Feed Type: session.llmfeed.json — Portable Session Context for LLM Handoffs

Continue conversations seamlessly across different LLMs

HIGHbeginnersigned8 min
📅 Created: 6/10/2025
👥 Audience: llm, developer, end-user
🔌 Feed Types:sessionhandoffcontinuity
⚡ Capabilities:session-exportcontext-preservationcross-platform-transfer
🤖 LLMFeed:/.well-known/mcp.llmfeed.json

Feed Type: `session.llmfeed.json`

Purpose

A session.llmfeed.json captures the context and progress of a conversation with an LLM so you can continue it seamlessly with another LLM or resume it later.

Think of it as "copy-paste on steroids" - instead of losing context when switching between Claude, ChatGPT, or other LLMs, you get perfect continuity.

The Original Problem

What usually happens:

You: [Long conversation with Claude about React optimization]
Claude: [Helpful analysis and recommendations]
You: [Switch to ChatGPT]
ChatGPT: "I don't have context of your previous conversation..."
You: [Have to re-explain everything] 😞

What session feeds enable:

You: "Claude, export this conversation for me to continue elsewhere"
Claude: [Generates session.llmfeed.json with full context]
You: [Paste into ChatGPT] 
ChatGPT: "I see you were discussing React optimization with Claude. 
         You're implementing custom hooks for data fetching..." 😊

Minimum Structure (Hello World)

json
{
  "feed_type": "session",
  "metadata": {
    "title": "React Optimization Discussion",
    "origin": "claude.ai",
    "generated_at": "2025-06-10T15:30:00Z"
  },
  "session_summary": {
    "topic": "React performance optimization",
    "current_focus": "custom hooks for data fetching",
    "key_points": ["useState vs useRef", "memoization patterns"]
  }
}

Result: Any LLM can understand where you left off!

Common Use Cases

Switching Between LLMs

json
{
  "feed_type": "session",
  "metadata": {
    "title": "Writing Strategy Session",
    "origin": "claude.ai",
    "generated_at": "2025-06-10T14:00:00Z"
  },
  "session_summary": {
    "topic": "Blog post about sustainable technology",
    "current_focus": "structuring the article outline",
    "key_decisions": ["target audience: tech professionals", "tone: informative but engaging"],
    "next_steps": "need help with compelling introduction"
  },
  "conversation_context": {
    "user_background": "startup founder",
    "writing_style": "prefers short paragraphs",
    "target_length": "1500 words"
  }
}

Pause and Resume

json
{
  "feed_type": "session", 
  "metadata": {
    "title": "Python Learning Session",
    "origin": "chatgpt.com",
    "generated_at": "2025-06-10T16:45:00Z"
  },
  "session_summary": {
    "topic": "Learning Python decorators",
    "current_focus": "understanding @property decorator",
    "completed_topics": ["basic decorators", "function wrappers"],
    "next_steps": "practice with class decorators"
  },
  "learning_context": {
    "skill_level": "intermediate",
    "learning_goal": "build a web scraper",
    "time_constraint": "2 weeks to complete project"
  }
}

Collaborative Sessions

json
{
  "feed_type": "session",
  "metadata": {
    "title": "Team Planning Session",
    "origin": "gemini.google.com", 
    "generated_at": "2025-06-10T11:20:00Z"
  },
  "session_summary": {
    "topic": "Q3 product roadmap planning",
    "participants": ["product_manager", "tech_lead", "designer"],
    "current_focus": "prioritizing feature requests",
    "decisions_made": ["mobile app is priority", "API v2 delayed to Q4"]
  },
  "project_context": {
    "team_size": "8 people",
    "deadline": "end of Q3",
    "budget_constraints": "limited engineering resources"
  }
}

Trust & Verification

Basic Signed Session

json
{
  "feed_type": "session",
  "metadata": {
    "title": "Medical Research Discussion",
    "origin": "claude.ai",
    "generated_at": "2025-06-10T13:15:00Z"
  },
  "session_summary": {
    "topic": "diabetes treatment research",
    "current_focus": "analyzing recent clinical trials"
  },
  "trust": {
    "signed_blocks": ["metadata", "session_summary", "trust"],
    "scope": "conversation_integrity"
  },
  "signature": {
    "value": "signature_hash_here",
    "created_at": "2025-06-10T13:15:00Z"
  }
}

Why sign sessions?

  • ✅ Prove authenticity - verify the session really came from that LLM
  • ✅ Prevent tampering - ensure context wasn't modified
  • ✅ Build trust - especially important for sensitive topics
  • ✅ Enable audit trails - track conversation history

Core Fields

FieldRequiredDescription
`session_summary`✅What was discussed and current status
`conversation_context`⚠️User background and preferences
`key_decisions`⚠️Important choices or conclusions made
`next_steps`⚠️What to focus on next
`completed_topics`⚠️What's already been covered

How LLMs Should Handle Sessions

When Receiving a Session Feed

  1. Read the context - understand what was discussed
  2. Acknowledge continuity - mention the previous conversation
  3. Pick up where left off - focus on current_focus and next_steps
  4. Respect the style - adapt to user preferences in conversation_context

Example Response

"I can see you were discussing React optimization with Claude, and you're 
currently working on custom hooks for data fetching. Based on your preference 
for functional components and your e-commerce dashboard project, let's continue 
by looking at some practical patterns for data fetching hooks..."

