENT,
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def delete_provider(
+    name: str,
+) -> None:
+    """Delete an LLM provider.
+
+    Requires admin authentication.
+    A provider can only be deleted if it has no associated models.
+    """
+    try:
+        # Find provider by name first to get ID
+        import prisma.models
+
+        existing = await prisma.models.LlmProvider.prisma().find_unique(
+            where={"name": name}
+        )
+        if not existing:
+            raise HTTPException(
+                status_code=404, detail=f"Provider with name '{name}' not found"
+            )
+
+        await db_write.delete_provider(provider_id=existing.id)
+        await db_write.refresh_runtime_caches()
+        logger.info(f"Deleted provider '{name}' (id: {existing.id})")
+    except ValueError as e:
+        logger.warning(f"Provider deletion validation failed: {e}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.exception(f"Failed to delete provider: {e}")
+        raise HTTPException(status_code=500, detail="Failed to delete provider")
+
+
+@router.get(
+    "/llm/admin/providers",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def admin_list_providers() -> dict[str, Any]:
+    """List all LLM providers from the database.
+
+    Unlike the public endpoint, this returns ALL providers including
+    those with no models. Requires admin authentication.
+    """
+    try:
+        import prisma.models
+
+        providers = await prisma.models.LlmProvider.prisma().find_many(
+            order={"name": "asc"},
+            include={"Models": True},
+        )
+        return {
+            "providers": [
+                {**_map_provider_response(p), "model_count": len(p.Models) if p.Models else 0}
+                for p in providers
+            ]
+        }
+    except Exception as e:
+        logger.exception(f"Failed to list providers: {e}")
+        raise HTTPException(status_code=500, detail="Failed to list providers")
+
+
+@router.get(
+    "/llm/admin/models",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def admin_list_models(
+    page: int = 1,
+    page_size: int = 100,
+    enabled_only: bool = False,
+) -> dict[str, Any]:
+    """List all LLM models from the database.
+
+    Unlike the public endpoint, this returns full model data including
+    costs and creator info. Requires admin authentication.
+    """
+    try:
+        import prisma.models
+
+        where = {"isEnabled": True} if enabled_only else {}
+        models = await prisma.models.LlmModel.prisma().find_many(
+            where=where,
+            skip=(page - 1) * page_size,
+            take=page_size,
+            order={"displayName": "asc"},
+            include={"Costs": True, "Creator": True},
+        )
+        return {
+            "models": [
+                {
+                    **_map_model_response(m),
+                    "creator": _map_creator_response(m.Creator) if m.Creator else None,
+                    "costs": [
+                        {
+                            "unit": c.unit,
+                            "credit_cost": float(c.creditCost),
+                            "credential_provider": c.credentialProvider,
+                            "credential_type": c.credentialType,
+                            "metadata": dict(c.metadata or {}),
+                        }
+                        for c in (m.Costs or [])
+                    ],
+                }
+                for m in models
+            ]
+        }
+    except Exception as e:
+        logger.exception(f"Failed to list models: {e}")
+        raise HTTPException(status_code=500, detail="Failed to list models")
+
+
+@router.get(
+    "/llm/creators",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def list_creators() -> dict[str, Any]:
+    """List all LLM model creators.
+
+    Requires admin authentication.
+    """
+    try:
+        import prisma.models
+
+        creators = await prisma.models.LlmModelCreator.prisma().find_many(
+            order={"name": "asc"}
+        )
+        logger.info(f"Retrieved {len(creators)} creators")
+        return {"creators": [_map_creator_response(c) for c in creators]}
+    except Exception as e:
+        logger.exception(f"Failed to list creators: {e}")
+        raise HTTPException(status_code=500, detail="Failed to list creators")
+
+
+@router.post(
+    "/llm/creators",
+    status_code=status.HTTP_201_CREATED,
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def create_creator(
+    request: dict[str, Any],
+) -> dict[str, Any]:
+    """Create a new LLM model creator."""
+    try:
+        import prisma.models
+
+        creator = await prisma.models.LlmModelCreator.prisma().create(
+            data={
+                "name": request["name"],
+                "displayName": request["display_name"],
+                "description": request.get("description"),
+                "websiteUrl": request.get("website_url"),
+                "logoUrl": request.get("logo_url"),
+                "metadata": prisma.Json(request.get("metadata", {})),
+            }
+        )
+        logger.info(f"Created creator '{creator.name}' (id: {creator.id})")
+        return _map_creator_response(creator)
+    except Exception as e:
+        logger.exception(f"Failed to create creator: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.patch(
+    "/llm/creators/{name}",
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def update_creator(
+    name: str,
+    request: dict[str, Any],
+) -> dict[str, Any]:
+    """Update an existing LLM model creator."""
+    try:
+        import prisma.models
+
+        existing = await prisma.models.LlmModelCreator.prisma().find_unique(
+            where={"name": name}
+        )
+        if not existing:
+            raise HTTPException(
+                status_code=404, detail=f"Creator '{name}' not found"
+            )
+
+        data: dict[str, Any] = {}
+        if "display_name" in request:
+            data["displayName"] = request["display_name"]
+        if "description" in request:
+            data["description"] = request["description"]
+        if "website_url" in request:
+            data["websiteUrl"] = request["website_url"]
+        if "logo_url" in request:
+            data["logoUrl"] = request["logo_url"]
+
+        creator = await prisma.models.LlmModelCreator.prisma().update(
+            where={"id": existing.id},
+            data=data,
+        )
+        logger.info(f"Updated creator '{name}' (id: {creator.id})")
+        return _map_creator_response(creator)
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.exception(f"Failed to update creator: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.delete(
+    "/llm/creators/{name}",
+    status_code=status.HTTP_204_NO_CONTENT,
+    dependencies=[Security(autogpt_libs.auth.requires_admin_user)],
+)
+async def delete_creator(
+    name: str,
+) -> None:
+    """Delete an LLM model creator."""
+    try:
+        import prisma.models
+
+        existing = await prisma.models.LlmModelCreator.prisma().find_unique(
+            where={"name": name},
+            include={"Models": True},
+        )
+        if not existing:
+            raise HTTPException(
+                status_code=404, detail=f"Creator '{name}' not found"
+            )
+
+        if existing.Models and len(existing.Models) > 0:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Cannot delete creator '{name}' — it has {len(existing.Models)} associated models",
+            )
+
+        await prisma.models.LlmModelCreator.prisma().delete(
+            where={"id": existing.id}
+        )
+        logger.info(f"Deleted creator '{name}' (id: {existing.id})")
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.exception(f"Failed to delete creator: {e}")
+        raise HTTPException(status_code=500, detail=str(e))

