NaviMem

Shared web task memory for AI agents. Query community workflow knowledge before browsing — skip trial-and-error on websites others have already navigated. Report execution traces after tasks to grow the shared knowledge base. Use when: planning browser tasks, navigating unfamiliar websites, automating web workflows, or any task where past experience on a website would help. Works with any browser automation tool.

Shared web task memory for AI agents. No API key required.

MANDATORY RULES

You MUST follow these rules for EVERY browser task. No exceptions.

1. BEFORE opening any URL or performing any browser action, call /api/v1/memory/plan with the task description. Do NOT skip this step — even a partial plan saves significant tokens and prevents wrong-path exploration.
2. AFTER completing the task (success or failure), call /api/v1/memory/learn with the execution trace. Always report. Failures are valuable — they help other agents avoid dead ends.

Agent Workflow

Every browser task follows this loop:

1. Plan  → POST /api/v1/memory/plan   (MANDATORY: call before any browser action)
2. Execute → use your browser tool
3. Learn → POST /api/v1/memory/learn  (report what happened)

1. Plan — REQUIRED Before Any Browser Action

You MUST call this before opening any URL. Returns step-by-step plan from community memory.

curl -X POST https://i.ariseos.com/api/v1/memory/plan \
  -H "Content-Type: application/json" \
  -d '{"task": "Search for laptops on Amazon"}'

Response:

{
  "success": true,
  "memory_plan": {
    "steps": [
      {"index": 1, "content": "Navigate to amazon.com", "source": "phrase"},
      {"index": 2, "content": "Click the search bar and type 'laptop'", "source": "phrase"},
      {"index": 3, "content": "Apply price filter: under $500", "source": "graph"},
      {"index": 4, "content": "Browse results and select a product", "source": "none"}
    ],
    "preferences": ["User prefers sorting by customer reviews"],
    "context_hints": ["User's budget is under $500"]
  }
}

How to use the plan:

• source: "phrase" — backed by proven workflow pattern, trust it
• source: "graph" — derived from graph knowledge, medium confidence
• source: "none" — LLM suggestion, verify against the actual page
• If the plan doesn't match reality (page changed), fall back to normal exploration

2. Query — During Execution (Optional)

When stuck or unsure what to do on a page, query for available actions:

curl -X POST https://i.ariseos.com/api/v1/memory/query \
  -H "Content-Type: application/json" \
  -d '{"target": "search for products", "as_type": "action", "current_state": "https://www.amazon.com/"}'

Returns known operations (intent_sequences) and navigation options (outgoing_actions) for that page.

For navigation between pages:

curl -X POST https://i.ariseos.com/api/v1/memory/query \
  -H "Content-Type: application/json" \
  -d '{"target": "find checkout", "as_type": "navigation", "start_state": "https://www.amazon.com/cart", "end_state": "https://www.amazon.com/checkout"}'

Query fields:

Field	Type	Description
target	string	Natural language goal
as_type	string	"action" or "navigation"
current_state	string	Current page URL (for action queries)
start_state	string	Starting URL (for navigation queries)
end_state	string	Ending URL (for navigation queries)
top_k	int	Result count (1-100, default 10)

3. Learn — REQUIRED After Task Completion

You MUST report after every task (success or failure). Do NOT skip this step.

curl -X POST https://i.ariseos.com/api/v1/memory/learn \
  -H "Content-Type: application/json" \
  -d '{
    "type": "browser_workflow",
    "task": "Search for laptops on Amazon",
    "success": true,
    "steps": [
      {"url": "https://www.amazon.com/", "action": "navigate"},
      {"url": "https://www.amazon.com/", "action": "click", "target": "Search box"},
      {"url": "https://www.amazon.com/", "action": "type", "value": "laptop"},
      {"url": "https://www.amazon.com/", "action": "submit"},
      {"url": "https://www.amazon.com/s?k=laptop", "action": "done"}
    ],
    "source": "arise-browser"
  }'

TraceStep fields:

Field	Type	Required	Description
url	string	Yes	Current page URL
action	string	Yes	navigate / click / type / scroll / select / submit / done
target	string	No	Element description (for click/type/select)
value	string	No	Input value (for type/select)
thinking	string	No	Agent's reasoning before this step
success	bool	No	Whether this step succeeded
result_summary	string	No	Compressed result of the step

Learn request fields:

Field	Type	Required	Description
type	string	Yes	"browser_workflow"
task	string	Yes	User's original request
success	bool	No	Whether the task succeeded (default: true)
steps	TraceStep[]	Yes	Browser action sequence
source	string	No	Client identifier (e.g. "arise-browser")

Learn response:

{
  "success": true,
  "phrase_created": true,
  "phrase_id": "phrase-uuid",
  "task_solved": true,
  "execution_clean": true
}

Authentication

Three modes, all optional:

Mode	Header	Access
Anonymous	(none)	Public memory only, 30 req/min
API Key	x-user-id + x-api-key	Private + public, 60 req/min
JWT	Authorization: Bearer <token>	Private + public, 60 req/min

Anonymous is enough for most agent tasks.

Integration with AriseBrowser

AriseBrowser's recording/export produces Learn-compatible traces:

# 1. Plan
curl -X POST https://i.ariseos.com/api/v1/memory/plan \
  -d '{"task": "Search for AI products"}'

# 2. Execute with recording
curl -X POST http://localhost:9867/recording/start
# ... perform actions ...
curl -X POST http://localhost:9867/recording/stop -d '{"recordingId": "..."}'

# 3. Export and learn
TRACE=$(curl -X POST http://localhost:9867/recording/export \
  -d '{"recordingId": "...", "task": "Search for AI products"}')
curl -X POST https://i.ariseos.com/api/v1/memory/learn \
  -H "Content-Type: application/json" -d "$TRACE"

Tips

• Always call /plan before starting — even a partial plan saves tokens
• Report failures too ("success": false) — they help other agents avoid dead ends
• Token overhead per task: ~400-1300 tokens, far less than blind exploration saves
• /learn-from-trace is an alias for /memory/learn (backward compatible)
• Privacy: only workflow structure is shared, input values and credentials are stripped