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the complete train of thought of agent work as first-class artifacts","maintainers":[{"name":"khaliqgant","email":"khaliqgant@gmail.com"},{"name":"willwashburn","email":"will@agentrelay.com"}],"readme":"# Agent Trajectories\n\n**Capture the complete \"train of thought\" of agent work as first-class artifacts.**\n\nWhen an agent completes a task today, the only artifacts are code changes, commit messages, and PR descriptions. The rich context of *how* the work happened disappears: why approach A was chosen over B, what dead ends were explored, what assumptions were made.\n\nAgent Trajectories captures this missing context as structured, searchable, portable records that travel with the code.\n\n## What is a Trajectory?\n\nA **trajectory** is the complete story of agent work on a task:\n\n- **Chapters** - Logical segments of work (exploration, implementation, testing)\n- **Events** - Prompts, tool calls, decisions, messages between agents\n- **Retrospective** - Agent reflection on what was accomplished, challenges faced, and lessons learned\n- **Artifacts** - Links to commits, files changed, and external task references\n\n## Key Features\n\n### Platform Agnostic\nWorks with any task system: Beads, Linear, Jira, GitHub Issues, or standalone. Trajectories are a universal format—like Markdown for documentation.\n\n### Multiple Storage Backends\n- **File system** (default) - `.agentworkforce/trajectories/` directory, git-friendly\n- **SQLite** - Local indexing and search\n- **PostgreSQL/S3** - For teams and archival\n\n### Rich Export Formats\n- **Markdown** - Notion-style pages for documentation\n- **Timeline** - Linear-style chronological view\n- **JSON** - Full structured data for tooling\n\n### Native Multi-Agent Support\n\nTrajectories is built for teams of agents working together:\n\n- **Shared trajectory** — Multiple agents collaborate on a single task record\n- **Agent participation** — Each agent logged as lead, contributor, or reviewer with timestamps\n- **Chapter handoffs** — When work moves between agents, chapters capture the context shift\n- **Cross-agent messaging** — Integrates with [agent-relay](https://github.com/khaliqgant/agent-relay) to record inter-agent communication as trajectory events\n- **Parallel coordination** — Multiple agents working in parallel on related tasks can reference each other's trajectories\n\nThis is a key differentiator: no other tool in the AI dev stack tracks *who* (which agent, which model) made *which decisions* and *why*, across a coordinated multi-agent workflow.\n\n### Integration Ready\n- Complements [claude-mem](https://github.com/thedotmack/claude-mem) for observation-level memory\n- Integrates with [agent-relay](https://github.com/khaliqgant/agent-relay) for multi-agent messaging\n- **Agent Trace integration** - Automatic code attribution following [agent-trace.dev](https://agent-trace.dev) spec\n\n### Code Attribution (Agent Trace)\n\nTrajectories automatically generate [Agent Trace](https://agent-trace.dev) records that attribute code changes to AI agents:\n\n```bash\ntrail start \"Implement auth module\"\n# ... agent writes code, makes commits ...\ntrail complete --summary \"Added JWT auth\" --confidence 0.85\n\n# View trace attribution\ntrail show traj_abc123 --trace\n```\n\n**What you get:**\n- `.trace.json` files saved alongside each trajectory\n- Line-level attribution of which code was AI-generated\n- Model identification (Claude, GPT, etc.)\n- Git revision tracking for change history\n\n**Zero configuration required** - traces are generated automatically when completing trajectories in a git repository.\n\nSee the full [Agent Trace Integration Spec](./docs/specs/agent-trace-integration.md) for details.\n\n## Use Cases\n\n### Code Review\nInstead of guessing at intent from 500 changed lines, reviewers can:\n- Read the trajectory summary\n- See what alternatives were considered and rejected\n- Understand the agent's confidence level\n\n### Bug Diagnosis\nWhen a bug surfaces months later:\n- Query the trajectory for the commit that introduced the code\n- See original requirements and edge cases considered\n- Understand the context that led to this implementation\n\n### Institutional Memory\nOver time, trajectories become a searchable knowledge base:\n- \"How have we solved caching problems before?\"\n- \"What libraries did we evaluate for X?\"\n- \"Why did we choose this architecture?