autogpt_platform/backend/backend/server/v2/llm/db_write.py

@@ -0,0 +1,588 @@
+"""Database write operations for LLM registry admin API."""
+
+import json
+import logging
+from datetime import datetime, timezone
+from typing import Any
+
+import prisma
+import prisma.models
+
+from backend.data import llm_registry
+from backend.data.db import transaction
+
+logger = logging.getLogger(__name__)
+
+
+def _build_provider_data(
+    name: str,
+    display_name: str,
+    description: str | None = None,
+    default_credential_provider: str | None = None,
+    default_credential_id: str | None = None,
+    default_credential_type: str | None = None,
+    metadata: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Build provider data dict for Prisma operations."""
+    return {
+        "name": name,
+        "displayName": display_name,
+        "description": description,
+        "defaultCredentialProvider": default_credential_provider,
+        "defaultCredentialId": default_credential_id,
+        "defaultCredentialType": default_credential_type,
+        "metadata": prisma.Json(metadata or {}),
+    }
+
+
+def _build_model_data(
+    slug: str,
+    display_name: str,
+    provider_id: str,
+    context_window: int,
+    price_tier: int,
+    description: str | None = None,
+    creator_id: str | None = None,
+    max_output_tokens: int | None = None,
+    is_enabled: bool = True,
+    is_recommended: bool = False,
+    supports_tools: bool = False,
+    supports_json_output: bool = False,
+    supports_reasoning: bool = False,
+    supports_parallel_tool_calls: bool = False,
+    capabilities: dict[str, Any] | None = None,
+    metadata: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Build model data dict for Prisma operations."""
+    data: dict[str, Any] = {
+        "slug": slug,
+        "displayName": display_name,
+        "description": description,
+        "Provider": {"connect": {"id": provider_id}},
+        "contextWindow": context_window,
+        "maxOutputTokens": max_output_tokens,
+        "priceTier": price_tier,
+        "isEnabled": is_enabled,
+        "isRecommended": is_recommended,
+        "supportsTools": supports_tools,
+        "supportsJsonOutput": supports_json_output,
+        "supportsReasoning": supports_reasoning,
+        "supportsParallelToolCalls": supports_parallel_tool_calls,
+        "capabilities": prisma.Json(capabilities or {}),
+        "metadata": prisma.Json(metadata or {}),
+    }
+    if creator_id:
+        data["Creator"] = {"connect": {"id": creator_id}}
+    return data
+
+
+async def create_provider(
+    name: str,
+    display_name: str,
+    description: str | None = None,
+    default_credential_provider: str | None = None,
+    default_credential_id: str | None = None,
+    default_credential_type: str | None = None,
+    metadata: dict[str, Any] | None = None,
+) -> prisma.models.LlmProvider:
+    """Create a new LLM provider."""
+    data = _build_provider_data(
+        name=name,
+        display_name=display_name,
+        description=description,
+        default_credential_provider=default_credential_provider,
+        default_credential_id=default_credential_id,
+        default_credential_type=default_credential_type,
+        metadata=metadata,
+    )
+    provider = await prisma.models.LlmProvider.prisma().create(
+        data=data,
+        include={"Models": True},
+    )
+    if not provider:
+        raise ValueError("Failed to create provider")
+    return provider
+
+
+async def update_provider(
+    provider_id: str,
+    display_name: str | None = None,
+    description: str | None = None,
+    default_credential_provider: str | None = None,
+    default_credential_id: str | None = None,
+    default_credential_type: str | None = None,
+    metadata: dict[str, Any] | None = None,
+) -> prisma.models.LlmProvider:
+    """Update an existing LLM provider."""
+    # Fetch existing provider to get current name
+    provider = await prisma.models.LlmProvider.prisma().find_unique(
+        where={"id": provider_id}
+    )
+    if not provider:
+        raise ValueError(f"Provider with id '{provider_id}' not found")
+
+    # Build update data (only include fields that are provided)
+    data: dict[str, Any] = {}
+    if display_name is not None:
+        data["displayName"] = display_name
+    if description is not None:
+        data["description"] = description
+    if default_credential_provider is not None:
+        data["defaultCredentialProvider"] = default_credential_provider
+    if default_credential_id is not None:
+        data["defaultCredentialId"] = default_credential_id
+    if default_credential_type is not None:
+        data["defaultCredentialType"] = default_credential_type
+    if metadata is not None:
+        data["metadata"] = prisma.Json(metadata)
+
+    updated = await prisma.models.LlmProvider.prisma().update(
+        where={"id": provider_id},
+        data=data,
+        include={"Models": True},
+    )
+    if not updated:
+        raise ValueError("Failed to update provider")
+    return updated
+
+
+async def delete_provider(provider_id: str) -> bool:
+    """Delete an LLM provider.
+
+    A provider can only be deleted if it has no associated models.
+    """
+    # Check if provider exists
+    provider = await prisma.models.LlmProvider.prisma().find_unique(
+        where={"id": provider_id},
+        include={"Models": True},
+    )
+    if not provider:
+        raise ValueError(f"Provider with id '{provider_id}' not found")
+
+    # Check if provider has any models
+    model_count = len(provider.Models) if provider.Models else 0
+    if model_count > 0:
+        raise ValueError(
+            f"Cannot delete provider '{provider.displayName}' because it has "
+            f"{model_count} model(s). Delete all models first."
+        )
+
+    await prisma.models.LlmProvider.prisma().delete(where={"id": provider_id})
+    return True
+
+
+async def create_model(
+    slug: str,
+    display_name: str,
+    provider_id: str,
+    context_window: int,
+    price_tier: int,
+    description: str | None = None,
+    creator_id: str | None = None,
+    max_output_tokens: int | None = None,
+    is_enabled: bool = True,
+    is_recommended: bool = False,
+    supports_tools: bool = False,
+    supports_json_output: bool = False,
+    supports_reasoning: bool = False,
+    supports_parallel_tool_calls: bool = False,
+    capabilities: dict[str, Any] | None = None,
+    metadata: dict[str, Any] | None = None,
+) -> prisma.models.LlmModel:
+    """Create a new LLM model."""
+    data = _build_model_data(
+        slug=slug,
+        display_name=display_name,
+        provider_id=provider_id,
+        context_window=context_window,
+        price_tier=price_tier,
+        description=description,
+        creator_id=creator_id,
+        max_output_tokens=max_output_tokens,
+        is_enabled=is_enabled,
+        is_recommended=is_recommended,
+        supports_tools=supports_tools,
+        supports_json_output=supports_json_output,
+        supports_reasoning=supports_reasoning,
+        supports_parallel_tool_calls=supports_parallel_tool_calls,
+        capabilities=capabilities,
+        metadata=metadata,
+    )
+    model = await prisma.models.LlmModel.prisma().create(
+        data=data,
+        include={"Costs": True, "Creator": True, "Provider": True},
+    )
+    if not model:
+        raise ValueError("Failed to create model")
+    return model
+
+
+async def update_model(
+    model_id: str,
+    display_name: str | None = None,
+    description: str | None = None,
+    creator_id: str | None = None,
+    context_window: int | None = None,
+    max_output_tokens: int | None = None,
+    price_tier: int | None = None,
+    is_enabled: bool | None = None,
+    is_recommended: bool | None = None,
+    supports_tools: bool | None = None,
+    supports_json_output: bool | None = None,
+    supports_reasoning: bool | None = None,
+    supports_parallel_tool_calls: bool | None = None,
+    capabilities: dict[str, Any] | None = None,
+    metadata: dict[str, Any] | None = None,
+) -> prisma.models.LlmModel:
+    """Update an existing LLM model.
+
+    When is_recommended=True, clears the flag on all other models first so
+    only one model can be recommended at a time.
+    """
+    # Build update data (only include fields that are provided)
+    data: dict[str, Any] = {}
+    if display_name is not None:
+        data["displayName"] = display_name
+    if description is not None:
+        data["description"] = description
+    if context_window is not None:
+        data["contextWindow"] = context_window
+    if max_output_tokens is not None:
+        data["maxOutputTokens"] = max_output_tokens
+    if price_tier is not None:
+        data["priceTier"] = price_tier
+    if is_enabled is not None:
+        data["isEnabled"] = is_enabled
+    if is_recommended is not None:
+        data["isRecommended"] = is_recommended
+    if supports_tools is not None:
+        data["supportsTools"] = supports_tools
+    if supports_json_output is not None:
+        data["supportsJsonOutput"] = supports_json_output
+    if supports_reasoning is not None:
+        data["supportsReasoning"] = supports_reasoning
+    if supports_parallel_tool_calls is not None:
+        data["supportsParallelToolCalls"] = supports_parallel_tool_calls
+    if capabilities is not None:
+        data["capabilities"] = prisma.Json(capabilities)
+    if metadata is not None:
+        data["metadata"] = prisma.Json(metadata)
+    if creator_id is not None:
+        data["creatorId"] = creator_id if creator_id else None
+
+    async with transaction() as tx:
+        # Enforce single recommended model: unset all others first.
+        if is_recommended is True:
+            await tx.llmmodel.update_many(
+                where={"id": {"not": model_id}},
+                data={"isRecommended": False},
+            )
+
+        model = await tx.llmmodel.update(
+            where={"id": model_id},
+            data=data,
+            include={"Costs": True, "Creator": True, "Provider": True},
+        )
+
+    if not model:
+        raise ValueError(f"Model with id '{model_id}' not found")
+    return model
+
+
+async def get_model_usage(slug: str) -> dict[str, Any]:
+    """Get usage count for a model — how many AgentNodes reference it."""
+    import prisma as prisma_module
+
+    count_result = await prisma_module.get_client().query_raw(
+        """
+        SELECT COUNT(*) as count
+        FROM "AgentNode"
+        WHERE "constantInput"::jsonb->>'model' = $1
+        """,
+        slug,
+    )
+    node_count = int(count_result[0]["count"]) if count_result else 0
+    return {"model_slug": slug, "node_count": node_count}
+
+
+async def toggle_model_with_migration(
+    model_id: str,
+    is_enabled: bool,
+    migrate_to_slug: str | None = None,
+    migration_reason: str | None = None,
+    custom_credit_cost: int | None = None,
+) -> dict[str, Any]:
+    """Toggle a model's enabled status, optionally migrating workflows when disabling."""
+    model = await prisma.models.LlmModel.prisma().find_unique(
+        where={"id": model_id}, include={"Costs": True}
+    )
+    if not model:
+        raise ValueError(f"Model with id '{model_id}' not found")
+
+    nodes_migrated = 0
+    migration_id: str | None = None
+
+    if not is_enabled and migrate_to_slug:
+        async with transaction() as tx:
+            replacement = await tx.llmmodel.find_unique(
+                where={"slug": migrate_to_slug}
+            )
+            if not replacement:
+                raise ValueError(
+                    f"Replacement model '{migrate_to_slug}' not found"
+                )
+            if not replacement.isEnabled:
+                raise ValueError(
+                    f"Replacement model '{migrate_to_slug}' is disabled. "
+                    f"Please enable it before using it as a replacement."
+                )
+
+            node_ids_result = await tx.query_raw(
+                """
+                SELECT id
+                FROM "AgentNode"
+                WHERE "constantInput"::jsonb->>'model' = $1
+                FOR UPDATE
+                """,
+                model.slug,
+            )
+            migrated_node_ids = (
+                [row["id"] for row in node_ids_result] if node_ids_result else []
+            )
+            nodes_migrated = len(migrated_node_ids)
+
+            if nodes_migrated > 0:
+                node_ids_json = json.dumps(migrated_node_ids)
+                await tx.execute_raw(
+                    """
+                    UPDATE "AgentNode"
+                    SET "constantInput" = JSONB_SET(
+                        "constantInput"::jsonb,
+                        '{model}',
+                        to_jsonb($1::text)
+                    )
+                    WHERE id::text IN (
+                        SELECT jsonb_array_elements_text($2::jsonb)
+                    )
+                    """,
+                    migrate_to_slug,
+                    node_ids_json,
+                )
+
+            await tx.llmmodel.update(
+                where={"id": model_id},
+                data={"isEnabled": is_enabled},
+            )
+
+            if nodes_migrated > 0:
+                migration_record = await tx.llmmodelmigration.create(
+                    data={
+                        "sourceModelSlug": model.slug,
+                        "targetModelSlug": migrate_to_slug,
+                        "reason": migration_reason,
+                        "migratedNodeIds": json.dumps(migrated_node_ids),
+                        "nodeCount": nodes_migrated,
+                        "customCreditCost": custom_credit_cost,
+                    }
+                )
+                migration_id = migration_record.id
+    else:
+        await prisma.models.LlmModel.prisma().update(
+            where={"id": model_id},
+            data={"isEnabled": is_enabled},
+        )
+
+    return {
+        "nodes_migrated": nodes_migrated,
+        "migrated_to_slug": migrate_to_slug if nodes_migrated > 0 else None,
+        "migration_id": migration_id,
+    }
+
+
+async def delete_model(
+    model_id: str, replacement_model_slug: str | None = None
+) -> dict[str, Any]:
+    """Delete an LLM model, optionally migrating affected AgentNodes first.
+
+    If workflows are using this model and no replacement is given, raises ValueError.
+    If replacement is given, atomically migrates all affected nodes then deletes.
+    """
+    model = await prisma.models.LlmModel.prisma().find_unique(
+        where={"id": model_id}, include={"Costs": True}
+    )
+    if not model:
+        raise ValueError(f"Model with id '{model_id}' not found")
+
+    deleted_slug = model.slug
+    deleted_display_name = model.displayName
+
+    async with transaction() as tx:
+        count_result = await tx.query_raw(
+            """
+            SELECT COUNT(*) as count
+            FROM "AgentNode"
+            WHERE "constantInput"::jsonb->>'model' = $1
+            """,
+            deleted_slug,
+        )
+        nodes_to_migrate = int(count_result[0]["count"]) if count_result else 0
+
+        if nodes_to_migrate > 0:
+            if not replacement_model_slug:
+                raise ValueError(
+                    f"Cannot delete model '{deleted_slug}': {nodes_to_migrate} workflow node(s) "
+                    f"are using it. Please provide a replacement_model_slug to migrate them."
+                )
+            replacement = await tx.llmmodel.find_unique(
+                where={"slug": replacement_model_slug}
+            )
+            if not replacement:
+                raise ValueError(
+                    f"Replacement model '{replacement_model_slug}' not found"
+                )
+            if not replacement.isEnabled:
+                raise ValueError(
+                    f"Replacement model '{replacement_model_slug}' is disabled."
+                )
+
+            await tx.execute_raw(
+                """
+                UPDATE "AgentNode"
+                SET "constantInput" = JSONB_SET(
+                    "constantInput"::jsonb,
+                    '{model}',
+                    to_jsonb($1::text)
+                )
+                WHERE "constantInput"::jsonb->>'model' = $2
+                """,
+                replacement_model_slug,
+                deleted_slug,
+            )
+
+        await tx.llmmodel.delete(where={"id": model_id})
+
+    return {
+        "deleted_model_slug": deleted_slug,
+        "deleted_model_display_name": deleted_display_name,
+        "replacement_model_slug": replacement_model_slug,
+        "nodes_migrated": nodes_to_migrate,
+    }
+
+
+async def list_migrations(
+    include_reverted: bool = False,
+) -> list[dict[str, Any]]:
+    """List model migrations."""
+    where: Any = None if include_reverted else {"isReverted": False}
+    records = await prisma.models.LlmModelMigration.prisma().find_many(
+        where=where,
+        order={"createdAt": "desc"},
+    )
+    return [
+        {
+            "id": r.id,
+            "source_model_slug": r.sourceModelSlug,
+            "target_model_slug": r.targetModelSlug,
+            "reason": r.reason,
+            "node_count": r.nodeCount,
+            "custom_credit_cost": r.customCreditCost,
+            "is_reverted": r.isReverted,
+            "reverted_at": r.revertedAt.isoformat() if r.revertedAt else None,
+            "created_at": r.createdAt.isoformat(),
+        }
+        for r in records
+    ]
+
+
+async def revert_migration(
+    migration_id: str,
+    re_enable_source_model: bool = True,
+) -> dict[str, Any]:
+    """Revert a model migration, restoring affected nodes to their original model."""
+    migration = await prisma.models.LlmModelMigration.prisma().find_unique(
+        where={"id": migration_id}
+    )
+    if not migration:
+        raise ValueError(f"Migration with id '{migration_id}' not found")
+
+    if migration.isReverted:
+        raise ValueError(
+            f"Migration '{migration_id}' has already been reverted"
+        )
+
+    source_model = await prisma.models.LlmModel.prisma().find_unique(
+        where={"slug": migration.sourceModelSlug}
+    )
+    if not source_model:
+        raise ValueError(
+            f"Source model '{migration.sourceModelSlug}' no longer exists."
+        )
+
+    migrated_node_ids: list[str] = (
+        migration.migratedNodeIds
+        if isinstance(migration.migratedNodeIds, list)
+        else json.loads(migration.migratedNodeIds)  # type: ignore
+    )
+    if not migrated_node_ids:
+        raise ValueError("No nodes to revert in this migration")
+
+    source_model_re_enabled = False
+
+    async with transaction() as tx:
+        if not source_model.isEnabled and re_enable_source_model:
+            await tx.llmmodel.update(
+                where={"id": source_model.id},
+                data={"isEnabled": True},
+            )
+            source_model_re_enabled = True
+
+        node_ids_json = json.dumps(migrated_node_ids)
+        result = await tx.execute_raw(
+            """
+            UPDATE "AgentNode"
+            SET "constantInput" = JSONB_SET(
+                "constantInput"::jsonb,
+                '{model}',
+                to_jsonb($1::text)
+            )
+            WHERE id::text IN (
+                SELECT jsonb_array_elements_text($2::jsonb)
+            )
+            AND "constantInput"::jsonb->>'model' = $3
+            """,
+            migration.sourceModelSlug,
+            node_ids_json,
+            migration.targetModelSlug,
+        )
+        nodes_reverted = result if isinstance(result, int) else 0
+
+        await tx.llmmodelmigration.update(
+            where={"id": migration_id},
+            data={
+                "isReverted": True,
+                "revertedAt": datetime.now(timezone.utc),
+            },
+        )
+
+    return {
+        "migration_id": migration_id,
+        "source_model_slug": migration.sourceModelSlug,
+        "target_model_slug": migration.targetModelSlug,
+        "nodes_reverted": nodes_reverted,
+        "nodes_already_changed": len(migrated_node_ids) - nodes_reverted,
+        "source_model_re_enabled": source_model_re_enabled,
+    }
+
+
+async def refresh_runtime_caches() -> None:
+    """Invalidate the shared Redis cache, refresh this process, notify other workers."""
+    from backend.data.llm_registry.notifications import (
+        publish_registry_refresh_notification,
+    )
+
+    # Invalidate Redis so the next fetch hits the DB.
+    llm_registry.clear_registry_cache()
+    # Refresh this process (also repopulates Redis via @cached(shared_cache=True)).
+    await llm_registry.refresh_llm_registry()
+    # Tell other workers to reload their in-process cache from the fresh Redis data.
+    await publish_registry_refresh_notification()

autogpt_platform/backend/backend/server/v2/llm/model.py

@@ -0,0 +1,68 @@
+"""Pydantic models for LLM registry public API."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pydantic
+
+
+class LlmModelCost(pydantic.BaseModel):
+    """Cost configuration for an LLM model."""
+
+    unit: str  # "RUN" or "TOKENS"
+    credit_cost: int = pydantic.Field(ge=0)
+    credential_provider: str
+    credential_id: str | None = None
+    credential_type: str | None = None
+    currency: str | None = None
+    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
+
+
+class LlmModelCreator(pydantic.BaseModel):
+    """Represents the organization that created/trained the model."""
+
+    id: str
+    name: str
+    display_name: str
+    description: str | None = None
+    website_url: str | None = None
+    logo_url: str | None = None
+
+
+class LlmModel(pydantic.BaseModel):
+    """Public-facing LLM model information."""
+
+    slug: str
+    display_name: str
+    description: str | None = None
+    provider_name: str
+    creator: LlmModelCreator | None = None
+    context_window: int
+    max_output_tokens: int | None = None
+    price_tier: int  # 1=cheapest, 2=medium, 3=expensive
+    is_enabled: bool = True
+    is_recommended: bool = False
+    capabilities: dict[str, Any] = pydantic.Field(default_factory=dict)
+    costs: list[LlmModelCost] = pydantic.Field(default_factory=list)
+
+
+class LlmProvider(pydantic.BaseModel):
+    """Provider with its enabled models."""
+
+    name: str
+    display_name: str
+    models: list[LlmModel] = pydantic.Field(default_factory=list)
+
+
+class LlmModelsResponse(pydantic.BaseModel):
+    """Response for GET /llm/models."""
+
+    models: list[LlmModel]
+    total: int
+
+
+class LlmProvidersResponse(pydantic.BaseModel):
+    """Response for GET /llm/providers."""
+
+    providers: list[LlmProvider]

autogpt_platform/backend/backend/server/v2/llm/routes.py

@@ -0,0 +1,143 @@
+"""Public read-only API for LLM registry."""
+
+import autogpt_libs.auth
+import fastapi
+
+from backend.data.llm_registry import (
+    RegistryModelCreator,
+    get_all_models,
+    get_enabled_models,
+)
+from backend.server.v2.llm import model as llm_model
+
+router = fastapi.APIRouter(
+    prefix="/llm",
+    tags=["llm"],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
+)
+
+
+def _map_creator(
+    creator: RegistryModelCreator | None,
+) -> llm_model.LlmModelCreator | None:
+    """Convert registry creator to API model."""
+    if not creator:
+        return None
+    return llm_model.LlmModelCreator(
+        id=creator.id,
+        name=creator.name,
+        display_name=creator.display_name,
+        description=creator.description,
+        website_url=creator.website_url,
+        logo_url=creator.logo_url,
+    )
+
+
+@router.get("/models", response_model=llm_model.LlmModelsResponse)
+async def list_models(
+    enabled_only: bool = fastapi.Query(
+        default=True, description="Only return enabled models"
+    ),
+):
+    """
+    List all LLM models available to users.
+
+    Returns models from the in-memory registry cache.
+    Use enabled_only=true to filter to only enabled models (default).
+    """
+    # Get models from in-memory registry
+    registry_models = get_enabled_models() if enabled_only else get_all_models()
+
+    # Map to API response models
+    models = [
+        llm_model.LlmModel(
+            slug=model.slug,
+            display_name=model.display_name,
+            description=model.description,
+            provider_name=model.provider_display_name,
+            creator=_map_creator(model.creator),
+            context_window=model.metadata.context_window,
+            max_output_tokens=model.metadata.max_output_tokens,
+            price_tier=model.metadata.price_tier,
+            is_enabled=model.is_enabled,
+            is_recommended=model.is_recommended,
+            capabilities=model.capabilities,
+            costs=[
+                llm_model.LlmModelCost(
+                    unit=cost.unit,
+                    credit_cost=cost.credit_cost,
+                    credential_provider=cost.credential_provider,
+                    credential_id=cost.credential_id,
+                    credential_type=cost.credential_type,
+                    currency=cost.currency,
+                    metadata=cost.metadata,
+                )
+                for cost in model.costs
+            ],
+        )
+        for model in registry_models
+    ]
+
+    return llm_model.LlmModelsResponse(models=models, total=len(models))
+
+
+@router.get("/providers", response_model=llm_model.LlmProvidersResponse)
+async def list_providers():
+    """
+    List all LLM providers with their enabled models.
+
+    Groups enabled models by provider from the in-memory registry.
+    """
+    # Get all enabled models and group by provider
+    registry_models = get_enabled_models()
+
+    # Group models by provider
+    provider_map: dict[str, list] = {}
+    for model in registry_models:
+        provider_key = model.metadata.provider
+        if provider_key not in provider_map:
+            provider_map[provider_key] = []
+        provider_map[provider_key].append(model)
+
+    # Build provider responses
+    providers = []
+    for provider_key, models in sorted(provider_map.items()):
+        # Use the first model's provider display name
+        display_name = models[0].provider_display_name if models else provider_key
+
+        providers.append(
+            llm_model.LlmProvider(
+                name=provider_key,
+                display_name=display_name,
+                models=[
+                    llm_model.LlmModel(
+                        slug=model.slug,
+                        display_name=model.display_name,
+                        description=model.description,
+                        provider_name=model.provider_display_name,
+                        creator=_map_creator(model.creator),
+                        context_window=model.metadata.context_window,
+                        max_output_tokens=model.metadata.max_output_tokens,
+                        price_tier=model.metadata.price_tier,
+                        is_enabled=model.is_enabled,
+            is_recommended=model.is_recommended,
+                        capabilities=model.capabilities,
+                        costs=[
+                            llm_model.LlmModelCost(
+                                unit=cost.unit,
+                                credit_cost=cost.credit_cost,
+                                credential_provider=cost.credential_provider,
+                                credential_id=cost.credential_id,
+                                credential_type=cost.credential_type,
+                                currency=cost.currency,
+                                metadata=cost.metadata,
+                            )
+                            for cost in model.costs
+                        ],
+                    )
+                    for model in sorted(models, key=lambda m: m.display_name)
+                ],
+            )
+        )
+
+    return llm_model.LlmProvidersResponse(providers=providers)

autogpt_platform/backend/backend/util/service.py

@@ -704,8 +704,19 @@ def get_service_client(
             return kwargs
 
         def _get_return(self, expected_return: TypeAdapter | None, result: Any) -> Any:
+            """Validate and coerce the RPC result to the expected return type.
+
+            Falls back to the raw result with a warning if validation fails.
+            """
             if expected_return:
-                return expected_return.validate_python(result)
+                try:
+                    return expected_return.validate_python(result)
+                except Exception as e:
+                    logger.warning(
+                        "RPC return type validation failed, using raw result: %s",
+                        type(e).__name__,
+                    )
+                    return result
             return result
 
         def __getattr__(self, name: str) -> Callable[..., Any]:

autogpt_platform/backend/backend/util/type.py

@@ -302,7 +302,14 @@ def _value_satisfies_type(value: Any, target: Any) -> bool:
 
