virtual-context 0.3.4

OS-style virtual memory for LLM session context management

_{virtual-context cloud: running 3 million virtual token window at 80k actual tokens}

virtual-context

100x your agent's context by virtualizing it. Better reasoning. Persistent memory. Shared across platforms. Lower costs.

95% accuracy vs 33% baseline on the same model, at half the cost. See benchmark →

Your client sets contextWindow: 20000000 (20 million). Your model's real window is 200K. virtual-context sits between them and makes it work, the same way your OS lets a process address more memory than physically exists. The client sends its full conversation history. VC compresses, indexes, and pages. The model sees a dense 60K window where every token is signal.

Virtualizing the context window has many advantages:

• Compression: Topic-level summarization with structured fact extraction, tool chain stubbing (52 tool call/response pairs collapse to a single retrievable stub), and image scaling (a 391KB base64 screenshot becomes ~40KB, cutting payload size by ~90%). A 937K-token payload collapses to ~65K. Everything is stored, indexed, and recoverable at full fidelity.
• Memory: Your agent recalls what the user said at turn 12 when it reaches turn 1000. Facts, preferences, and decisions persist across the full conversation, not just what fits in the raw window.
• Reasoning quality: A curated 60K window of dense signal produces measurably better answers than a raw 200K window full of noise. The model reasons over what matters, not over everything.
• Cost: Smaller payloads, fewer tokens billed. A conversation running at a 1M-token virtual window regularly produces 60-90K actual payloads, a fraction of the raw cost. The payload is organized to maximize prompt cache hits, so even compressed conversations achieve significant caching in most cases.
• Cache-Aware Payload Compaction: VC compacts conversations in the background but defers rewriting the request payload until the provider's prompt cache has expired or the context window is nearly full. This preserves the byte-identical prefix that providers use for cache hits, giving you compaction savings without sacrificing cached-token discounts. You get full compaction savings when they're free and full cache savings when they matter.
• Collaboration: VCATTACH lets agents share memory across platforms and sessions. Custom agents, local tools, and API clients can all work from the same context. Multiple agents collaborate through shared memory. Conversations survive client restarts, platform switches, and session boundaries.

This is what makes virtual-context fundamentally different from memory systems that bolt a vector database onto your LLM. Those systems are additive: they retrieve chunks and compete for the context window your agent is working in right now. They do nothing to evict or curate what's already there.

virtual-context manages the window itself: compressing by topic, extracting structured facts, paging in what's needed, and paging out what's not. The client thinks it has 20M tokens. The model sees 60K of curated signal. Nothing is lost. Everything is addressable, at varying levels of compression.

Layer 0: Raw conversation turns              (active memory, in the context window)
Layer 1: Segment summaries + Facts per tag   (compressed pages, per-topic summaries)
Layer 2: Tag summaries via greedy set cover   (working set descriptors, bird's-eye view)

Full documentation → including architecture and pipeline, features deep dive, proxy internals, design decisions, and user commands.

Cloud Offering

https://virtual-context.com is the fastest way to get going. Sign up and change your base-url. Statistics, visibility into the context window, and cost savings reports included.

Install

pip install virtual-context

Python 3.11+, all core dependencies in the base install.

Optional storage backends: pip install virtual-context[postgres], [neo4j], or [falkordb].

Integration

virtual-context runs as a local HTTP proxy between your client and the upstream LLM API. Point your client at localhost:5757 instead of the upstream. The proxy handles everything transparently: tagging, retrieval, history filtering, compaction, tool interception. Auto-detects Anthropic, OpenAI (Chat + Codex/Responses), and Gemini request formats.

virtual-context proxy --upstream https://api.anthropic.com
# OR
virtual-context proxy --upstream https://api.openai.com
# OR
virtual-context proxy --upstream https://generativelanguage.googleapis.com

No config file needed for basic usage. For customization:

cp virtual-context.yaml.example virtual-context.yaml
virtual-context -c virtual-context.yaml proxy

