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Restore expired sessions via summary-based catch-up (#52)
Aksel Joonas ReediUnverified signatureWhen the backend loses a session (typically: HF Space restarted), the chat now shows a small inline banner instead of silently erroring: Where were we? Let me skim the conversation so far and pick up right where we left off — or we can start something new. [ Catch me up ] [ Start fresh ] Flow, in one pass: * Frontend stashes the raw backend messages in localStorage on every mount-hydrate and turn_complete. When the backend 404s for the session id, the SSE transport and the mount effect both fire onSessionDead → the session is flagged `expired` in sessionStore; the sidebar marks it "needs a catch-up". * Catch me up → POST /api/session/restore-summary with the cached messages. Backend creates a fresh session, runs the existing summarizer (factored into summarize_messages() and shared with in-session compaction) with a restore-specific prompt that preserves the tool-call trail, and seeds the new session with that summary wrapped in a [SYS…962191f -
Read max context from litellm + compact at 90% (#54)
Aksel Joonas ReediUnverified signature* Read max context from litellm.get_model_info + compact at 90% The local _MAX_TOKENS_MAP had Claude Opus 4.6 pinned at 200k, triggering compaction at ~20% of its real 1M window. Swap the hand-maintained table for litellm.get_model_info()['max_input_tokens'], which LiteLLM keeps in sync upstream (Opus 4.6=1M, GPT-5=272k, Sonnet 4.5=200k, etc.). HF router-only ids (MiniMax, Kimi, GLM) aren't in litellm's catalog and fall through to the 200k default — close enough to their advertised ranges and safe if the model lies. Also shifts compaction to fire at 90% of max_context instead of >100%. The old condition waited until context had already overshot, risking the next LLM call hitting ContextWindowExceededError before the compact finished. 90% gives headroom for the summary call + one more turn. * Drop the legacy -10k buffer — the 90% ratio is the headroom now ContextManager was subtracting a fixed 10k tokens from max_context on top of the new 90% compaction threshold, so a 1M-window Opu…28b8f2b -
Route HF inference through /v1 auto-router + add reasoning_effort knob
Users paste bare HF model ids (MiniMaxAI/MiniMax-M2.7, moonshotai/Kimi-K2.6) with an optional :fastest|cheapest|preferred|<provider> suffix; the router picks a provider and handles failover. /model does a live preflight against /v1/models and prints providers, pricing, context, tool support — warn-and- allow for unknowns with fuzzy suggestions. Friendly messages replace LiteLLM's raw traceback for model/provider mismatches, and the noisy 'Give Feedback' banner is suppressed. Adds a reasoning_effort config + /effort command (default high). OpenAI and Anthropic get the top-level param natively; HF router gets it via extra_body with minimal->low normalization for models like MiniMax M2 that require reasoning. Frontend + backend model selectors updated to the bare-id format.0a9e96d
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Fall back to user HF token for router when INFERENCE_TOKEN is unset (#39)
Aksel Joonas Reedi +1: Claude Opus 4.7 (1M context)Unverified signatureThe HF router code path only read INFERENCE_TOKEN, which is the shared server-side key set on the hosted Space so inference is free for users. On the CLI / self-hosted path that env var is absent, so requests went out with no bearer token and the router returned 401 — surfaced to users as "Authentication failed" even with a valid HF_TOKEN (issue #36). Resolve api_key in this order: 1. INFERENCE_TOKEN env (unchanged Space behavior — shared billing) 2. session.hf_token (user's OAuth / CLI token) 3. HF_TOKEN env (belt-and-suspenders for CLI) Applied to _resolve_hf_router_params, research_tool._resolve_llm_params, and ContextManager.compact. Fixes #36 Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>182ddee
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Add message edit and regenerate from any point in conversation
Backend: add truncate_to_user_message() on ContextManager, POST /api/truncate/{session_id} endpoint, and session_manager.truncate() helper to slice conversation history to before a given user message. Frontend: add editAndRegenerate callback in useAgentChat that truncates backend + frontend messages then resubmits via existing chat flow. UserMessage gains an inline edit mode with pencil icon on hover, TextField for editing, Enter/Escape to confirm/cancel.0611031 -
Improve agent loop, tools, and research subagent for long-running autonomous tasks
- Add --no-stream and --max-iterations CLI flags for headless operation - Simplify context manager: remove staged pruning, track actual token usage - Add doom-loop detection and context budget to research subagent - Improve file tools: better enforcement, error messages, descriptions - Improve bash tool: longer timeouts, background execution guidance - Add training logging and HP sweep guidance to system prompt - Relax Python requirement to >=3.11 - Remove unused file_content_cache73882d9
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feat: add headless CLI mode with local filesystem tools and rich terminal rendering
- Add argparse-based headless mode: `python -m agent.main "prompt here"` - Local tool specs override sandbox /app references for CLI mode - System prompt injects CLI-specific context (working directory, no sandbox) - Extract streaming/non-streaming LLM call helpers with LLMResult dataclass - Add shimmer thinking animation (truecolor gradient sweep) - Progressive markdown rendering via rich Live display - Clean MCP shutdown sequence, suppress asyncio teardown noise - Add rich dependency to agent extras577ec48 -
refactor: append dynamic session context after static system prompt for cache stability