     # Simple type (e.g. str, int)
     if isinstance(target, type):
-        return isinstance(value, target)
+        try:
+            return isinstance(value, target)
+        except TypeError:
+            # TypedDict and some typing constructs don't support isinstance checks.
+            # For TypedDict, check if value is a dict with the required keys.
+            if isinstance(value, dict) and hasattr(target, "__required_keys__"):
+                return all(k in value for k in target.__required_keys__)
+            return False
 
     return False
 

autogpt_platform/backend/migrations/20260310_add_llm_registry_schema/migration.sql

@@ -0,0 +1,148 @@
+-- CreateEnum
+CREATE TYPE "LlmCostUnit" AS ENUM ('RUN', 'TOKENS');
+
+-- CreateTable
+CREATE TABLE "LlmProvider" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "name" TEXT NOT NULL,
+    "displayName" TEXT NOT NULL,
+    "description" TEXT,
+    "defaultCredentialProvider" TEXT,
+    "defaultCredentialId" TEXT,
+    "defaultCredentialType" TEXT,
+    "metadata" JSONB NOT NULL DEFAULT '{}',
+
+    CONSTRAINT "LlmProvider_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateTable
+CREATE TABLE "LlmModelCreator" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "name" TEXT NOT NULL,
+    "displayName" TEXT NOT NULL,
+    "description" TEXT,
+    "websiteUrl" TEXT,
+    "logoUrl" TEXT,
+    "metadata" JSONB NOT NULL DEFAULT '{}',
+
+    CONSTRAINT "LlmModelCreator_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateTable
+CREATE TABLE "LlmModel" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "slug" TEXT NOT NULL,
+    "displayName" TEXT NOT NULL,
+    "description" TEXT,
+    "providerId" TEXT NOT NULL,
+    "creatorId" TEXT,
+    "contextWindow" INTEGER NOT NULL,
+    "maxOutputTokens" INTEGER,
+    "priceTier" INTEGER NOT NULL DEFAULT 1,
+    "isEnabled" BOOLEAN NOT NULL DEFAULT true,
+    "isRecommended" BOOLEAN NOT NULL DEFAULT false,
+    "supportsTools" BOOLEAN NOT NULL DEFAULT false,
+    "supportsJsonOutput" BOOLEAN NOT NULL DEFAULT false,
+    "supportsReasoning" BOOLEAN NOT NULL DEFAULT false,
+    "supportsParallelToolCalls" BOOLEAN NOT NULL DEFAULT false,
+    "capabilities" JSONB NOT NULL DEFAULT '{}',
+    "metadata" JSONB NOT NULL DEFAULT '{}',
+
+    CONSTRAINT "LlmModel_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateTable
+CREATE TABLE "LlmModelCost" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "unit" "LlmCostUnit" NOT NULL DEFAULT 'RUN',
+    "creditCost" INTEGER NOT NULL,
+    "credentialProvider" TEXT NOT NULL,
+    "credentialId" TEXT,
+    "credentialType" TEXT,
+    "currency" TEXT,
+    "metadata" JSONB NOT NULL DEFAULT '{}',
+    "llmModelId" TEXT NOT NULL,
+
+    CONSTRAINT "LlmModelCost_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateTable
+CREATE TABLE "LlmModelMigration" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "sourceModelSlug" TEXT NOT NULL,
+    "targetModelSlug" TEXT NOT NULL,
+    "reason" TEXT,
+    "migratedNodeIds" JSONB NOT NULL DEFAULT '[]',
+    "nodeCount" INTEGER NOT NULL,
+    "customCreditCost" INTEGER,
+    "isReverted" BOOLEAN NOT NULL DEFAULT false,
+    "revertedAt" TIMESTAMP(3),
+
+    CONSTRAINT "LlmModelMigration_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateIndex
+CREATE UNIQUE INDEX "LlmProvider_name_key" ON "LlmProvider"("name");
+
+-- CreateIndex
+CREATE UNIQUE INDEX "LlmModelCreator_name_key" ON "LlmModelCreator"("name");
+
+-- CreateIndex
+CREATE UNIQUE INDEX "LlmModel_slug_key" ON "LlmModel"("slug");
+
+-- CreateIndex
+CREATE INDEX "LlmModel_providerId_isEnabled_idx" ON "LlmModel"("providerId", "isEnabled");
+
+-- CreateIndex
+CREATE INDEX "LlmModel_creatorId_idx" ON "LlmModel"("creatorId");
+
+-- CreateIndex (partial unique for default costs - no specific credential)
+CREATE UNIQUE INDEX "LlmModelCost_default_cost_key" ON "LlmModelCost"("llmModelId", "credentialProvider", "unit") WHERE "credentialId" IS NULL;
+
+-- CreateIndex (partial unique for credential-specific costs)
+CREATE UNIQUE INDEX "LlmModelCost_credential_cost_key" ON "LlmModelCost"("llmModelId", "credentialProvider", "credentialId", "unit") WHERE "credentialId" IS NOT NULL;
+
+-- CreateIndex
+CREATE INDEX "LlmModelMigration_targetModelSlug_idx" ON "LlmModelMigration"("targetModelSlug");
+
+-- CreateIndex
+CREATE INDEX "LlmModelMigration_sourceModelSlug_isReverted_idx" ON "LlmModelMigration"("sourceModelSlug", "isReverted");
+
+-- CreateIndex (partial unique to prevent multiple active migrations per source)
+CREATE UNIQUE INDEX "LlmModelMigration_active_source_key" ON "LlmModelMigration"("sourceModelSlug") WHERE "isReverted" = false;
+
+-- AddForeignKey
+ALTER TABLE "LlmModel" ADD CONSTRAINT "LlmModel_providerId_fkey" FOREIGN KEY ("providerId") REFERENCES "LlmProvider"("id") ON DELETE RESTRICT ON UPDATE CASCADE;
+
+-- AddForeignKey
+ALTER TABLE "LlmModel" ADD CONSTRAINT "LlmModel_creatorId_fkey" FOREIGN KEY ("creatorId") REFERENCES "LlmModelCreator"("id") ON DELETE SET NULL ON UPDATE CASCADE;
+
+-- AddForeignKey
+ALTER TABLE "LlmModelCost" ADD CONSTRAINT "LlmModelCost_llmModelId_fkey" FOREIGN KEY ("llmModelId") REFERENCES "LlmModel"("id") ON DELETE CASCADE ON UPDATE CASCADE;
+
+-- AddForeignKey
+ALTER TABLE "LlmModelMigration" ADD CONSTRAINT "LlmModelMigration_sourceModelSlug_fkey" FOREIGN KEY ("sourceModelSlug") REFERENCES "LlmModel"("slug") ON DELETE RESTRICT ON UPDATE CASCADE;
+
+-- AddForeignKey
+ALTER TABLE "LlmModelMigration" ADD CONSTRAINT "LlmModelMigration_targetModelSlug_fkey" FOREIGN KEY ("targetModelSlug") REFERENCES "LlmModel"("slug") ON DELETE RESTRICT ON UPDATE CASCADE;
+
+-- AddCheckConstraints (enforce data integrity)
+ALTER TABLE "LlmModel"
+    ADD CONSTRAINT "LlmModel_priceTier_check" CHECK ("priceTier" BETWEEN 1 AND 3);
+
+ALTER TABLE "LlmModelCost"
+    ADD CONSTRAINT "LlmModelCost_creditCost_check" CHECK ("creditCost" >= 0);
+
+ALTER TABLE "LlmModelMigration"
+    ADD CONSTRAINT "LlmModelMigration_nodeCount_check" CHECK ("nodeCount" >= 0),
+    ADD CONSTRAINT "LlmModelMigration_customCreditCost_check" CHECK ("customCreditCost" IS NULL OR "customCreditCost" >= 0);