Claude Code

Point Claude Code at the proxy. Either set the environment variable:

export ANTHROPIC_BASE_URL=http://127.0.0.1:5757

Or add it to your shell profile (~/.bashrc, ~/.zshrc) to make it permanent:

alias claudevc='ANTHROPIC_BASE_URL=http://127.0.0.1:5757 claude'

Claude Code's tool chains (file reads, searches, command output) are automatically compressed. A 937K-token payload with 52 tool chains collapses to ~65K. When Claude Code truncates history to manage its own context window, virtual-context detects the truncation and recovers stored context transparently.

OpenClaw

Set these to allow OpenClaw to maintain large context windows from a client perspective:

  // 1. History limits (the real bottleneck most users will hit)
  // channels.<provider> (e.g. channels.telegram)
  "historyLimit": 99999,
  "dmHistoryLimit": 99999

  // global fallback
  "messages": { "groupChat": { "historyLimit": 99999 } }

  // 2. Model context window: must be on the provider in the per-agent models.json, with
  // explicit model entries:
  "anthropic": {
    "baseUrl": "https://anthropic.virtual-context.com?vckey=...",
    "api": "anthropic-messages",
    "models": [
      {
        "id": "claude-opus-4-6",
        "contextWindow": 2000000,  // Note this is 2M
        ...
      }
    ]
  }

Just setting baseUrl alone isn't enough. Without model entries, it falls back to pi-ai's hardcoded 200K. And models.overrides in the global config is display only; it doesn't affect actual windowing.

  3. Context pruning: disable it so the proxy controls windowing:
  "agents": {
    "defaults": {
      "contextPruning": { "mode": "off" },
      "contextTokens": 2000000 // Note this is 2M
    }
  }

  4. Session idle timeout: prevent OpenClaw from resetting sessions too early.
  Without this, sessions reset after 12 hours by default, wiping the client-side
  history before VC can manage it:
  "session": {
    "resetByType": {
      "group": { "idleMinutes": 2880 }   // 48 hours (default is 720 / 12h)
    }
  }

A dedicated OpenClaw plugin is also in progress, using lifecycle hooks for sync retrieval (message.pre) and fire-and-forget compaction (agent.post).

Other Clients (Cursor, Continue, any OpenAI-compatible client)

Any client that lets you set a base URL works. Point it at http://127.0.0.1:5757 (Anthropic format) or http://127.0.0.1:5757/v1 (OpenAI format):

# Python (anthropic SDK)
import anthropic
client = anthropic.Anthropic(base_url="http://127.0.0.1:5757")

# Python (openai SDK)
from openai import OpenAI
client = OpenAI(base_url="http://127.0.0.1:5757/v1")

Multi-instance mode runs multiple providers on different ports in one process:

proxy:
  instances:
    - port: 5757
      upstream: https://api.anthropic.com
      label: anthropic
    - port: 5758
      upstream: https://api.openai.com
      label: openai
    - port: 5760
      upstream: https://generativelanguage.googleapis.com
      label: gemini

Daemon mode runs the proxy as a background service:

virtual-context daemon install --upstream https://api.anthropic.com
virtual-context onboard --install-daemon

Daemon lifecycle: daemon status | start | stop | restart | uninstall

Full setup docs (macOS launchd, Linux systemd --user, Windows Task Scheduler): docs/install.md

Python SDK

Two function calls wrap your existing LLM pipeline:

from virtual_context import VirtualContextEngine, Message

engine = VirtualContextEngine(config_path="./virtual-context.yaml")

# BEFORE sending to LLM: retrieve relevant stored context
assembled = engine.on_message_inbound(
    message="What was the Henninger filing deadline?",
    conversation_history=messages,
)
# assembled.prepend_text → enriched system prompt with retrieved summaries
# assembled.matched_tags → ["legal", "filing"]

# AFTER LLM responds: tag, index, compact if needed
report = engine.on_turn_complete(messages)
if report:
    print(f"Compacted {report.segments_compacted} segments, freed {report.tokens_freed:,} tokens")

MCP Server

virtual-context also exposes an MCP server for Claude Desktop, Cursor, or any MCP-compatible client. The model calls tools like recall_all, remember_when, find_quote, query_facts, expand_topic, and collapse_topic internally to build robust memory. These are not user-facing commands; the model decides when to use them based on what the conversation needs.