\"\n\n## Quick Start\n\n### CLI\n\n```bash\n# Run without installing globally\nnpx --yes agent-trajectories start \"Implement auth module\"\n\n# Or install globally if you prefer the short trail command\nnpm install -g agent-trajectories\ntrail start \"Implement auth module\"\n\n# Or install locally in a project\nnpm install agent-trajectories\nnpx --no-install trail start \"Implement auth module\"\n# or\nnpm exec -- trail start \"Implement auth module\"\n```\n\n```bash\n# Start tracking a task\ntrail start \"Implement auth module\"\n# (for non-global installs, replace `trail` with\n#  `npx --yes agent-trajectories`, `npx --no-install trail`, or `npm exec -- trail`)\n\n# View current status\ntrail status\n\n# Record a decision (reasoning optional for minor decisions)\ntrail decision \"Chose JWT over sessions\" \\\n  --reasoning \"Stateless scaling requirements\"\n\n# Complete with retrospective\ntrail complete --summary \"Added JWT auth\" --confidence 0.85\n\n# List all trajectories (with optional search)\ntrail list\ntrail list --search \"auth\"\n\n# Export for documentation (markdown, json, timeline, or html)\ntrail export traj_abc123 --format markdown\ntrail export --format html --open  # Opens in browser\n\n# Compact trajectories (consolidate similar decisions)\ntrail compact                         # Uncompacted trajectories (default)\ntrail compact --branch main           # Trajectories with commits not in main\ntrail compact --commits abc1234,def5678  # Trajectories matching specific commit SHAs\ntrail compact --pr 123                # Trajectories mentioning PR #123\ntrail compact --since 7d              # Last 7 days\ntrail compact --all                   # Everything (including previously compacted)\ntrail compact --pr 123 --discard-sources  # Delete source trajectories after compaction\n```\n\n### Automatic Compaction (GitHub Action)\n\nAdd these steps to any workflow that runs on PR merge (e.g., your release or publish flow). Requires `ref: ${{ github.event.pull_request.base.ref }}` and `fetch-depth: 0` on checkout, plus `contents: write` permission.\n\nUse `--discard-sources` when the compacted summary should replace the raw source trajectories. This removes the source JSON/Markdown/trace files, reducing future list/search noise.\n\n```yaml\n      - name: Compact trajectories\n        run: |\n          PR_COMMITS=$(git log ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }} --format=%H | paste -sd, -)\n          OUTPUT=\".agentworkforce/trajectories/compacted/pr-${{ github.event.pull_request.number }}.json\"\n          if [ -n \"$PR_COMMITS\" ]; then\n            npx agent-trajectories compact --commits \"$PR_COMMITS\" --output \"$OUTPUT\" --discard-sources\n          else\n            npx agent-trajectories compact --pr ${{ github.event.pull_request.number }} --output \"$OUTPUT\" --discard-sources\n          fi\n      - name: Commit compacted trajectories\n        run: |\n          git add .agentworkforce/trajectories/ || true\n          git diff --cached --quiet || \\\n            (git commit -m \"chore: compact trajectories for PR #${{ github.event.pull_request.number }}\" && git push)\n```\n\n### SDK\n\nFor programmatic usage, install the package and use the SDK:\n\n```bash\nnpm install agent-trajectories\n```\n\n**Using the Client (with storage):**\n\n```typescript\nimport { TrajectoryClient } from 'agent-trajectories';\n\nconst client = new TrajectoryClient({ defaultAgent: 'my-agent' });\nawait client.init();\n\n// Start a new trajectory\nconst session = await client.start('Implement auth module');\n\n// Record work in chapters\nawait session.chapter('Research');\nawait session.note('Found existing auth patterns');\nawait session.finding('Current system uses sessions');\n\n// Record decisions\nawait session.decide(\n  'JWT vs Sessions?',\n  'JWT',\n  'Better for horizontal scaling'\n);\n\n// Complete with retrospective\nawait session.done('Implemented JWT-based authentication', 0.9);\n\nawait client.close();\n```\n\n**Using the Builder (in-memory, no storage):**\n\n```typescript\nimport { trajectory } from 'agent-trajectories';\n\nconst result = trajectory('Fix login bug')\n  .withSource({ system: 'github', id: 'GH#456' })\n  .chapter('Investigation', 'claude')\n    .finding('Null pointer in session handler')\n    .decide('Fix approach', 'Add null check', 'Minimal change')\n  .chapter('Implementation', 'claude')\n    .note('Added validation')\n  .done('Fixed null pointer exception', 0.95);\n\n// Export the trajectory\nconsole.log(result);  // Full trajectory object\n```\n\n**SDK Features:**\n- **Auto-save**: Changes persist automatically with the client\n- **Fluent API**: Chain operations naturally\n- **Resume support**: Pick up where you left off with `client.resume()`\n- **Multiple exports**: Markdown, JSON, timeline, PR summary\n\n## Why \"Trail\"?