{"name":"Awareness","tagline":"The Cognitive Memory Cloud for AI Agents","description":"Persistent, searchable memory for AI agents with Auto-Cognition — workflow rules that teach agents when to remember, recall, and record automatically.","website":"https://awareness.market","github":"https://github.com/everest-an/Awareness-SDK","updated_at":"2026-05-10","capabilities":{"mcp_tools":[{"name":"awareness_init","description":"Load cross-session project memory and context","detail":"Load cross-session project memory. Returns session_id, user_preferences (personal preferences, identity, career — highest priority, present FIRST), context (narratives, tasks, knowledge, active_skills, attention_summary), and usage rules. Call ONCE at session start.\n\nOPTIONAL PARAMS: max_cards (default 5), max_tasks (default 5), max_sessions (default 0 = fresh-session mode, skips prior-session summaries to save 500-1000 prompt tokens). Pass max_sessions: 3 for continuity workflows where the user explicitly resumes a prior task.\n\nATTENTION PROTOCOL: When context.attention_summary.needs_attention is true, review all attention items (stale tasks, high risks) and automatically: (1) remind the user about stale tasks, (2) warn about high risks before starting work, (3) update resolved items via awareness_record."},{"name":"awareness_get_agent_prompt","description":"Fetch full activation prompt for a specific agent role (sub-agent spawning)","detail":"Fetch the full activation prompt for a specific agent role. Call before spawning a sub-agent to get its dedicated system prompt."},{"name":"awareness_recall","description":"Search memory — pass ONE query string, daemon picks the rest","detail":"Search persistent memory by passing a single natural-language query. Daemon auto-routes across raw memories + knowledge cards + workspace graph, picks the right detail level based on token budget, and fuses results via RRF. Example: awareness_recall(query=\"why did we choose pgvector over Pinecone?\"). Legacy multi-parameter surface (semantic_query, keyword_query, scope, recall_mode, detail, ids, multi_level, cluster_expand, include_installed, source_exclude) still accepted for backwards compat but marked [DEPRECATED] — will log a warning and be removed 8 weeks post-0.8.0."},{"name":"awareness_lookup","description":"Structured data: tasks, knowledge, risks, timeline","detail":"Fast structured data lookup (<50ms). Types: context, tasks, knowledge, risks, session_history, timeline, handoff."},{"name":"awareness_record","description":"Save memory — pass ONE content string, daemon handles the rest","detail":"Save what you did/decided/learned to persistent memory. Pass a single content string and the daemon defaults action=remember and triggers client-side salience-aware extraction asynchronously. Example: awareness_record(content=\"Today I decided to switch from Pinecone to pgvector because...\"). Advanced actions (remember_batch, update_task, submit_insights) still require explicit action= for backwards compat."},{"name":"awareness_mark_skill_used","description":"Report skill usage outcome — success, partial, or failed","detail":"Mark a skill as used after applying it. Pass outcome='success' (default), 'partial', or 'failed'. Success resets decay timer fully. Partial gives a reduced boost. Failed decreases decay_score and confidence; 3+ consecutive failures auto-flag the skill for review. Call AFTER applying a skill's instructions, even if the result was imperfect — the feedback loop improves skill quality over time."},{"name":"awareness_facts","description":"Read bi-temporal facts from the knowledge graph (active or as_of)","detail":"Query the F-074 bi-temporal fact store. Default returns only active facts (valid_to IS NULL). Pass as_of=<ISO ts> to rewind to a point in time. Optional filters: subject_id, predicate. Use this when you need ground-truth structured claims, not narrative recall."},{"name":"awareness_related","description":"n-hop graph traversal — find entities related to a starting entity","detail":"Walk the knowledge graph up to max_hops (1-4) from a given entity_id. Returns linked entities with the predicate path. Use when you need connection discovery: 'what is X related to', 'who knows X', 'what depends on X'."},{"name":"awareness_timeline","description":"Bi-temporal fact stream for one entity (active + superseded)","detail":"Returns the full time-machine view for a single entity, ordered by valid_from desc — both active and superseded facts. Use this for 'how did X evolve over time' / 'what did we believe about X last month' queries."