What It Does

Automatic Topic Tagging

There are no predefined domains to configure. An LLM tagger reads each turn and generates semantic tags (database, auth, fitness, legal) that naturally converge over the session. A vocabulary feedback loop passes known tags back into the tagger prompt, so it reuses storage instead of inventing data-persistence or file-management. When synonyms slip through (db vs database), a canonicalizer detects aliases via edit distance and normalizes them automatically.

When a tag appears on too many turns and loses discriminative power, virtual-context detects this and automatically splits it into narrower sub-tags. In a 143-turn OpenClaw session, reservation-request (43 turns, 30%) was split into reservation-platform-troubleshooting, reservation-availability-search, reservation-browser-access, and reservation-general. The vocabulary evolves toward maximum precision without manual curation.

Structured Fact Extraction

Summaries compress information but inevitably lose specific details. When the user says "I run 5K every morning" at turn 14, a summary might retain "runs regularly" but drop the exact distance and timing.

virtual-context extracts structured facts during compaction: subject, verb, object, fact type (preference, biographical, decision, plan, routine, medical, financial), temporal status (active, completed, planned, abandoned, recurring), session provenance, and source turn numbers. Facts are queryable by any combination of these fields.

When new information contradicts a stored fact ("I moved from NYC to LA"), the supersession checker detects the conflict and marks the old fact as superseded. Facts have typed relationships (SUPERSEDES, CAUSED_BY, PART_OF, CONTRADICTS, SAME_AS, RELATED_TO) that are automatically detected and traversed during queries.

Tool Chain Compression

Agent conversations are dominated by tool calls. A coding session with 50 tool rounds might have 900K tokens of tool output but only 60K of actual conversation.

virtual-context collapses entire tool chains into compact stubs:

Before (3 messages, ~18K tokens):
  assistant: [tool_use: Read file.py]
  user:      [tool_result: <full 500-line file contents>]
  assistant: "The file has a bug on line 42..."

After (2 messages, ~200 tokens):
  user:      [compacted turn: Read(file.py)]
  assistant: "The file has a bug on line 42..."

Handles all four provider formats (Anthropic, OpenAI Chat, OpenAI Responses, Gemini). Full raw tool output is stored durably and recoverable on demand. Past a configurable age threshold, stubs are dropped entirely (the segment summaries already cover that content).

Media Compression

Base64 images in API payloads are enormous: a single screenshot is 300-500KB of base64. Providers process images through vision encoders with fixed token costs based on dimensions, not base64 string length, but payload size still matters for bandwidth, latency, and TTFB. virtual-context compresses images on first sight: a 391KB screenshot becomes ~40KB, cutting payload size by ~90%. Originals are stored to disk for recovery. This runs on both passthrough and active paths, so even conversations that haven't triggered compaction benefit.

Virtual Memory Paging

RAG retrieves content and appends it to the context window. It never frees space from what's already there. virtual-context treats the context window as managed memory with bidirectional paging:

Tag summaries  <------->  Segment summaries  <------->  Full stored text
     ^                          ^                            ^
  collapse                   default                      expand
  (~200t)                  (~2,000t)                   (~8,000t+)

When the model needs more detail on a topic, it expands that topic from summary to full stored text. When budget pressure hits, cold topics are automatically collapsed. The working set persists across turns, so expansion decisions are stateful.

