LLM sometimes emits plain text (e.g. 'Now I'll write the tests...')
without calling tools, which the loop treated as final output. Now
the loop detects this and injects a user message nudging the LLM
to either continue using tools or output frontmatter with ---.
Agent was using all continue turns to keep calling tools instead of
outputting the required frontmatter. Now continue runs with noTools=true,
forcing LLM to emit text-only response.
Also supports null tools in chatCompletionWithTools to omit tools from
the API request entirely.
Agent was wasting turns exploring the filesystem because it didn't
know its working directory. Now the system prompt includes:
'Your working directory is: /path/to/cwd'
- Add stripPreamble() to handle LLM output with text before ---
- Strengthen system prompt: CRITICAL instruction for --- at position 0
- Fixes frontmatter parsing failures on first output turn
Previously runBuiltinWithMessages deleted the session jsonl after each
run/continue call. This meant the createAgent retry mechanism (which
calls continue on frontmatter validation failure) would lose all
previous turn data — each continue started with an empty jsonl.
Now the session jsonl accumulates across run + continue calls, so the
final storeBuiltinDetail captures all turns. The jsonl file is left
behind for debugging; it's small and can be cleaned up on next startup.
Also add a workflow hint to the system prompt reminding the LLM to use
tools before outputting frontmatter, preventing premature text-only
responses on the first turn.
Each turn (assistant response / tool result) is appended to a JSONL file
at ~/.uncaged/workflow/sessions/<sessionId>.jsonl during the loop.
On completion, the JSONL is read back, each turn is stored as a CAS node,
and the detail payload references them as a flat turns[] array in
chronological order. The session file is then deleted.
Benefits:
- Real-time observability: tail -f the JSONL to watch loop progress
- Crash recovery: partial JSONL survives process death
- Zero write contention: one file per session
- Detail stays a flat array for easy consumption by CLI/dashboard
Changes:
- New session.ts: initSessionDir, appendSessionTurn, readSessionTurns, removeSession
- loop.ts: append JSONL each turn instead of accumulating in-memory
- detail.ts: reads session JSONL → persists turns to CAS → stores detail
- agent.ts: passes storageRoot/sessionId to loop, cleans up session on completion
- types.ts: remove index from TurnPayload (order is implicit in JSONL/array)
- schemas.ts: sync with type changes
Ref: #433
- StepRecord adds edgePrompt field (backward compat: defaults to "")
- StepNode CAS schema includes edgePrompt
- writeStepNode persists ctx.edgePrompt
- buildHistory exposes edgePrompt in StepContext
- buildBuiltinMessages reconstructs multi-turn moderator↔agent conversation:
system = role prompt + output format (stable prefix)
per prior visit: user (edgePrompt + inter-step summary) + assistant (output)
current: user (edgePrompt + recent summary)
- Zero extra persistence — pure function of CAS chain
- Stable prefix for LLM prompt cache hits
- 10 builtin tests pass, all other package tests pass
System message = agent identity (role prompt + output format instruction)
User message = moderator speech (task + edge prompt + history)
This reflects the workflow's core model: moderator speaks to agent
via the graph's edge prompt. Previously all content was in a single
system message with no user message, causing Claude API 400 errors.
- buildBuiltinPrompt now returns { system, user } instead of string
- agent.ts sends system + user as separate messages
- Tests updated accordingly
- Replace resolvePathInWorkspace with simple resolvePath (no boundary check)
- Remove UWF_BUILTIN_ALLOW_SHELL env gate from run_command
- Update tests accordingly
Per review: sandbox was false security with shell=true, and path
restrictions are unnecessary for a trusted agent environment.
Built-in role agent that uses workflow config models directly,
with its own tool-calling run loop. No external agent dependency.
- OpenAI-compatible chat completion client with tool_calls support
- P0 toolkit: read_file, write_file, run_command
- Integrates via createAgent factory from workflow-agent-kit
- CAS detail recording for each turn
- Path sandboxing and shell opt-in (UWF_BUILTIN_ALLOW_SHELL)
xingyue