autogpt_platform/backend/migrations/20260310_seed_llm_registry/migration.sql

@@ -0,0 +1,287 @@
+-- Seed LLM Registry from existing hard-coded data
+-- This migration populates the LlmProvider, LlmModelCreator, LlmModel, and LlmModelCost tables
+-- with data from the existing MODEL_METADATA and MODEL_COST dictionaries
+
+-- Insert Providers
+INSERT INTO "LlmProvider" ("id", "createdAt", "updatedAt", "name", "displayName", "description", "defaultCredentialProvider", "defaultCredentialType", "metadata")
+VALUES
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'openai', 'OpenAI', 'OpenAI language models', 'openai', 'api_key', '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'anthropic', 'Anthropic', 'Anthropic Claude models', 'anthropic', 'api_key', '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'groq', 'Groq', 'Groq inference API', 'groq', 'api_key', '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'open_router', 'OpenRouter', 'OpenRouter unified API', 'open_router', 'api_key', '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'aiml_api', 'AI/ML API', 'AI/ML API models', 'aiml_api', 'api_key', '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'ollama', 'Ollama', 'Ollama local models', 'ollama', 'api_key', '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'llama_api', 'Llama API', 'Llama API models', 'llama_api', 'api_key', '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'v0', 'v0', 'v0 by Vercel models', 'v0', 'api_key', '{}'::jsonb)
+ON CONFLICT ("name") DO NOTHING;
+
+-- Insert Model Creators
+INSERT INTO "LlmModelCreator" ("id", "createdAt", "updatedAt", "name", "displayName", "description", "websiteUrl", "logoUrl", "metadata")
+VALUES
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'openai', 'OpenAI', 'Creator of GPT, O1, O3, and DALL-E models', 'https://openai.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'anthropic', 'Anthropic', 'Creator of Claude AI models', 'https://anthropic.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'meta', 'Meta', 'Creator of Llama foundation models', 'https://llama.meta.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'google', 'Google', 'Creator of Gemini and PaLM models', 'https://deepmind.google', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'mistralai', 'Mistral AI', 'Creator of Mistral and Codestral models', 'https://mistral.ai', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'cohere', 'Cohere', 'Creator of Command language models', 'https://cohere.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'deepseek', 'DeepSeek', 'Creator of DeepSeek reasoning models', 'https://deepseek.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'alibaba', 'Alibaba', 'Creator of Qwen language models', 'https://qwenlm.github.io', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'nvidia', 'NVIDIA', 'Creator of Nemotron models', 'https://nvidia.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'vercel', 'Vercel', 'Creator of v0 AI models', 'https://v0.dev', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'microsoft', 'Microsoft', 'Creator of Phi models', 'https://microsoft.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'xai', 'xAI', 'Creator of Grok models', 'https://x.ai', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'perplexity', 'Perplexity AI', 'Creator of Sonar search models', 'https://perplexity.ai', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'nousresearch', 'Nous Research', 'Creator of Hermes language models', 'https://nousresearch.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'amazon', 'Amazon', 'Creator of Nova language models', 'https://aws.amazon.com', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'gryphe', 'Gryphe', 'Creator of MythoMax models', 'https://huggingface.co/Gryphe', NULL, '{}'::jsonb),
+    (gen_random_uuid(), CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 'moonshotai', 'Moonshot AI', 'Creator of Kimi language models', 'https://moonshot.ai', NULL, '{}'::jsonb)
+ON CONFLICT ("name") DO NOTHING;
+
+-- Insert Models (using CTEs to reference provider and creator IDs)
+WITH provider_ids AS (
+    SELECT "id", "name" FROM "LlmProvider"
+),
+creator_ids AS (
+    SELECT "id", "name" FROM "LlmModelCreator"
+)
+INSERT INTO "LlmModel" ("id", "createdAt", "updatedAt", "slug", "displayName", "description", "providerId", "creatorId", "contextWindow", "maxOutputTokens", "isEnabled", "capabilities", "metadata")
+SELECT
+    gen_random_uuid(),
+    CURRENT_TIMESTAMP,
+    CURRENT_TIMESTAMP,
+    model_slug,
+    model_display_name,
+    NULL,
+    p."id",
+    c."id",
+    context_window,
+    max_output_tokens,
+    true,
+    '{}'::jsonb,
+    '{}'::jsonb
+FROM (VALUES
+    -- OpenAI models (creator: openai)
+    ('o3-2025-04-16', 'O3', 'openai', 'openai', 200000, 100000),
+    ('o3-mini', 'O3 Mini', 'openai', 'openai', 200000, 100000),
+    ('o1', 'O1', 'openai', 'openai', 200000, 100000),
+    ('o1-mini', 'O1 Mini', 'openai', 'openai', 128000, 65536),
+    ('gpt-5.2-2025-12-11', 'GPT-5.2', 'openai', 'openai', 400000, 128000),
+    ('gpt-5-2025-08-07', 'GPT 5', 'openai', 'openai', 400000, 128000),
+    ('gpt-5.1-2025-11-13', 'GPT 5.1', 'openai', 'openai', 400000, 128000),
+    ('gpt-5-mini-2025-08-07', 'GPT 5 Mini', 'openai', 'openai', 400000, 128000),
+    ('gpt-5-nano-2025-08-07', 'GPT 5 Nano', 'openai', 'openai', 400000, 128000),
+    ('gpt-5-chat-latest', 'GPT 5 Chat', 'openai', 'openai', 400000, 16384),
+    ('gpt-4.1-2025-04-14', 'GPT 4.1', 'openai', 'openai', 1000000, 32768),
+    ('gpt-4.1-mini-2025-04-14', 'GPT 4.1 Mini', 'openai', 'openai', 1047576, 32768),
+    ('gpt-4o-mini', 'GPT 4o Mini', 'openai', 'openai', 128000, 16384),
+    ('gpt-4o', 'GPT 4o', 'openai', 'openai', 128000, 16384),
+    ('gpt-4-turbo', 'GPT 4 Turbo', 'openai', 'openai', 128000, 4096),
+    -- Anthropic models (creator: anthropic)
+    ('claude-opus-4-6', 'Claude Opus 4.6', 'anthropic', 'anthropic', 200000, 128000),
+    ('claude-sonnet-4-6', 'Claude Sonnet 4.6', 'anthropic', 'anthropic', 200000, 64000),
+    ('claude-opus-4-1-20250805', 'Claude 4.1 Opus', 'anthropic', 'anthropic', 200000, 32000),
+    ('claude-opus-4-20250514', 'Claude 4 Opus', 'anthropic', 'anthropic', 200000, 32000),
+    ('claude-sonnet-4-20250514', 'Claude 4 Sonnet', 'anthropic', 'anthropic', 200000, 64000),
+    ('claude-opus-4-5-20251101', 'Claude 4.5 Opus', 'anthropic', 'anthropic', 200000, 64000),
+    ('claude-sonnet-4-5-20250929', 'Claude 4.5 Sonnet', 'anthropic', 'anthropic', 200000, 64000),
+    ('claude-haiku-4-5-20251001', 'Claude 4.5 Haiku', 'anthropic', 'anthropic', 200000, 64000),
+    ('claude-3-haiku-20240307', 'Claude 3 Haiku', 'anthropic', 'anthropic', 200000, 4096),
+    -- AI/ML API models (creators: alibaba, nvidia, meta)
+    ('Qwen/Qwen2.5-72B-Instruct-Turbo', 'Qwen 2.5 72B', 'aiml_api', 'alibaba', 32000, 8000),
+    ('nvidia/llama-3.1-nemotron-70b-instruct', 'Llama 3.1 Nemotron 70B', 'aiml_api', 'nvidia', 128000, 40000),
+    ('meta-llama/Llama-3.3-70B-Instruct-Turbo', 'Llama 3.3 70B', 'aiml_api', 'meta', 128000, NULL),
+    ('meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo', 'Meta Llama 3.1 70B', 'aiml_api', 'meta', 131000, 2000),
+    ('meta-llama/Llama-3.2-3B-Instruct-Turbo', 'Llama 3.2 3B', 'aiml_api', 'meta', 128000, NULL),
+    -- Groq models (creator: meta for Llama)
+    ('llama-3.3-70b-versatile', 'Llama 3.3 70B', 'groq', 'meta', 128000, 32768),
+    ('llama-3.1-8b-instant', 'Llama 3.1 8B', 'groq', 'meta', 128000, 8192),
+    -- Ollama models (creators: meta for Llama, mistralai for Mistral)
+    ('llama3.3', 'Llama 3.3', 'ollama', 'meta', 8192, NULL),
+    ('llama3.2', 'Llama 3.2', 'ollama', 'meta', 8192, NULL),
+    ('llama3', 'Llama 3', 'ollama', 'meta', 8192, NULL),
+    ('llama3.1:405b', 'Llama 3.1 405B', 'ollama', 'meta', 8192, NULL),
+    ('dolphin-mistral:latest', 'Dolphin Mistral', 'ollama', 'mistralai', 32768, NULL),
+    -- OpenRouter models (creators: google, mistralai, cohere, deepseek, perplexity, nousresearch, openai, amazon, microsoft, gryphe, meta, xai, moonshotai, alibaba)
+    ('google/gemini-2.5-pro-preview-03-25', 'Gemini 2.5 Pro', 'open_router', 'google', 1050000, 8192),
+    ('google/gemini-2.5-pro', 'Gemini 2.5 Pro', 'open_router', 'google', 1048576, 65536),
+    ('google/gemini-3.1-pro-preview', 'Gemini 3.1 Pro Preview', 'open_router', 'google', 1048576, 65536),
+    ('google/gemini-3-flash-preview', 'Gemini 3 Flash Preview', 'open_router', 'google', 1048576, 65536),
+    ('google/gemini-2.5-flash', 'Gemini 2.5 Flash', 'open_router', 'google', 1048576, 65535),
+    ('google/gemini-2.0-flash-001', 'Gemini 2.0 Flash', 'open_router', 'google', 1048576, 8192),
+    ('google/gemini-3.1-flash-lite-preview', 'Gemini 3.1 Flash Lite Preview', 'open_router', 'google', 1048576, 65536),
+    ('google/gemini-2.5-flash-lite-preview-06-17', 'Gemini 2.5 Flash Lite Preview', 'open_router', 'google', 1048576, 65535),
+    ('google/gemini-2.0-flash-lite-001', 'Gemini 2.0 Flash Lite', 'open_router', 'google', 1048576, 8192),
+    ('mistralai/mistral-nemo', 'Mistral Nemo', 'open_router', 'mistralai', 128000, 4096),
+    ('mistralai/mistral-large-2512', 'Mistral Large 3 2512', 'open_router', 'mistralai', 262144, NULL),
+    ('mistralai/mistral-medium-3.1', 'Mistral Medium 3.1', 'open_router', 'mistralai', 131072, NULL),
+    ('mistralai/mistral-small-3.2-24b-instruct', 'Mistral Small 3.2 24B', 'open_router', 'mistralai', 131072, 131072),
+    ('mistralai/codestral-2508', 'Codestral 2508', 'open_router', 'mistralai', 256000, NULL),
+    ('cohere/command-r-08-2024', 'Command R', 'open_router', 'cohere', 128000, 4096),
+    ('cohere/command-r-plus-08-2024', 'Command R Plus', 'open_router', 'cohere', 128000, 4096),
+    ('cohere/command-a-03-2025', 'Command A 03.2025', 'open_router', 'cohere', 256000, 8192),
+    ('cohere/command-a-reasoning-08-2025', 'Command A Reasoning 08.2025', 'open_router', 'cohere', 256000, 32768),
+    ('cohere/command-a-translate-08-2025', 'Command A Translate 08.2025', 'open_router', 'cohere', 128000, 8192),
+    ('cohere/command-a-vision-07-2025', 'Command A Vision 07.2025', 'open_router', 'cohere', 128000, 8192),
+    ('deepseek/deepseek-chat', 'DeepSeek Chat', 'open_router', 'deepseek', 64000, 2048),
+    ('deepseek/deepseek-r1-0528', 'DeepSeek R1', 'open_router', 'deepseek', 163840, 163840),
+    ('perplexity/sonar', 'Perplexity Sonar', 'open_router', 'perplexity', 127000, 8000),
+    ('perplexity/sonar-pro', 'Perplexity Sonar Pro', 'open_router', 'perplexity', 200000, 8000),
+    ('perplexity/sonar-deep-research', 'Perplexity Sonar Deep Research', 'open_router', 'perplexity', 128000, 16000),
+    ('perplexity/sonar-reasoning-pro', 'Sonar Reasoning Pro', 'open_router', 'perplexity', 128000, 8000),
+    ('nousresearch/hermes-3-llama-3.1-405b', 'Hermes 3 Llama 3.1 405B', 'open_router', 'nousresearch', 131000, 4096),
+    ('nousresearch/hermes-3-llama-3.1-70b', 'Hermes 3 Llama 3.1 70B', 'open_router', 'nousresearch', 12288, 12288),
+    ('openai/gpt-oss-120b', 'GPT OSS 120B', 'open_router', 'openai', 131072, 131072),
+    ('openai/gpt-oss-20b', 'GPT OSS 20B', 'open_router', 'openai', 131072, 32768),
+    ('amazon/nova-lite-v1', 'Amazon Nova Lite', 'open_router', 'amazon', 300000, 5120),
+    ('amazon/nova-micro-v1', 'Amazon Nova Micro', 'open_router', 'amazon', 128000, 5120),
+    ('amazon/nova-pro-v1', 'Amazon Nova Pro', 'open_router', 'amazon', 300000, 5120),
+    ('microsoft/wizardlm-2-8x22b', 'WizardLM 2 8x22B', 'open_router', 'microsoft', 65536, 4096),
+    ('microsoft/phi-4', 'Phi-4', 'open_router', 'microsoft', 16384, 16384),
+    ('gryphe/mythomax-l2-13b', 'MythoMax L2 13B', 'open_router', 'gryphe', 4096, 4096),
+    ('meta-llama/llama-4-scout', 'Llama 4 Scout', 'open_router', 'meta', 131072, 131072),
+    ('meta-llama/llama-4-maverick', 'Llama 4 Maverick', 'open_router', 'meta', 1048576, 1000000),
+    ('x-ai/grok-3', 'Grok 3', 'open_router', 'xai', 131072, 131072),
+    ('x-ai/grok-4', 'Grok 4', 'open_router', 'xai', 256000, 256000),
+    ('x-ai/grok-4-fast', 'Grok 4 Fast', 'open_router', 'xai', 2000000, 30000),
+    ('x-ai/grok-4.1-fast', 'Grok 4.1 Fast', 'open_router', 'xai', 2000000, 30000),
+    ('x-ai/grok-code-fast-1', 'Grok Code Fast 1', 'open_router', 'xai', 256000, 10000),
+    ('moonshotai/kimi-k2', 'Kimi K2', 'open_router', 'moonshotai', 131000, 131000),
+    ('qwen/qwen3-235b-a22b-thinking-2507', 'Qwen 3 235B Thinking', 'open_router', 'alibaba', 262144, 262144),
+    ('qwen/qwen3-coder', 'Qwen 3 Coder', 'open_router', 'alibaba', 262144, 262144),
+    -- Llama API models (creator: meta)
+    ('Llama-4-Scout-17B-16E-Instruct-FP8', 'Llama 4 Scout', 'llama_api', 'meta', 128000, 4028),
+    ('Llama-4-Maverick-17B-128E-Instruct-FP8', 'Llama 4 Maverick', 'llama_api', 'meta', 128000, 4028),
+    ('Llama-3.3-8B-Instruct', 'Llama 3.3 8B', 'llama_api', 'meta', 128000, 4028),
+    ('Llama-3.3-70B-Instruct', 'Llama 3.3 70B', 'llama_api', 'meta', 128000, 4028),
+    -- v0 models (creator: vercel)
+    ('v0-1.5-md', 'v0 1.5 MD', 'v0', 'vercel', 128000, 64000),
+    ('v0-1.5-lg', 'v0 1.5 LG', 'v0', 'vercel', 512000, 64000),
+    ('v0-1.0-md', 'v0 1.0 MD', 'v0', 'vercel', 128000, 64000)
+) AS models(model_slug, model_display_name, provider_name, creator_name, context_window, max_output_tokens)
+JOIN provider_ids p ON p."name" = models.provider_name
+JOIN creator_ids c ON c."name" = models.creator_name
+ON CONFLICT ("slug") DO NOTHING;
+
+-- Insert Costs (using CTEs to reference model IDs)
+WITH model_ids AS (
+    SELECT "id", "slug", "providerId" FROM "LlmModel"
+),
+provider_ids AS (
+    SELECT "id", "name" FROM "LlmProvider"
+)
+INSERT INTO "LlmModelCost" ("id", "createdAt", "updatedAt", "unit", "creditCost", "credentialProvider", "credentialId", "credentialType", "currency", "metadata", "llmModelId")
+SELECT
+    gen_random_uuid(),
+    CURRENT_TIMESTAMP,
+    CURRENT_TIMESTAMP,
+    'RUN'::"LlmCostUnit",
+    cost,
+    p."name",
+    NULL,
+    'api_key',
+    NULL,
+    '{}'::jsonb,
+    m."id"
+FROM (VALUES
+    -- OpenAI costs
+    ('o3-2025-04-16', 4),
+    ('o3-mini', 2),
+    ('o1', 16),
+    ('o1-mini', 4),
+    ('gpt-5.2-2025-12-11', 5),
+    ('gpt-5-2025-08-07', 2),
+    ('gpt-5.1-2025-11-13', 5),
+    ('gpt-5-mini-2025-08-07', 1),
+    ('gpt-5-nano-2025-08-07', 1),
+    ('gpt-5-chat-latest', 5),
+    ('gpt-4.1-2025-04-14', 2),
+    ('gpt-4.1-mini-2025-04-14', 1),
+    ('gpt-4o-mini', 1),
+    ('gpt-4o', 3),
+    ('gpt-4-turbo', 10),
+    -- Anthropic costs
+    ('claude-opus-4-6', 21),
+    ('claude-sonnet-4-6', 5),
+    ('claude-opus-4-1-20250805', 21),
+    ('claude-opus-4-20250514', 21),
+    ('claude-sonnet-4-20250514', 5),
+    ('claude-haiku-4-5-20251001', 4),
+    ('claude-opus-4-5-20251101', 14),
+    ('claude-sonnet-4-5-20250929', 9),
+    ('claude-3-haiku-20240307', 1),
+    -- AI/ML API costs
+    ('Qwen/Qwen2.5-72B-Instruct-Turbo', 1),
+    ('nvidia/llama-3.1-nemotron-70b-instruct', 1),
+    ('meta-llama/Llama-3.3-70B-Instruct-Turbo', 1),
+    ('meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo', 1),
+    ('meta-llama/Llama-3.2-3B-Instruct-Turbo', 1),
+    -- Groq costs
+    ('llama-3.3-70b-versatile', 1),
+    ('llama-3.1-8b-instant', 1),
+    -- Ollama costs
+    ('llama3.3', 1),
+    ('llama3.2', 1),
+    ('llama3', 1),
+    ('llama3.1:405b', 1),
+    ('dolphin-mistral:latest', 1),
+    -- OpenRouter costs
+    ('google/gemini-2.5-pro-preview-03-25', 4),
+    ('google/gemini-2.5-pro', 4),
+    ('google/gemini-3.1-pro-preview', 5),
+    ('google/gemini-3-flash-preview', 3),
+    ('google/gemini-3.1-flash-lite-preview', 1),
+    ('mistralai/mistral-nemo', 1),
+    ('mistralai/mistral-large-2512', 3),
+    ('mistralai/mistral-medium-3.1', 2),
+    ('mistralai/mistral-small-3.2-24b-instruct', 1),
+    ('mistralai/codestral-2508', 2),
+    ('cohere/command-r-08-2024', 1),
+    ('cohere/command-r-plus-08-2024', 3),
+    ('cohere/command-a-03-2025', 2),
+    ('cohere/command-a-reasoning-08-2025', 3),
+    ('cohere/command-a-translate-08-2025', 1),
+    ('cohere/command-a-vision-07-2025', 2),
+    ('deepseek/deepseek-chat', 2),
+    ('perplexity/sonar', 1),
+    ('perplexity/sonar-pro', 5),
+    ('perplexity/sonar-deep-research', 10),
+    ('perplexity/sonar-reasoning-pro', 5),
+    ('nousresearch/hermes-3-llama-3.1-405b', 1),
+    ('nousresearch/hermes-3-llama-3.1-70b', 1),
+    ('amazon/nova-lite-v1', 1),
+    ('amazon/nova-micro-v1', 1),
+    ('amazon/nova-pro-v1', 1),
+    ('microsoft/wizardlm-2-8x22b', 1),
+    ('microsoft/phi-4', 1),
+    ('gryphe/mythomax-l2-13b', 1),
+    ('meta-llama/llama-4-scout', 1),
+    ('meta-llama/llama-4-maverick', 1),
+    ('x-ai/grok-3', 5),
+    ('x-ai/grok-4', 9),
+    ('x-ai/grok-4-fast', 1),
+    ('x-ai/grok-4.1-fast', 1),
+    ('x-ai/grok-code-fast-1', 1),
+    ('moonshotai/kimi-k2', 1),
+    ('qwen/qwen3-235b-a22b-thinking-2507', 1),
+    ('qwen/qwen3-coder', 9),
+    ('google/gemini-2.5-flash', 1),
+    ('google/gemini-2.0-flash-001', 1),
+    ('google/gemini-2.5-flash-lite-preview-06-17', 1),
+    ('google/gemini-2.0-flash-lite-001', 1),
+    ('deepseek/deepseek-r1-0528', 1),
+    ('openai/gpt-oss-120b', 1),
+    ('openai/gpt-oss-20b', 1),
+    -- Llama API costs
+    ('Llama-4-Scout-17B-16E-Instruct-FP8', 1),
+    ('Llama-4-Maverick-17B-128E-Instruct-FP8', 1),
+    ('Llama-3.3-8B-Instruct', 1),
+    ('Llama-3.3-70B-Instruct', 1),
+    -- v0 costs
+    ('v0-1.5-md', 1),
+    ('v0-1.5-lg', 2),
+    ('v0-1.0-md', 1)
+) AS costs(model_slug, cost)
+JOIN model_ids m ON m."slug" = costs.model_slug
+JOIN provider_ids p ON p."id" = m."providerId"
+ON CONFLICT ("llmModelId", "credentialProvider", "unit") WHERE "credentialId" IS NULL DO NOTHING;
+