Cross-Vocabulary Retrieval

Users don't use the same words every time. "Materialized views for feed performance" at turn 46 might be recalled as "that caching trick for the feed" at turn 71. Pure tag overlap finds nothing.

virtual-context uses 3-signal retrieval scoring via Reciprocal Rank Fusion: IDF-weighted tag overlap, BM25 keyword search on summaries, and embedding cosine similarity. Related tags generated at both write time and query time bridge vocabulary gaps. When tag-based retrieval misses entirely, full-text and semantic search across stored conversation text provide a fallback.

Time-Scoped Recall

Queries like "going back to the very beginning, what were the key decisions?" or "between June and July, what changed?" reference a position in time, not just a topic. virtual-context combines semantic query matching with structured time ranges. Date math is backend-resolved, not LLM-resolved, so results are deterministic. Session dates propagate through the entire pipeline: every segment knows when it happened, and temporal ordering is always accurate.

Configurable Context Ceiling

Most teams set context_window to whatever the model supports and let it fill up. This is expensive and degrades quality. Research on "lost in the middle" shows that LLM attention degrades in long contexts: facts buried in 200K tokens of raw history are missed more often than the same facts concentrated in a managed window.

context_window: 60000  # run a 200K model at 60K
compaction:
  soft_threshold: 0.70
  hard_threshold: 0.90

A 200K-capable model running at 60K uses ~70% fewer input tokens per request. The model's attention is concentrated on curated, high-signal context rather than spread across mostly-stale history.

Store-Backed Recovery

Clients (Claude Code, OpenClaw) sometimes truncate conversation history to manage their own context windows. virtual-context detects the truncation and recovers from its durable store: chain snapshots, recent raw turns, sanitized and restored transparently. The payload that reaches the LLM contains the recovered context as if it had never been truncated.

User Commands

Type these as normal messages in any client connected through the proxy. Case-insensitive. The proxy intercepts them before they reach the LLM, so no tokens are consumed.

Command	What it does
VCATTACH <label|id>	Reattach to another conversation by label or UUID
VCLABEL <name>	Set label on current conversation (no arg = show current)
VCSTATUS	Show conversation ID, label, turns, segments, working set, active tags
VCRECALL <query>	Search stored context, promote matching tags to working set for next turn
VCCOMPACT	Force compaction of uncompacted turns
VCLIST	List all conversations with labels and turn counts
VCFORGET <tag>	Delete segments and summaries for a specific tag

VCATTACH: Shared Memory Across Platforms

Every conversation gets a stable identity derived from the system prompt hash and conversation markers embedded in assistant responses. This identity persists across restarts, deploys, and client changes.

When identity detaches (system prompt changes, client truncation loses the marker, a deploy produces a different hash), type VCATTACH <label> to reconnect to the original conversation with all segments, facts, and tags intact.

Cross-platform shared memory. Build up deep context in Claude Code (architecture decisions, code patterns, debugging history), then type VCATTACH code-project in a Telegram conversation with a different model. Both clients now share the same conversation identity: messages from either platform enrich the same compacted knowledge base. This isn't document sharing or chat mirroring. It's shared memory across platforms and models.

Multi-agent collaboration. Two agents (or two humans using different clients) can work on the same problem space simultaneously. Agent A researches in Claude Code, compacting findings. Agent B drafts a proposal in Telegram, pulling from the same segments. Each agent's contributions are compacted into the shared store. The virtual context IS the shared workspace.

Conversation merging. Two conversations about the same topic? Pick the one with richer context and VCATTACH the other to it. The old conversation is deleted; the target keeps all its compacted data. The alias table is persistent, so stale markers follow the alias instead of creating orphans.

Virtual-Context vs RAG vs Compaction

These approaches are complementary. RAG, other memory systems, and compaction can all run alongside virtual-context.