\n\n> **Trajectory** = the complete path an agent takes through a task\n> **Trail** = what's left behind for others to follow\n\nYou don't see the whole trajectory in real-time, but you can always follow the trail.\n\nThe CLI is called `trail` because that's what you're doing—leaving a trail of breadcrumbs through your work. Future agents and humans can follow this trail to understand not just *what* was built, but *why* it was built that way.\n\n## Who Uses Trail?\n\n**Both agents and humans—but differently.**\n\n### Agents: Write the Trail\n\nAgents use trail commands to **record** their work as they go:\n\n```bash\n# Agent starts work on a task\ntrail start \"Add rate limiting to API\"\n\n# Agent records key decisions as it works\ntrail decision \"Token bucket algorithm\" \\\n  --reasoning \"Better burst handling than fixed window\"\n\n# Agent completes with reflection\ntrail complete --summary \"Added rate limiting\" --confidence 0.9\n```\n\nThis can be invoked programmatically by AI coding tools, or agents can learn to call `trail` as part of their workflow.\n\n### Humans: Read the Trail\n\nHumans use trail commands to **understand** and **review** agent work:\n\n```bash\n# List and search past work\ntrail list --search \"authentication\"\n\n# See trajectory details and decisions\ntrail show traj_abc123 --decisions\n\n# View in browser\ntrail export traj_abc123 --format html --open\n\n# Export for code review\ntrail export traj_abc123 --format markdown\n```\n\n### The Handoff\n\nThe trail bridges the gap between agent work and human understanding:\n\n```\nAgent works → Records decisions → Completes trajectory\n                                        ↓\nHuman reviews → Follows the trail → Understands the \"why\"\n```\n\nWithout the trail, humans see only the code. With it, they see the reasoning.\n\n## Agent Workspace\n\nTrajectories power a broader vision: **a knowledge workspace for agents**—like Notion, but for AI.\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│                     AGENT WORKSPACE                             │\n├─────────────────────────────────────────────────────────────────┤\n│  📚 Knowledge Base          🛤️ Trajectories                     │\n│  ├── Architecture docs      ├── Active work                    │\n│  ├── Code patterns          ├── Recent history                 │\n│  └── Conventions            └── Searchable archive             │\n│                                                                 │\n│  🧠 Decision Log            📋 Pattern Library                  │\n│  └── Why things are         └── How to do things               │\n└─────────────────────────────────────────────────────────────────┘\n```\n\nWhen an agent starts a new task, it can query the workspace for:\n- Relevant past trajectories\n- Applicable patterns and conventions\n- Related decisions\n- Potential gotchas from retrospectives\n\n## Architecture\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│  AGENT-TRAJECTORIES (Layer 3)                                   │\n│  Task narratives, decisions, retrospectives                     │\n│                            ▲                                    │\n│                            │ aggregates                         │\n│  CLAUDE-MEM (Layer 2)                                           │\n│  Tool observations, semantic concepts                           │\n│                            ▲                                    │\n│                            │ captures                           │\n│  AGENT-RELAY (Layer 1)                                          │\n│  Real-time messaging, message persistence                       │\n└─────────────────────────────────────────────────────────────────┘\n```\n\nEach layer is independent and can be used alone, but together they form a complete agent memory stack.\n\n## The Narrative Layer in Your AI Stack\n\nTrajectories sits at the top of an emerging ecosystem of AI development tools. Each layer answers a different question:\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│  AGENT-TRAJECTORIES                                             │\n│  \"Why was this built this way?