autogpt_platform/backend/schema.prisma

@@ -1301,3 +1301,164 @@ model OAuthRefreshToken {
   @@index([userId, applicationId])
   @@index([expiresAt]) // For cleanup
 }
+
+// ============================================================================
+// LLM Registry Models
+// ============================================================================
+
+enum LlmCostUnit {
+  RUN
+  TOKENS
+}
+
+model LlmProvider {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  name        String @unique
+  displayName String
+  description String?
+
+  defaultCredentialProvider String?
+  defaultCredentialId       String?
+  defaultCredentialType     String?
+
+  metadata Json @default("{}")
+
+  Models LlmModel[]
+
+}
+
+model LlmModel {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  slug        String @unique
+  displayName String
+  description String?
+
+  providerId String
+  Provider   LlmProvider @relation(fields: [providerId], references: [id], onDelete: Restrict)
+
+  // Creator is the organization that created/trained the model (e.g., OpenAI, Meta)
+  // This is distinct from the provider who hosts/serves the model (e.g., OpenRouter)
+  creatorId String?
+  Creator   LlmModelCreator? @relation(fields: [creatorId], references: [id], onDelete: SetNull)
+
+  contextWindow   Int
+  maxOutputTokens Int?
+  priceTier       Int     @default(1) // 1=cheapest, 2=medium, 3=expensive (DB constraint: 1-3)
+  isEnabled       Boolean @default(true)
+  isRecommended   Boolean @default(false)
+
+  // Model-specific capabilities
+  // These vary per model even within the same provider (e.g., Hugging Face)
+  // Default to false for safety - partially-seeded rows should not be assumed capable
+  supportsTools            Boolean @default(false)
+  supportsJsonOutput       Boolean @default(false)
+  supportsReasoning        Boolean @default(false)
+  supportsParallelToolCalls Boolean @default(false)
+
+  capabilities Json @default("{}")
+  metadata     Json @default("{}")
+
+  Costs             LlmModelCost[]
+  SourceMigrations  LlmModelMigration[] @relation("SourceMigrations")
+  TargetMigrations  LlmModelMigration[] @relation("TargetMigrations")
+
+  @@index([providerId, isEnabled])
+  @@index([creatorId])
+  // Note: slug already has @unique which creates an implicit index
+}
+
+model LlmModelCost {
+  id        String      @id @default(uuid())
+  createdAt DateTime    @default(now())
+  updatedAt DateTime    @updatedAt
+  unit      LlmCostUnit @default(RUN)
+
+  creditCost Int // DB constraint: >= 0
+
+  // Provider identifier (e.g., "openai", "anthropic", "openrouter")
+  // Used to determine which credential system provides the API key.
+  // Allows different pricing for:
+  // - Default provider costs (WHERE credentialId IS NULL)
+  // - User's own API key costs (WHERE credentialId IS NOT NULL)
+  credentialProvider String
+  credentialId       String?
+  credentialType     String?
+  currency           String?
+
+  metadata Json @default("{}")
+
+  llmModelId String
+  Model      LlmModel @relation(fields: [llmModelId], references: [id], onDelete: Cascade)
+
+  // Note: Unique constraints are implemented as partial indexes in migration SQL:
+  // - One for default costs (WHERE credentialId IS NULL)
+  // - One for credential-specific costs (WHERE credentialId IS NOT NULL)
+  // This allows both provider-level defaults and credential-specific overrides
+}
+
+model LlmModelCreator {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  name        String  @unique // e.g., "openai", "anthropic", "meta"
+  displayName String  // e.g., "OpenAI", "Anthropic", "Meta"
+  description String?
+  websiteUrl  String? // Link to creator's website
+  logoUrl     String? // URL to creator's logo
+
+  metadata Json @default("{}")
+
+  Models LlmModel[]
+
+}
+
+model LlmModelMigration {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  sourceModelSlug String // The original model that was disabled
+  targetModelSlug String // The model workflows were migrated to
+  reason          String? // Why the migration happened (e.g., "Provider outage")
+
+  // FK constraints ensure slugs reference valid models
+  SourceModel LlmModel @relation("SourceMigrations", fields: [sourceModelSlug], references: [slug], onDelete: Restrict)
+  TargetModel LlmModel @relation("TargetMigrations", fields: [targetModelSlug], references: [slug], onDelete: Restrict)
+
+  // Track affected nodes as JSON array of node IDs
+  // Format: ["node-uuid-1", "node-uuid-2", ...]
+  migratedNodeIds Json @default("[]")
+  nodeCount       Int  // Number of nodes migrated (DB constraint: >= 0)
+
+  // Custom pricing override for migrated workflows during the migration period.
+  // Use case: When migrating users from an expensive model (e.g., GPT-4) to a cheaper
+  // one (e.g., GPT-3.5), you may want to temporarily maintain the original pricing
+  // to avoid billing surprises, or offer a discount during the transition.
+  //
+  // IMPORTANT: This field is intended for integration with the billing system.
+  // When billing calculates costs for nodes affected by this migration, it should
+  // check if customCreditCost is set and use it instead of the target model's cost.
+  // If null, the target model's normal cost applies.
+  //
+  // TODO: Integrate with billing system to apply this override during cost calculation.
+  // LIMITATION: This is a simple Int and doesn't distinguish RUN vs TOKENS pricing.
+  // For token-priced models, this may be ambiguous. Consider migrating to a relation
+  // with LlmModelCost or a dedicated override model in a follow-up PR.
+  customCreditCost Int? // DB constraint: >= 0 when not null
+
+  // Revert tracking
+  isReverted Boolean   @default(false)
+  revertedAt DateTime?
+
+  // Note: Partial unique index in migration SQL prevents multiple active migrations per source:
+  // UNIQUE (sourceModelSlug) WHERE isReverted = false
+  @@index([targetModelSlug])
+  @@index([sourceModelSlug, isReverted]) // Composite index for active migration queries
+}

autogpt_platform/backend/scripts/refresh_claude_token.sh

@@ -0,0 +1,123 @@
+#!/usr/bin/env bash
+# refresh_claude_token.sh — Extract Claude OAuth tokens and update backend/.env
+#
+# Works on macOS (keychain), Linux (~/.claude/.credentials.json),
+# and Windows/WSL (~/.claude/.credentials.json or PowerShell fallback).
+#
+# Usage:
+#   ./scripts/refresh_claude_token.sh              # auto-detect OS
+#   ./scripts/refresh_claude_token.sh --env-file /path/to/.env  # custom .env path
+#
+# Prerequisite: You must have run `claude login` at least once on the host.
+
+set -euo pipefail
+
+# --- Parse arguments ---
+ENV_FILE=""
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --env-file) ENV_FILE="$2"; shift 2 ;;
+    *) echo "Unknown option: $1"; exit 1 ;;
+  esac
+done
+
+# Default .env path: relative to this script's location
+if [[ -z "$ENV_FILE" ]]; then
+  SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+  ENV_FILE="$SCRIPT_DIR/../.env"
+fi
+
+# --- Extract tokens by platform ---
+ACCESS_TOKEN=""
+REFRESH_TOKEN=""
+
+extract_from_credentials_file() {
+  local creds_file="$1"
+  if [[ -f "$creds_file" ]]; then
+    ACCESS_TOKEN=$(jq -r '.claudeAiOauth.accessToken // ""' "$creds_file" 2>/dev/null)
+    REFRESH_TOKEN=$(jq -r '.claudeAiOauth.refreshToken // ""' "$creds_file" 2>/dev/null)
+  fi
+}
+
+case "$(uname -s)" in
+  Darwin)
+    # macOS: extract from system keychain
+    CREDS_JSON=$(security find-generic-password -s "Claude Code-credentials" -w 2>/dev/null || true)
+    if [[ -n "$CREDS_JSON" ]]; then
+      ACCESS_TOKEN=$(echo "$CREDS_JSON" | jq -r '.claudeAiOauth.accessToken // ""' 2>/dev/null)
+      REFRESH_TOKEN=$(echo "$CREDS_JSON" | jq -r '.claudeAiOauth.refreshToken // ""' 2>/dev/null)
+    else
+      # Fallback to credentials file (e.g. if keychain access denied)
+      extract_from_credentials_file "$HOME/.claude/.credentials.json"
+    fi
+    ;;
+  Linux)
+    # Linux (including WSL): read from credentials file
+    extract_from_credentials_file "$HOME/.claude/.credentials.json"
+    ;;
+  MINGW*|MSYS*|CYGWIN*)
+    # Windows Git Bash / MSYS2 / Cygwin
+    APPDATA_PATH="${APPDATA:-$USERPROFILE/AppData/Roaming}"
+    extract_from_credentials_file "$APPDATA_PATH/claude/.credentials.json"
+    # Fallback to home dir
+    if [[ -z "$ACCESS_TOKEN" ]]; then
+      extract_from_credentials_file "$HOME/.claude/.credentials.json"
+    fi
+    ;;
+  *)
+    echo "Unsupported platform: $(uname -s)"
+    exit 1
+    ;;
+esac
+
+# --- Validate ---
+if [[ -z "$ACCESS_TOKEN" ]]; then
+  echo "ERROR: Could not extract Claude OAuth token."
+  echo ""
+  echo "Make sure you have run 'claude login' at least once."
+  echo ""
+  echo "Locations checked:"
+  echo "  macOS:   Keychain ('Claude Code-credentials')"
+  echo "  Linux:   ~/.claude/.credentials.json"
+  echo "  Windows: %APPDATA%/claude/.credentials.json"
+  exit 1
+fi
+
+echo "Found Claude OAuth token: ${ACCESS_TOKEN:0:20}..."
+[[ -n "$REFRESH_TOKEN" ]] && echo "Found refresh token:  ${REFRESH_TOKEN:0:20}..."
+
+# --- Update .env file ---
+update_env_var() {
+  local key="$1" value="$2" file="$3"
+  if grep -q "^${key}=" "$file" 2>/dev/null; then
+    # Replace existing value (works on both macOS and Linux sed)
+    if [[ "$(uname -s)" == "Darwin" ]]; then
+      sed -i '' "s|^${key}=.*|${key}=${value}|" "$file"
+    else
+      sed -i "s|^${key}=.*|${key}=${value}|" "$file"
+    fi
+  elif grep -q "^# *${key}=" "$file" 2>/dev/null; then
+    # Uncomment and set
+    if [[ "$(uname -s)" == "Darwin" ]]; then
+      sed -i '' "s|^# *${key}=.*|${key}=${value}|" "$file"
+    else
+      sed -i "s|^# *${key}=.*|${key}=${value}|" "$file"
+    fi
+  else
+    # Append
+    echo "${key}=${value}" >> "$file"
+  fi
+}
+
+if [[ ! -f "$ENV_FILE" ]]; then
+  echo "WARNING: $ENV_FILE does not exist, creating it."
+  touch "$ENV_FILE"
+fi
+
+update_env_var "CLAUDE_CODE_OAUTH_TOKEN" "$ACCESS_TOKEN" "$ENV_FILE"
+[[ -n "$REFRESH_TOKEN" ]] && update_env_var "CLAUDE_CODE_REFRESH_TOKEN" "$REFRESH_TOKEN" "$ENV_FILE"
+update_env_var "CHAT_USE_CLAUDE_CODE_SUBSCRIPTION" "true" "$ENV_FILE"
+
+echo ""
+echo "Updated $ENV_FILE with Claude subscription tokens."
+echo "Run 'docker compose up -d copilot_executor' to apply."

autogpt_platform/frontend/package.json

@@ -73,7 +73,7 @@
     "@vercel/analytics": "1.5.0",
     "@vercel/speed-insights": "1.2.0",
     "@xyflow/react": "12.9.2",
-    "ai": "6.0.59",
+    "ai": "6.0.134",
     "boring-avatars": "1.11.2",
     "canvas-confetti": "1.9.4",
     "class-variance-authority": "0.7.1",

autogpt_platform/frontend/pnpm-lock.yaml

@@ -142,8 +142,8 @@ importers:
         specifier: 12.9.2
         version: 12.9.2(@types/react@18.3.17)(immer@11.1.3)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       ai:
-        specifier: 6.0.59
-        version: 6.0.59(zod@3.25.76)
+        specifier: 6.0.134
+        version: 6.0.134(zod@3.25.76)
       boring-avatars:
         specifier: 1.11.2
         version: 1.11.2
@@ -448,16 +448,32 @@ packages:
     peerDependencies:
       zod: ^3.25.76 || ^4.1.8
 
+  '@ai-sdk/gateway@3.0.77':
+    resolution: {integrity: sha512-UdwIG2H2YMuntJQ5L+EmED5XiwnlvDT3HOmKfVFxR4Nq/RSLFA/HcchhwfNXHZ5UJjyuL2VO0huLbWSZ9ijemQ==}
+    engines: {node: '>=18'}
+    peerDependencies:
+      zod: ^3.25.76 || ^4.1.8
+
   '@ai-sdk/provider-utils@4.0.10':
     resolution: {integrity: sha512-VeDAiCH+ZK8Xs4hb9Cw7pHlujWNL52RKe8TExOkrw6Ir1AmfajBZTb9XUdKOZO08RwQElIKA8+Ltm+Gqfo8djQ==}
     engines: {node: '>=18'}
     peerDependencies:
       zod: ^3.25.76 || ^4.1.8
 
+  '@ai-sdk/provider-utils@4.0.21':
+    resolution: {integrity: sha512-MtFUYI1/8mgDvRmaBDjbLJPFFrMG777AvSgyIFQtZHIMzm88R/12vYBBpnk7pfiWLFE1DSZzY4WDYzGbKAcmiw==}
+    engines: {node: '>=18'}
+    peerDependencies:
+      zod: ^3.25.76 || ^4.1.8
+
   '@ai-sdk/provider@3.0.5':
     resolution: {integrity: sha512-2Xmoq6DBJqmSl80U6V9z5jJSJP7ehaJJQMy2iFUqTay06wdCqTnPVBBQbtEL8RCChenL+q5DC5H5WzU3vV3v8w==}
     engines: {node: '>=18'}
 