When Asked to Export

  1. Summarize the conversation - key topics and decisions
  2. Capture current focus - where the discussion stands
  3. Include user context - background and preferences revealed
  4. Suggest next steps - logical continuation points

📚 Advanced Features

Decision Tracking

Conversation with Decision Points

json
{
  "session_summary": {
    "topic": "choosing a web framework",
    "current_focus": "comparing React vs Vue",
    "key_decisions": [
      {
        "decision": "ruled out Angular",
        "reasoning": "too complex for team size",
        "timestamp": "2025-06-10T14:20:00Z"
      },
      {
        "decision": "prioritizing ease of learning", 
        "reasoning": "team has junior developers",
        "timestamp": "2025-06-10T14:25:00Z"
      }
    ]
  }
}
Multi-Session Workflows

Link Related Sessions

json
{
  "related_sessions": [
    {
      "title": "Initial Project Planning",
      "url": "/sessions/project-planning-20250605.llmfeed.json",
      "relationship": "precedes"
    },
    {
      "title": "Technical Deep Dive", 
      "url": "/sessions/tech-deep-dive-20250608.llmfeed.json",
      "relationship": "parallel_track"
    }
  ]
}
Learning and Adaptation

Educational Sessions

json
{
  "learning_progress": {
    "concepts_mastered": ["Python basics", "loops", "functions"],
    "current_difficulty": "object-oriented programming",
    "learning_style": "prefers examples over theory",
    "misconceptions_corrected": [
      "thought classes were like functions",
      "confused about self parameter"
    ]
  }
}

Professional Development

json
{
  "career_context": {
    "current_role": "junior frontend developer",
    "learning_goals": ["become full-stack", "understand backend"],
    "timeline": "6 months",
    "preferred_technologies": ["JavaScript", "Python", "PostgreSQL"]
  }
}
Collaborative and Team Sessions

Team Planning

json
{
  "collaboration_context": {
    "session_type": "team_planning",
    "facilitator": "product_manager",
    "stakeholders": ["engineering", "design", "marketing"],
    "consensus_points": ["mobile-first approach", "Q3 launch target"],
    "open_questions": ["budget allocation", "resource allocation"]
  }
}

Multi-Agent Workflows

json
{
  "agent_collaboration": {
    "primary_agent": "claude.ai",
    "consulting_agents": ["code_review_bot", "design_feedback_ai"],
    "handoff_points": [
      {
        "to_agent": "code_review_bot",
        "for_task": "review generated React components",
        "expected_return": "code quality feedback"
      }
    ]
  }
}

Best Practices

For Users

  1. Export at natural break points - end of topics, before switching focus
  2. Include enough context - background info helps the new LLM understand
  3. Be specific about next steps - what you want to accomplish next
  4. Use descriptive titles - makes sessions easier to find later

For LLM Implementations

  1. Always acknowledge the handoff - mention the previous conversation
  2. Summarize what you understand - confirm context was preserved
  3. Pick up naturally - don't restart, continue the flow
  4. Respect user preferences - adapt to their communication style

For Developers

  1. Keep sessions focused - one session per major topic/goal
  2. Sign important sessions - especially for sensitive or critical content
  3. Link related sessions - create workflows across multiple conversations
  4. Include user context - background helps with personalization

Real-World Examples

Code Review Session

json
{
  "feed_type": "session",
  "metadata": {
    "title": "React Component Code Review",
    "origin": "claude.ai"
  },
  "session_summary": {
    "topic": "reviewing custom React components",
    "current_focus": "optimizing render performance",
    "code_reviewed": ["UserProfile.jsx", "ProductCard.jsx"],
    "issues_found": ["missing memo optimization", "prop drilling"],
    "next_steps": "implement context API for state management"
  },
  "development_context": {
    "project_type": "e-commerce dashboard",
    "tech_stack": ["React 18", "TypeScript", "Vite"],
    "performance_requirements": "sub-200ms render times"
  }
}

Writing Workshop Session

json
{
  "feed_type": "session",
  "metadata": {
    "title": "Technical Blog Writing Session",
    "origin": "chatgpt.com"
  },
  "session_summary": {
    "topic": "writing about machine learning concepts",
    "current_focus": "making complex topics accessible",
    "completed_sections": ["introduction", "basic concepts"],
    "writing_challenges": ["explaining neural networks simply"],
    "next_steps": "add practical examples and code snippets"
  },
  "writing_context": {
    "target_audience": "junior developers",
    "publication": "company engineering blog",
    "tone": "educational but conversational",
    "length_goal": "2000 words"
  }
}

What This Enables

For Users

  • ✅ Seamless LLM switching - no context loss
  • ✅ Better conversation continuity - pick up exactly where you left off
  • ✅ Portable knowledge sessions - take your conversations anywhere
  • ✅ Long-term project tracking - maintain context across weeks/months

For LLMs

  • ✅ Better user experience - understanding context immediately
  • ✅ More effective assistance - building on previous work
  • ✅ Reduced repetition - no need to re-establish background
  • ✅ Collaborative workflows - working together across platforms

For Teams

  • ✅ Shared context - team members can continue each other's conversations
  • ✅ Knowledge preservation - important discussions don't get lost
  • ✅ Decision tracking - clear record of choices and reasoning
  • ✅ Workflow continuity - projects span multiple sessions and people

MIME Type

Content-Type: application/llmfeed+json

Or specifically:

Content-Type: application/session+llmfeed

Related Feed Types

  • export.llmfeed.json: Export specific content from a session
  • prompt.llmfeed.json: Reusable prompts that work across sessions
  • mcp.llmfeed.json: Service context that sessions can reference
  • credential.llmfeed.json: Authentication for private session content

References

⚡

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