	RAG	Compaction-only	virtual-context
Primary mechanism	Query-time retrieval by embedding similarity	Summarize old history to fit window	Tagged memory + retrieval + compaction + paging tools
What gets kept	External documents + recent raw chat	Summaries of old turns + recent raw chat	Multi-layer memory (raw turns, segment summaries, tag summaries)
Specific fact lookup	Depends on embedding/query phrasing alignment	Lossy after summarization	Structured fact queries + full-text search + summary drill-down
Broad overview	Weak unless special orchestration	Can summarize, but often generic	All topic summaries loaded within budget
Time-scoped recall	Custom logic outside core RAG	Requires date fidelity in summaries	Backend-resolved time ranges with session date propagation
Vocabulary mismatch tolerance	Embedding-dependent	Low	3-signal RRF fusion + related-tag expansion + semantic search fallback
Context budget control	Append retrieved chunks	Compression with limited rehydration	Explicit paging: expand/collapse topics with bounded assembly
Cost at scale	Grows with corpus size	Grows with conversation length	Configurable ceiling: run a 200K model at 30K
Best fit	Knowledge/doc retrieval	Simple long-chat cost reduction	Long-running agent memory with mixed query types

Proxy Features

The proxy includes a live dashboard at http://localhost:5757/dashboard with request grid, turn inspector, session stats, telemetry, and SSE live updates.

• Conversation continuity via invisible markers in assistant responses, with stable identity derived from system prompt hash
• Redis session cache for lossless restarts across container deploys (falls back gracefully if Redis is unavailable)
• Four-format support auto-detected per request (Anthropic, OpenAI Chat, OpenAI Responses, Gemini)
• History ingestion bootstraps the tag index from existing conversation on the first request
• Streaming with zero added latency (SSE forwarded byte-for-byte, text accumulated in background)
• Error-resilient (engine failures fall back to unmodified passthrough; bloat fallback reverts to original payload)
• Envelope stripping extracts sender identity and timestamps from metadata blocks (group chat participants appear as real names)
• Image-aware token counting using Anthropic formula, not raw base64 tokenization
• Per-port config for multi-instance setups with isolated engines and storage
• Telemetry on every LLM call: token counts, cost, timing across five components (compactor, tagger, tool_loop, fact_curator, proxy_upstream)

CLI

virtual-context proxy -u https://api.anthropic.com  # start proxy
virtual-context status                               # tag stats and token usage
virtual-context tags                                 # list all tags
virtual-context domains                              # tags with turn counts and summaries
virtual-context recall auth                          # retrieve stored summaries for a tag
virtual-context retrieve -m "What about auth?"       # tag + retrieve (JSON)
virtual-context transform -m "What about auth?"      # tag + retrieve + assemble
virtual-context compact -i msgs.json                 # manual compaction
virtual-context aliases list|suggest|add             # tag alias management
virtual-context init coding                          # create config from preset
virtual-context onboard [--upstream URL]              # guided setup (interactive wizard)
virtual-context daemon install|status|start|stop     # background service
virtual-context config validate                      # check config syntax
virtual-context telemetry [--verbose] [--json]       # cost, tokens, timing
virtual-context chat [--headless] [--replay ...]     # interactive TUI or headless

Interactive Chat (TUI)

virtual-context chat --config virtual-context.yaml

Terminal chat interface with live context visualization: tag panel with activity levels, real-time budget bar, turn inspector (Ctrl+I), manual compaction (/compact or Ctrl+K), session export (Ctrl+S). Headless mode (--headless --replay prompts.txt) for automated testing and regression validation.

Stress-Tested

Validated against adversarial 100-turn conversations with deliberately overlapping domains, vocabulary mismatches, ambiguous callbacks, and cross-domain synthesis queries, using a 3,000-token context window with Claude Haiku. 89% pass rate on 28 deliberately adversarial prompts. Tag vocabulary stabilizes within 10-15 turns via the feedback loop.

Also validated in production with OpenClaw (Telegram) handling real multi-topic conversations: tool chain preservation across 90-message conversations (52 messages filtered to 27 without breaking a single tool dependency), live embedding matching against 40+ tag vocabularies, and single-pass history ingestion of 43 pre-existing turns.