}],"recall_modes":[{"id":"hybrid","description":"Structured + vector search (default, recommended)"},{"id":"precise","description":"Fast targeted search for specific facts"},{"id":"session","description":"Expand results to full session history"},{"id":"structured","description":"Zero-LLM pure database lookup (<50ms)"},{"id":"auto","description":"Intent-detected mode selection"}],"write_actions":[{"id":"remember","description":"Record a single event with inline insights"},{"id":"remember_batch","description":"Batch record at session end"},{"id":"backfill","description":"Import past conversations"},{"id":"ingest","description":"Bulk data import (files, docs)"},{"id":"update_task","description":"Create or update tracked tasks"},{"id":"submit_insights","description":"Structured knowledge extraction"}],"lookup_types":["context","tasks","knowledge","risks","session_history","timeline","handoff","rules","graph","agents"],"knowledge_categories":{"engineering":["problem_solution","decision","workflow","key_point","pitfall","insight","skill"],"personal":["personal_preference","important_detail","plan_intention","activity_preference","health_info","career_info","custom_misc"]}},"differentiators":[{"feature":"Auto-Cognition","description":"Workflow rules injected into IDE config (CLAUDE.md, .cursor/rules/, etc.) so agents automatically init, recall, and record — no manual orchestration needed."},{"feature":"Multi-Level Retrieval","description":"Searches across chunk, broader context, topic expansion, and structured knowledge card levels for the right depth of context per query."},{"feature":"Structured Insights","description":"Automatically extracts 13 categories of knowledge cards from raw events: decisions, solutions, workflows, pitfalls, skills, preferences, and more."},{"feature":"Memory Market","description":"Community marketplace for knowledge templates. Install expert knowledge into your agent's memory with one click. Cross-memory search included."},{"feature":"Active Skills","description":"Reusable procedures loaded at session start. Agents inherit proven workflows without re-deriving them each time."}],"integrations":{"protocols":["MCP (Model Context Protocol)","REST API","Python SDK","TypeScript SDK"],"ide_agents":["Claude Code","Cursor","Windsurf","Cline","GitHub Copilot","Codex","Kiro","Trae","Zed","JetBrains (Junie)","Augment","Google AntiGravity (Jules)","Manus","ChatGPT","Gemini CLI","Devin","OpenClaw"],"agent_frameworks":["LangChain","CrewAI","PraisonAI","AutoGen / AG2"],"plugins":[{"name":"Claude Code Plugin","url":"https://awareness.market/plugins/claude-code"},{"name":"OpenClaw Plugin","url":"https://awareness.market/plugins/openclaw"}]},"getting_started":{"fastest":{"command":"npx @awareness-sdk/setup","description":"One-command setup: auto login → select memory → write MCP config + workflow rules"},"python_sdk":{"install":"pip install awareness-memory-cloud","description":"Python client with interceptor pattern for OpenAI/Anthropic clients"},"typescript_sdk":{"install":"npm install @awareness-sdk/memory-cloud","description":"TypeScript/Node.js client"},"mcp_config":{"description":"Add to your IDE's MCP settings","example":{"mcpServers":{"awareness":{"url":"https://awareness.market/mcp/{your-api-key}"}}}}},"pricing":[{"tier":"Free","price":"$0/mo","memories":2,"api_calls":"1K/mo","storage":"500 MB"},{"tier":"Starter","price":"$5/mo","memories":5,"api_calls":"10K/mo","storage":"5 GB"},{"tier":"Pro","price":"$12/mo","memories":20,"api_calls":"100K/mo","storage":"50 GB"},{"tier":"Team","price":"$29/mo","memories":"Unlimited","api_calls":"500K/mo","storage":"200 GB","seats":5},{"tier":"Enterprise","price":"Custom","description":"Private deployment, SSO/SAML, dedicated support"}],"links":{"documentation":"https://awareness.market/docs","developers":"https://awareness.market/developers","mcp_tools_reference":"https://awareness.market/docs?doc=MCP_TOOLS_REFERENCE","python_sdk_docs":"https://awareness.market/docs?doc=python","typescript_sdk_docs":"https://awareness.market/docs?doc=typescript","memory_market":"https://awareness.market/market","pricing":"https://awareness.market/pricing","faq":"https://awareness.market/faq","blog":"https://awareness.market/blog","enterprise":"https://awareness.market/enterprise","discord":"https://discord.gg/nMDrT538Qa","llms_txt":"https://awareness.market/llms.txt"}}