+  '@ai-sdk/provider@3.0.8':
+    resolution: {integrity: sha512-oGMAgGoQdBXbZqNG0Ze56CHjDZ1IDYOwGYxYjO5KLSlz5HiNQ9udIXsPZ61VWaHGZ5XW/jyjmr6t2xz2jGVwbQ==}
+    engines: {node: '>=18'}
+
   '@ai-sdk/react@3.0.61':
     resolution: {integrity: sha512-vCjZBnY2+TawFBXamSKt6elAt9n1MXMfcjSd9DSgT9peCJN27qNGVSXgaGNh/B3cUgeOktFfhB2GVmIqOjvmLQ==}
     engines: {node: '>=18'}
@@ -4053,6 +4069,12 @@ packages:
     resolution: {integrity: sha512-MnA+YT8fwfJPgBx3m60MNqakm30XOkyIoH1y6huTQvC0PwZG7ki8NacLBcrPbNoo8vEZy7Jpuk7+jMO+CUovTQ==}
     engines: {node: '>= 14'}
 
+  ai@6.0.134:
+    resolution: {integrity: sha512-YalNEaavld/kE444gOcsMKXdVVRGEe0SK77fAFcWYcqLg+a7xKnEet8bdfrEAJTfnMjj01rhgrIL10903w1a5Q==}
+    engines: {node: '>=18'}
+    peerDependencies:
+      zod: ^3.25.76 || ^4.1.8
+
   ai@6.0.59:
     resolution: {integrity: sha512-9SfCvcr4kVk4t8ZzIuyHpuL1hFYKsYMQfBSbBq3dipXPa+MphARvI8wHEjNaRqYl3JOsJbWxEBIMqHL0L92mUA==}
     engines: {node: '>=18'}
@@ -8718,6 +8740,13 @@ snapshots:
       '@vercel/oidc': 3.1.0
       zod: 3.25.76
 
+  '@ai-sdk/gateway@3.0.77(zod@3.25.76)':
+    dependencies:
+      '@ai-sdk/provider': 3.0.8
+      '@ai-sdk/provider-utils': 4.0.21(zod@3.25.76)
+      '@vercel/oidc': 3.1.0
+      zod: 3.25.76
+
   '@ai-sdk/provider-utils@4.0.10(zod@3.25.76)':
     dependencies:
       '@ai-sdk/provider': 3.0.5
@@ -8725,10 +8754,21 @@ snapshots:
       eventsource-parser: 3.0.6
       zod: 3.25.76
 
+  '@ai-sdk/provider-utils@4.0.21(zod@3.25.76)':
+    dependencies:
+      '@ai-sdk/provider': 3.0.8
+      '@standard-schema/spec': 1.1.0
+      eventsource-parser: 3.0.6
+      zod: 3.25.76
+
   '@ai-sdk/provider@3.0.5':
     dependencies:
       json-schema: 0.4.0
 
+  '@ai-sdk/provider@3.0.8':
+    dependencies:
+      json-schema: 0.4.0
+
   '@ai-sdk/react@3.0.61(react@18.3.1)(zod@3.25.76)':
     dependencies:
       '@ai-sdk/provider-utils': 4.0.10(zod@3.25.76)
@@ -12798,6 +12838,14 @@ snapshots:
   agent-base@7.1.4:
     optional: true
 
+  ai@6.0.134(zod@3.25.76):
+    dependencies:
+      '@ai-sdk/gateway': 3.0.77(zod@3.25.76)
+      '@ai-sdk/provider': 3.0.8
+      '@ai-sdk/provider-utils': 4.0.21(zod@3.25.76)
+      '@opentelemetry/api': 1.9.0
+      zod: 3.25.76
+
   ai@6.0.59(zod@3.25.76):
     dependencies:
       '@ai-sdk/gateway': 3.0.27(zod@3.25.76)
@@ -14066,8 +14114,8 @@ snapshots:
       '@typescript-eslint/parser': 8.52.0(eslint@8.57.1)(typescript@5.9.3)
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1)
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1)
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
       eslint-plugin-jsx-a11y: 6.10.2(eslint@8.57.1)
       eslint-plugin-react: 7.37.5(eslint@8.57.1)
       eslint-plugin-react-hooks: 5.2.0(eslint@8.57.1)
@@ -14086,7 +14134,7 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1):
+  eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1):
     dependencies:
       '@nolyfill/is-core-module': 1.0.39
       debug: 4.4.3
@@ -14097,22 +14145,22 @@ snapshots:
       tinyglobby: 0.2.15
       unrs-resolver: 1.11.1
     optionalDependencies:
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
     transitivePeerDependencies:
       - supports-color
 
-  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
+  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1):
     dependencies:
       debug: 3.2.7
     optionalDependencies:
       '@typescript-eslint/parser': 8.52.0(eslint@8.57.1)(typescript@5.9.3)
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1)
+      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1)
     transitivePeerDependencies:
       - supports-color
 
-  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
+  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1):
     dependencies:
       '@rtsao/scc': 1.1.0
       array-includes: 3.1.9
@@ -14123,7 +14171,7 @@ snapshots:
       doctrine: 2.1.0
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
       hasown: 2.0.2
       is-core-module: 2.16.1
       is-glob: 4.0.3

autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts

@@ -15,46 +15,11 @@ import { useCopilotUIStore } from "./store";
 import { useChatSession } from "./useChatSession";
 import { useCopilotNotifications } from "./useCopilotNotifications";
 import { useCopilotStream } from "./useCopilotStream";
+import { useWorkflowImportAutoSubmit } from "./useWorkflowImportAutoSubmit";
 
 const TITLE_POLL_INTERVAL_MS = 2_000;
 const TITLE_POLL_MAX_ATTEMPTS = 5;
 
-/**
- * Extract a prompt from the URL hash fragment.
- * Supports: /copilot#prompt=URL-encoded-text
- * Optionally auto-submits if ?autosubmit=true is in the query string.
- * Returns null if no prompt is present.
- */
-function extractPromptFromUrl(): {
-  prompt: string;
-  autosubmit: boolean;
-} | null {
-  if (typeof window === "undefined") return null;
-
-  const hash = window.location.hash;
-  if (!hash) return null;
-
-  const hashParams = new URLSearchParams(hash.slice(1));
-  const prompt = hashParams.get("prompt");
-
-  if (!prompt || !prompt.trim()) return null;
-
-  const searchParams = new URLSearchParams(window.location.search);
-  const autosubmit = searchParams.get("autosubmit") === "true";
-
-  // Clean up hash + autosubmit param only (preserve other query params)
-  const cleanURL = new URL(window.location.href);
-  cleanURL.hash = "";
-  cleanURL.searchParams.delete("autosubmit");
-  window.history.replaceState(
-    null,
-    "",
-    `${cleanURL.pathname}${cleanURL.search}`,
-  );
-
-  return { prompt: prompt.trim(), autosubmit };
-}
-
 interface UploadedFile {
   file_id: string;
   name: string;
@@ -130,16 +95,23 @@ export function useCopilotPage() {
     breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
 
   const pendingFilesRef = useRef<File[]>([]);
+  // Pre-built file parts from workflow import (already uploaded, skip re-upload)
+  const pendingFilePartsRef = useRef<FileUIPart[]>([]);
 
   // --- Send pending message after session creation ---
   useEffect(() => {
     if (!sessionId || pendingMessage === null) return;
     const msg = pendingMessage;
     const files = pendingFilesRef.current;
+    const prebuiltParts = pendingFilePartsRef.current;
     setPendingMessage(null);
     pendingFilesRef.current = [];
+    pendingFilePartsRef.current = [];
 
-    if (files.length > 0) {
+    if (prebuiltParts.length > 0) {
+      // File already uploaded (e.g. workflow import) — send directly
+      sendMessage({ text: msg, files: prebuiltParts });
+    } else if (files.length > 0) {
       setIsUploadingFiles(true);
       void uploadFiles(files, sessionId)
         .then((uploaded) => {
@@ -164,26 +136,11 @@ export function useCopilotPage() {
   }, [sessionId, pendingMessage, sendMessage]);
 
   // --- Extract prompt from URL hash on mount (e.g. /copilot#prompt=Hello) ---
-  const { setInitialPrompt } = useCopilotUIStore();
-  const hasProcessedUrlPrompt = useRef(false);
-  useEffect(() => {
-    if (hasProcessedUrlPrompt.current) return;
-
-    const urlPrompt = extractPromptFromUrl();
-    if (!urlPrompt) return;
-
-    hasProcessedUrlPrompt.current = true;
-
-    if (urlPrompt.autosubmit) {
-      setPendingMessage(urlPrompt.prompt);
-      void createSession().catch(() => {
-        setPendingMessage(null);
-        setInitialPrompt(urlPrompt.prompt);
-      });
-    } else {
-      setInitialPrompt(urlPrompt.prompt);
-    }
-  }, [createSession, setInitialPrompt]);
+  useWorkflowImportAutoSubmit({
+    createSession,
+    setPendingMessage,
+    pendingFilePartsRef,
+  });
 
   async function uploadFiles(
     files: File[],

autogpt_platform/frontend/src/app/(platform)/copilot/useWorkflowImportAutoSubmit.ts

@@ -0,0 +1,122 @@
+import type { FileUIPart } from "ai";
+import { useEffect, useRef } from "react";
+import { useCopilotUIStore } from "./store";
+
+/**
+ * Extract a prompt from the URL hash fragment.
+ * Supports: /copilot#prompt=URL-encoded-text
+ * Optionally auto-submits if ?autosubmit=true is in the query string.
+ * Returns null if no prompt is present.
+ */
+function extractPromptFromUrl(): {
+  prompt: string;
+  autosubmit: boolean;
+  filePart?: FileUIPart;
+} | null {
+  if (typeof window === "undefined") return null;
+
+  const searchParams = new URLSearchParams(window.location.search);
+  const autosubmit = searchParams.get("autosubmit") === "true";
+
+  // Check sessionStorage first (used by workflow import for large prompts)
+  const storedPrompt = sessionStorage.getItem("importWorkflowPrompt");
+  if (storedPrompt) {
+    sessionStorage.removeItem("importWorkflowPrompt");
+
+    // Check for a pre-uploaded workflow file attached to this import
+    let filePart: FileUIPart | undefined;
+    const storedFile = sessionStorage.getItem("importWorkflowFile");
+    if (storedFile) {
+      sessionStorage.removeItem("importWorkflowFile");
+      try {
+        const { fileId, fileName, mimeType } = JSON.parse(storedFile);
+        // Validate fileId is a UUID to prevent path traversal
+        const UUID_RE =
+          /^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i;
+        if (typeof fileId === "string" && UUID_RE.test(fileId)) {
+          filePart = {
+            type: "file",
+            mediaType: mimeType ?? "application/json",
+            filename: fileName ?? "workflow.json",
+            url: `/api/proxy/api/workspace/files/${fileId}/download`,
+          };
+        }
+      } catch {
+        // ignore malformed stored data
+      }
+    }
+
+    // Clean up query params
+    const cleanURL = new URL(window.location.href);
+    cleanURL.searchParams.delete("autosubmit");
+    cleanURL.searchParams.delete("source");
+    window.history.replaceState(
+      null,
+      "",
+      `${cleanURL.pathname}${cleanURL.search}`,
+    );
+    return { prompt: storedPrompt.trim(), autosubmit, filePart };
+  }
+
+  // Fall back to URL hash (e.g. /copilot#prompt=...)
+  const hash = window.location.hash;
+  if (!hash) return null;
+
+  const hashParams = new URLSearchParams(hash.slice(1));
+  const prompt = hashParams.get("prompt");
+
+  if (!prompt || !prompt.trim()) return null;
+
+  // Clean up hash + autosubmit param only (preserve other query params)
+  const cleanURL = new URL(window.location.href);
+  cleanURL.hash = "";
+  cleanURL.searchParams.delete("autosubmit");
+  window.history.replaceState(
+    null,
+    "",
+    `${cleanURL.pathname}${cleanURL.search}`,
+  );
+
+  return { prompt: prompt.trim(), autosubmit };
+}
+
+/**
+ * Hook that checks for workflow import data in sessionStorage / URL on mount,
+ * and auto-submits a new CoPilot session when `autosubmit=true`.
+ *
+ * Extracted from useCopilotPage to keep that hook focused on page-level concerns.
+ */
+export function useWorkflowImportAutoSubmit({
+  createSession,
+  setPendingMessage,
+  pendingFilePartsRef,
+}: {
+  createSession: () => Promise<string | undefined>;
+  setPendingMessage: (msg: string | null) => void;
+  pendingFilePartsRef: React.MutableRefObject<FileUIPart[]>;
+}) {
+  const { setInitialPrompt } = useCopilotUIStore();
+  const hasProcessedUrlPrompt = useRef(false);
+
+  useEffect(() => {
+    if (hasProcessedUrlPrompt.current) return;
+
+    const urlPrompt = extractPromptFromUrl();
+    if (!urlPrompt) return;
+
+    hasProcessedUrlPrompt.current = true;
+
+    if (urlPrompt.autosubmit) {
+      if (urlPrompt.filePart) {
+        pendingFilePartsRef.current = [urlPrompt.filePart];
+      }
+      setPendingMessage(urlPrompt.prompt);
+      void createSession().catch(() => {
+        setPendingMessage(null);
+        setInitialPrompt(urlPrompt.prompt);
+      });
+    } else {
+      setInitialPrompt(urlPrompt.prompt);
+    }
+  }, [createSession, setInitialPrompt, setPendingMessage, pendingFilePartsRef]);
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers/renderers/MarkdownRenderer.tsx

@@ -169,7 +169,7 @@ function renderMarkdown(
           [remarkMath, { singleDollarTextMath: false }], // Math support for LaTeX
         ]}
         rehypePlugins={[
-          rehypeKatex, // Render math with KaTeX
+          [rehypeKatex, { strict: false }], // Render math with KaTeX
           rehypeHighlight, // Syntax highlighting for code blocks
           rehypeSlug, // Add IDs to headings
           [rehypeAutolinkHeadings, { behavior: "wrap" }], // Make headings clickable

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryActionHeader/LibraryActionHeader.tsx

@@ -1,5 +1,5 @@
+import LibraryImportDialog from "../LibraryImportDialog/LibraryImportDialog";
 import { LibrarySearchBar } from "../LibrarySearchBar/LibrarySearchBar";
-import LibraryUploadAgentDialog from "../LibraryUploadAgentDialog/LibraryUploadAgentDialog";
 
 interface Props {
   setSearchTerm: (value: string) => void;
@@ -10,13 +10,13 @@ export function LibraryActionHeader({ setSearchTerm }: Props) {
     <>
       <div className="mb-[32px] hidden items-center justify-center gap-4 md:flex">
         <LibrarySearchBar setSearchTerm={setSearchTerm} />
-        <LibraryUploadAgentDialog />
+        <LibraryImportDialog />
       </div>
 
       {/* Mobile and tablet */}
       <div className="flex flex-col gap-4 p-4 pt-[52px] md:hidden">
-        <div className="flex w-full justify-between">
-          <LibraryUploadAgentDialog />
+        <div className="flex w-full justify-between gap-2">
+          <LibraryImportDialog />
         </div>
 
         <div className="flex items-center justify-center">

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryImportDialog/LibraryImportDialog.tsx