Benchmark Results

LongMemEval (100 Questions)

100 random questions from LongMemEval-500 (5 batches x 20, seeds 42/99/777/1234/2025).

Configuration:

• VC: MiMo-V2-Flash (ingestion) + Claude Sonnet 4.5 (reader) + Gemini 3 Pro Preview (judge)
• Baseline: Claude Sonnet 4.5 with full conversation history (~118K tokens) + Gemini 3 Pro Preview (judge)

Metric	VC	Baseline
Accuracy	95/100 (95%)	33/100 (33%)
Avg Tokens/Question	52,347	117,582
Avg Cost/Question	$0.16	$0.36
Total Cost	$15.99	$35.56
Token Reduction	2.2x fewer	--

Accuracy by Question Type

Category	Count	VC	Baseline
knowledge-update	17	100.0% (17/17)	29.4% (5/17)
multi-session	26	88.5% (23/26)	15.4% (4/26)
temporal-reasoning	28	92.9% (26/28)	32.1% (9/28)
single-session-user	13	100.0% (13/13)	46.2% (6/13)
single-session-assistant	11	100.0% (11/11)	72.7% (8/11)
single-session-preference	5	100.0% (5/5)	20.0% (1/5)

Click to expand full results table (100 questions)

ID	Type	BL	BL Tokens	BL Cost	VC	VC Tokens	VC Cost
07741c44	knowledge-update	FAIL	116,404	$0.35	pass	49,721	$0.15
0977f2af	knowledge-update	FAIL	117,359	$0.35	pass	49,734	$0.15
0ddfec37	knowledge-update	FAIL	115,848	$0.35	pass	43,780	$0.13
2133c1b5_abs	knowledge-update	pass	116,186	$0.36	pass	56,533	$0.17
2698e78f_abs	knowledge-update	FAIL	118,841	$0.36	pass	36,039	$0.11
3ba21379	knowledge-update	FAIL	116,604	$0.35	pass	46,034	$0.14
4b24c848	knowledge-update	pass	117,107	$0.35	pass	32,494	$0.10
4d6b87c8	knowledge-update	FAIL	115,104	$0.35	pass	47,262	$0.14
50635ada	knowledge-update	FAIL	118,682	$0.36	pass	41,677	$0.13
5a4f22c0	knowledge-update	pass	118,775	$0.36	pass	35,437	$0.11
6071bd76	knowledge-update	FAIL	117,904	$0.36	pass	36,618	$0.11
6aeb4375	knowledge-update	pass	115,001	$0.35	pass	38,984	$0.12
89941a94	knowledge-update	FAIL	117,038	$0.35	pass	45,347	$0.14
8fb83627	knowledge-update	pass	115,488	$0.35	pass	35,041	$0.11
a1eacc2a	knowledge-update	FAIL	117,513	$0.35	pass	46,401	$0.14
cf22b7bf	knowledge-update	FAIL	115,784	$0.35	pass	49,002	$0.15
ed4ddc30	knowledge-update	FAIL	118,045	$0.36	pass	37,708	$0.11
099778bb	multi-session	FAIL	118,622	$0.36	pass	33,375	$0.10
09ba9854	multi-session	FAIL	115,128	$0.35	FAIL	36,120	$0.11
0ea62687	multi-session	FAIL	116,840	$0.36	pass	36,910	$0.11
21d02d0d	multi-session	FAIL	119,667	$0.36	pass	44,069	$0.13
36b9f61e	multi-session	FAIL	116,713	$0.35	pass	42,919	$0.13
3fe836c9	multi-session	FAIL	117,954	$0.35	pass	45,463	$0.14
46a3abf7	multi-session	FAIL	117,783	$0.35	pass	132,933	$0.40
6456829e_abs	multi-session	FAIL	117,467	$0.35	pass	42,898	$0.13