3ed5324 -
feat: LLM-based pruning for complex tool outputs (hf_jobs)
Tools in _LLM_PRUNE_TOOLS get a cheap LLM call (≤600 tokens) that extracts the answer from the raw output, preserving job IDs, metrics, and error messages exactly. Falls back to deterministic truncation if the LLM call fails. Three tool lists now control pruning behavior: - _PRUNE_SKIP_TOOLS: never pruned (research, plan_tool) - _LLM_PRUNE_TOOLS: LLM-summarised (hf_jobs) - Everything else: deterministic 80-char truncation Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>d4b1495 -
fix: also skip plan_tool from deterministic pruning
Research and plan_tool outputs are critical for task continuity across compaction — they should never be truncated. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>e561d10 -
fix: skip research tool outputs in deterministic pruning
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>c130bba -
Add deterministic pruning of old tool outputs before LLM compaction
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>64437d0
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refactor: simplify tool call validation and make interrupts cancel tool execution
- Handle finish_reason=length by dropping truncated tool calls before they enter context - Single json.loads per tool call instead of 4 redundant parses - Parsed args flow through to approval/execution without re-parsing - Replace recover_malformed_tool_calls (90 lines) with inline validation in agent loop — bad calls get error results, good calls execute - Make tool execution cancellable: asyncio.wait races gather against cancel event so Ctrl+C/frontend interrupt stops immediately instead of waiting for all tools to finish - Keep _patch_dangling_tool_calls as the only safety net in get_messages() for any remaining edge cases Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>f03cbf4
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fix: correct context_length init and emit tool_call events for malformed calls
- Initialize context_length to max_context (not token-estimate of system prompt) and reserve 10k token buffer to prevent overflows - Emit synthetic tool_call events before tool_output errors for malformed calls so the frontend renders matching dynamic-tool parts288473a
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fix: handle interrupt during streaming tool calls without corrupting context
When the user clicks interrupt while the LLM is streaming a tool call, the accumulated deltas can have empty IDs and truncated arguments. Previously these got added to the context and caused litellm API errors on the next call. - Add cancellation check inside the streaming loop so we stop early - Drop tool_calls with empty IDs when building from stream deltas - recover_malformed_tool_calls() now strips empty-ID tool_calls from assistant messages as a safety net Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>4eb958e -
fix: recover malformed tool calls instead of crashing session
When the LLM produces malformed JSON in tool call arguments (common with large write calls), the session would crash. Now ContextManager.recover_malformed_tool_calls() sanitizes the JSON, injects an error result telling the agent to retry with smaller content, and the agent loop skips execution of those tools. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>c7899fc
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fix: protect system prompt in undo, scan backwards for dangling tool calls, gate input on SDK status
a13e8cc -
fix: patch dangling tool calls on error, move undo to context manager
a1ce5bc
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fix: improve compaction summary prompt, remove sidebar status bar
13ae315
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fix: route all HF token usage through session.hf_token from OAuth
Remove all os.environ.get("HF_TOKEN") fallbacks. Token now flows exclusively through OAuth -> session.hf_token -> tools. - Pass hf_token through Session -> ContextManager -> _get_hf_username - Add session param to docs, repo_files, repo_git handlers - Jobs use HF_ADMIN_TOKEN for creation, user token for job secrets - Remove HF_TOKEN env setup from backend/main.py - Remove module-level username cache (broken for multi-user)33f29a8
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feat: restore sandbox tools and proactive compaction
a644598 -
feat: merge HF Space improvements
79b2fcc
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v3 prompt and tool desc rework
66647c8 -
adding compaction
369d828
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frontend update
463e470
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adding whoami to system prompt
d9a3b65
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integrated hf skills into the prompts
6735687
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loading date in system prompt, search code update (still not there)
64a9ca9
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Added tools to create repos, view them, upload files to them (private). added a clock tool.
Henri Bonamy82baf19
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added the explore subagent, quality ok but not perf
b6155b0
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Fixed assistant -> assistant messages after compaction
Henri Bonamya3268b6 -
prompt update and loading from file
5099f9d
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prompt change
Henri Bonamy579e839
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added compaction every x tokens
Henri Bonamy8c943c2
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intermediary commit
1e9763b
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working events and session
63e9959
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minimal impl. of the agent
5fe810b