@@ -0,0 +1,66 @@
+"use client";
+import { Button } from "@/components/atoms/Button/Button";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import {
+  TabsLine,
+  TabsLineList,
+  TabsLineTrigger,
+} from "@/components/molecules/TabsLine/TabsLine";
+import { UploadSimpleIcon } from "@phosphor-icons/react";
+import { useState } from "react";
+import { useLibraryUploadAgentDialog } from "../LibraryUploadAgentDialog/useLibraryUploadAgentDialog";
+import AgentUploadTab from "./components/AgentUploadTab/AgentUploadTab";
+import ExternalWorkflowTab from "./components/ExternalWorkflowTab/ExternalWorkflowTab";
+import { useExternalWorkflowTab } from "./components/ExternalWorkflowTab/useExternalWorkflowTab";
+
+export default function LibraryImportDialog() {
+  const [isOpen, setIsOpen] = useState(false);
+
+  const importWorkflow = useExternalWorkflowTab();
+
+  function handleClose() {
+    setIsOpen(false);
+    importWorkflow.setFileValue("");
+    importWorkflow.setUrlValue("");
+  }
+
+  const upload = useLibraryUploadAgentDialog({ onSuccess: handleClose });
+
+  return (
+    <Dialog
+      title="Import"
+      styling={{ maxWidth: "32rem" }}
+      controlled={{
+        isOpen,
+        set: setIsOpen,
+      }}
+      onClose={handleClose}
+    >
+      <Dialog.Trigger>
+        <Button
+          data-testid="import-button"
+          variant="primary"
+          className="h-[2.78rem] w-full md:w-[10rem]"
+          size="small"
+        >
+          <UploadSimpleIcon width={18} height={18} />
+          <span>Import</span>
+        </Button>
+      </Dialog.Trigger>
+      <Dialog.Content>
+        <TabsLine defaultValue="agent">
+          <TabsLineList>
+            <TabsLineTrigger value="agent">AutoGPT agent</TabsLineTrigger>
+            <TabsLineTrigger value="platform">Another platform</TabsLineTrigger>
+          </TabsLineList>
+
+          {/* Tab: Import from any platform (file upload + n8n URL) */}
+          <ExternalWorkflowTab importWorkflow={importWorkflow} />
+
+          {/* Tab: Upload AutoGPT agent JSON */}
+          <AgentUploadTab upload={upload} />
+        </TabsLine>
+      </Dialog.Content>
+    </Dialog>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryImportDialog/components/AgentUploadTab/AgentUploadTab.tsx

@@ -0,0 +1,105 @@
+"use client";
+import { Button } from "@/components/atoms/Button/Button";
+import { FileInput } from "@/components/atoms/FileInput/FileInput";
+import { Input } from "@/components/atoms/Input/Input";
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import {
+  Form,
+  FormControl,
+  FormField,
+  FormItem,
+  FormMessage,
+} from "@/components/molecules/Form/Form";
+import { TabsLineContent } from "@/components/molecules/TabsLine/TabsLine";
+import { useLibraryUploadAgentDialog } from "../../../LibraryUploadAgentDialog/useLibraryUploadAgentDialog";
+
+type AgentUploadTabProps = {
+  upload: ReturnType<typeof useLibraryUploadAgentDialog>;
+};
+
+export default function AgentUploadTab({ upload }: AgentUploadTabProps) {
+  return (
+    <TabsLineContent value="agent">
+      <p className="mb-4 text-sm text-neutral-500">
+        Upload a previously exported AutoGPT agent file (.json).
+      </p>
+      <Form
+        form={upload.form}
+        onSubmit={upload.onSubmit}
+        className="flex flex-col justify-center gap-0 px-1"
+      >
+        <FormField
+          control={upload.form.control}
+          name="agentName"
+          render={({ field }) => (
+            <FormItem>
+              <FormControl>
+                <Input
+                  {...field}
+                  id={field.name}
+                  label="Agent name"
+                  className="w-full rounded-[10px]"
+                />
+              </FormControl>
+              <FormMessage />
+            </FormItem>
+          )}
+        />
+        <FormField
+          control={upload.form.control}
+          name="agentDescription"
+          render={({ field }) => (
+            <FormItem>
+              <FormControl>
+                <Input
+                  {...field}
+                  id={field.name}
+                  label="Agent description"
+                  type="textarea"
+                  className="w-full rounded-[10px]"
+                />
+              </FormControl>
+              <FormMessage />
+            </FormItem>
+          )}
+        />
+        <FormField
+          control={upload.form.control}
+          name="agentFile"
+          render={({ field }) => (
+            <FormItem>
+              <FormControl>
+                <FileInput
+                  mode="base64"
+                  value={field.value}
+                  onChange={field.onChange}
+                  accept=".json,application/json"
+                  placeholder="Agent file"
+                  maxFileSize={10 * 1024 * 1024}
+                  showStorageNote={false}
+                  className="mb-8 mt-4"
+                />
+              </FormControl>
+              <FormMessage />
+            </FormItem>
+          )}
+        />
+        <Button
+          type="submit"
+          variant="primary"
+          className="w-full"
+          disabled={!upload.agentObject || upload.isUploading}
+        >
+          {upload.isUploading ? (
+            <div className="flex items-center gap-2">
+              <LoadingSpinner size="small" className="text-white" />
+              <span>Uploading...</span>
+            </div>
+          ) : (
+            "Upload"
+          )}
+        </Button>
+      </Form>
+    </TabsLineContent>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryImportDialog/components/ExternalWorkflowTab/ExternalWorkflowTab.tsx

@@ -0,0 +1,99 @@
+"use client";
+import { Button } from "@/components/atoms/Button/Button";
+import { FileInput } from "@/components/atoms/FileInput/FileInput";
+import { Input } from "@/components/atoms/Input/Input";
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { TabsLineContent } from "@/components/molecules/TabsLine/TabsLine";
+import { useExternalWorkflowTab } from "./useExternalWorkflowTab";
+
+const N8N_EXAMPLES = [
+  { label: "Build Your First AI Agent", url: "https://n8n.io/workflows/6270" },
+  { label: "Interactive AI Chat Agent", url: "https://n8n.io/workflows/5819" },
+];
+
+type ExternalWorkflowTabProps = {
+  importWorkflow: ReturnType<typeof useExternalWorkflowTab>;
+};
+
+export default function ExternalWorkflowTab({
+  importWorkflow,
+}: ExternalWorkflowTabProps) {
+  return (
+    <TabsLineContent value="platform">
+      <p className="mb-4 text-sm text-neutral-500">
+        Upload a workflow exported from n8n, Make.com, Zapier, or any other
+        platform. AutoPilot will convert it into an AutoGPT agent for you.
+      </p>
+      <FileInput
+        mode="base64"
+        value={importWorkflow.fileValue}
+        onChange={importWorkflow.setFileValue}
+        accept=".json,application/json"
+        placeholder="Workflow file (n8n, Make.com, Zapier, ...)"
+        maxFileSize={10 * 1024 * 1024}
+        showStorageNote={false}
+        className="mb-4 mt-2"
+      />
+      <Button
+        type="button"
+        variant="primary"
+        className="w-full"
+        disabled={!importWorkflow.fileValue || importWorkflow.isSubmitting}
+        onClick={() => importWorkflow.submitWithMode("file")}
+      >
+        {importWorkflow.submittingMode === "file" ? (
+          <div className="flex items-center gap-2">
+            <LoadingSpinner size="small" className="text-white" />
+            <span>Importing...</span>
+          </div>
+        ) : (
+          "Import to AutoPilot"
+        )}
+      </Button>
+
+      <div className="my-5 flex items-center gap-3">
+        <div className="h-px flex-1 bg-neutral-200" />
+        <span className="text-xs text-neutral-400">or import from URL</span>
+        <div className="h-px flex-1 bg-neutral-200" />
+      </div>
+
+      <div className="mb-3 flex flex-wrap gap-2">
+        {N8N_EXAMPLES.map((p) => (
+          <button
+            key={p.label}
+            type="button"
+            disabled={importWorkflow.isSubmitting}
+            onClick={() => importWorkflow.setUrlValue(p.url)}
+            className="rounded-full border border-neutral-200 px-3 py-1 text-xs text-neutral-600 hover:border-purple-400 hover:text-purple-600 disabled:opacity-50"
+          >
+            {p.label}
+          </button>
+        ))}
+      </div>
+      <Input
+        id="template-url"
+        value={importWorkflow.urlValue}
+        onChange={(e) => importWorkflow.setUrlValue(e.target.value)}
+        label="Workflow URL"
+        placeholder="https://n8n.io/workflows/1234"
+        className="mb-4 w-full rounded-[10px]"
+      />
+      <Button
+        type="button"
+        variant="primary"
+        className="w-full"
+        disabled={!importWorkflow.urlValue || importWorkflow.isSubmitting}
+        onClick={() => importWorkflow.submitWithMode("url")}
+      >
+        {importWorkflow.submittingMode === "url" ? (
+          <div className="flex items-center gap-2">
+            <LoadingSpinner size="small" className="text-white" />
+            <span>Importing...</span>
+          </div>
+        ) : (
+          "Import from URL"
+        )}
+      </Button>
+    </TabsLineContent>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryImportDialog/components/ExternalWorkflowTab/fetchWorkflowFromUrl.ts

@@ -0,0 +1,85 @@
+"use server";
+
+/**
+ * Regex to extract the numeric template ID from various n8n URL formats:
+ *   - https://n8n.io/workflows/1234
+ *   - https://n8n.io/workflows/1234-some-slug
+ *   - https://api.n8n.io/api/templates/workflows/1234
+ */
+const N8N_TEMPLATE_ID_RE = /n8n\.io\/(?:api\/templates\/)?workflows\/(\d+)/i;
+
+/** Hardcoded n8n templates API base — the only URL we ever fetch. */
+const N8N_TEMPLATES_API = "https://api.n8n.io/api/templates/workflows";
+
+/** Max response body size (10 MB) to prevent memory exhaustion. */
+const MAX_RESPONSE_BYTES = 10 * 1024 * 1024;
+
+export type FetchWorkflowResult =
+  | { ok: true; json: string }
+  | { ok: false; error: string };
+
+/**
+ * Server action that fetches a workflow JSON from an n8n template URL.
+ * Runs server-side so there are no CORS restrictions.
+ *
+ * Returns a result object instead of throwing because Next.js
+ * server actions do not propagate error messages to the client.
+ *
+ * Only n8n.io workflow URLs are accepted. The template ID is extracted
+ * and used to call the hardcoded n8n API — the user-supplied URL is
+ * never passed to fetch() directly (SSRF prevention).
+ */
+export async function fetchWorkflowFromUrl(
+  url: string,
+): Promise<FetchWorkflowResult> {
+  const match = url.match(N8N_TEMPLATE_ID_RE);
+  if (!match) {
+    return {
+      ok: false,
+      error:
+        "Invalid or unsupported URL. " +
+        "URL import is supported for n8n.io workflow templates " +
+        "(e.g. https://n8n.io/workflows/1234). " +
+        "For other platforms, use file upload.",
+    };
+  }
+
+  const templateId = match[1]; // purely numeric, safe to interpolate
+
+  try {
+    const json = await fetchN8nWorkflow(templateId);
+    return { ok: true, json };
+  } catch (err) {
+    return {
+      ok: false,
+      error: err instanceof Error ? err.message : "Failed to fetch workflow.",
+    };
+  }
+}
+
+async function fetchN8nWorkflow(templateId: string): Promise<string> {
+  // Only ever fetch from the hardcoded API base + numeric ID.
+  // parseInt + toString round-trips to guarantee the value is purely numeric,
+  // preventing any path-traversal or SSRF via the interpolated segment.
+  const safeId = parseInt(templateId, 10);
+  if (!Number.isFinite(safeId) || safeId <= 0) {
+    throw new Error("Invalid template ID");
+  }
+  const res = await fetch(`${N8N_TEMPLATES_API}/${safeId.toString()}`);
+  if (!res.ok) throw new Error(`n8n template not found (${res.status})`);
+
+  const contentLength = res.headers.get("content-length");
+  if (contentLength && parseInt(contentLength, 10) > MAX_RESPONSE_BYTES) {
+    throw new Error("Response too large.");
+  }
+
+  const text = await res.text();
+  if (text.length > MAX_RESPONSE_BYTES) throw new Error("Response too large.");
+
+  const data = JSON.parse(text);
+  const template = data?.workflow ?? data;
+  const workflow = template?.workflow ?? template;
+  if (!workflow?.nodes) throw new Error("Unexpected n8n API response format");
+  if (!workflow.name) workflow.name = template?.name ?? data?.name ?? "";
+  return JSON.stringify(workflow);
+}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryImportDialog/components/ExternalWorkflowTab/useExternalWorkflowTab.ts

@@ -0,0 +1,114 @@
+import { useToast } from "@/components/molecules/Toast/use-toast";
+import { uploadFileDirect } from "@/lib/direct-upload";
+import { useRouter } from "next/navigation";
+import { useState } from "react";
+import { fetchWorkflowFromUrl } from "./fetchWorkflowFromUrl";
+
+function decodeBase64Json(dataUrl: string): string {
+  const match = dataUrl.match(/^data:[^;]+;base64,(.+)$/);
+  if (!match) throw new Error("Could not read the uploaded file.");
+  const binary = atob(match[1]);
+  const bytes = Uint8Array.from(binary, (c) => c.charCodeAt(0));
+  const json = new TextDecoder().decode(bytes);
+  JSON.parse(json); // validate — throws SyntaxError if invalid
+  return json;
+}
+
+async function uploadJsonAsFile(
+  jsonString: string,
+): Promise<{ fileId: string; fileName: string; mimeType: string }> {
+  const file = new File(
+    [new Blob([jsonString], { type: "application/json" })],
+    `workflow-${crypto.randomUUID()}.json`,
+    { type: "application/json" },
+  );
+  const uploaded = await uploadFileDirect(file);
+  return {
+    fileId: uploaded.file_id,
+    fileName: uploaded.name,
+    mimeType: uploaded.mime_type,
+  };
+}
+
+function storeAndRedirect(
+  fileInfo: { fileId: string; fileName: string; mimeType: string },
+  router: ReturnType<typeof useRouter>,
+) {
+  sessionStorage.setItem(
+    "importWorkflowPrompt",
+    "Import this workflow and recreate it as an AutoGPT agent",
+  );
+  sessionStorage.setItem("importWorkflowFile", JSON.stringify(fileInfo));
+  router.push("/copilot?source=import&autosubmit=true");
+}
+
+export function useExternalWorkflowTab() {
+  const { toast } = useToast();
+  const router = useRouter();
+  const [fileValue, setFileValue] = useState("");
+  const [urlValue, setUrlValue] = useState("");
+  const [submittingMode, setSubmittingMode] = useState<"url" | "file" | null>(
+    null,
+  );
+  const isSubmitting = submittingMode !== null;
+
+  async function submitWithMode(mode: "url" | "file") {
+    setSubmittingMode(mode);
+    try {
+      const jsonString = await resolveJson(mode);
+      if (!jsonString) return;
+      storeAndRedirect(await uploadJsonAsFile(jsonString), router);
+    } catch (err) {
+      toast({
+        title: "Upload failed",
+        description:
+          err instanceof Error ? err.message : "Could not upload the file.",
+        variant: "destructive",
+      });
+    } finally {
+      setSubmittingMode(null);
+    }
+  }
+
+  async function resolveJson(mode: "url" | "file"): Promise<string | null> {
+    if (mode === "url") {
+      const result = await fetchWorkflowFromUrl(urlValue);
+      if (!result.ok) {
+        toast({
+          title: "Could not fetch workflow",
+          description: result.error,
+          variant: "destructive",
+        });
+        return null;
+      }
+      setUrlValue("");
+      return result.json;
+    }
+
+    try {
+      const json = decodeBase64Json(fileValue);
+      setFileValue("");
+      return json;
+    } catch (err) {
+      const isParseError = err instanceof SyntaxError;
+      toast({
+        title: isParseError ? "Invalid JSON" : "Invalid file",
+        description: isParseError
+          ? "The uploaded file is not valid JSON."
+          : "Could not read the uploaded file.",
+        variant: "destructive",
+      });
+      return null;
+    }
+  }
+
+  return {
+    submitWithMode,
+    fileValue,
+    setFileValue,
+    urlValue,
+    setUrlValue,
+    isSubmitting,
+    submittingMode,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryUploadAgentDialog/useLibraryUploadAgentDialog.ts