681a1674	multi-session	FAIL	118,545	$0.36	pass	62,141	$0.19
720133ac	multi-session	FAIL	120,053	$0.37	pass	50,205	$0.15
7405e8b1	multi-session	FAIL	118,694	$0.36	pass	50,989	$0.16
88432d0a	multi-session	FAIL	118,401	$0.36	pass	46,391	$0.14
88432d0a_abs	multi-session	pass	119,275	$0.36	pass	55,463	$0.17
9d25d4e0	multi-session	FAIL	117,978	$0.36	pass	83,295	$0.25
a11281a2	multi-session	FAIL	119,807	$0.36	pass	49,939	$0.15
a346bb18	multi-session	FAIL	118,452	$0.36	pass	44,404	$0.14
a96c20ee	multi-session	FAIL	117,282	$0.35	pass	42,068	$0.13
bf659f65	multi-session	FAIL	114,781	$0.35	FAIL	41,952	$0.13
d682f1a2	multi-session	FAIL	117,856	$0.35	pass	48,821	$0.15
dd2973ad	multi-session	pass	117,351	$0.36	pass	56,463	$0.17
e56a43b9	multi-session	pass	119,177	$0.36	pass	47,528	$0.14
e6041065	multi-session	FAIL	117,316	$0.35	pass	38,473	$0.12
eeda8a6d	multi-session	FAIL	118,197	$0.36	pass	45,726	$0.14
ef66a6e5	multi-session	FAIL	116,328	$0.35	pass	152,680	$0.46
gpt4_372c3eed	multi-session	pass	117,552	$0.36	FAIL	46,299	$0.14
gpt4_d84a3211	multi-session	FAIL	116,459	$0.35	pass	51,487	$0.16
0db4c65d	temporal-reasoning	FAIL	115,780	$0.35	pass	45,639	$0.14
2ebe6c90	temporal-reasoning	FAIL	115,113	$0.35	pass	39,883	$0.12
6613b389	temporal-reasoning	pass	119,268	$0.37	pass	41,228	$0.13
a3045048	temporal-reasoning	FAIL	116,689	$0.35	pass	47,120	$0.14
b29f3365	temporal-reasoning	FAIL	118,078	$0.36	pass	43,563	$0.13
c8090214_abs	temporal-reasoning	pass	116,460	$0.35	pass	79,046	$0.24
cc6d1ec1	temporal-reasoning	pass	116,218	$0.35	pass	47,747	$0.15
eac54adc	temporal-reasoning	FAIL	119,492	$0.36	pass	40,470	$0.12
f0853d11	temporal-reasoning	pass	116,117	$0.35	pass	46,903	$0.14
gpt4_18c2b244	temporal-reasoning	FAIL	119,183	$0.36	pass	53,922	$0.17
gpt4_1a1dc16d	temporal-reasoning	FAIL	120,646	$0.37	pass	52,119	$0.16
gpt4_1e4a8aec	temporal-reasoning	pass	118,208	$0.36	pass	48,286	$0.15
gpt4_21adecb5	temporal-reasoning	FAIL	119,249	$0.36	pass	125,864	$0.38
gpt4_483dd43c	temporal-reasoning	FAIL	117,942	$0.35	pass	43,327	$0.13
gpt4_4929293b	temporal-reasoning	FAIL	118,774	$0.37	pass	58,869	$0.18
gpt4_4cd9eba1	temporal-reasoning	pass	119,611	$0.36	pass	46,083	$0.14
gpt4_5438fa52	temporal-reasoning	FAIL	114,753	$0.35	pass	51,194	$0.16
gpt4_65aabe59	temporal-reasoning	FAIL	115,392	$0.35	pass	39,931	$0.12
gpt4_70e84552	temporal-reasoning	FAIL	117,453	$0.35	pass	42,109	$0.13
gpt4_7ca326fa	temporal-reasoning	FAIL	116,432	$0.35	pass	51,589	$0.16
gpt4_7de946e7	temporal-reasoning	pass	117,096	$0.35	pass	44,183	$0.14
gpt4_8279ba02	temporal-reasoning	FAIL	115,780	$0.35	pass	156,923	$0.47