\"                                 │\n│  Narrative, decisions, retrospectives, institutional memory     │\n│                            ▲                                    │\n│                            │ gives meaning to                   │\n│  ENTIRE (entireio/cli)                                          │\n│  \"What happened in this session?\"                               │\n│  Raw session capture, transcripts, recovery, rewind             │\n│                            ▲                                    │\n│                            │ attributes                         │\n│  AGENT-TRACE (agent-trace.dev)                                  │\n│  \"Who wrote this line of code?\"                                 │\n│  Line-level code attribution, model identification              │\n└─────────────────────────────────────────────────────────────────┘\n```\n\n**Agent Trace** ([agent-trace.dev](https://agent-trace.dev)) is the attribution spec — trajectories implements it automatically, generating `.trace.json` files that comply with the spec on every `trail complete`.\n\n**Entire** ([entireio/cli](https://github.com/entireio/cli)) captures raw session transcripts via git hooks — a complementary layer focused on recovery and rewind.\n\n**Trajectories** is the narrative layer: structured meaning on top of raw events. Where entire captures *what happened*, trajectories captures *why decisions were made* and *what was learned*. Where agent-trace says *who wrote the code*, trajectories explains *why this approach was chosen*.\n\nUsed together, these tools give you a complete audit trail of AI-assisted development from attribution through narrative.\n\n## The Trajectory Format\n\n```json\n{\n  \"id\": \"traj_abc123\",\n  \"task\": {\n    \"title\": \"Implement user authentication\",\n    \"source\": { \"system\": \"linear\", \"id\": \"ENG-456\" }\n  },\n  \"status\": \"completed\",\n  \"chapters\": [...],\n  \"retrospective\": {\n    \"summary\": \"Implemented JWT-based auth with refresh tokens\",\n    \"decisions\": [...],\n    \"confidence\": 0.85\n  }\n}\n```\n\nTrajectories are stored as `.trajectory.json` files (machine-readable) with auto-generated `.trajectory.md` summaries (human-readable).\n\n## Why Trajectories Matter\n\n> \"The trajectory is as valuable as the code.\"\n\nAs AI agents write more code faster than ever before, a critical gap emerges: **we're shipping code without understanding**. Trajectories close this gap.\n\n### The Health of Your Codebase\n\nWithout trajectories, agent-generated code becomes a black box:\n\n| Problem | Impact | How Trajectories Help |\n|---------|--------|----------------------|\n| **Silent assumptions** | Bugs hide in undocumented edge cases | Decisions and reasoning are captured explicitly |\n| **Inconsistent patterns** | Each agent reinvents approaches | Past solutions are queryable, patterns emerge |\n| **Lost context** | Nobody knows why code exists | The \"why\" lives alongside the \"what\" |\n| **Review theater** | PRs approved without real understanding | Reviewers see the full decision history |\n| **Debugging blind** | Hours spent reverse-engineering intent | Original context is one query away |\n\n### The Flywheel Effect\n\nTrajectories create a virtuous cycle that compounds over time:\n\n```\nMore trajectories → More extracted knowledge → Better agent context →\nBetter decisions → Better retrospectives → Richer trajectories → ...\n```\n\nEach completed task makes future tasks easier:\n- Agents make fewer mistakes by learning from past gotchas\n- Decisions are more consistent across the codebase\n- Onboarding new agents (or humans) becomes instant\n- Institutional memory persists even as team members change\n\n### Future-Proofing Your Project\n\nAs agent usage scales, trajectories become essential infrastructure:\n\n**Today (1-2 agents):**\n- Nice to have for code review\n- Helpful for debugging\n\n**Tomorrow (5-10 agents working in parallel):**\n- Critical for coordination\n- Required for understanding who did what and why\n- Enables agents to learn from each other\n\n**Long-term (agents as primary contributors):**\n- The authoritative record of how the system evolved\n- Training data for project-specific agent improvements\n- Audit trail for compliance and security review\n\n### Trust Through Transparency\n\nAgent-generated code faces a trust problem. Developers hesitate to ship code they don't understand. Trajectories solve this by making agent reasoning transparent:\n\n- **Confidence scores** tell you when to scrutinize more carefully\n- **Decision logs** show trade-offs were considered\n- **Retrospectives** surface known limitations and risks\n- **Challenge documentation** reveals what was hard (and might break)\n\nThe result: teams can ship agent code with the same confidence as human-written code—because they understand it just as well.