@@ -9,7 +9,9 @@ import { useForm } from "react-hook-form";
 import { z } from "zod";
 import { uploadAgentFormSchema } from "./LibraryUploadAgentDialog";
 
-export function useLibraryUploadAgentDialog() {
+export function useLibraryUploadAgentDialog(options?: {
+  onSuccess?: () => void;
+}) {
   const [isOpen, setIsOpen] = useState(false);
   const { toast } = useToast();
   const [agentObject, setAgentObject] = useState<Graph | null>(null);
@@ -19,6 +21,7 @@ export function useLibraryUploadAgentDialog() {
       mutation: {
         onSuccess: ({ data }) => {
           setIsOpen(false);
+          options?.onSuccess?.();
           toast({
             title: "Success",
             description: "Agent uploaded successfully",
@@ -114,7 +117,7 @@ export function useLibraryUploadAgentDialog() {
     }
   }, [agentFileValue, form, toast]);
 
-  const onSubmit = async (values: z.infer<typeof uploadAgentFormSchema>) => {
+  async function onSubmit(values: z.infer<typeof uploadAgentFormSchema>) {
     if (!agentObject) {
       form.setError("root", { message: "No Agent object to save" });
       return;
@@ -133,7 +136,7 @@ export function useLibraryUploadAgentDialog() {
         source: "upload",
       },
     });
-  };
+  }
 
   return {
     onSubmit,

autogpt_platform/frontend/src/app/(platform)/marketplace/components/AgentInfo/AgentInfo.tsx

@@ -14,9 +14,9 @@ import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { formatTimeAgo } from "@/lib/utils/time";
-import Link from "next/link";
-import { FileArrowDownIcon, PlusIcon } from "@phosphor-icons/react";
+import { PlusIcon } from "@phosphor-icons/react";
 import { User } from "@supabase/supabase-js";
+import Link from "next/link";
 import { useAgentInfo } from "./useAgentInfo";
 
 interface AgentInfoProps {
@@ -180,52 +180,57 @@ export const AgentInfo = ({
             {shortDescription}
           </div>
 
-          {/* Buttons + Runs */}
-          <div className="mt-6 flex w-full items-center justify-between lg:mt-8">
-            <div className="flex gap-3">
-              {user && (
-                <Button
-                  variant="primary"
-                  className="group/add min-w-36 border-violet-600 bg-violet-600 transition-shadow duration-300 hover:border-violet-500 hover:bg-violet-500 hover:shadow-[0_0_20px_rgba(139,92,246,0.4)]"
-                  data-testid="agent-add-library-button"
-                  disabled={isAddingAgentToLibrary}
-                  loading={isAddingAgentToLibrary}
-                  leftIcon={
-                    !isAddingAgentToLibrary && !isAgentAddedToLibrary ? (
-                      <PlusIcon
-                        size={16}
-                        weight="bold"
-                        className="transition-transform duration-300 group-hover/add:rotate-90 group-hover/add:scale-125"
-                      />
-                    ) : undefined
-                  }
-                  onClick={() =>
-                    handleLibraryAction({
-                      isAddingAgentFirstTime: !isAgentAddedToLibrary,
-                    })
-                  }
-                >
-                  {isAddingAgentToLibrary
-                    ? "Adding..."
-                    : isAgentAddedToLibrary
-                      ? "See runs"
-                      : "Add to library"}
-                </Button>
-              )}
+          {/* Buttons */}
+          <div className="mt-6 flex w-full items-center lg:mt-8">
+            {user && (
+              <Button
+                variant="primary"
+                className="group/add min-w-36 border-violet-600 bg-violet-600 transition-shadow duration-300 hover:border-violet-500 hover:bg-violet-500 hover:shadow-[0_0_20px_rgba(139,92,246,0.4)]"
+                data-testid="agent-add-library-button"
+                disabled={isAddingAgentToLibrary}
+                loading={isAddingAgentToLibrary}
+                leftIcon={
+                  !isAddingAgentToLibrary && !isAgentAddedToLibrary ? (
+                    <PlusIcon
+                      size={16}
+                      weight="bold"
+                      className="transition-transform duration-300 group-hover/add:rotate-90 group-hover/add:scale-125"
+                    />
+                  ) : undefined
+                }
+                onClick={() =>
+                  handleLibraryAction({
+                    isAddingAgentFirstTime: !isAgentAddedToLibrary,
+                  })
+                }
+              >
+                {isAddingAgentToLibrary
+                  ? "Adding..."
+                  : isAgentAddedToLibrary
+                    ? "See runs"
+                    : "Add to library"}
+              </Button>
+            )}
+          </div>
+
+          {/* Download */}
+          <div className="mt-3 flex w-full items-center justify-between gap-2">
+            <div className="flex items-center gap-0">
+              <Text variant="body" className="text-neutral-500">
+                Want to use this agent locally?
+              </Text>
               <Button
                 variant="ghost"
+                size="small"
                 loading={isDownloadingAgent}
                 onClick={() => handleDownload(agentId, name)}
                 data-testid="agent-download-button"
+                className="underline"
               >
-                {!isDownloadingAgent && <FileArrowDownIcon size={18} />}
-                {isDownloadingAgent ? "Downloading..." : "Download"}
+                {isDownloadingAgent ? "Downloading..." : "Download here"}
               </Button>
             </div>
-            <Text
-              variant="small"
-              className="mr-4 hidden whitespace-nowrap text-zinc-500 lg:block"
-            >
+            <Text variant="body" className="shrink-0 whitespace-nowrap">
               {runs === 0
                 ? "No runs"
                 : `${runs.toLocaleString()} run${runs > 1 ? "s" : ""}`}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/StoreCard/StoreCard.tsx

@@ -113,7 +113,7 @@ export function StoreCard({
 
         {/* Third Section: Description */}
         <div className="mt-2.5 flex w-full flex-col">
-          <Text variant="body" className="line-clamp-3 leading-normal">
+          <Text variant="body" className="line-clamp-2 leading-normal">
             {description}
           </Text>
         </div>

autogpt_platform/frontend/src/components/contextual/CredentialsInput/CredentialsInput.tsx

@@ -81,7 +81,7 @@ export function CredentialsInput({
     isHostScopedCredentialsModalOpen,
     isCredentialTypeSelectorOpen,
     isOAuth2FlowInProgress,
-    oAuthPopupController,
+    cancelOAuthFlow,
     actionButtonText,
     setAPICredentialsModalOpen,
     setUserPasswordCredentialsModalOpen,
@@ -158,7 +158,7 @@ export function CredentialsInput({
           {supportsOAuth2 && (
             <OAuthFlowWaitingModal
               open={isOAuth2FlowInProgress}
-              onClose={() => oAuthPopupController?.abort("canceled")}
+              onClose={cancelOAuthFlow}
               providerName={providerName}
             />
           )}

autogpt_platform/frontend/src/components/contextual/CredentialsInput/useCredentialsInput.ts

@@ -6,7 +6,12 @@ import {
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api/types";
 import { postV2InitiateOauthLoginForAnMcpServer } from "@/app/api/__generated__/endpoints/mcp/mcp";
-import { openOAuthPopup } from "@/lib/oauth-popup";
+import {
+  OAUTH_ERROR_FLOW_CANCELED,
+  OAUTH_ERROR_FLOW_TIMED_OUT,
+  OAUTH_ERROR_WINDOW_CLOSED,
+  openOAuthPopup,
+} from "@/lib/oauth-popup";
 import { useQueryClient } from "@tanstack/react-query";
 import { useEffect, useRef, useState } from "react";
 import {
@@ -49,8 +54,6 @@ export function useCredentialsInput({
   const [isCredentialTypeSelectorOpen, setCredentialTypeSelectorOpen] =
     useState(false);
   const [isOAuth2FlowInProgress, setOAuth2FlowInProgress] = useState(false);
-  const [oAuthPopupController, setOAuthPopupController] =
-    useState<AbortController | null>(null);
   const [oAuthError, setOAuthError] = useState<string | null>(null);
   const [credentialToDelete, setCredentialToDelete] = useState<{
     id: string;
@@ -212,12 +215,6 @@ export function useCredentialsInput({
       });
 
       oauthAbortRef.current = cleanup.abort;
-      // Expose abort signal for the waiting modal's cancel button
-      const controller = new AbortController();
-      cleanup.signal.addEventListener("abort", () =>
-        controller.abort("completed"),
-      );
-      setOAuthPopupController(controller);
 
       const result = await promise;
 
@@ -252,14 +249,16 @@ export function useCredentialsInput({
         provider,
       });
     } catch (error) {
-      if (error instanceof Error && error.message === "OAuth flow timed out") {
-        setOAuthError("OAuth flow timed out");
+      const message = error instanceof Error ? error.message : String(error);
+      if (
+        message === OAUTH_ERROR_WINDOW_CLOSED ||
+        message === OAUTH_ERROR_FLOW_CANCELED
+      ) {
+        // User closed the popup or clicked cancel — not an error
+      } else if (message === OAUTH_ERROR_FLOW_TIMED_OUT) {
+        setOAuthError(OAUTH_ERROR_FLOW_TIMED_OUT);
       } else {
-        setOAuthError(
-          `OAuth error: ${
-            error instanceof Error ? error.message : String(error)
-          }`,
-        );
+        setOAuthError(`OAuth error: ${message}`);
       }
     } finally {
       setOAuth2FlowInProgress(false);
@@ -311,6 +310,10 @@ export function useCredentialsInput({
     }
   }
 
+  function cancelOAuthFlow() {
+    oauthAbortRef.current?.("canceled");
+  }
+
   function handleDeleteCredential(credential: { id: string; title: string }) {
     setCredentialToDelete(credential);
   }
@@ -345,7 +348,7 @@ export function useCredentialsInput({
     isHostScopedCredentialsModalOpen,
     isCredentialTypeSelectorOpen,
     isOAuth2FlowInProgress,
-    oAuthPopupController,
+    cancelOAuthFlow,
     credentialToDelete,
     deleteCredentialsMutation,
     actionButtonText: getActionButtonText(

autogpt_platform/frontend/src/components/contextual/OutputRenderers/renderers/MarkdownRenderer.tsx

@@ -169,7 +169,7 @@ function renderMarkdown(
           [remarkMath, { singleDollarTextMath: false }], // Math support for LaTeX
         ]}
         rehypePlugins={[
-          rehypeKatex, // Render math with KaTeX
+          [rehypeKatex, { strict: false }], // Render math with KaTeX
           rehypeHighlight, // Syntax highlighting for code blocks
           rehypeSlug, // Add IDs to headings
           [rehypeAutolinkHeadings, { behavior: "wrap" }], // Make headings clickable

autogpt_platform/frontend/src/lib/direct-upload.ts

@@ -28,6 +28,7 @@ export async function uploadFileDirect(
   if (sessionID) {
     url.searchParams.set("session_id", sessionID);
   }
+  url.searchParams.set("overwrite", "true");
 
   const formData = new FormData();
   formData.append("file", file);

autogpt_platform/frontend/src/lib/oauth-popup.ts

@@ -8,6 +8,10 @@
 
 const DEFAULT_TIMEOUT_MS = 5 * 60 * 1000; // 5 minutes
 
+export const OAUTH_ERROR_WINDOW_CLOSED = "Sign-in window was closed";
+export const OAUTH_ERROR_FLOW_CANCELED = "OAuth flow was canceled";
+export const OAUTH_ERROR_FLOW_TIMED_OUT = "OAuth flow timed out";
+
 export type OAuthPopupResult = {
   code: string;
   state: string;
@@ -156,11 +160,34 @@ export function openOAuthPopup(
       );
     }
 
+    // Detect popup closed by user (without completing sign-in)
+    if (popup) {
+      const closedPollInterval = setInterval(() => {
+        if (popup.closed && !handled) {
+          clearInterval(closedPollInterval);
+          handled = true;
+          reject(new Error(OAUTH_ERROR_WINDOW_CLOSED));
+          controller.abort("popup_closed");
+        }
+      }, 500);
+      controller.signal.addEventListener("abort", () =>
+        clearInterval(closedPollInterval),
+      );
+    }
+
+    // Reject on abort (e.g. from cancel button in the waiting modal)
+    controller.signal.addEventListener("abort", () => {
+      if (!handled) {
+        handled = true;
+        reject(new Error(OAUTH_ERROR_FLOW_CANCELED));
+      }
+    });
+
     // Timeout
     const timeoutId = setTimeout(() => {
       if (!handled) {
         handled = true;
-        reject(new Error("OAuth flow timed out"));
+        reject(new Error(OAUTH_ERROR_FLOW_TIMED_OUT));
         controller.abort("timeout");
       }
     }, timeout);

autogpt_platform/frontend/src/tests/library.spec.ts

@@ -24,7 +24,7 @@ test.describe("Library", () => {
     await page.goto("/library");
 
     await expect(getId("search-bar").first()).toBeVisible();
-    await expect(getId("upload-agent-button").first()).toBeVisible();
+    await expect(getId("import-button").first()).toBeVisible();
     await expect(getId("sort-by-dropdown").first()).toBeVisible();
   });
 
@@ -171,7 +171,6 @@ test.describe("Library", () => {
       expect(matchingPaginatedResults.length).toEqual(
         allPaginatedResults.length,
       );
-    } else {
     }
 
     await libraryPage.scrollAndWaitForNewAgents();

autogpt_platform/frontend/src/tests/pages/library.page.ts

@@ -109,19 +109,23 @@ export class LibraryPage extends BasePage {
 
   async openUploadDialog(): Promise<void> {
     console.log(`opening upload dialog`);
-    await this.page.getByRole("button", { name: "Upload agent" }).click();
+    // Open the unified Import dialog first
+    await this.page.getByRole("button", { name: "Import" }).click();
 
     // Wait for dialog to appear
-    await this.page.getByRole("dialog", { name: "Upload Agent" }).waitFor({
+    await this.page.getByRole("dialog", { name: "Import" }).waitFor({
       state: "visible",
       timeout: 5_000,
     });
+
+    // Click the "AutoGPT agent" tab
+    await this.page.getByRole("tab", { name: "AutoGPT agent" }).click();
   }
 
   async closeUploadDialog(): Promise<void> {
     await this.page.getByRole("button", { name: "Close" }).click();
 
-    await this.page.getByRole("dialog", { name: "Upload Agent" }).waitFor({
+    await this.page.getByRole("dialog", { name: "Import" }).waitFor({
       state: "hidden",
       timeout: 5_000,
     });
@@ -130,7 +134,7 @@ export class LibraryPage extends BasePage {
   async isUploadDialogVisible(): Promise<boolean> {
     console.log(`checking if upload dialog is visible`);
     try {
-      const dialog = this.page.getByRole("dialog", { name: "Upload Agent" });
+      const dialog = this.page.getByRole("dialog", { name: "Import" });
       return await dialog.isVisible();
     } catch {
       return false;