gpt4_88806d6e	temporal-reasoning	FAIL	119,052	$0.36	pass	33,463	$0.10
gpt4_98f46fc6	temporal-reasoning	pass	117,366	$0.36	pass	58,524	$0.18
gpt4_d6585ce9	temporal-reasoning	FAIL	115,862	$0.35	pass	50,320	$0.15
gpt4_d9af6064	temporal-reasoning	pass	116,298	$0.35	pass	48,037	$0.15
gpt4_f420262c	temporal-reasoning	FAIL	116,610	$0.35	FAIL	134,691	$0.41
gpt4_f420262d	temporal-reasoning	FAIL	118,803	$0.36	FAIL	52,815	$0.16
001be529	ss-user	FAIL	117,394	$0.35	pass	40,375	$0.12
15745da0	ss-user	FAIL	120,384	$0.37	pass	53,318	$0.16
19b5f2b3	ss-user	pass	115,688	$0.35	pass	42,046	$0.13
19b5f2b3_abs	ss-user	pass	116,214	$0.35	pass	44,256	$0.14
37d43f65	ss-user	FAIL	117,911	$0.35	pass	72,955	$0.22
4fd1909e	ss-user	FAIL	119,200	$0.36	pass	50,759	$0.15
577d4d32	ss-user	pass	116,583	$0.35	pass	48,225	$0.15
60d45044	ss-user	FAIL	119,224	$0.36	pass	47,125	$0.14
853b0a1d	ss-user	FAIL	116,684	$0.35	pass	48,110	$0.15
8e9d538c	ss-user	pass	118,317	$0.36	pass	42,345	$0.13
ad7109d1	ss-user	FAIL	114,263	$0.34	pass	49,802	$0.15
af8d2e46	ss-user	pass	114,690	$0.35	pass	53,504	$0.16
f4f1d8a4_abs	ss-user	pass	118,760	$0.36	pass	46,426	$0.14
0e5e2d1a	ss-assistant	pass	118,067	$0.35	pass	45,569	$0.14
1de5cff2	ss-assistant	FAIL	118,432	$0.36	pass	45,809	$0.14
28bcfaac	ss-assistant	pass	118,509	$0.36	pass	44,713	$0.14
41275add	ss-assistant	FAIL	118,490	$0.36	pass	51,010	$0.16
58470ed2	ss-assistant	pass	118,116	$0.36	pass	80,240	$0.25
6222b6eb	ss-assistant	pass	118,378	$0.36	pass	41,408	$0.13
8aef76bc	ss-assistant	pass	118,739	$0.36	pass	32,131	$0.10
ceb54acb	ss-assistant	pass	118,463	$0.37	pass	45,166	$0.14
dc439ea3	ss-assistant	pass	118,782	$0.36	pass	57,967	$0.18
e3fc4d6e	ss-assistant	FAIL	115,974	$0.35	pass	51,285	$0.16
f523d9fe	ss-assistant	pass	119,321	$0.36	pass	58,638	$0.18
1a1907b4	ss-preference	FAIL	117,865	$0.35	pass	51,663	$0.16
1da05512	ss-preference	FAIL	120,425	$0.37	pass	54,796	$0.17
b0479f84	ss-preference	FAIL	117,425	$0.36	pass	48,987	$0.15
b6025781	ss-preference	FAIL	119,376	$0.36	pass	46,189	$0.14
fca70973	ss-preference	pass	117,421	$0.36	pass	59,228	$0.19
Total	100	33	11,758,181	$35.56	95	5,234,716	$15.99

Development

git clone https://github.com/virtual-context/virtual-context.git
cd virtual-context
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
python -m pytest tests/ -v --ignore=tests/ollama    # ~1500 unit tests
python -m pytest tests/ollama/ -v -m ollama          # integration (requires local LLM)

License

AGPL-3.0, Copyright Y. Ahmed Kidwai

For commercial licensing inquiries, contact: [e-mail]