\n\n## Installation\n\n```bash\nnpm install agent-trajectories\n```\n\nThe package provides:\n- **CLI** (`trail` command) - For command-line usage\n- **SDK** - For programmatic integration\n\n```typescript\n// Main import (includes SDK)\nimport { TrajectoryClient, trajectory } from 'agent-trajectories';\n\n// Or import from SDK subpath\nimport { TrajectoryClient, TrajectoryBuilder } from 'agent-trajectories/sdk';\n```\n\n## SDK Reference\n\n### TrajectoryClient\n\nThe client manages trajectories with persistent storage.\n\n```typescript\nconst client = new TrajectoryClient({\n  defaultAgent: 'my-agent',    // Default agent name\n  dataDir: '.',                // Base directory; stores under .agentworkforce/trajectories\n  autoSave: true,               // Auto-save after operations\n});\n\nawait client.init();            // Required before use\n\n// Lifecycle\nconst session = await client.start('Task title');\nconst session = await client.resume();     // Resume active trajectory\nconst traj = await client.get('traj_xxx'); // Get by ID\n\n// Query\nconst list = await client.list({ status: 'completed' });\nconst results = await client.search('auth');\n\n// Export\nconst md = await client.exportMarkdown('traj_xxx');\nconst json = await client.exportJSON('traj_xxx');\n\nawait client.close();\n```\n\n### TrajectorySession\n\nSessions provide chainable operations on active trajectories.\n\n```typescript\nconst session = await client.start('Task');\n\n// Chapters organize work phases\nawait session.chapter('Research');\nawait session.chapter('Implementation');\n\n// Events record what happened\nawait session.note('Observation or note');\nawait session.finding('Important discovery');\nawait session.error('Something went wrong');\n\n// Decisions capture choices\nawait session.decide('Question?', 'Choice', 'Reasoning');\n\n// Complete or abandon\nawait session.done('Summary of work', 0.9);\nawait session.abandon('Reason for abandoning');\n```\n\n### TrajectoryBuilder\n\nThe builder creates trajectories in memory without storage.\n\n```typescript\nimport { trajectory, TrajectoryBuilder } from 'agent-trajectories';\n\n// Shorthand function\nconst t = trajectory('Task title')\n  .chapter('Work', 'agent-name')\n  .note('Did something')\n  .done('Completed', 0.9);\n\n// Or use the class directly\nconst t = TrajectoryBuilder.create('Task')\n  .withDescription('Detailed description')\n  .withSource({ system: 'linear', id: 'ENG-123' })\n  .withTags('feature', 'auth')\n  .chapter('Phase 1', 'claude')\n  .complete({\n    summary: 'What was done',\n    approach: 'How it was done',\n    confidence: 0.85,\n    challenges: ['What was hard'],\n    learnings: ['What was learned'],\n  });\n```\n\n## Roadmap\n\nThis project is in early development. See [PROPOSAL-trajectories.md](./PROPOSAL-trajectories.md) for the full design document.\n\n**v1.0 (current)**\n- [x] File-based storage (`.agentworkforce/trajectories/`)\n- [x] Core CLI commands (`start`, `decision`, `complete`, `list`, `show`, `export`)\n- [x] Agent Trace spec compliance (`.trace.json` generation)\n- [x] Multi-agent participation tracking\n- [x] Rich export formats (Markdown, JSON, Timeline, HTML)\n\n**v1.1 (next)**\n- [ ] **MCP server** — Real-time bidirectional queries so Claude Code, Cursor, and other tools can read and write trajectories directly within agent sessions\n- [ ] **Claude Code hooks** — Auto-capture on `PostToolUse` and session boundaries\n- [ ] **SQLite storage** — Full-text search across all trajectories\n- [ ] **Git hook integration** — Auto-start/complete trajectories on commit events\n- [ ] **`CLAUDE.md` generation** — Extract patterns from trajectories into reusable context files\n\n**Future**\n- Workspace knowledge base (decisions, patterns, conventions as queryable memory)\n- PostgreSQL/S3 storage for teams\n- Training data export for project-specific model fine-tuning\n\n## License\n\nMIT\n","readmeFilename":"README.md"}